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

Interactions of Rear-Facing Child Restraints in Frontal Crash Tests

09-14-01-0010

4/10/2026

The aims of this study were to investigate the kinematics of child anthropomorphic test devices in a large sample of rear-facing child restraint system installations and the effects of anti-rebound features and load legs on the kinematics of rear-facing child anthropomorphic test devices. The test matrix included a general sample of 70 rear-facing child restraint system installations to observe trends in frontal crash tests; 14 full-scale crash tests with paired comparisons to investigate the effect of anti-rebound features; and five paired comparisons of rear-facing child restraint systems installed with and without a load leg. The paired t-test was used to determine the statistical significance of differences in kinematic responses. In the general sample, 84% of anthropomorphic test devices in infant seats with the base in outboard seats interacted with the first-row seat. In 52% of tests, the anthropomorphic test device head directly contacted the front seatback. Head accelerations

Tylko, Suzanne, Tang, Kathy

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

09-14-01-0026

4/10/2026

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

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

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

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

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

Acoustic Improvements to a Passenger Service Unit Panel with Core Filler and Facesheet Patches using Topology Optimization

2026-01-0511

4/7/2026

Passenger comfort is becoming the forefront of luxury private jets where noise needs to be kept to a minimum. One source of structure-borne noise is the vibration of the Passenger Service Unit (PSU) panel. These vibrations originate from the outer skin, excited by turbulent boundary layer, and are transmitted through the fuselage frame to the PSU panel. This panel resides overhead of passenger seating, it is composed of a corrugated honeycomb core sandwiched between thin face-sheets. This paper presents a systematic approach to improve the vibro-acoustic performance of a honeycomb core sandwich structure by employing core filler and facesheet patches. Topology Optimization (TO) is used to determine the optimal layouts of these design modifications. The vibro-acoustic performance of the PSU panel with facesheet patches and core filler is evaluated using a frequency response analysis in the commercial finite element solver OptiStruct. The effectiveness of vibration reduction will be

Russo, Connor, Whetstone, Isobel, Patel, Anuj, Wotten, Erik, Kim, Il Yong

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

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

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

ProGuard: A Real-Time Preemptive Artificial Intelligence-Based Anti-Pinch System for Bus Entryways on Low-Power Edge Devices

2026-01-0100

4/7/2026

This paper proposes ProGuard, a novel approach to preemptive pinch detection systems for buses. ProGuard utilizes state-of-the-art AI object detection algorithms to identify potential pinching events in bus entryways before pinching occurs. Modern conventional anti-pinch systems, such as pressure sensors or hall effect sensors, often rely on mechanical contact before triggering. While these systems are established safety mechanisms, they are reactive and therefore require some level of pinching before triggering. This reactive approach presents numerous safety concerns for passengers, especially when considering children on school buses. Existing preemptive detection methods, such as infrared or ultrasonic sensors, solve the problems presented by these reactive detection systems. However, these systems either lack the range or environmental resilience needed for reliable operation in buses. The critical nature of anti-pinch systems requires a robust and reliable solution that can adapt

Bradley, Hudson, Zadeh, Mehrdad, Tan, Teik-Khoon

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

09-14-01-0013

4/7/2026

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

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

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

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

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.

HaloBus: Edge Computing-Enabled Real-Time Boarding and Exit Detection for Enhanced Transportation Safety Using Lightweight AI

2026-01-0555

4/7/2026

This paper proposes HaloBus, an innovative, edge-computing solution designed to mitigate this risk by detecting student boarding and exiting in real time using lightweight AI based methods. A persistent challenge in elementary school transportation is the issue of missing students after they exit their buses, which disproportionately impacts low-income households. Current safety systems place the burden of implementation on individual households, often requiring independent methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and ensuring child safety is a primary concern for parents and caregivers. That is why HaloBus was invented. The system employs YOLOv5us—an Ultralytics-enhanced, anchor-free, split-head architecture that offers a superior accuracy speed trade-off. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby

Getz, Grayson, Zadeh, Mehrdad, Tan, Teik-Khoon

Multi-Stage Packaging and Routing Optimization for Automotive Component Layout, Wire Harness and Duct Route Design

2026-01-0507

4/7/2026

Modern vehicle design involves complex considerations and tradeoffs between system integration and layout which have a direct impact on performance, efficiency, and cost. The placement of equipment including control boards, motors, and fans as well as the routing of ducts and wire harnesses poses a time-consuming and intricate problem for design engineers. This paper presents an automated methodology to determine the optimal component packaging configuration, duct routing, and wire harnessing layout to maximize component packing density and minimize the total routing length. A two-stage optimization framework has been developed where the first stage packages the components within the design space with considerations for space utilization, component overlap, proximity relationships, point-to-point accessibility, and component mounting. The second stage implements a custom A* path-finding algorithm and gradient based optimization to determine the optimal route layout between port points

LeFrancois, Richard, Kim, Il Yong

Sensorless Estimation of In-Cabin CO ₂ Concentration for Model-Based Air Recirculation Control

2026-01-0130

4/7/2026

This paper introduces a sensorless approach for data-driven modeling of in-cabin CO2 concentration to optimize air recirculation flap control without the need for a dedicated CO2 sensor. Elevated CO2 concentrations, resulting from passenger exhalation, can impair occupants’ cognitive function and comfort. Current state-of-the-art solutions rely either on time-based control strategies, which lack responsiveness to actual cabin conditions, or on direct CO2 measurements via sensors, which increase system complexity and costs. In contrast, the proposed approach aims to replicate the benefits of sensor-based control without requiring physical sensors. In this study, a model-based methodology is presented, utilizing empirical CO2 measurement data collected from real-world test drives at varying occupancies, fan stages, vehicle speeds, and flap positions. Data acquisition involves a multi-gas analyzer positioned within the passengers’ breathing zone under controlled operation of the vehicle’s

Stürmer, Michael, Geier, Bertram, Hofstetter, Martin, Hirz, Mario

Data-Driven Study on Chassis Suspension Performance Degradation

2026-01-0582

4/7/2026

The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Implementing real-time health monitoring for chassis systems contributes to preventing severe consequences such as increased body roll or loss of handling stability caused by shock absorber softening or spring stiffness degradation under deteriorating operating conditions, while circumventing the substantial costs associated with professional facility-based chassis inspections. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve online monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a half-car model incorporating both vertical and pitch motions was established to simulate bumpy road conditions, with the aim of constructing a dataset that includes key vehicle suspension parameters and vehicle states related to their

Liao, Yinsheng, Lei, Yisong, Su, Ailin, Wang, Zhenfeng, Shi, Shuai, Zhang, Lei, Zhang, Junzhi, Ma, Changye

Impact of User-Adjustable Vehicle-Dynamics Tuning on Vehicle Durability

2026-01-0194

4/7/2026

Software-defined vehicles offer customers a greater degree of customization of vehicle controls and driving experience. One such feature is user-adjustable tuning of vehicle ride and handling, where customers can vary ride height, damper stiffness, front-rear torque balance, and other aspects of vehicle dynamics. While promising a great customer experience, such a feature can expose the vehicle to a wider range of structural loads than those in the nominal design condition, particularly when such tuning is extended to cover spirited “sport” mode driving, off-road driving, etc. In this paper we present a novel methodology combining Road Load Data Acquisition (RLDA) data and real-world telemetry data to estimate the impact of user-adjustable vehicle-dynamics tuning on structural durability. In doing so, the method combines the physics of damage accumulation (from RLDA data) with user behavior (from telemetry data) to present an accurate assessment of the impact on durability, moving

Demiri, Albion, Ramakrishnan, Sankaran, White, Dylan, Khapane, Prashant, Borton, Zackery

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

Target Cascading Kinematics and Compliance Tables to Suspension Design

2026-01-0584

4/7/2026

During the initial design phase, automotive Original Equipment Manufacturers (OEMs) require the adaptability to examine various suspension system architectures while maintaining focus on the specific performance objectives. Those requirements are expressed by Kinematics and Compliance (K&C) look-up tables and represent the footprint of what the suspension should look like in real-world applications. However, translating those requirements into the full geometric hardpoint layout is not straightforward. This process often relies on trial-and-error approaches, making it time consuming and requiring significant expertise. This challenge, known as ”target cascading,” remains a major hurdle for many engineers. The main objective of this paper is to cascade the suspension requirements from K&C look-up tables to hardpoint locations by adopting an automatic workflow and ensuring respect for constructive and feasibility constraints. Design space exploration was conducted using a robust

Brigida, Piero, Di Carlo, Paolo, Di Gioia, Niccolò, Geluk, Theo, Tong, Son, Alirand, Marc, Gorgoretti, Davide, Occhineri, Marco, Tassini, Nicola, Berzi, Lorenzo

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

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

Modified Hybrid A* Collision-Free Path-Planning for Reverse Parking a Vehicle in Tight Spaces

2026-01-0046

4/7/2026

Parking a vehicle in tight spaces is a challenging task to perform due to the scarcity of feasible paths that are also collision-free. This paper presents a strategy to tackle this kind of maneuver with a modified Hybrid-A* path-planning algorithm that combines the feasibility guarantee inherent in the standard Hybrid A* algorithm with the addition of static obstacle collision avoidance. A kinematic single-track model is derived to describe the low-speed motion of the vehicle, which is subsequently used as the motion model in the Hybrid A* path-planning algorithm to generate feasible motion primitive branches. The model states are also used to reconstruct the vehicle centerline, which, in conjunction with an inflated binary occupancy map, facilitates static obstacle collision avoidance functions. Simulation study and animation are set up to test the efficacy of the approach, and the proposed algorithm proves to consistently provide kinematically feasible trajectories that are also

Cao, Xincheng, Chen, Haochong, Aksun Guvenc, Bilin, Guvenc, Levent

Research on Driving Performance of Vehicle Body Control Using Vehicle Height Adjustment System

2026-01-0626

4/7/2026

In recent years, premium vehicles have increasingly incorporated suspension systems capable of adjusting ride height. The primary function of these systems is to enable the vehicle to traverse uneven terrain by elevating the chassis, thereby preventing contact between the underbody and the road surface. Notably, air spring-based mechanisms enhance ride comfort by modulating the wheel rate. The system proposed in this study achieves ride height adjustment through vertical displacement of the spring’s lower seat. By constructing a detailed mechanical topology model using a dynamic simulation tool, this research aims to evaluate the feasibility of improving driving performance not only through height regulation but also by actively controlling the vehicle’s posture during motion.

Park, Jaeyong, Sang Hoon, Lee, Jong Min, Kim, Choi, Jang Han

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 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

A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)

2026-01-0147

4/7/2026

The shared autonomy framework has become an option with great potential in the field of autonomous vehicles. Human and machine control decisions typically demonstrate strengths in different scenarios. As a result, the robustness of systems can be enhanced by the collaboration between humans and autonomy. A shared autonomy architecture that takes into account both human and environmental factors was proposed in this work. The authority distribution between the human operator and the autonomy algorithm was determined by the Shared Autonomy Arbiter (SAB). Designed with a two-tier structure, the SAB incorporated a policy-level decision module, as well as a numerical-level arbitration tuning module. A fuzzy inference system (FIS) was incorporated to enhance the noise tolerance of the policy selection module. Furthermore, the human factor was taken into account by applying a projection to the users’ control input. The human operator’s control decision was projected by the Adaptive

Sang, I-Chen, Norris, William, Patterson, Albert, Sreenivas, Ramavarapu S., Soylemezoglu PhD, Ahmet, Nottage, Dustin S.

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

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

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

Adaptive Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches

2026-01-0628

4/7/2026

To effectively improve the performance of chassis control of distributed drive intelligent electric vehicles (EVs) under difference road conditions, especially in combing road information and chassis control for improving road handling and ride comfort, is a challenging task for the distributed drive intelligent EVs. Simultaneously, inaccurate chassis control and uncertainty with system input, are always existing, e.g., varying road input or control parameters. Due to the higher fatality rate caused by variable factors, how to precisely chose and enforce the reasonable chassis control strategy of distributed drive intelligent EVs become a hot topic in both academia and industry. To issue the above mentioned, an adaptive torque vector hierarchical controller based on road level and adhesion is proposed, which optimizes the comprehensive. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and the rotor of the in-wheel motor, a

Wang, Zhenfeng, Zhao, Gaoming, Zhang, Zhijie, Zhou, Zitao, Huang, Taishuo, Ma, Changye

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

High Biofidelity Modification of Thoracolumbar Structure in THOR Model for Reclined Occupant

2026-01-0196

4/7/2026

With the rapid development of automated driving and the increasing adoption of “zero-gravity” seats, the crash safety of highly reclined occupants has become a critical issue. The current THOR dummy, designed for frontal impacts in the standard upright posture, exhibits limitations when directly applied to reclined seating configurations, including insufficient spinal flexion capability and excessive posterior pelvic rotation. In this study, the thoracolumbar spine kinematics of the THUMS human body model, reconstructed against post-mortem human subject (PMHS) tests, were analyzed. A two-segment linear fitting was employed to characterize a “dummy-like” spinal flexion response, yielding a virtual rotational hinge located near the thoracolumbar joint of the original THOR model. The characteristic rotation angle obtained from THUMS showed a strong linear correlation with the flexion moment of the T12–L1 vertebrae. Based on this relationship, the rotational joint of the THOR dummy was

Guo, Wencheng, Kuang, Gaoyuan, Shen, Wenxuan, Tan, Puyuan, Zhou, Qing

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

Multi-Objective Reinforcement Learning Framework for Transmission Shift Schedule Optimization

2026-01-0162

4/7/2026

Building upon previous work that successfully employed a Reinforcement Learning (RL) agent for the autonomous optimization of transmission shift programs to enhance fuel efficiency, this paper addresses a critical limitation of that approach: the neglect of human-centric factors. While the prior methodology achieved substantial fuel consumption reductions by training an RL agent in a Software-in-the-Loop (SiL) environment, it did not explicitly account for aspects such as driver comfort and preferences, which are paramount for real-world user acceptance and drivability. This work presents a multi-objective optimization framework extending the artificial calibrator to simultaneously maximize fuel efficiency and enhance driver comfort. The method introduces a modified RL reward function that penalizes undesirable shift behavior to ensure a smooth driving experience (drivability). This new methodology also incorporates a mechanism to capture and integrate driver preferences, moving beyond

Kengne Dzegou, Thierry Junior, Schober, Florian, Rebesberger, Ron, Henze, Roman, Sturm, Axel

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