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

Schecker, Daniel, Rittenschober, Thomas

Field-Based Evaluation of Physiological Fatigue Reduction in Fuel Cell Refuse Collection Vehicle Using Heart Rate Variability

2026-01-0690

6/20/2026

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

Utsumi, Atsuko, Yakoh, Takahiro

Ergonomic Assessment of Glove-Box Design in Passenger Vehicles for the Indian Scenario

15-19-02-0009

6/10/2026

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

Jujjavarapu, Sreeram, Rajakumaran, Sriram, Kota, Srinivas, Kotkunde, Nitin, Jasti, Naga Vamsi Krishna

Can Large Language Models Enable Sustainable Driving Behavior? A Study on Large Language Model-Based Eco-Driving

13-07-02-0006

6/9/2026

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

Wang, Haoyu, Li, Zhenning, Wang, Siying, Zhou, Zijing, Zhang, Xiang, Yang, Zhifeng, Ou, Shiqi (Shawn), Qi, Hao

Mitigating Cracking in COP Optics through Annealing and Step-Torquing

2026-26-0764

6/1/2026

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

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

6/1/2026

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

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

6/1/2026

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

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

Injury and Kinematics of Obese PMHS in Frontal Impacts with Reclined Seating

09-14-01-0003

5/4/2026

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

Somasundaram, Karthik, Yoganandan, Narayan, Pintar, Frank

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

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

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

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

Geometric Variabilities and Their Effects on Impact Responses in Midsize Male and Female Occupants

09-14-01-0025

5/4/2026

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

Hu, Jingwen, Lin, Yang-Shen, Boyle, Kyle, Khandare, Sujata, Bonifas, Anne, Reed, Matthew P., Hasija, Vikas

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

Characterization of Pedestrian Automatic Emergency Braking Acceleration in Modern Light Vehicles: Potential Implications for Occupant Safety

09-14-01-0007

5/4/2026

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

Witmer, Maitland, Kidd, David, Graci, Valentina

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

Dog Clutch Actuator Control to Mitigate NVH

2026-01-0003

4/7/2026

Dog clutches have long been employed in the automotive industry across various applications, including transmission systems, transfer cases, axle disconnects, and hybrid driveline architectures. Their ability to provide direct mechanical engagement makes it ideal for torque transmission with minimal energy loss. However, the transition between engaged and disengaged states can introduce noise, vibration, and harshness (NVH), which may be perceptible to vehicle occupants and affect overall driving comfort. A typical dog clutch relies on interlocking teeth for torque transfer, and its actuation can result in NVH due to factors such as friction between mating surfaces, backlash between engagement components, teeth-on-teeth contact during synchronization, and impact forces during clutch engagement. This paper presents Stellantis’s approach to controlling the actuator system to mitigate NVH effects during clutch engagement and disengagement, focusing on strategies that enhance drivability

Xu, Chengyi, Madireddy, Krishna Chaitanya, Verhun, Brandon

A Sub Optimal Fuzzy Control Strategy for Balancing Driver Demand and Actuator Acceleration in Electrified Powertrains

2026-01-0451

4/7/2026

Electrified powertrains—such as Power Splits, Series Hybrids, and EVs with Disconnect Actuators—enable flexible management of actuator acceleration and torque from shared power sources. In power-limited or high-demand conditions, the Hybrid Supervisor must balance available power to sustain performance and drivability; poor coordination can cause control imbalance, reduced actuator performance, and unintended motion. Conventional methods often favor a single control objective, compromising overall system efficiency. This paper introduces FLAIR (Fuzzy Learning Adaptive Integral Response) Control, a supervisory strategy for actuator speed profiling and driver demand tracking in single-input multi-output (SIMO) systems. FLAIR integrates an integral of tracking error with fuzzy inferencing to dynamically weigh multiple control goals, adapting acceleration limits in real time while preserving driver power demand tracking. It enables bi-directional power-flow decisions—allocating system

Banuso, Abdulquadri, Sha, Hangxing, Shenoy, Aayush, Madireddy, Krishna Chaitanya

Study of Automatic Emergency Braking Effects on Human and Surrogate Passengers

2026-01-0556

4/7/2026

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

Bartholomew, Meredith, Dahiya, Akshay, Russell, Caleb, Morr, Douglas, Castro, Elaine, Nguyen, An

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

Quasi-Static Head Restraint Pull-Tests to Assess Retention Performance: Maximum Load and Damage Characteristic

2026-01-0564

4/7/2026

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

Parenteau, Chantal, Burnett, Roger, Davidson, Russell

Use Inertial Sliding of Reclined Seat to Enhance Occupant Retention under Rear-End Crash

2026-01-0563

4/7/2026

Autonomous vehicles may attract more passengers to recline their seat for comfort. However, under severe rear-end crashes and large reclining angle, the backward inertia could completely throw occupant out of seat. Even if the occupant body can be restrained by seatbelt, the occupant’s head could slide out of the head restraint area. Any of these situations may cause severe injuries. To address this safety concern, we developed a sliding seat system designed to enhance occupant retention. Activated by impact inertia of rear-end collision, the system allows the seat sliding backward along its track in a controlled manner, and the sliding stroke is accompanied by a restraint force and absorbs some amount of kinetic energy during the sliding. Thus, occupant retention can be enhanced, and injury risks of head and neck can be reduced. To demonstrate this concept, we built a MADYMO model and conducted a parametric analysis. The model includes a 50th percentile human model, a vehicle seat

Dai, Rui, Zhou, Qing, Puyuan, Tan, Shen, Wenxuan

Evaluating the Effects of Improper Positioning and Unbuckling of a Restraint System on a 10-Year-Old Child ATD in a Far Side Side-Impact Environment

2026-01-0562

4/7/2026

Five sled tests were performed with a Hybrid III (H-III) 10-year-old child sized Anthropomorphic Test Device (ATD) positioned in the 2nd row left seat of a three row 2006 Sport Utility Vehicle (SUV). A HYGE Sled buck was positioned to represent/replicate a side impact collision to the passenger (right) side of the SUV, with a Principal Direction of Force (PDOF) of 60 degrees, resulting in a far side side-impact for the ATD. Of the 5 tests performed, three of the five tests were performed with a delta-V of 17 mph, and two of the tests at a delta-V of 24 mph. Of the 17 mph tests, one test was performed with a properly restrained ATD, and two tests performed with improper restraint positioning. Both of the 24 mph tests were performed with improper restraint positioning, effectively identical to the two 17 mph delta-V tests. The two improper restraint use tests (at both 17 and 24 mph delta-V) included two different improper restraint scenarios. The first scenario of improper restraint

Luepke, Peter, Hewett, Natalie, Betts, Kevin, Van Arsdell, William, Weber, Paul, Stankewich, Charles, Miller, Gregory, Watson, Richard, Sochor, Mark

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

Leveraging Machine Learning for Occupant Body Size Estimation via B-Pillar Seatbelt Systems

2026-01-0559

4/7/2026

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

Wang, Da, Ahmed, Jawwad, Rowe, Mike, Brase, Dan

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

Injury Risk Analysis of Occupant with Different Lower Limb Postures under the Frontal Impact Sled Conditions

2026-01-0572

4/7/2026

To investigate the characteristics of injuries sustained by occupant with different lower limb postures under the frontal impact sled conditions. Using the finite element method a series of simulation analyses were conducted on THUMS (Total Human Model for Safety) AM50 human body model with four different postures, including standing posture, lower limb bent at 100°, 90°, and crossed forward-backward, under the frontal impact scenario at 56 km/h in this study. The simulation results indicated that the overall injury risk predicted by the THUMS AM50 huma body model with lower limb crossed forward-backward was higher than that predicted by the model with other postures. The values of injury criteria including of HIC (Head Injury Criterion), head resultant acceleration, and thoracic VC (Viscous Criterion) predicted by the THUMS AM50 huma body model with lower limb crossed forward-backward were highest in these series simulations. Also, the biomechanical responses, including stress or

Li, Dongqiang, jiang, Yejie, Tan, Chun, Li, Yanyan, Li, Yihui, Wu, Hequan, Jiang, Binhui, Zhu, Feng

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

Numerical Investigation on Thorax Rib Deflections of 50th Male in Vehicle Side Oblique Pole Impact Using Human Body Model

2026-01-0566

4/7/2026

The WorldSID-50M dummy is widely adopted in regulatory and third-party testing programs (e.g., ECE, Euro-NCAP, C-NCAP) owing to its advanced design and superior biofidelity. However, in vehicle side oblique pole crash tests involving shoulder-covered side airbags - an expanded testing modality - excessive deflection of the upper thoracic ribs was observed. Notably, this phenomenon was absent in standard side moving deformable barrier (SMDB) tests. This study pursued two core objectives: (1) to systematically document the excessive upper thoracic rib deflection of the WorldSID-50M dummy in side oblique pole crash tests; and (2) to investigate the influence of arm-thorax interaction on such deflection using a Human Body Model (HBM) representative of a 50th percentile male occupant. Numerical simulation results reveal that while arm-thorax interaction does contribute to rib deflection, its impact on the excessive deflection of the upper thoracic ribs is negligible.

Zhou, DY, Chen, Shaopeng, Yan, Li, Wu, Jing, Liu, Chong, Lv, Xiaojiang, Yang, Heping

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.

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