Browse Topic: Vehicle occupants

Items (6,557)
Find
A Driver Behavior Detection Method Based on Improved YOLOv11 and an Attention Mechanism2026-01-50468/6/2026
Driver’s distraction and fatigue are among the major contributing factors of traffic accidents. This study presents a methodology to identify driver’s distraction using a refined You Only Look Once (YOLO) model, denoted as YOLOv11.To address the inconsistent performance of earlier versions of YOLO, especially with regard to lack of systematic evaluations, this study proposes an improved YOLOv11 model. A mixed local channel attention (MLCA) module is further introduced to enhance small object feature extractions considering the use of Wise-Intersection over Union (IoU) v3 loss function to improve localization accuracy and training stability. Experiments demonstrated that this model outperforms competing models across all metrics, achieving 99.13% mAP at 0.5 and 82.54% mAP at 0.5:0.95, while also achieving minimal bounding box loss. The proposed model demonstrated higher accuracy and robustness, making it suitable for real-world driver monitoring system (DMS) deployments.
Ma, Bao, Taghavifar, Hamid, Fu, Zhijun, Karangwa, Jules, Rakheja, Subhash
Automated Driving Fallback Test Driver Skill CertificationJ3300/3_202607 (Current)7/20/2026
This document is intended to establish a procedure to certify AD fallback test driver skill levels as an endorsement to SAE J3300 foundational level certification. The SAE J3300/3 endorsement can be used by the individual driver to qualify their skills as a test driver of vehicles with automated driving features. The SAE J3300/3 endorsement levels may also be used by test facilities or other organizations when seeking test or professional drivers with these skills. This document provides directions for obtaining the endorsement, including associated AD fallback test driving skill examination requirements, through SAE J3300-certified Examiners (refer to SAE J3300 for definition). Endorsement registration and associated records are administered through Probitas Authentication®. Probitas Authentication® is the current Independent Program Administrator for the SAE J3300 series. This document is a supplement to SAE J3300, providing information specific to the AD fallback test driver skill endorsement and clarifying the application of the rules set forth in SAE J3300 to the AD fallback test driver endorsement. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the AD fallback test driver endorsement, they are not repeated in this document.
Driving Skills Standards Committee
On Disturbance-Aware Minimum-Time Trajectory Planning: Evidence from Tests on a Dynamic Driving Simulator10-10-03-00267/13/2026
This work aims to investigate how disturbance-aware, robustness-embedding reference trajectories translate into actual driving performance when executed by professional drivers in a dynamic driving simulator. The study compares three planned reference trajectories against a free-driving baseline (NO-REF) to assess the trade-offs between lap time (LT) performance and steering effort: NOM, the nominal time-optimal trajectory; TLC, a track-limit-robust, time-optimal trajectory obtained by tightening margins to the track edges; and FLC, a friction-limit-robust, time-optimal trajectory obtained by tightening against axle/tire saturation. All reference trajectories share the same minimum LT objective with a small steering-smoothness regularizer, and are evaluated with two professional drivers driving a high-performance car on a virtual track. The reference trajectories stem from a disturbance-aware minimum-LT framework recently proposed by some of the authors, where worst-case disturbance growth is propagated over a finite horizon and used to tighten tire-friction and track-limit constraints, preserving performance while delivering probabilistic safety margins. LT and steering energy (SE) are evaluated as indicators of driving performance and steering effort, respectively, while RMS values of lateral deviation, speed error, and drift angle are used to characterize driving style. The results reveal a Pareto-like trade-off between LT and SE: NOM achieves the shortest LT, but with the highest SE, TLC minimizes SE at the expense of longer LT, while FLC lies near the efficient frontier, markedly reducing SE relative to NOM with only a minor LT increase. Removing reference trajectories (NO-REF) leads to both higher SE and longer LT, confirming that trajectory guidance improves pace and control efficiency. Overall, the findings highlight reference-based and disturbance-aware planning, particularly the FLC variant, as effective tools for training and for achieving fast yet stable trajectories.
Masoni, Matteo, Palermo, Vincenzo, Gabiccini, Marco, Gulisano, Martino, Previati, Giorgio, Gobbi, Massimiliano, Comolli, Francesco, Mastinu, Gianpiero, Guiggiani, Massimo
The Chain of Visual Factors, Visual Characteristics, and Driving Behavior at Highway Tunnel Entrances2026-99-05567/10/2026
To explore the impact of guiding and warning visual combination factors at the entrance sections of highway tunnels on drivers’ visual characteristics and driving behavior, this study recruited 16 drivers to conduct on-road vehicle experiments at the entrance sections of the Yunling Tunnel’s left bore (with visual combination factors) and right bore (without visual combination factors). Seven visual characteristics and driving behavior indicators, including pupil diameter and vehicle speed, were collected and statistically analyzed. Representative indicators such as pupil diameter, standard deviation of fixation point position, and vehicle speed were selected to establish a trend surface model of visual characteristics and driving behavior. The results indicate that when driving at the entrance section of the left bore, drivers’ pupil diameter and fixation duration were significantly lower than those at the entrance section of the right bore. With the increase in the sweeping view angle, there was a more dispersed distribution of fixation points. Additionally, there were significant differences in the acceleration and lateral deviation of the driving vehicle, with the range of variation narrowing by 52.5% and 35.7%, respectively. The trend surface model results show that under the influence of visual combination factors, the reduction in drivers’ vehicle speed was smaller, and the impact of pupil diameter and standard deviation of fixation point position on vehicle speed was less pronounced. Overall, under the influence of visual combination factors, drivers’ visual characteristics showed significant changes, with improved speed control and manipulation levels, leading to more stable vehicle operation.
Ma, Yanpeng, Huang, He, Huang, Yong, Yuan, Chen
Impact of Driving Style on Electric Vehicle Energy Consumption2026-99-05077/10/2026
The way we drive has a big effect on how much energy electric cars use, so making better driving habits can help make electric cars use less energy. By utilizing a set of real EV driving data, this paper classifies and analyzes EVs from the perspective of energy consumption, and establishes an intelligent scoring system for EV driving behavior based on a decision tree model. Experimental results show that this method is able to successfully distinguish different driving behaviours and the critical driving behavior factors, such as vehicle speed, accelerator pedal change rate, etc., and braking behavior are identified. Use intelligent scoring to give driver suggestions; this way, they can improve on their driving techniques and lower their energy consumption.
Liang, Yongkai, Zhang, Hao, Liu, Yu, Yu, Hanzhengnan
Research on Predicting Occupant Height Using CNN-Based Pressure and Posture in Zero-Gravity Seats2026-99-05217/10/2026
Zero-gravity seats alleviate prolonged sitting fatigue by optimizing human body pressure distribution, but the correlation mechanism between body size parameters and pressure distribution remains unclear. This study proposes a deep learning model based on multimodal data fusion, combining pressure matrices and postural angle data to construct a convolutional neural network (CNN) with a height prediction error ⩽3 cm. Experiments collected pressure and posture data from 100 participants with diverse anthropometric percentiles. Through the fusion of features and the optimization of the model, the study managed to quantify how height and weight impact pressure gradients. The results indicate that the model achieved a prediction R2 value of 0.73, which confirms that there is a strong correlation between pressure distribution and body size parameters. The findings offer theoretical and technical support for the adaptive adjustment systems within intelligent cabins.
Bi, Tengfei, Nie, Jiacheng, Du, Changjiang, Ji, Yuechen, Wang, Song, Sun, Jiawei
The Influence of Spatial and Aspatial Factors on the Safety Driving Behavior of Motorcycle Couriers in the Gig-Economy Era: A Case Study of East Jakarta, Indonesia09-14-02-00057/3/2026
This study aims to analyze the impact of spatial and aspatial factors on the safety driving behavior of motorcycle couriers in East Jakarta within the context of the gig economy. Both factors are integrated to clarify how spatial conditions and individual characteristics jointly shape couriers’ safety driving behavior. The Partial Least Squares Structural Equation Modeling (PLS-SEM) method was employed to examine the relationship between spatial and aspatial factors on safety driving behavior. Data were collected through questionnaires from 253 motorcycle couriers operating in three subdistricts in East Jakarta, namely Cakung, Pasar Rebo, and Pulo Gadung. The results show that safety driving behavior is significantly influenced by aspatial factors, particularly socioeconomic characteristics and personality traits. In contrast, spatial factors such as road conditions and daily activity patterns do not directly influence safety driving behavior, but exert indirect effects through the couriers’ personality traits.
Wahyuddin, Yasser, Sitorus, Paldibo Alfriramson, Putri, Kharunia, Maharani, Garnierita
Mind the Gap: Quantifying Behavioral Fidelity in CARLA Using Naturalistic Drone Data2026-01-07717/1/2026
Rigorous validation of SAE Levels 3 and 4 autonomous systems increasingly relies on simulation. However, the simulation-reality gap remains a challenge for human-in-the-loop assessments. This study empirically quantifies the behavioral fidelity of the Car-Learning-to-Act (CARLA) simulator by recreating specific real-world traffic scenarios using the high-precision exiD drone dataset. Twenty-five participants performed a series of maneuvers, including lane changes and time-critical cut-ins. Their performance was analyzed using Dynamic Time Warping (DTW), driver profiling, and Time-to-Collision (TTC) metrics. The findings reveal a clear distinction between relative and absolute behavioral validity. In strategic decision-making tasks, the simulation demonstrated remarkably high temporal fidelity. DTW analysis explained 94% of the trajectory variance. Participants initiated lane changes with an average lag of -9 frames (0.36 s) compared to naturalistic references. These results indicate that, despite the absence of peripheral optical flow, the simulator successfully elicits temporally correlated decision-making patterns suitable for assessing strategic driver intent. However, physical execution in reactive scenarios revealed significant absolute discrepancies. Although the high Pearson correlation (r ≈ 0.89) in velocity profiles proves that drivers recognize and react to hazards with realistic timing, their physical inputs were exaggerated. Participants displayed digital, over-modulated braking responses and maintained a negative safety bias of -11.26 m, a deviation attributed to the lack of vestibular g-force feedback and geometric minification. Furthermore, distinct driver profiles emerged. Risk-oriented participants exhibited a gaming effect by neglecting safety margins. In conclusion, while CARLA is highly valid for testing the temporal logic of driver interactions, absolute dynamics require calibration functions, such as force-feedback (pedal) tuning and visual deceleration cues like camera shake, to compensate for sensory limitations before it can be used for safety-critical validation.
Rebling, Patrick, Alphan, Metehan, Nenninger, Philipp
UMV Peoplemover: Towards a Modular Fully Automated Vehicle Research Platform2026-01-07847/1/2026
The UMV Peoplemover 2+2 is part of a modular vehicle family (Urban Modular Vehicle) that includes derivatives for passenger and cargo transport in urban environments. The platform supports automated movers as well as conventionally controlled vehicles with a human driver, ensuring high flexibility across applications. The modular platform enables the extensive use of common parts, allowing the efficient and cost-effective realization of multiple vehicle variants. The increased share of common parts also improves sustainability by reducing derivative-specific parts, material usage, and production complexity. A drivable demonstrator of the UMV Peoplemover 2+2 has already been realized. The vehicle is designed for the automated transport of up to four occupants in a 2+2 vis-à-vis seating arrangement and is targeted at demand-oriented shuttle services. While the drivable demonstrator validated the proof of concept, it lacked the core Level 4 hardware and software stack for automated driving functions. To address this limitation, we deployed a software-defined vehicle architecture to the concept. This paper introduces the novel e/e-architecture and software stack enabling the Peoplemover 2+2 to initiate its first shuttle service at the German Aerospace Center (DLR e.V.) in Stuttgart. We further detail the deployed multi-modal sensor suite, comprising modern solid-state LiDARs and a 4D imaging radar, which were carefully selected to meet the operational design domain requirements while also serving as a versatile research platform for future advanced perception studies. Finally, we analyze the SDV-based modular software stack, which facilitates rapid application development through straightforward switching between commercial, open-source, and in-house software domains, and supports parallel execution of domain-specific functions across all three software sources.
Pohl, Eric, Schmid, Fabian, Münster, Marco, Siefkes, Tjark, Stuebler, Tillmann, Mohammed, Shawan
Estimating Driver Field of View in Active Safety Systems2026-01-07757/1/2026
Driver monitoring systems are an important component of active safety systems, continuously evaluating the driver’s state and issuing real-time warnings. As defined by the SAE Levels of Automation, driving tasks are increasingly transferred from the driver to the vehicle from Level 0 to Level 2, however, the driver remains fully responsible for monitoring the driving environment. Current implementations, such as driver drowsiness and attention warning, assess driver alertness, while advanced driver distraction warning ensures that the driver maintains visual focus. Nevertheless, these systems do not identify the specific objects or regions the driver is observing. This limitation motivates the presented research question: can an in-car monitoring system be integrated with external environment perception sensors to infer the driver’s field of view (FoV)? This paper presents a system consisting of a driver-facing camera and a front-view camera. Facial features, including gaze direction, head pose, and iris offset are extracted using computer vision techniques. These features, together with cropped eye images, are used as inputs to a multi-modal network. Training labels were generated using a driving simulator study with 16 participants who sequentially fixated on visual targets displayed on a front screen. Experimental results show that the proposed system can predict driver visual attention and approximate FoV with a mean pixel error of 35.40 px, enabling identification of the regions of the road scene observed by the driver in real time. This work provides a foundation for explicitly modeling driver perception and its correspondence with vehicle perception systems.
Ji, Dejie, Lausch, Hendryk, Flormann, Maximilian, Henze, Roman
Context-Dependent Assessment of Driving Dynamics and User Perception in Automated Driving2026-01-07577/1/2026
As automation advances and occupants transition from active drivers to passive passengers, understanding how automated driving behavior is evaluated becomes increasingly important. While longitudinal and lateral vehicle dynamics are known to influence perceived comfort and safety, it remains unclear to what extent motion–perception relationships remain stable across urban traffic contexts. This study compares two real-world investigations of automated driving: a left-turn maneuver at a signalized intersection on a test track and a roundabout maneuver with a shuttle in public traffic. Both datasets include high-resolution vehicle dynamics and structured subjective ratings. A consistent objectification approach was applied to examine the transferability of motion–perception relationships across contexts. However, differences in vehicle platform, automation level, trajectory characteristics, and study design limit direct comparability and require cautious interpretation. Despite partially overlapping ranges in selected peak-based dynamic parameters, such as longitudinal acceleration, subjective comfort and safety ratings were consistently higher in the roundabout scenario. Furthermore, strong associations were observed between motion parameters and subjective evaluations in the intersection context (adj. R2 up to 0.891), whereas objective parameters showed only limited explanatory power in the roundabout scenario (adj. R2 ≤ 0.06). The results indicate that motion–perception relationships derived within a specific context may not be directly transferable across different traffic scenarios. The findings highlight limitations of globally derived motion-based evaluation models and underline the importance of validating objectification approaches across diverse operational environments.
Panzer, Anna, Strenge, Emma, Iatropoulos, Jannes, Henze, Roman
Model-Guided Scenario Expansion for Data-Driven Autonomous-Vehicle Safety Testing: A Feasibility Demonstration12-09-04-00326/29/2026
This article presents a data-driven pipeline for autonomous-vehicle (AV) safety testing. The pipeline integrates real-world traffic observations with model-guided scenario expansion and safety-metric evaluation to enable an end-to-end AV safety testing framework, demonstrated on a canonical highway scenario. The framework enhances test diversity, realism, and coverage by generating statistically informed variants of observed driving behaviors. Key parameters such as vehicle speed, trajectories, and headways are extracted from naturalistic data and used to train a probabilistic model of traffic dynamics. Scenario variants are sampled from this model and encoded as behavior trees (BTs) for modular, simulation-ready execution. Each scenario is simulated using a consistent AV control configuration, and safety metrics such as minimum safe distance violation, minimum safe distance factor, time to collision, and aggressive driving are applied to evaluate safety outcomes independently of system-specific tuning. A case study based on the highD dataset (110,000+ trajectories) demonstrates the framework’s ability to generate realistic and safety-relevant scenarios, providing an initial demonstration of pipeline feasibility and metric-based evaluation. This initial study is intentionally scoped to a single scenario class and a simplified parametric model to isolate and validate the end-to-end integration of the pipeline.
Elshenawy, Mohamed, Aboudina, Aya, Abdelmotaleb, Anhar, Amr, Mariam, El-darieby, Mohamed
Field-Based Evaluation of Physiological Fatigue Reduction in Fuel Cell Refuse Collection Vehicle Using Heart Rate Variability2026-01-06906/20/2026
Understanding the physiological impact of vehicle electrification on operators remains an important but underexplored issue in commercial vehicle research. This study quantitatively evaluates the physiological fatigue of drivers and onboard crew members during real-world operation of commercial refuse-collection vehicles by comparing a diesel-powered vehicle with a fuel cell electric vehicle (FCEV). Both vehicles were operated on the same routes under comparable real-world operating conditions, including similar time periods and operational tasks, during municipal waste collection service. Heart Rate Variability (HRV) metrics were obtained from R-R interval (RRI) data recorded using a Polar heart rate sensor. The Root Mean Square of Successive Differences (RMSSD), a time-domain index reflecting short-term parasympathetic activity, and Poincaré (Lorenz) plot area (LP area), a nonlinear HRV index reflecting overall autonomic nervous system modulation, were calculated. In-cabin vibration and noise levels were also measured as supplementary context to support the interpretation of physiological responses. The results indicate that both RMSSD and LP area were higher during FCEV operation than during diesel vehicle operation. For the driver, RMSSD increased by approximately 61.65% and the LP area by approximately 49.91%. For the onboard crew member, RMSSD increased by approximately 18.79% and the LP area by approximately 46.02%. These findings suggest a consistent association between reduced vibration and noise characteristics in the FCEV and increased HRV indices, indicating reduced physiological fatigue during operation. This study provides quantitative evidence that fuel cell electric commercial vehicles are associated with improved occupational conditions, extending beyond conventional environmental benefits.
Utsumi, Atsuko, Yakoh, Takahiro
Spatial Noise Acquisition at the Driver’s Position in an Electric Vehicle with Increased Accuracy2026-01-07236/20/2026
In vehicles with electrified powertrains, high-frequency tonal noise components have become increasingly prominent and can be perceived as particularly annoying by the driver. While recent advancements in international standardization — such as ECMA-74 [1] and ECMA-418 [2] — have led to powerful new algorithms for tonal noise visualization and analysis, including Tonality-Heatmaps, the measurement side still lacks sensor setups that adequately reflect the spatial sensitivity of noise, especially for tonal components. This challenge is amplified in enclosed vehicle cabins, where room modes create local minima and maxima that become increasingly dense at higher frequencies. As a result, even small head movements can lead to noticeable differences in perceived tonal noise. Current measurement approaches do not sufficiently account for this spatial variability. This contribution addresses the absence of tailored solutions for the driver’s position by introducing an improved microphone arrangement that significantly reduces the uncertainty of measured noise levels. The proposed setup considers spatial variability without compromising comfort or crash safety requirements. By enhancing the precision of tonal noise quantification, this approach provides noise-vibration-harshness (NVH) engineers with a valuable complement to modern software-based tonal analysis methods. The paper discusses the technical implementation constraints and demonstrates the comparability of the new measurement technique with conventional setups.
Schecker, Daniel, Rittenschober, Thomas
Ergonomic Assessment of Glove-Box Design in Passenger Vehicles for the Indian Scenario15-19-02-00096/10/2026
Passenger vehicles experience severe packaging constraints around the instrument panel, rendering glove-box operation a critical yet ergonomically underexplored interaction. Although glove-box interaction occurs frequently during routine vehicle use, its potential implications for ergonomic risk remain largely unexamined in existing automotive research. To isolate the influence of driver-side packaging constraints from component-level design effects, this study adopts a comparative evaluation of driver and co-driver glove-box interaction as a built-in control condition. This study introduces a discomfort-based evaluation framework that integrates Digital Human Modeling with India-specific anthropometric datasets. A composite loss-function scoring model is developed to quantify functional usability differences across four glove-box configurations, defined by variations in latch placement (center or side) and storage-bin mechanisms (fixed or rotating). Indians are utilized to assess reachability and visibility during glove-box interaction. Ergonomic performance is analyzed through reach and visibility metrics for both latch actuation and storage-access tasks. For the co-driver, all configurations exhibit 0% loss, confirming that usability remains unaffected. In contrast, the driver assessment reveals pronounced limitations. Center-mounted latches prove inaccessible from a neutral seated posture, reflecting an approximate loss function of 55%. Among the side-latch alternatives, the rotating-bin configuration achieves the lowest discomfort score (41%), supported by more favorable access posture and smoother hand-entry alignment. The findings specify that ergonomic limitations stem primarily from driver-side packaging constraints rather than inherent flaws in the glove box unit. Based on the reach and visibility loss values obtained through the developed framework, the Side-Latch + Rotating-Bin configuration emerges as the most suitable design option for passenger-vehicle layout. The proposed methodology offers a practical decision-support tool for early stage ergonomic evaluation of glove-box configurations in passenger vehicles.
Jujjavarapu, Sreeram, Kota, Srinivas, Kotkunde, Nitin, Jasti, Naga Vamsi Krishna
Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving13-07-02-00066/9/2026
Large language models (LLMs) have shown remarkable capabilities for perceiving driving environments and making interpretable, logical decisions for autonomous driving. However, their potential for more comprehensive driving strategies, especially concerning energy efficiency, remains underexplored. Most existing studies primarily focus on driving safety, which may inadvertently increase energy consumption. To address this issue, this study explores the use of LLMs as high-level controllers to jointly optimize driving safety and energy efficiency. A textual prompt is designed for the LLM, incorporating few-shot examples that describe scenarios, states, and actions. The LLM processes the scenario and state prompts describing the surrounding traffic environment. It generates a high-level control signal, which is then translated into low-level vehicle motion commands in a high-fidelity traffic simulator with realistic physics, vehicle dynamics, road slopes, and network topology. Experiments in campus-scale digital twin car-following scenarios demonstrate that the proposed LLM-based framework achieves an average reduction of 4.16% in energy consumption compared to the reinforcement learning paradigm, while maintaining driving safety and providing interpretable high-level decision-making. This study highlights the potential of LLMs for longitudinal eco-driving applications under the evaluated simulation settings, extending previous LLM-based autonomous driving research that primarily focused on safety to also consider energy efficiency.
Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao
Hitting the road IN THE ALL-NEW VNR26TOFHP06_016/1/2026
Volvo Trucks' revised VNR brings updated safety tech, improved fuel economy and driver comfort features to the regional haul segment. Volvo Trucks has continued its rollout of new models for every sector of the commercial truck market. The redesigned VNR is the latest model to see the spotlight. The new VNR naturally carries all of Volvo's latest safety tech, but also prioritizes maneuverability, fuel efficiency and configurability for a wide variety of fleet uses. “The VNR is an incredibly versatile truck,” said Maddie Sullivan, product marketing manager. “There are so many different configurations to meet our customer's needs. We offer four different cab sizes, three different axle configurations and two different chassis configurations.”
Wolfe, Matt
Zero Waste Manufacturing in Aerospace: Circular Design Approaches from Concept to End-of-Life2026-26-07756/1/2026
Achieving zero-waste manufacturing in aerospace requires a shift from end-of-pipe waste mitigation toward circular design principles embedded early in product development. This paper presents a practical framework for integrating circularity into aerospace systems through five design pillars: design for modularity and disassembly, material substitution to enhance recyclability, waste segregation and characterization, component-level circularity readiness scoring, and collaborative supplier engagement. To operationalize this approach, a Circularity Readiness Assessment Tool (CRAT) is developed to evaluate design alternatives against criteria such as disassembly ease, material recyclability, manufacturing waste potential, end-of-life recovery pathways, and supplier take-back mechanisms. The framework supports multi-criteria decision-making by complementing traditional aerospace design drivers including weight, performance, cost, and safety. The methodology is demonstrated through a case study of an aircraft seating system. Scenario-based analysis indicates that targeted circular design interventions can reduce material waste and lifecycle carbon emissions while maintaining functional and regulatory requirements. Emphasizing practical engineering workflows rather than exhaustive lifecycle modeling, this work provides a scalable foundation for embedding circular design into aerospace product development and advancing zero-waste manufacturing objectives.
S, Chaitra
AI-Led Sustainability Strategy: Driving Product Value from Birth to Disposal2026-26-07816/1/2026
Aerospace products operate within highly complex, safety-critical environments and endure extended lifecycles, often spanning decades. Sustaining their operational value requires rigorous management of Safety, Reliability, and Availability (SRA), while global Environmental, Social, and Governance (ESG) mandates demand parallel progress toward sustainability goals. This paper introduces an AI-driven strategy that integrates these dual imperatives—Sustenance Management and Sustainability Management—within a unified Product Lifecycle (PLC) framework. The proposed approach leverages Artificial Intelligence across five PLC phases: Generative Design, Detailed Design & Verification, Manufacturing & Industrialization, Operations & Maintenance, and End-of-Life Circularity. Anchored by a certified Digital Thread, this framework ensures seamless, auditable data flow from concept to disposal. Using Life-Limiting Parts (LLPs)—such as high-stress turbine discs—as a case study, the paper demonstrates how AI interventions enhance operational efficiency while reducing embedded carbon emissions. For example, Generative AI optimizes component geometry for performance and material efficiency, Physics-Informed Machine Learning (PIML) improves Remaining Useful Life (RUL) predictions for certification readiness, and predictive analytics extend Time-on-Wing (ToW), deferring Scope 3 emissions from replacement manufacturing. At end-of-life, AI-guided valuation of Used Serviceable Material (USM) enables circularity and compliance with ISO 14067 and ISO 14040/14044 standards. The paper also discusses sustainability metrics such as Design Simulation Energy Intensity (DSEI) and the Sustainable AI Quotient (SAIQ) [25], to address the AI-energy paradox, ensuring that digital transformation remains net-positive for environmental stewardship. By positioning sustenance as the most immediate lever for sustainability, this AI-led framework delivers measurable improvements in lifecycle cost, operational resilience, and carbon footprint reduction. The discussion concludes with challenges in data governance, regulatory compliance, and model explainability, offering mitigation strategies for safe and scalable adoption.
Srinivasan, Karthik, G.V.V., Ravi Kumar, Vaderahobli, Devaraja Holla, Bhate, Ujwal, Veluri, Sastry
Mitigating Cracking in COP Optics through Annealing and Step-Torquing2026-26-07646/1/2026
Polymeric optical materials such as Cyclo Olefin Polymer (COP) are adopted in aerospace lighting systems due to their excellent optical clarity, dimensional stability, moldability and weight saving advantages over glass. However, their relatively low toughness and the presence of residual molding stress make them prone to crack initiation during mechanical fastening. During its installation, crack formation was consistently observed around self-tapping screw interfaces, raising concerns over reliability, maintainability, and compliance with durability requirements. A structured Design of Experiments (DOE) was performed to identify root causes and evaluate potential mitigation methods. The investigation revealed that residual stresses in the COP material, combined with localized stress concentrations during screw tightening, were the primary drivers of crack initiation. Two complementary process improvements were identified and validated as part of mitigation plan: (i) annealing of the optics prior to assembly to relieve internal stress, and (ii) using step-torquing method to fasten the screws, to gradually distribute applied loads and reduce localized stress peaks. Post-assembly observation over three days confirmed a significant reduction in crack initiation. The combined annealing and step-torquing approach demonstrated a substantial reduction in crack generation probability, providing a practical and repeatable process for enhancing the robustness of polymeric optic assemblies. This work contributes a generalizable methodology for mitigating assembly-induced failures in advanced polymer materials and supports broader adoption of lightweight, high-performance optics in aerospace applications.
S, Nikhil, Singh, Abhimanyu Kumar, Katageri, Praveen, SP, Pradeep, Chandra, Praveen
Driving Behavior Heterogeneity Identification with Action Patterns Relationship2026-99-07525/15/2026
Identifying driving heterogeneity is critical for enhancing the strategy learning capabilities of autonomous driving systems, as well as improving their safety and efficiency. This research proposes a novel driving heterogeneity identification framework. The framework consists of three core processes: action phase extraction, action relationship modeling, and behavior heterogeneity identification. First, a rule-based segmentation method is employed to systematically decode and interpret the inherent variations in human driving behavior. Subsequently, an action relationship modeling method is introduced to characterize the temporal relations between the acquired action phases. Finally, to mitigate the inaccurate identification caused by the sparse distribution of critical driving events in long-sequence data, a semantic encoding method is applied to remap the driving behavior space. Experimental results on the Lyft level-5 dataset validate the effectiveness of the proposed framework, which outperforms multiple traditional clustering algorithms. This demonstrates its significant potential to enhance behavior detection and learning in personalized advanced driver-assistance systems (ADAS) and advanced autonomous vehicle (AV) design.
Yin, Hui, Zhang, Qinyao, Li, Xiaojian, Mo, Hangjie
Letter from the Guest Editor09-14-01-00345/13/2026
Letter from the Guest Editor
Tylko, Suzanne
SAE TOMORROW TODAY - Why Curing Motion Sickness Is Key to AV Success135675/11/2026
Did you know that motion sickness affects 1 in 3 people? As AVs gain traction and drivers become passengers, it may be the biggest barrier to the future of autonomous mobility. With a focus on addressing the root cause of motion sickness, Motion Sync offers breakthrough solution: a haptic pre-activation system that helps passengers anticipate vehicle movement with a personalized in-cabin experience that could redefine comfort and productivity. Listen in as we sit down with Dr. Daniel Sousa Schulman, CEO and Founder of Motion Sync, to unpack the science behind motion sickness and why it's a critical issue for EVs, robotaxis, and self-driving cars. You'll learn why passengers get sick, why drivers don't, and how EVs can actually make symptoms worse. As a topic critical to the success of autonomous vehicles, it's a conversation you don't want to miss. We'd love to hear from you. Share your comments, questions and ideas for future topics and guests to podcast@sae.org. Don't forget to take a moment to follow SAE Tomorrow Today--a podcast where we discuss emerging technology and trends in mobility with the leaders, innovators and strategists making it all happen--and give us a review on your preferred podcasting platform. Follow SAE on LinkedIn, Instagram, Facebook, X, and YouTube. Follow host Grayson Brulte on LinkedIn, X, and Instagram.
Patterson, Lori
Full State Feedback Controller for a HelicopterF-0082-2026-00415/5/2026
Full state feedback offers theoretically guaranteed multi-axis stability, making it superior to conventional PID controllers. There is however one drawback, a full state controller has a mathematical difficulty if the B matrix is not square and thus not invertible. This is the case for helicopters with 6 degrees of freedom and 4 inceptors. Variations of linear quadratic regulators are a work around, however complexity dramatically increases. Best would be a direct solution to the original problem. This is the breakthrough result of this paper. This paper documents an approach which removes the analysis roadblock by partitioning the 6 x 6 system "A" matrix into two groups of 4 x 4 matrices. The 4x4 matrices are individually stabilized with full state gain matrices. One matrix is designated “Driver Matrix” which provides actuator commands. The other matrix is designated "Reference Matrix" which provides references. The two matrices are coupled together by requiring that the driver matrix follow references generated by the reference matrix. With each matrix individually stabilized, the coupled combination is also stabilized. Computation of flight dynamics states (u, v, w, p, q, r) is shared between the matrices. Initial results are very encouraging, showing an originally sluggish, heavy lift helicopter having now concise decoupled responses to pitch and roll commands. Stability derivatives are recomputed during flight allowing coverage over the whole flight envelope. A handling qualities task has been defined to relocate a 40 ft standard seaborne container directed by a pilot in a ground control station. Cooper Harper ratings of this task have demonstrated favorable Level 1 handling qualities if use is made of an automated lateral repositioning command.
Mouritsen, Stephen, Piasecki, Fred
The Advanced Tiltrotor Aircraft by Leonardo: Design Drivers and FeaturesF-0082-2026-02425/5/2026
This paper describes the characteristics of the Leonardo Advanced Tiltrotor Aircraft (ATA) concept, focusing on the relationship between goals, targeted improvements and enabling design features. The paper shows the design drivers such as performance, operational capabilities, and maneuverability and it describes how the attributes of the concept originated, showing trade-off and compromises approached during the genesis of the concept. The design drivers are translated into areas of interests, including download, drag, aerodynamic efficiency, rolling and yawing inertia, detectability, maintainability and engine retrofit ability. Finally, these areas are linked to the physical features of the concept, showing how they have been selected and combined to achieve the best overall benefit at platform level.
Bianco Mengotti, Riccardo, Viganò, Luca, Cassinelli, Carlo, Sampugnaro, Luca, Pecoraro, Matteo, Lilliu, Cristian, Medici, Luca
An Integrated Workflow for Structural Dynamics, Comfort, and Airbag Deployment for Virtual Seat DesignF-0082-2026-03295/5/2026
This paper presents an integrated simulation workflow for aircraft seat development that combines (i) structural dynamics and certification load cases, (ii) occupant comfort and living-space assessment using finite-element digital humans, and (iii) airbag folding, deployment, and calibration using a coupled gas-dynamics solver suited to early-time transients. The workflow is built around a single manufacturing-aware, as-built seat model that is reused across comfort, certification, and restraint-system studies, allowing design iterations to move upstream before design freeze. Each stage is paired with validation or industrial case examples, and the airbag-calibration process is accelerated through reduced-order modeling (ROM) of parameter identification. The result is a practical virtual-seat-development methodology that is sufficiently predictive to de-risk physical testing while remaining fast enough for concept iteration and late-stage compliance support.
Dwarampudi, Ramesh, Vaz, Ignatius
An Investigation of Drowsy Lane Departures from Driving Simulator Studies09-14-01-00145/4/2026
NHTSA is conducting research to evaluate the current state-of-the-art technology for lane departure warning (LDW) and lane-keeping assistance (LKA) technology. NHTSA is undertaking research to understand the nature of real-world lane departures and recovery behaviors. While some information about lane departures can be learned from crash datasets, the purpose of this work was to mine simulator datasets for lane departures, analyze them in greater detail than is possible from crash reports or naturalistic studies, and link their characteristics to driver drowsiness. The objective of the study was to determine whether there are differences in lane departure characteristics as a function of driver drowsiness. This research used a novel approach by combining data from six different driving simulator studies on driver drowsiness. The dataset included a sample of 380 drivers. Study drives occurred during overnight hours after periods of sleep deprivation, with participants being awake for at least 16 h prior to driving. Study drives ranged in duration from relatively short 45-min to nearly 4 h. The datasets were reduced to characterize 5805 individual lane departures. Lane departures were delineated into three phases (pre-departure, departure, and recovery) and two transition points (onset and reentry) to capture driver behaviors under drowsiness. We hypothesized that lane departures would look different under different levels of drowsiness. Drives took place across a range of roadway environments that included interstate highways, rural highways, rural roads, and low-speed urban areas. Drowsiness was sampled at points before, during, and after the drive using self-ratings [Karolinska Sleepiness Scale (KSS) or Stanford Sleepiness Scale (SSS)] as well as the expert Observational Rating of Drowsiness (ORD). High levels of drowsiness were associated with a narrow speed range at highway speeds and the least amount of throttle input, while low levels of drowsiness had more steering activity, more throttle input, and a broader range of speeds. The results of this study will improve understanding of vehicle kinematics and driver behavior in drowsy lane departures using a safe methodology to help address crash dataset limitations.
Schwarz, Chris, Gaspar, John, Shull, Emily, Venegas, Michael
Advanced Tool for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction09-14-01-00335/4/2026
Traffic collision reconstruction traditionally relies on human expertise and, when performed properly, can be incredibly accurate. However, attempting to perform pre-crash reconstruction, i.e., reconstructing the driver and vehicle behaviors that preceded the actual crash, poses significantly more challenges. This study develops a multi-agent artificial intelligence (AI) framework that reconstructs pre-crash scenarios and infers vehicle behaviors from fragmented collision data. We present a two-phase collaborative framework combining reconstruction and reasoning phases. The system processes 277 rear-end lead vehicle deceleration (LVD) collisions from the Crash Investigation Sampling System (CISS; 2017–2022), integrating textual crash reports, structured tabular data, and visual scene diagrams. Phase I generates natural language crash reconstructions from multimodal inputs. Phase II performs in-depth crash reasoning by combining these reconstructions with the temporal event data recorder (EDR). This enables precise identification of striking and struck vehicles while isolating the EDR records most relevant to the collision moment, thereby revealing crucial pre-crash driving behaviors. For validation, we applied it to all LVD cases, focusing on a subset of 39 complicated EDR cases where multiple EDR records per collision introduced possible ambiguity (e.g., due to missing or conflicting data). Ground truth was established via consensus between manual annotations (two independent researchers), with a separate large language model (LLM) used only to flag possible conflicts for re-checking. In the full end-to-end evaluation, the framework achieved 100% accuracy across all 4155 trials (277 cases × 5 runs × 3 models), with three reasoning models producing identical outputs, confirming that performance derives from the structured prompt design rather than model-specific characteristics. In contrast, research analysts without specialized reconstruction training achieved 92.31% accuracy on the same 39 complex cases. In separate ablation experiments on the 39 complicated EDR cases, where one randomly selected Phase I output from the full end-to-end evaluation was fixed as the unified input for Phase II and each model was tested with 10 independent runs, removing the structured reasoning anchors reduced case-level accuracy from 99.7% to 96.5%, with errors spreading from a single output type to multiple analytical dimensions. The system maintained robust performance even when processing incomplete data. This zero-shot evaluation, conducted without any domain-specific training or fine-tuning, demonstrates that the framework’s effectiveness stems from its multi-agent architecture and prompt engineering, offering a scalable approach for AI-assisted pre-crash analysis.
Xu, Gerui, Chen, Boyou, Guo, Huizhong, LeBlanc, Dave, Kusari, Arpan, Yarbasi, Efe, Ahmed, Ananna, Sun, Zhaonan, Bao, Shan
Characterization of Pedestrian Automatic Emergency Braking Acceleration in Modern Light Vehicles: Potential Implications for Occupant Safety09-14-01-00075/4/2026
The objective of this study was to characterize and compare pedestrian automatic emergency braking (PAEB) pulses in modern light vehicles to understand the loading environment that vehicle occupants are being exposed to during PAEB maneuvers. PAEB tests (n = 8008) conducted using 2018–2023 vehicle model years were analyzed. Pulse, vehicle, and impact characteristics (e.g., jerk, peak acceleration, pedestrian scenario, etc.) were derived from each PAEB test. Two k-means clustering analyses were used to group PAEB pulses with and without target collisions based on their similarity between characteristics. One-way ANOVA and Kruskal–Wallis tests were performed on the PAEB pulse characteristics to examine differences between clusters (p < 0.05). Two non-collision clusters (NC1 and NC2) were identified for PAEB pulses without collisions: NC1 had a statistically significant lower jerk (0.8 ± 0.4 g/s) and peak acceleration (1.0 ± 0.1 g) compared to NC2 (1.6 ± 0.8 g/s and 0.9 ± 0.1 g, respectively, p < 0.001). NC1 was mostly represented by stationary adult (88.6%), 60 km/h (99.5%), and 40 km/h (62.2%) tests. NC2 was mostly represented by crossing scenarios (child: 92.3%; adult: 70.5%) and 20 km/h (96.2%) tests. Three collision clusters (C1, C2, and C3) were identified for PAEB pulses with collisions. C3 showed a greater jerk (1.5 ± 0.8 g/s) compared to C1 (0.9 ± 0.6 g/s) and C2 (1.1 ± 0.9 g/s, p < 0.001). These results suggest that with successful avoidance, deceleration begins earlier with higher speeds and a stationary pedestrian, resulting in potentially milder loading conditions for vehicle occupants (i.e., lower jerk in NC1 vs NC2). With unsuccessful avoidance, in daytime pedestrian crossing scenarios, lower impact speeds were observed, resulting in potentially non-optimal loading conditions (i.e., higher jerk and peak acceleration in C3) for vehicle occupants. At night with low beams, C2 may result in advantageous loading conditions for vehicle occupants (i.e., lower jerk and peak acceleration), but it may lead to the worst outcome for pedestrians (i.e., greatest impact speed).
Witmer, Maitland, Kidd, David, Graci, Valentina
Understanding and Modeling Effectiveness of Predictive Risk Notifications for Early Assistance for Safe Driving: A Public Road Testing Study09-14-01-00265/4/2026
While an enlarged lead time from risk notifications to collisions is widely acknowledged to facilitate safe driving, it remains challenging to effectively notify drivers of invisible risks and non-apparent risks coming from uncertain behaviors on the part of road users. The current study examined whether verbal notifications are able to assist early awareness of predictive risks. We also attempted to identify human and environmental factors that could possibly improve the effectiveness of predictive risk information. Twenty-eight licensed drivers participated in a public road test conducted in two different urban areas on 3 days. They drove predefined courses on which potential risk locations were identified prior to the test, using a sport utility vehicle equipped with an automatic verbal notification system triggered based on the distance to the potential risk locations. After passing through the locations each time, the participants were instructed to verbally evaluate the shift in awareness provided by the notification and the usefulness of the assistance. After the driving test was completed, we acquired a subjective evaluation on annoyance acceptability and a self-report of participants’ road usage frequency at notified locations in daily life, as well as questionnaires on their driving style and workload sensitivity. We found that the effectiveness of verbal notifications increased by conveying uncertainty risks at visible locations and by using interrogative sentences or expressions of risk target perspective, although it decreased as a function of age. Our model showed strong performance in predicting positive ratings for the notifications, but this was not the case for negative ratings. We identified individual characteristics and the risk factor of uncertainty as important features in our model. In conclusion, the findings provide an important reference for understanding the early notification of predictive risk and constructing a numerical model for the implementation of assistance systems in vehicles and nomadic devices.
Maruyama, Masaki, Koyama, Keiichiro, Ezaki, Toru, Sakamoto, Junichi, Sawada, Yuta, Matsuoka, Takahiro
Driver Understanding of Yellow Indication Laws and Preferences for Configuration of In-Vehicle Alerts for Dilemma Zones09-14-01-00065/4/2026
Drivers frequently encounter Type II dilemma zones at signalized intersections, where the decision to stop or proceed during the onset of a yellow indication can be ambiguous. Decision-making relies on drivers’ expectations of the yellow change interval duration and behavioral factors. While boundaries of these zones are well studied, less is known about how familiar drivers are with their local yellow indication laws, which vary from state to state, and whether their typical reactions to yellow indications align with the laws. Existing interventions like signal timing adjustments, improved vehicle detection, and advance warning signs reduce the number of drivers caught in dilemma zones but may not reach distracted drivers. In-vehicle alerts tailored to dilemma zone scenarios are a potential solution not yet implemented widely in North America. This study addresses how drivers may interpret these alerts. A web-based survey of 640 licensed drivers in Michigan and Washington (ages 18–85) assessed respondents’ knowledge of their state’s law, typical responses to yellow indications, interpretations of proposed in-vehicle alerts, and preferences for alert modality, frequency, and placement. These states were selected for their differing yellow indication laws—restrictive in Michigan, permissive in Washington. Nine alert visuals were tested, including pairs of implicit and explicit messages, and were inspired by or designed to address gaps in prior research. Respondents evaluated these alerts in response to hypothetical intersection scenarios that varied by the presence of other vehicles. Results revealed a prevalent misunderstanding of local yellow indication laws across both states. Statistical analyses showed significant differences in rankings among the nine alert visuals, and explicit messages showed higher rates of correct interpretation. Findings show overall driver support for dilemma zone alerts, but higher receptivity in drivers who more frequently use other ADAS features and lower receptivity in drivers within older, but not the oldest, age groups. Future research could explore whether these alerts promote safe behaviors aimed at crash avoidance.
Anderson, Erika, Jashami, Hisham, Ahmed, Ananna, Hurwitz, David
Increasing the Competency and Confidence of Older Drivers in the Use of Advanced Vehicle Technologies: The MOVETech Randomized Controlled Trial09-14-01-00135/4/2026
Programs that teach older drivers how to confidently and competently use advanced vehicle technologies (AVTs) are limited. The MOVETech study evaluated a training program specifically designed to teach older drivers how to use these technologies. Participants (n = 119) were randomized to the intervention (training program) or control group (brochure). The intervention involved an in-person classroom education session on the use and benefits of AVTs, and an on-road driving session where participants drove along a pre-defined route in a dual-controlled vehicle with instruction on AVT use by a driving instructor. All participants completed in-person and telephone assessments at baseline and 3 months. Driving performance and on-road AVT competence assessments were the primary outcomes. Self-reported driving confidence, competence, and confidence in use of AVT, crashes, citations, and count of vehicle damage were the secondary outcomes. Program fidelity was also evaluated using a checklist. At 3 months, overall driving performance was high (96/100) and similar between groups. The intervention group, however, had slightly higher competence in AVT use (77 vs 73), but the between-group difference was not statistically significant (4.14, 95% CI −4.85 to 13.13). There were no differences in secondary outcomes. Program fidelity was high for all classroom sessions but varied for on-road sessions due to external and environmental factors, which impacted how AVT was demonstrated. The findings indicate AVT competence and confidence may be improved by combining classroom and on-road sessions, and importantly, that this type of program is feasible and very well-accepted among older drivers. Future work could target drivers with new vehicles who are unfamiliar with AVT to determine potential real-world benefits. This study provides evidence for vehicle manufacturers and policymakers to explore efficient ways of providing support to older drivers with AVT.
Nguyen, Helen, Ren, Kerrie, Coxon, Kristy, Neville, Nick, O’Donnell, Joan, Cheal, Beth, Brown, Julie, Keay, Lisa
Are Drivers More Distracted with Level 2 Automated Technologies? A Naturalistic Driving Study Analysis of Frequency, Duration, and Proportion of Time Drivers Engage in Secondary Tasks09-14-01-00285/4/2026
This project was designed to better understand how the activation of SAE International Level 2 (L2) system features affect the duration of secondary task engagement. Four naturalistic driving datasets were used: one that included drivers without L2 experience, two that included drivers with L2 experienced, and one that included drivers of L0 vehicles. Dependent variables that were assessed include frequency of secondary tasks, duration of secondary task, and proportion of time that drivers engaged in cell phone tasks when L2 systems were active compared to when L2 systems were available but inactive. Results suggest that both the frequency and proportion of time drivers engaged in secondary tasks were significantly higher when L2 systems were active compared to when systems were available but inactive. Drivers without L2 experience took longer to perform tasks involving the center stack/instrument panel compared to experienced L2 drivers. These results suggest that drivers demonstrate a tendency to shift their attention away from the driving task when L2 is engaged.
Klauer, Sheila, Dunn, Naomi, Anderson, Gabrial T., Barnes, Ellen, Han, Shu, Fincannon, Thomas, Weaver, Starla
Visual–Language Model–Driven Annotation and Analysis of Maneuver Data for Enhanced Vehicle Safety09-14-01-00325/4/2026
Vehicle maneuver data are essential for perception and planning in advanced driver-assistance systems (ADAS) and automated driving systems (ADS). While high-quality annotations improve machine-learning performance, existing maneuver datasets remain fragmented, labor-intensive to annotate, and inconsistent in semantic richness. Challenges persist in scalability, interpretability, and contextual labeling. This article establishes a structured framework for maneuver data analysis by combining a systematic review of existing resources with the development of a new multimodal dataset. First, we conduct a systematic review of publicly available datasets such as HDD, KITTI, BDD-X, D2CAV, Brain4Cars, DrivingDojo, and the Driving Behavior Database. We further evaluate the data modality and sensor configurations including event data recorders, onboard logging systems, and smartphone sensing. We then propose the Matt3r Data Collection System with modern metadata management, which integrates video, GPS, and IMU signals into temporally coherent clips. Next, we outline the limitations of traditional annotation approaches, which rely on manual labeling and rule-based methods. To address the limitations of traditional manual and semi-automated labeling, we propose a Vision–Language Model (VLM)–driven annotation pipeline. VLMs generate maneuver categories and causal explanations through prompt-based reasoning, with selected outputs refined through human-in-the-loop verification. Finally, we propose an annotation quality evaluation based on accuracy, inter-annotator agreement, credibility, consistency, and efficiency gain. In summary, this article bridges the gap between the environment perception requirements of existing ADAS and ADS systems and the developing capabilities of generative artificial intelligence. By providing a novel and scalable research approach for AI-driven maneuver data annotation and analysis, this article supports data engineering efforts for both research and practical applications aimed at enhancing vehicle safety.
Bai, Ling, Yuan, Chongyu, Osman, Islam, Lin, Zirui, Mirab, Ghazal, Saheb, Amir, Parnian, Neda, Shapiro, Evgeny, Shehata, Mohamed S., Liu, Zheng
Injury and Kinematics of Obese PMHS in Frontal Impacts with Reclined Seating09-14-01-00035/4/2026
Objective: This study investigated injury outcomes and body kinematics in obese occupants exposed to frontal impacts while seated in reclined postures. With increasing interest in non-traditional seating configurations and a growing population of obese vehicle occupants, the objective was to evaluate how seat stiffness and restraint features influence injury patterns and whole-body excursions. Methods: Nine obese post-mortem human surrogates (PMHS; mean age: 64 years, stature: 1.70 m, body mass: 102 kg, BMI: 35 kg/m2) were tested under frontal impact conditions simulating a delta-V of 50 kph. All specimens were seated on a spring-controlled seat with a 45° reclined seatback and restrained by a three-point belt system with pretensioner and load limiter. Three configurations were evaluated: (1) stiffer seat, (2) softer seat, and (3) stiffer seat with a knee bolster 100 mm from the knees. Each subject underwent one test. Whole-body kinematics were captured using a VICON motion analysis system, and injury outcomes were assessed through radiographs, CT imaging, and autopsy, with severity classified by Maximum Abbreviated Injury Scale (MAIS) and Injury Severity Score (ISS). Results: Softer seats produced substantially greater downward (+Z) pelvic displacement compared with stiffer seats. The knee bolster effectively reduced both downward and forward excursions of the lower torso. Rib cage and pelvic injuries were most frequent, with the highest severity observed in softer seat tests. Mean ISS values were: STIFF—27, 13, 27; SOFT—34, 27, 14; STIFF-KB—22, 0, 13. Discussion: Reduced seat stiffness combined with increased occupant mass contributed to greater excursions and anterior torso injuries, whereas the knee bolster mitigated excursion and injury severity. Findings are limited by sample size and test conditions; broader evaluation with production seats is needed to confirm trends and support countermeasure design.
Somasundaram, Karthik, Yoganandan, Narayan, Pintar, Frank
Geometric Variabilities and Their Effects on Impact Responses in Midsize Male and Female Occupants09-14-01-00255/4/2026
The objective of this study is to use parametric human body models (HBMs) to understand how geometric variability among individuals who have the same sex, stature, and body weight may affect the impact responses and injury outcomes, using midsize male and midsize female populations as representative cases. Methods were developed to quantify skeletal and external body surface variations using principal component analysis, regression, and residual error analysis. Based on this analysis, nine midsize male and nine midsize female geometric models were created, focusing on ribcage and pelvis variations, which account for most of the observed variability. These geometries were then applied to morph the simplified Global Human Body Model Consortium (GHBMC) midsize male model, producing 18 distinct HBMs. Each morphed HBM was subjected to nine impact scenarios, resulting in a total of 162 simulations to assess the effects of geometric variability. Substantial geometric variation was observed in the ribcage and pelvis, while the femur and tibia showed minimal variability for both midsize males and females. All morphed HBMs had good mesh quality, and all crash simulations terminated normally without error. Component-level tests showed relatively minor differences in impact responses among HBMs with identical sex, stature, and body weight. However, the United States New Car Assessment Program (US-NCAP) frontal crash simulations revealed considerable differences in injury risk, especially in the front passenger position. These findings highlight the importance of accounting for geometric variability, even among HBMs with the same sex, stature, and body weight, when evaluating injury risks in severe frontal crashes. It is especially important to consider ribcage geometry variations, which could impact occupant sitting height, posture, and injury risks at different body regions in frontal crashes. This study demonstrated that future virtual testing frameworks using HBMs should consider human geometric variations, especially in the ribcage and pelvis, when assessing injury risks in vehicle frontal crashes.
Hu, Jingwen, Lin, Yang-Shen, Boyle, Kyle, Khandare, Sujata, Bonifas, Anne, Reed, Matthew P., Hasija, Vikas
Cars and Planes Could Avoid Hazardous Ice and Freezing Rain with New SensorsTBMG-550635/1/2026
Pilots, drivers, and automated safety systems in cars and airplanes could be alerted to icy hazards by a pair of sensors developed at the University of Michigan. Ice on roads causes around 20 percent of weather-related car crashes each year, and ice build-up on planes causes roughly 10 percent of all fatal air carrier crashes by interfering with aerodynamics and controls. For instance, a flight from Brazilian airliner Voepass Linhas Aéreas crashed near São Paulo on August 9, 2024, after the plane’s de-icing systems failed, according to a report in the Aviation Safety Network. An Air France flight also crashed in the Atlantic Ocean on June 1, 2009, after ice blocked the probes that measure the plane’s speed. In both cases, all occupants died.
Hierarchical Control Strategy Evaluation and Migration for a Four-Degree-of-Freedom Quarter Commercial Vehicle02-19-03-00144/9/2026
In order to improve the comfort performance in commercial vehicles, this study proposes a hierarchical control strategy that integrates the evaluation and migration of control algorithms. First, a quarter-vehicle model with four-degree-of-freedom (4-DOF) is constructed, incorporating the dynamics of the wheel, frame, driver’s cab, and seat. The key modal characteristics of the model are then verified through amplitude–frequency analysis, confirming their consistency with the typical vibration patterns observed in actual commercial vehicles, which provides the foundation for subsequent control strategy evaluation and migration. Then, based on a standard two-degree-of-freedom (2-DOF) suspension model, a weighted comprehensive evaluation function is developed to account for comfort, structural safety, handling stability, and both time- and frequency-domain performance indicators. Using this evaluation function, various control algorithms—including Skyhook control (SH), acceleration-based damping control (ADD), and proportional–integral–derivative control (PID)—are systematically assessed. The control algorithm is migrated to the 4-DOF model to carry out the hierarchical collaborative control. The results show that this method can effectively inhibit vibration transmission to enhance ride comfort and improve structural safety at the same time, while maintaining an acceptable level of handling performance. The transferability and applicability of the hierarchical control method are validated for the considered vertical dynamics scenarios. This article provides a new theoretical method and technical pathway for the comfort-oriented performance optimization of commercial vehicles.
Pan, Ting, Pang, Jianzhong, Wu, Jinglai, Zhang, Jiuxiang, Kang, Gong, Zhang, Yunqing
A Dynamic Driver Comfort Index for Stabilizing Mixed Traffic Behavior2026-01-00994/7/2026
Despite remarkable advances in vehicle technology - enhancing comfort, safety, and automation – productivity of transportation over the road continues to decline. Stop-and-go driving remains one of the most persistent inefficiencies in modern mobility systems, leading to greater travel delays, energy waste, emissions, and accident risk. As vehicle volumes rise, these effects compound into systemic challenges, including driver frustration, unstable flow dynamics, and elevated greenhouse gas (GHG) emissions. To address these issues, an extensive data-driven evaluation was performed characterizing the underlying causes of traffic instability and uncovering hidden behavioral parameters influencing traffic flow. This research led to the identification of a previously unrecognized metric - the Driver Comfort Index (DCI) - which quantifies an inter-vehicle spacing behavior that reflects intrinsic human driving behavior. Building on this discovery, mixed traffic is explored to identify its phenomena, where human-driven and machine-controlled vehicles coexist and share the road. It appears that adaptive cruise control (ACC) and connected autonomous vehicles (CAV) are controlled by a non-intrinsic parameter so that traffic mix suffers from a mismatch of vehicle dynamics. This mismatch is explored, and it is proposed to harmonize traffic dynamics by adopting the natural DCI parameter as the single control mechanism. Analytical studies demonstrate that DCI-based traffic flow orchestration, applied integrally to human- and machine-controlled vehicles, enhances traffic flow stability, mitigates stop-and-go oscillations, and significantly improves network efficiency, safety, and environmental performance.
Schlueter, Georg J.
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-01654/7/2026
Energy efficiency and range optimization remain critical challenges to the widespread adoption of battery electric vehicles (BEVs). As a result, there is a growing demand for intelligent driver assistance systems that can extend the operating range and reduce range anxiety. This paper presents an adaptive eco-feedback and driver rating system based on proximal policy optimization (PPO) reinforcement learning, designed to support drivers with the target to reduce energy consumption and maximize driving range. The system processes real-time driving data, such as velocity, acceleration and powertrain status. Map data of high quality is used to anticipate traffic events, including but not limited to speed limits, curves, gradients, preceding vehicles and traffic lights. This contextual awareness allows the system to continuously assess driving behavior and provide personalized, context-aware visual feedback alongside a dynamic driving behavior rating. A PPO agent learns optimal feedback strategies through continuous interaction and evaluates the impact of specific guidance actions, such as but not limited to “release accelerator pedal”, “brake” and “recuperate”, on immediate energy efficiency and long-term driver adaptation patterns. Feedback intensity and modality are dynamically tailored to individual driver profiles based on observed reaction patterns and feedback adherence. This approach encourages drivers to prioritize energy efficiency while aiming to minimize cognitive distraction and discomfort. The algorithm is implemented and validated within a driving simulation environment that replicates diverse and realistic conditions. Virtual driving tests conducted in various scenarios, such as congested urban areas, suburban routes, mountain roads and highways demonstrate that the proposed PPO-based eco-driving assistance system can reduce energy losses by about 28% compared to conventional driving behavior.
Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin
Development of a Lightweight and Quick-to-Install Pedal Robot for Unmanned Chassis Dynamometer Testing2026-01-01974/7/2026
As the adoption of electric vehicles continues to accelerate, the demand for their development and testing using chassis dynamometers has also increased significantly. Compared with internal combustion engine vehicles, chassis dynamometer testing for electric vehicles typically requires test durations several to several dozen times longer, resulting in substantially increased labor requirements. In addition, low-temperature testing is often required, further intensifying the workload associated with vehicle testing. To address these challenges, this study developed and evaluated a pedal robot designed to enable unmanned and automated testing. The pedal robot developed in this study weighs only 12 kg and can be installed within a few minutes. It is, to the authors’ knowledge, the world’s first pedal robot that mimics human driving behavior by using a single foot to operate both the accelerator and brake pedals. Unlike conventional driving robots, the actuators of the proposed system do not require direct mechanical attachment to the vehicle pedals, allowing for rapid installation. Furthermore, the robot is mounted on the driver-side floor, eliminating the need for attachment to the seat structure. The pedal robot features three degrees of freedom driven by three motors and employs artificial intelligence to recognize the shape and position of pedals across different vehicle models, thereby enabling automated test initiation without manual adjustment. The performance of the pedal robot was evaluated under UDDS, HWFET, and WLTC driving modes, and the results were analyzed in accordance with the SAE J2951 standard. Comparative evaluations demonstrated that the pedal robot achieved superior speed-tracking performance relative to that of an experienced human test driver. The developed pedal robot is currently being utilized for vehicle certification testing of electric and other vehicles at the Mobile Environment Research Center of the National Institute of Environmental Research in Korea. This paper presents a detailed analysis of the corresponding experimental results.
Lee, Daeyup, Kang, Ji Myeong, Jo, Yechan, Choi, SeongUn, Shin, Jaesik, Kim, Jongmin, Kang, Keonwoo
Personalized Driver Model for On-Ramp Merging Using LSTM2026-01-05254/7/2026
This study develops a personalized driver model for expressway merging, embedding individual driving characteristics into automated longitudinal and lateral control via Long Short-Term Memory (LSTM) networks. Uniform assistance (Advanced Driver Assist System, ADAS) can feel uncomfortable when it does not match a driver’s style; we therefore target the merge maneuver—a safety-critical task requiring anticipation and timing—and test whether merging-related context improves model fidelity. Driving data were collected in a high-fidelity motion-base simulator across two merging scenarios (13 licensed drivers in total). Inputs comprised ego speed, Headway distance and relative speed to the lead vehicle, and geometric context variables (distance to the end of the acceleration lane and to the hard/soft nose); outputs were longitudinal and, in the cross-scenario study, lateral accelerations. Models were trained per driver and evaluated by root mean square error (RMSE). Including merging context reduced longitudinal error in Experiment 1 (Gotemba IC) by about 30% on average relative to models without context, while errors remained below 0.5 m/s2. In Experiment 2 (Tokyo–Nagoya Expressway vs. Tokyo Metropolitan Expressway), longitudinal and lateral errors were low across both geometries; group-mean trends favored context but were non-significant, reflecting small sample size and inter-individual variability. Questionnaire-based evaluations in the simulator showed ratings close to real driving for discomfort, merge timing, and perceived safety; similarity and willingness to use were slightly higher in the urban expressway scenario, suggesting good user acceptance in constrained conditions. These findings indicate that incorporating merging context enables personalized control that better reflects individual driving behavior, while pointing to future work on generalization across geometries, speed ranges, and richer interaction semantics.
Shen, Shuncong, Hirose, Toshiya
Quasi-Static Head Restraint Pull-Tests to Assess Retention Performance: Maximum Load and Damage Characteristic2026-01-05644/7/2026
Head restraint requirements and designs have evolved to minimize the delay in head support and reduce differential loading in the neck. As a result, head restraints have become bigger and more angled forward, sitting, closer to the occupant’s head. Head restraints separation from seatbacks are sometimes observed in the field. Are head restraint detachments resulting from occupant comfort issues prior to the crash, occupant loading during the crash or were they removed by emergency personnel for extrication? Understanding the retention strength of head restraints and the type of evidence left behind by a forced removal may help researchers resolve the question of how a head restraint may be found post-crash separated from the seat. Quasistatic pull tests were conducted to measure vertical retention capabilities, compare vertical adjustment and release mechanisms, and document deformation and damage. Eighteen different front seat head restraint designs were evaluated. The model years ranged from 2014 to 2019. All head restraints complied with FMVSS 202A requirements. Each head restraint design was tested in two tension loading configurations: In-line nominal pull and 45-degree pull. In the nominal configuration, the head restraints were pulled vertically upwards, in line with the adjustment posts. Additionally, head restraints were pulled at a 45-degree angle to the ground in the 45-degree configuration. The force at detachment averaged 1,758 ± 559 N for the in-line tests and 2,505 ± 662 N for the 45 degree pull tests. Damage was observed in all 36 tests and was evidenced by deformation in the locking mechanism and/or the guide sleeve being displaced out of the seatback. Detachment occurred due to overload or deformation of the locking mechanism or the guide sleeve being pulled out of the seatback. Bypassing detachment occurred in 21 of the tests while detachment from guide sleeve separation resulted in 15 of the tests. Half of the head restraints were equipped with two locking sides and half with one side. However, there does not appear to be a correlation between peak force and number of locking sides. There are currently no head restraint retention regulations for tensile loading. This study is a first to document head restraint separation resulting from forceful loading. The damage was documented in detail. This information may assist in answering the questions posed above.
Parenteau, Chantal, Burnett, Roger, Davidson, Russell
Leveraging Machine Learning for Occupant Body Size Estimation via B-Pillar Seatbelt Systems2026-01-05594/7/2026
Occupant body size in vehicles varies significantly, encompassing differences in height, mass, and overall body composition. Adaptive restraint systems, featuring adjustable parameters such as belt load limiters, steering column load limiters and stroke, seat pan stiffness, and airbag pressure, can offer more equitable protection tailored to individual body sizes. In this study, a test rig modeled after the Volvo XC90 (2016) was used to collect data from 46 participants who were dressed in typical summer clothing and seated upright, without slouching or leaning sideways. Stepwise adjustments of the seat pan and seatback were performed. The collected measurements include seat pan movements (front-back and up-down), seatback recline, and key seatbelt-related parameters, such as belt payout length, D-ring angle, lap belt length, and buckle tension. The collected data was then used to train machine learning models to predict individual occupant characteristics: standing height, mass, and seated height. This study shows the challenges and opportunities for occupant body size estimation from seatbelt and seat location inputs. The prediction’s root mean square error across validation dataset was as follows: standing height 8.76cm, mass 11.33kg, and seated height 5.61cm. The prediction of mass fulfilled the defined criterion, while the prediction accuracy for standing height and seated height require further improvement. Our analysis reveals that a key improvement could be achieved by implementing an upgraded lap belt position sensor, given that lap belt length was identified as a dominant feature across models. Furthermore, the analysis suggests that D-ring angle, buckle tension, and seatback recline can be excluded from the input feature set.
Wang, Da, Ahmed, Jawwad, Rowe, Mike, Brase, Dan
Study of Automatic Emergency Braking Effects on Human and Surrogate Passengers2026-01-05564/7/2026
The influence of modern Automatic Emergency Braking (AEB) on the head and neck behavior of the occupants in a vehicle continues to be an active area of research. Occupant kinematics and kinetics were evaluated using a vehicle equipped with a pedestrian AEB system. The vehicle was tested in several different scenarios with speeds between 15 and 45 mph. Two instrumented 50th-percentile male Hybrid-III Anthropomorphic Test Devices (ATD) were positioned in certain seats of the vehicle, while minimally instrumented human volunteers occupied the remaining seats. Displacement transducers and video analysis were utilized to capture the kinematics of each occupant. The findings of this study indicate that in AEB-only events with belted-occupants, the test vehicle did not result in any occupant motion that would have placed the occupants out-of-position (OOP) had an impact occurred immediately following the AEB event. This means that when evaluating real-world AEB events, it may not be necessary to analyze and model properly seated and restrained occupant kinematics prior to an impact event when only AEB occurs. Consistent with the published literature, the kinetic results continue to show that the belted occupant exposure is significantly below any accepted injury criteria and is comparable to routine activities of daily living. Tests were also completed in two seating configurations with unbelted ATDs to evaluate the difference in vehicle braking (if any) and the excursion differences when unbelted. The study found greater excursion for unbelted ATDs compared to that of belted volunteers and provides a sample of unbelted ATD kinematics via AEB activation.
Bartholomew, Meredith, Dahiya, Akshay, Russell, Caleb, Morr, Douglas, Castro, Elaine, Nguyen, An
Investigation of a System for Headlight Glare Control Based on Photometric Zones2026-01-05154/7/2026
Headlight glare remains a constant problem among the driving public. Following several decades of mostly incremental progression in headlight design, the past twenty years have witnessed rapid evolutions in technology and design that have made substantial differences in the appearance and performance of automotive headlights. Most obviously, there has been a transition from yellowish-white sealed beam and halogen lamps, to high-intensity discharge and more conclusively, light-emitting diode sources with a distinct, cool-white color appearance. This transition has increased perceptions of brightness, both of the forward road scene (potentially benefiting the headlight user) and of the headlights themselves (increasing visual discomfort for opposing drivers). The mix of vehicles has also increased in size, resulting in higher-mounted headlights and the potential for higher light levels at other drivers’ eyes. Variability in headlight vertical aim has possibly decreased in very recent years, but still remains quite variable. Perhaps most crucial for the latest rounds of complaints about headlight glare, the peak luminous intensities of headlights over the past two decades have increased and the vertical inclination of these peak values has also increased, along with the sharpness of the gradient between the upward regions of low intensity and the downward regions of increasingly high intensity. Because roads are not perfectly straight or flat, these developments can increase the severity of glare episodes. Described in this paper is a zone-based concept for glare control based on the likelihood of headlight illumination in a particular angular zone to reach the eyes of other drivers. Zones more likely to glare other drivers would have more stringent intensity limits to reduce the probability of bothersome glare. Possibly in conjunction with some overall upper limits on low-beam luminous intensity, the impacts of such a system on glare and visibility are assessed.
Bullough, John D.
A Cooperative AFS/DYC Lateral Stability Control for FWDIEV Based on Dissipative Energy Method2026-01-06194/7/2026
To enhance the lateral stability of four-wheel-drive intelligent electric vehicles (FWDIEV) under extreme operating conditions, this paper proposes a cooperative control strategy integrating active front steering (AFS) and direct yaw moment control (DYC) based on dissipative energy method. A nonlinear three-degree-of-freedom vehicle model is established to analyze the evolution of the vehicle state phase trajectory. A quantitative lateral stability index is constructed using dissipative energy to accurately evaluate the vehicle’s lateral dynamics. Utilizing dissipative energy and its gradient information, a time-varying stability boundary is defined under dynamic constraints, and adaptive weighting coordination between the AFS and DYC systems is designed to achieve coordinated control of front steering angle and additional yaw moment. A feedforward–model predictive control (FF-MPC) framework is developed, in which a feedforward module generates compensation based on driver intent to improve system responsiveness, while the model predictive controller predicts real-time vehicle states and optimizes the front steering angle and yaw moment control inputs. This enables cooperative tracking of the yaw rate and sideslip angle, effectively suppressing lateral motion errors. Furthermore, an optimal torque distribution strategy is formulated with the objective of maximizing tire–road friction utilization, incorporating constraints such as tire load rate and motor output capability to prevent wheel slip and improve handling stability. The effectiveness of the proposed control strategy is validated through both CarSim/Simulink co-simulation and real vehicle tests under typical maneuvers such as high-speed double lane change on various road surfaces. Results demonstrate that the proposed method significantly reduces tracking errors in yaw rate and sideslip angle compared to conventional MPC strategies, thereby enhancing lateral stability and ensuring driving safety under extreme conditions.
Zhao, Kun, Zhao, Zhiguo, Wang, Yutao, Xia, Xue, Chen, Xi, Hu, Yingjia
Quantifying Accuracy and Robustness to Guide Fidelity Selection for Multi-Fidelity Vehicle Dynamics Models2026-01-01494/7/2026
Accurate and reliable simulation models are essential for design, development, and performance evaluation during virtual vehicle testing. However, fidelity assessment and validation remain a challenge. While error metrics are used to evaluate simulations, they alone do not capture how reliable predictions are, or how robust models are to varying driving scenarios and modeling assumptions. This work develops a systematic quantitative approach for evaluating vehicle dynamics model fidelity, moving beyond traditional visual or qualitative comparisons. A dimensionless fidelity metric is proposed that integrates error and uncertainty into a single measure, enabling objective accuracy assessment of variable-fidelity simulations. This framework supports fidelity selection in vehicle dynamics, providing clearer insight into tradeoffs between computational cost and achievable accuracy, and advancing the goal of reliable virtual testing. This approach is demonstrated on an open-loop vehicle dynamics simulation for a compact vehicle under both cruising and evasive maneuvers, comparing a library of multi-fidelity models developed through three approaches: Co-Kriging surrogate models that combine low- and high-fidelity data, adaptive fidelity models that switch between fidelities during simulation, and mixed-component fidelity models that integrate low and high-fidelity subsystems. These models were executed under varying operating conditions to evaluate how driving scenarios influence accuracy. The fidelity metric incorporates three components: error relative to the high-fidelity reference, input parameter uncertainty propagated though Monte Carlo sampling, and model form uncertainty measured through Bayesian inference. Error and uncertainty results were combined in a single dimensionless fidelity metric that provides insight into both accuracy and robustness. Demonstration on open-loop driver simulations shows that models with low errors can still exhibit high uncertainty, indicating the importance of considering both error and uncertainty in models. The framework also enables evaluation of parametric scenarios through visual analytics, offering clearer insight into fidelity evaluation, and strengthening the role of virtual testing in vehicle development.
Emara, Mariam, Balchanos, Michael, Mavris, Dimitri
Sensor Fusion Method for In-Cabin Humidity Prediction2026-01-01314/7/2026
Maintaining optimal in-cabin humidity levels is part of occupant comfort, air quality, and the effective operation of climate control systems, particularly for functions like windshield defogging. This paper introduces a novel sensor fusion methodology for predicting in-cabin humidity distribution without dedicated humidity sensor. The proposed approach leverages readily available vehicle data, integrating information from ambient temperature sensors, in-cabin temperature sensors, occupant detection systems, window status, and climate control settings. By intelligently fusing these diverse data streams, a predictive model is developed to infer the dynamic humidity conditions within the vehicle cabin. We discuss the complex interactions between these parameters, such as the moisture contribution from occupants, the influence of external air ingress through open windows, and the dehumidifying or humidifying effects of the Heating, Ventilation, and Air Conditioning system. The paper details the development and validation of the predictive algorithm, highlighting its capability to estimate humidity levels under various operational scenarios. Challenges in modeling the transient and non-linear relationships between inputs and humidity, as well as the evaluation of the model's accuracy against ground truth data, are presented. Alos, initial results demonstrate the feasibility and robustness of this sensor fusion approach, offering an integrated solution for intelligent services and cabin climate conditioning are summarized.
Ghannam, Mahmoud, Schroeter, Robert, Shaik, Faizan
Simulation Study on Rear-Wheel Steering Control of FSAE Vehicles Based on Temporal Convolutional Network2026-01-06504/7/2026
The Formula SAE (FSAE) race track is characterized by a large number of corners, making cornering performance a key factor affecting lap time. Based on the proportional control strategy for rear-wheel steering angles, this paper proposes a steering angle optimization method using a Temporal Convolutional Network (TCN). The TCN model features a faster training speed than traditional sequential neural networks. In addition, dilated convolutions enable an exponential expansion of the receptive field without increasing computational costs, making it particularly suitable for capturing the temporal dependencies of vehicle states. By processing vehicle dynamic parameters including front-wheel steering angle, vehicle speed, yaw rate and sideslip angle, the model calculates the correction value of the rear-wheel steering angle. This correction value is then superimposed with the reference value of the rear-wheel steering angle derived from the proportional control strategy, which serves as the control value for rear-wheel steering. Rear-wheel steering can reduce the turning radius during low-speed driving and enhance the racing car’s stability during high-speed cornering. This method was validated on a typical race track via CarSim-MATLAB co-simulation, resulting in reduced lap time. To meet the real-time computing requirements of FSAE, MATLAB was used to simulate the discretization results of vehicle parameters such as vehicle speed and front-wheel steering angle, generating a Look-Up Table for rear-wheel steering angles, which provides a feasible solution for real-vehicle tests. The racing car is equipped with a manual switch for the driver to operate. The driver can manually turn the rear-wheel steering function on or off when cornering or whenever they deem it necessary.
Liu, Xiyuan
Items per page:
1 – 50 of 6557