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

S, Nikhil, Singh, Abhimanyu Kumar, Katageri, Praveen, SP, Pradeep, Chandra, Praveen

AI-Led Sustainability Strategy: Driving Product Value from Birth to Disposal

2026-26-0781

To be published on 06/01/2026

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

Srinivasan, Karthik, G.V.V., Ravi Kumar, Vaderahobli, Devaraja Holla, Bhate, Ujwal, Veluri, Sastry

Zero Waste Manufacturing in Aerospace: Circular Design Approaches from Concept to End-of-Life

2026-26-0775

To be published on 06/01/2026

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

S, Chaitra

Driving Behavior Heterogeneity Identification with Action Patterns Relationship

2026-99-0752

5/15/2026

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

Yin, Hui, Zhang, Qinyao, Li, Xiaojian, Mo, Hangjie

Letter from the Guest Editor

09-14-01-0034

5/13/2026

Letter from the Guest Editor

Tylko, Suzanne

SAE TOMORROW TODAY - Why Curing Motion Sickness Is Key to AV Success

135675/11/2026

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

Patterson, Lori

Visual–Language Model–Driven Annotation and Analysis of Maneuver Data for Enhanced Vehicle Safety

09-14-01-0032

5/4/2026

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

Bai, Ling, Yuan, Chongyu, Osman, Islam, Lin, Zirui, Mirab, Ghazal, Saheb, Amir, Parnian, Neda, Shapiro, Evgeny, Shehata, Mohamed S., Liu, Zheng

Driver Understanding of Yellow Indication Laws and Preferences for Configuration of In-Vehicle Alerts for Dilemma Zones

09-14-01-0006

5/4/2026

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

Anderson, Erika, Jashami, Hisham, Ahmed, Ananna, Hurwitz, David

Understanding and Modeling Effectiveness of Predictive Risk Notifications for Early Assistance for Safe Driving: A Public Road Testing Study

09-14-01-0026

5/4/2026

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

Maruyama, Masaki, Koyama, Keiichiro, Ezaki, Toru, Sakamoto, Junichi, Sawada, Yuta, Matsuoka, Takahiro

Increasing the Competency and Confidence of Older Drivers in the Use of Advanced Vehicle Technologies: The MOVETech Randomized Controlled Trial

09-14-01-0013

5/4/2026

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

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 Tasks

09-14-01-0028

5/4/2026

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

Klauer, Sheila, Dunn, Naomi, Anderson, Gabrial T., Barnes, Ellen, Han, Shu, Fincannon, Thomas, Weaver, Starla

Advanced Tool for Traffic Crash Analysis: An AI-Driven Multi-Agent Approach to Pre-Crash Reconstruction

09-14-01-0033

5/4/2026

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

Xu, Gerui, Chen, Boyou, Guo, Huizhong, LeBlanc, Dave, Kusari, Arpan, Yarbasi, Efe, Ahmed, Ananna, Sun, Zhaonan, Bao, Shan

An Investigation of Drowsy Lane Departures from Driving Simulator Studies

09-14-01-0014

5/4/2026

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

Schwarz, Chris, Gaspar, John, Shull, Emily, Venegas, Michael

Hierarchical Control Strategy Evaluation and Migration for a Four-Degree-of-Freedom Quarter Commercial Vehicle

02-19-03-0014

4/9/2026

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

Pan, Ting, Pang, Jianzhong, Wu, Jinglai, Zhang, Jiuxiang, Kang, Gong, Zhang, Yunqing

Collision Avoidance Effectiveness of an Automated Driving System Using a Human Driver Behavior Reference Model in Reconstructed Fatal Collisions

2026-01-0520

4/7/2026

Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict (conflict avoidance effect) and (2) road user response should a conflict be entered (collision avoidance effect). This study examined the collision avoidance effect of the Waymo Driver, a currently deployed SAE level 4 automated driving system (ADS), using a human behavior reference model, designed to be representative of a human driver that is non-impaired, with eyes on the conflict (NIEON). Reliable performance benchmarking methodologies for assessing ADS performance are an essential component of determining system readiness. This consistently performing, always-attentive driver does not exist in the human population. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the Operational Design Domain of the Waymo ADS in Chandler, Arizona. Of 16 simulated conflicts entered, 12

Scanlon, John M., Kusano, Kristofer D., Engstrom, Johan, Victor, Trent

Driver Response Times to Turn-In-Path Events from Real World Responses in SHRP-2 Data

2026-01-0545

4/7/2026

This study analyzed driver behavior in Turn-In-Path (TIP) scenarios using the Second Strategic Highway Research Program (SHRP2) naturalistic driving dataset. A total of 167 real-world incidents, including both crashes and near-crashes, were examined to evaluate human driver perception-response times (PRT) and avoidance behaviors when an intruding vehicle (the principal other vehicle, or POV) turns into the path of a straight-moving subject vehicle (SV). The combined analysis includes TIP events involving POVs turning from intersecting roads to either cross or merge into the SV’s lane and continues in the direction of the SV. Each event was reviewed to identify the driver behavior in an emergency response event, with measurements taken from video and telematics data. Response time was measured across two different starting points. Key variables included time to conflict, POV behavior, SV driver engagement in secondary tasks, and environmental factors such as lighting and roadway

Dinakar, Swaroop, Muttart, Jeffrey, Maloney, Timothy, Adhikari, Bikram

Personalized Driver Model for On-Ramp Merging Using LSTM

2026-01-0525

4/7/2026

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

Shen, Shuncong, Hirose, Toshiya

Benchmarking Driver Responses in Head-On Conflicts: Expanding the Framework for TTC, Affordances, and Uncertainty

2026-01-0534

4/7/2026

Head-on emergency events present unique challenges for evaluating both human and automated-vehicle (AV) performance because they do not conform to a direct stimulus–response sequence. Instead, driver behavior in these scenarios follows a stimulus–wait–response pattern governed by time-to-conflict (TTC), uncertainty, and environmental affordances. Prior research has often failed to distinguish between conflict types, resulting in generalized reaction-time assumptions that do not account for contextual uncertainty. This study integrates simulator and naturalistic driving data from a four-part research program to establish objective benchmarks for driver responses in head-on encounters. When an encroaching vehicle crossed the centerline 2.5 s before impact, drivers initiated braking with a weighted average of approximately 1.0 s before impact. When the encroaching vehicle crossed or was first observed at approximately 3.5 s before impact, braking typically began with a weighted average of

Muttart, Jeffrey, Dinakar, Swaroop, Maloney, Timothy, Adikhari, Bikram, Gernhard-Macha, Suntasty

A Dynamic Driver Comfort Index for Stabilizing Mixed Traffic Behavior

2026-01-0099

4/7/2026

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

Schlueter, Georg J.

Simulation Study on Rear-Wheel Steering Control of FSAE Vehicles Based on Temporal Convolutional Network

2026-01-0650

4/7/2026

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

Liu, Xiyuan

From Prediction to Design: Using Context-Aware Graph Neural Networks and Explainable AI to Anticipate Transfer-of-Control

2026-01-0049

4/7/2026

Drivers often interact with partial automation (SAE Level 2) systems, initiating transfer of control (TOC) either by handing control over to the automation or by taking it back. Accurately predicting these interactions may inform the design of future automation systems that adapt proactively to the operating context, enhance comfort, and ultimately may improve safety. We present a context-aware framework that generates a unified driver–vehicle–environment representation by fusing data from in-cabin video of the driver and of the forward roadway with vehicle kinematics, driver glance, and hands-on-wheel behaviors. This representation was encoded in a hierarchical Graph Neural Network that classified driver-initiated TOCs to: (i) Manual-to-automation and (ii) Automation-to-manual transitions and predicted time-to-TOC. Shapley-based explainable AI was used to quantify how the importance of behavioral, contextual, and kinematic cues evolved in the seconds preceding a TOC. Analysis of a

Zhao, Zhouqiao, Gershon, Pnina

Quantifying Accuracy and Robustness to Guide Fidelity Selection for Multi-Fidelity Vehicle Dynamics Models

2026-01-0149

4/7/2026

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

Emara, Mariam, Balchanos, Michael, Mavris, Dimitri

Navigation Integrated End-To-End Path Planning Development and Vehicle Testing

2026-01-0032

4/7/2026

In order to achieve fully autonomous driving, point to point autonomous navigation is the most important task. Most existing end-to-end models output a short-horizon path which makes the decision process hard to interpret and unreliable at intersections and complex driving scenarios. In this research, we build a navigation-integrated end-to-end path planner on top of an openpilot open source model. We created a navigation branch that encodes route polyline geometry, distance-to-next-maneuver, and high-level instructions and combines with path plan branch using residual blocks and feed-forward layers. By adding minimal parameters, new model keeps the original openpilot tasks unchanged and have the path output based on the navigation information. The model is trained on diverse urban scenes’ intersections, and it shows improved route performance in vehicle testing. The proposed model is validated in a Comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test results

Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin

Characterizing Regenerative Braking of the Tesla Model 3 Using Chassis Dynamometer Testing

2026-01-0453

4/7/2026

Regenerative braking has a strong influence on the energy efficiency and drivability of battery-electric vehicles. This study establishes an empirical baseline analysis under controlled conditions of the regenerative braking behavior of the 2020 Tesla Model 3 to support the interpretation of on-road performance and serve as a reference for subsequent testing and analysis. The tests were performed on a four-wheel-drive chassis dynamometer at Argonne National Laboratory, combining Multi Cycle Testing (MCT) to simulate real world driving patterns (city, highway) with coast-down tests to isolate periods where the motor is operating in regen mode and compare the behavior across different parameters. Vehicle data was collected from the vehicle using taps in the Controller Area Network (CAN) bus as well as a high-resolution power analyzer. The vehicle displayed the highest efficiency during simulated city driving conditions (3.62 miles/kWh followed by highway (3.40 miles/kWh) and aggressive

Pierce, Benjamin Branch, Di Russo, Miriam, Das, Debashis, Zhan, Lu, Stutenberg, Kevin

A Study on the Injury Risk of Occupant with Different Head and Neck Rotating Postures under the Frontal Impact Sled Conditions

2026-01-0574

4/7/2026

Drivers obtain road information through head and neck rotation. In order to study the influences of head and neck rotation posture on occupant injury in frontal impact scenario, the THUMS (Total Human Model for Safety) AM50 human body model with five different head and neck rotation postures but without active muscles was adopted to study the biomechanical injury responses of occupant under the frontal impact scenario at 56 km/h in this study. Firstly, the kinematic responses of total body and head acceleration curves at the center of gravity predicted by PMHS (Post Mortem Human Subject) and THUMS AM50 human model under the sled test conditions were compared to verify the simulation model for subsequent study. Then, the THUMS AM50 human model with standard occupant seating posture was adjusted to have five different head and neck rotation postures with 0°, ±20°, and ±40° rotation angle, respectively. Finally, a series of frontal impact sled with or without airbag simulations were

Li, Dongqiang, jiang, Yejie, Tan, Chun, Li, Yanyan, Gong, Chuangye, Wu, Hequan, Jiang, Binhui

Behavioral Determinants of Electric Vehicle Battery Degradation: Evidence from Large-Scale Real-world Operations

2026-01-0458

4/7/2026

Electric vehicles (EVs) are central to sustainable transport, yet battery service life remains a limiting factor for cost and adoption. Distinct from traditional laboratory-based simulations that often fail to capture the complexity of field conditions, this study investigates how EV user behavior—including driving style and charging demands—influences capacity using large-scale, real-world operational data from daily EV usage. A data-driven framework is developed to quantify driving and charging behaviors through multidimensional feature extraction at the vehicle level and estimate battery State-of-Health (SOH) trajectories, enabling direct linkage between individual behavior patterns and degradation outcomes. Results reveal substantial heterogeneity in aging rates explicitly driven by diverse user behaviors: under identical urban conditions, vehicles with a radical driving style exhibit approximately 81% faster SOH decline per 20,000 km than those with a moderate style; regarding

Liu, Tianyi, Jing, Hao, Zhu, Jiankuan, Chen, Yongjian, Ou, Shiqi, Qian, Xiaodong

Comparison of Ammonia Emission Characteristics Between Different Gasoline Vehicles Equipped with Three-Way Catalysts

2026-01-0374

4/7/2026

In recent years, the tightening of vehicle emission regulations has led to a decreasing trend in regulated pollutants such as NOₓ and CO. However, the emission of ammonia (NH₃), which is unintentionally generated during the purification process in three-way catalyst of gasoline vehicles, has become a growing concern. NH₃ emissions from vehicles can serve as a precursor to PM2.5 and have been reported to cause local roadside pollution. Therefore, there is a growing need for on-road testing to identify conditions under which NH₃ is likely to be emitted. Furthermore, since engine control strategies vary among vehicle types, it is desirable to consider differences in emission behavior across different models. In this study, on-road NH₃ emissions were measured for multiple vehicle models with different powertrains, and the effects of engine behaviors and engine operating duration across vehicles on NH₃ emissions were investigated. To analyze differences in NH₃ emission behavior among

Ashizawa, Keigo, Fukunaga, Chisato, Gao, Tianyi, Sato, Susumu

Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization

2026-01-0165

4/7/2026

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

Stocker, Christoph, Hirz, Mario, Martin, Michael, Kreis, Alexander, Stadler, Severin

A Cooperative AFS/DYC Lateral Stability Control for FWDIEV Based on Dissipative Energy Method

2026-01-0619

4/7/2026

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

Zhao, Kun, Zhao, Zhiguo, Wang, Yutao, Xia, Xue, Chen, Xi, Hu, Yingjia

Investigation of a System for Headlight Glare Control Based on Photometric Zones

2026-01-0515

4/7/2026

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

Bullough, John D.

Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections

2026-01-0045

4/7/2026

Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents a novel approach to curvature prediction using deep neural networks trained on GPS-derived ground truth data, rather than model predictions, providing a more accurate training signal that reflects actual vehicle motion. We develop a multi-modal neural network architecture with temporal GRU encoders that processes vision features, driver intent signals, historical curvature, and vehicle state parameters to predict curvature. A key innovation is the use of GPS-based actual curvature measurements computed from vehicle motion data (κ = ωz/v) as training supervision, enabling the model to learn from real-world driving patterns. The model is trained on 5,322 samples from real-world driving data collected on The University of Oklahoma’s Norman Campus using a Comma 3X device and a 2025 Nissan Leaf electric vehicle. Experimental results

Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin

Development of a Lightweight and Quick-to-Install Pedal Robot for Unmanned Chassis Dynamometer Testing

2026-01-0197

4/7/2026

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

Lee, Daeyup, Kang, Ji Myeong, Jo, Yechan, Choi, SeongUn, Shin, Jaesik, Kim, Jongmin, Kang, Keonwoo

Advanced Integrated Chassis Control for Improved Energy Efficiency and Driving Experience in Electric Vehicle Applications

2026-01-0643

4/7/2026

Complexity of modern ground vehicles grows constantly, since car manufacturers want to provide functionality, while customers are expecting innovation and recent technologies to be integrated into the latest models released to the market. Recent advances in hard- and software opened the gates for new means of vehicle control and operation. Especially the transition to electric propulsion systems and decoupled chassis actuators offer completely new opportunities of dynamics control and manipulation. This paper presents an approach for integrated chassis and vehicle motion control in (battery) electric vehicle applications by using new and innovative controllers as well as mechatronic chassis systems. In several experiments on public roads with a fully instrumented vehicle demonstrator, that features in-wheel based rear-wheel drive and a hybrid brake-by-wire-system, the proposed control is tested under real environmental and traffic conditions with respect to aspects like energy

Heydrich, Marius, Mitsching, Thomas, Ivanov, Valentin

Automotive Engineering: April 2026

26AUTP044/2/2026

WCX 2026: Honda's crowdsourced PRMS can improve roads, drivers Proactive Roadway Maintenance System could tap into humanity's altruistic mode. Sue Bai's done the research and will deliver a keynote speech. Precise fit Why precision engineering is defining confidence in next-generation internal combustion engines. How precise GNSS unlocks scalable, cost-effective mapping for ADAS and autonomy The benefits of using the Global Navigation Satellite System and SD maps to keep ADAS/AVs up-to-date. Editorial It's war o'clock, again Supplier Eye Goodbye: A look at the past and thoughts on the future Caterham Project V EV on track for 2027 Joby looks to Toyota for investment and manufacturing Tensor goes with 433 Arms in upcoming Robocar Scout CEO: 160K reservations, no unsolvable Problems Ford announces 48-volt architecture for future electric truck BorgWarner to supply integrated drive and generator for EREV trucks The electric Mercedes G-Wagon: A pricey joy 2026 Audi A6: A sensible but fun

Startup Neumo says it can detect impaired drivers by scanning brain waves

26AUTP03_09

3/1/2026

A Detroit-based startup says its device can analyze brain activity to help figure out whether a driver is impaired. The impaired driver-detection business has been heating up since even before NHTSA announced in 2024 that it was working what would eventually be a mandate that vehicles be able to detect impaired drivers and mitigate the danger they represent to the motoring public.

Clonts, Chris

Chinese Drivers’ Preference for In-Vehicle Information Display: Insights from a Large-Scale Online Survey

12-09-03-0020

2/20/2026

Integrating intelligent and connected technologies in vehicles has significantly enriched the information environment for drivers, aiding them in making comprehensive driving decisions. However, inadequate information display may lead drivers to miss crucial information or increase their cognitive load, thereby affecting driving safety and user experience. It is essential to study drivers’ preferences for in-vehicle information display, the factors influencing these preferences, and to present information through appropriate modalities and carriers. Drawing on 695 valid questionnaire responses, this study investigates drivers’ preferences for recommendatory, explanatory, alerting, and warning information across three display modalities and six display carriers. A multivariate ordered probability model was further developed to examine the influence of user characteristics on these preferences. The results showed that drivers preferred visual cues over auditory ones, with a selection

He, Gang, Diao, Kai, Luo, Longfei, Xie, Bingjun, Zhong, Yixin, Qi, Jianping

Predicting of Transport Carbon Dioxide Emission of Light-Duty Gasoline Vehicles Based on Driving Behavior with Machine Learning Algorithm

2026-01-5012

2/20/2026

Driving behavior is a significant factor influencing vehicle emissions, and it must be carefully considered when modeling emissions for real road transportation vehicles. This study aims to contribute to this field by improving the intelligence and accuracy of distinguishing driving behavior volatility through the use of clustering algorithm. The research begins by processing raw emissions data collected from light-duty gasoline vehicle during real-driving emissions (RDE) test, which are used as input features for the clustering algorithm. Subsequently, a driving behavior classification method based on the gaussian mixture model (GMM) clustering algorithm is proposed. The results show that aggressive driving has a significantly higher CO2 emission rate compared to normal and calm driving. Specifically, the average CO2 emission rate for aggressive driving is 5.61 g/s, which is substantially higher than that of calm driving (2.40 g/s) and normal driving (2.91 g/s). Following this, the

Yu, Hao, Ma, Yi, Tan, Jianxun, Wang, Jing, Zhang, Honghao, Hu, Wei, Chen, Hao, Yu, Wenbin

Multi-Storey AI Based Parking System

2026-28-0129

2/12/2026

This paper presents a novel AI-based parking management system designed to enhance efficiency, reduce manual intervention, and optimize operational costs in modern parking facilities. By integrating computer vision with infrared (IR) sensors, the system continuously monitors parking areas in real time, accurately detecting vehicle occupancy and dynamically updating the space availability. The hybrid approach minimizes reliance on conventional sensors, improving accuracy and environmental robustness. Additional features include intelligent navigation assistance guiding drivers to available spots and integrated video surveillance for enhanced security through AI-driven suspicious activity detection. The user interface provides real-time updates ensuring a seamless and convenient parking experience. Overall, this system offers a comprehensive solution that advances parking technology through automation, real-time monitoring, and secure, user-friendly operation.

N, Kalaiarasi, Gupta, Shivansh, Hajarnis, Mihir, Anand, Vikas

Dynamic Stability Enhancement during Emergency Obstacle Avoidance under Inappropriate Driver Operation

10-10-02-0015

2/2/2026

In response to the decline in vehicle stability and the resulting safety risks caused by inappropriate driver operations during high-speed emergency obstacle avoidance, a human–machine cooperative control strategy based on driver operation recognition is proposed. The strategy establishes a vehicle controllability boundary by integrating real-time driver inputs with tire adhesion limits, enabling dynamic evaluation of the influence of operations on system controllability and identification of potential inappropriate operations. On this basis, a control authority allocation mechanism is developed, capable of adaptively adjusting to vehicle states and driver operations. By combining road boundary constraints with vehicle stability envelope constraints, the strategy dynamically regulates the steering angle, ensuring vehicle stability while retaining the driver’s effective intentions as much as possible. Unlike conventional path-tracking or single-envelope control approaches, the proposed

Liu, Yangyi, Zhou, Bing, Wu, Xiaojian, Jiang, Xiaokun, Cui, Qingjia

RDAS: A Shared Control Strategy Considering Real-Time Driving Ability in Driver Takeover Tasks

10-10-02-0014

1/24/2026

Autonomous vehicles require drivers to assume control of the vehicle in situations where the vehicle control system cannot perform its intended task. A shared control-based approach to driving authority transfer can effectively mitigate the driving risks associated with diminished driver capability due to prolonged disengagement, but it may readily precipitate human–machine conflicts—oscillatory steering behavior, excessive driver workload, and unstable control during weight transitions. Addressing the characteristics of driver capability variations during takeover tasks, a shared control strategy incorporating real-time driving ability, termed the real-time driving ability strategy (RDAS), is proposed. Initially, a real-time capability assessment strategy based on an expected steering angle model is developed. By collecting driving data under conditions of adequate driver capability to train an adaptive neuro-fuzzy inference system (ANFIS) neural network, the expected steering angle

Qi, Zhenliang, Liu, Ping, Duan, Haotian, Zhou, Zilong, Huang, Haibo

Human Detection Capacity of Vehicle Front Sonar Sensors in Light and Small Passenger Cars and Minivan

2025-22-0009

1/22/2026

Sonar sensor systems have been developed to prevent collisions between vehicles and surrounding objects by employing ultrasonic sensors mounted at the front of the vehicle. These systems warn drivers when nearby obstacles are detected. However, relatively few studies have examined the capacity of sonar to detect humans. This study aims to clarify the human detection capacity of front sonar sensors installed in two light passenger cars (LPC-I and LPC-II), one small passenger car (SPC), and one minivan (MNV). The LPC-I, SPC, and MNV were equipped with center and corner sensors, whereas the LPC-II had only corner sensors. Three volunteers—a child, an adult female, and an adult male—participated in the study. Human detectability was assessed using the “maximum detection distance ratio,” defined as the ratio of the maximum detection distance for a volunteer to that for a standard pipe. The results showed that both the center and corner sensors consistently detected front- and side-facing

Matsui, Yasuhiro, Oikawa, Shoko

Vehicle Usage Digital Twins: Mobility Analysis Test Case

2026-26-0394

1/16/2026

In the evolving landscape of the automotive industry, this study presents an innovative approach to developing digital twins for driver profiles, establishing a standardized and scalable procedure for collecting and analyzing driving data on a global scale. The proposed methodology centers on the development of a robust cloud infrastructure, including Data Lake and associated services, designed for efficient storage and processing of large volumes of data from multiple markets and vehicle types. The research introduces an adaptable procedure for data collection campaigns, applicable to diverse global markets and encompassing a wide range of vehicles, from internal combustion engines to electric and hybrid models. A key feature of this approach is the establishment of advanced data decoding protocols, enabling precise interpretation of CAN network information from vehicles of different manufacturers and models, even when the CAN structure is not previously known. The study defines

Arturo, Rubio, Marín Saltó, Anna, Diaz, Francisco, Olivencia, Sergio

Computer Vision Based Driver Health and Wellness Monitoring System: A) Fatigue Detection, B) Emotional Wellbeing

2026-26-0639

1/16/2026

Computer vision has evolved from a supportive driver-assistance tool into a core technology for intelligent, non-intrusive occupant health monitoring in modern vehicles. Leveraging deep learning, edge optimization, and adaptive image processing, this work presents a dual-module Driver Health and Wellness Monitoring System that simultaneously performs fatigue detection and emotional wellbeing assessment using existing in-cabin RGB cameras without requiring additional sensors or intrusive wearables. The fatigue module employs MediaPipe-based facial and skeletal landmark analysis to track Eye Aspect Ratio (EAR), Mouth Aspect Ratio (MAR), head posture, and gaze dynamics, detecting early drowsiness and postural deviations. Adaptive, driver-specific thresholds combined with CAN-bus data fusion minimize false positives, achieving over 92% detection accuracy even under variable lighting and demographics. The emotional wellbeing module analyzes micro-expressions and facial action units to

Iqbal, Shoaib, Imteyaz, Shahma

Driving Performance Impact Analysis on EV Components for Efficient, Safe, and Intelligent Mobility

2026-26-0655

1/16/2026

Electric vehicles (EVs) are the cornerstone of sustainable transportation, but their performance and component longevity are heavily influenced by driving behaviors. This study proposes a comprehensive analytical framework to assess how different driving styles affect the operational health of key EV components such as the battery pack, motor, and DC-DC converter. Various driving styles such as aggressive, moderate, and economical are discriminated against using dynamic vehicle operation signatures including acceleration and braking intensity, turning profiles, and load variations. These behavioral patterns are reflected in the electrical responses, namely current and voltage waveforms across power electronic systems. By analyzing these electrical signatures, a range of KPIs can be estimated for each component, offering insights into their operational stress and degradation trends. Experimental analysis using real-time EV datasets validates the framework’s ability to predict and

Deole, Kaushik, Kumar, Pankaj, Hivarkar, Umesh

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