Browse Topic: Vehicle occupants

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A study on the injury risk of occupant with different head and neck rotating positions2026-01-0574To be published on 04/07/2026
Drivers obtain road information through head and neck rotation. In order to study the impact of head and neck rotation posture on occupant injury in frontal collision, this paper studies the biomechanical response of occupants with five different head and neck rotation posture under 56 km/h frontal collision based on THUMS human model. Firstly, the kinematic response and head center-of-mass acceleration curves of the dead body and THUMS were compared to verify the validity of the vehicle model. Then, THUMS was adjusted to the standard occupant pose and five different head and neck rotation poses of ±20° and ±40°, and frontal collision simulation was conducted for each of the five poses with or without airbags. The results show that with the increase of head and neck rotation Angle, the risk of neck injury increases while the risk of chest injury decreases. 4 ribs are broken under standard posture, and only 2 ribs are broken when the left rotation of head and neck is 40°. The neck Nij
Li, Dongqiang, jiang, Yejie, Li, Yanyan, Tan, Chun, Jiang, Binhui
Benchmarking Driver Responses in Head-On Conflicts: Toward an Objective Protocol for Human and AV Evaluation2026-01-0534To be published on 04/07/2026
Head-on emergency conflicts present a unique challenge for evaluating driver and automated vehicle (AV) performance. Unlike most emergency scenarios that follow a direct stimulus–response sequence, head-on encounters are characterized by a stimulus–wait–response pattern in which time to contact (TTC) strongly shapes driver behavior. Drawing on naturalistic and simulator data, this study quantified human responses to head-on conflicts. When the opposing vehicle crossed the centerline with more than 2 seconds of TTC, drivers delayed braking until an average of 1.3 seconds (SD = 0.8) before impact, regardless of the timing of the intrusion. By contrast, when TTC was less than 2 seconds, braking was initiated only 0 to 1.0 second before impact. As a result, many drivers applied the brakes less than half a second before collision. These findings provide a meta-analytically grounded benchmark for comparing human behavior to AV decision-making processes and demonstrate the need for conflict
Muttart, Jeffrey, Dinakar, Swaroop, Maloney, Tim, Adikhari, Bikram, Gernhard-Macha, Suntasty
Leveraging Machine Learning for Occupant Body Size Estimation via B-Pillar Seatbelt Systems2026-01-0559To be published on 04/07/2026
Occupant body size in vehicles varies significantly, encompassing differences in height, weight, 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 was used to collect data from 47 seated participants. Key seatbelt-related parameters, including D-ring angle, belt payout length, lap belt length, and buckle tension, were measured. These measurements were used to train machine learning models to predict occupant characteristics: height, weight, sitting height. The results demonstrate that low-cost sensors embedded in the seatbelt system can provide sufficiently accurate occupant body size estimations to inform adaptive restraint systems. The prediction errors across both training and test datasets were as follows
Wang, Da, Ahmed, Jawwad, Rowe, Mike, Brase, Dan
Driver response times to turn-in-path events from real world responses in SHRP-2 data2026-01-0545To be published on 04/07/2026
This study analyses of driver behavior in Turn-In-Path (TIP) scenarios using the Second Strategic Highway Research Program (SHRP2) naturalistic driving dataset. A total of 259 real-world incidents, including both crashes and near-crashes, were examined to evaluate human driver perception-response times (PRT) and avoidance behaviors when another 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. Each event was reviewed to identify the triggering threshold that prompted an emergency response, 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 (e.g., stopped vs. rolling), SV driver engagement in secondary tasks, and environmental factors such as lighting and roadway geometry
Dinakar, Swaroop, Muttart, Jeffrey, Maloney, Timothy, Adhikari, Bikram
Use inertial sliding of reclined seat to enhance occupant retention under rear-end crash2026-01-0563To be published on 04/07/2026
Smart cabin of autonomous vehicle may lead more passengers to recline their seat for comfort. However, reclined seat is disadvantageous for holding occupant in seat when vehicle is subjected to rear-end crash. Under severe rear-end crash and large reclining angle, the backward inertia could completely throw occupant out of seat. Or, even if occupant body can be restrained well by seatbelt, the occupant head could slide out of the head restraint area. Any of these situations may potentially cause severe injuries. To address this safety concern, we developed a sliding seat system designed to enhance occupant retention. Activated by impact inertia from 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. Meanwhile, the backward sliding of seat, together with backward sliding of occupant, makes the ride-down process more
Dai, Rui, Zhou, Qing, Puyuan, Tan, Shen, Wenxuan
Characterizing Regenerative Braking of Tesla Model 3 Using Dynamometer Testing2026-01-0453To be published on 04/07/2026
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
Sensor Fusion Method for In-Cabin Humidity Prediction2026-01-0131To be published on 04/07/2026
Maintaining optimal in-cabin humidity levels is part of occupant comfort, air quality, and the effective operation of climate control systems, particularly for functions like windshield defogging. This paper introduces a novel sensor fusion methodology for predicting in-cabin humidity without the need for a dedicated humidity sensor. The proposed approach leverages readily available vehicle data, integrating information from ambient temperature sensors, in-cabin temperature sensors, occupant detection systems, window status (open/closed), and climate control settings. By intelligently fusing these diverse data streams, a predictive model is developed to infer the dynamic humidity conditions within the vehicle cabin. We discuss the complex interactions between these parameters, such as the moisture contribution from occupants, the influence of external air ingress through open windows, and the dehumidifying or humidifying effects of the HVAC system. The paper details the development and
Ghannam, Mahmoud, Schroeter, Robert, Shaik, Faizan
Development of Windshield Wetting Level Assessment Using Computer Vision2026-01-0524To be published on 04/07/2026
This paper introduces a novel methodology for evaluating windshield wetting (rain) levels by leveraging sensor fusion approach combined with advanced computer vision applications. The proposed method aims to enhance the traditional wetting detection systems by providing an alternative or granular classification of various wetting conditions. We will discuss the challenges encountered during the development and validation phases, including variability in rain intensity, droplet dynamics, and environmental factors. Furthermore, the evaluation process of critical parameters influencing wetting detection accuracy will be presented, detailing the metrics used for performance assessment. Initial results from experimental evaluations demonstrate the efficacy of the new method in diverse wetting scenarios, and an in-depth analysis of challenging edge cases, such as mist, glare, and partial wetting, will be provided. This research contributes to the driver experience.
Schroeter, Robert A., Ghannam, Mahmoud, Shaik, Faizan
Investigation of a System for Headlight Glare Control Based on Photometric Zones2026-01-0515To be published on 04/07/2026
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.
Real-time Detection of Vehicle Drivers’ Distractions using YOLOv112026-01-0048To be published on 04/07/2026
Distracted driving is one of the main causes of traffic accidents and poses serious threats to safety of drivers, other road users and public property. Existing methods for detecting distracted driving often suffer from low detection accuracy, poor real-time performance, and high computational demand. This paper proposes a distracted driving behavior detection algorithm based on the latest YOLOv11 object detection model. The YOLOv11's is exhibits enhanced feature extraction capabilities and lightweight network design, which could identify distracted driving behaviors in a highly efficient manner. The study focusses on many different scenarios, such as phone usage, drinking and smoking while maintaining detection accuracy, by aligning with realistic driving scenarios, so as to facilitate development of a more suitable for in-vehicle driver monitoring systems. This study is expected to contribute towards enhanced road traffic safety through efficient detection of distracted driving
Ma, Bao Bao, Taghavifar, Hamid, Fu, Zhijun, Rakheja, Subhash, Karangwa, Jules
Adaptive Eco-Driving Feedback for Battery Electric Vehicles: A Reinforcement Learning Approach Using Context-Aware Policy Optimization2026-01-0165To be published on 04/07/2026
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
Dog Clutch actuator Control to mitigate NVH2026-01-0003To be published on 04/07/2026
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 them 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, Sagi, Rama Rohit Varma, Madireddy, Krishna Chaitanya, Verhun, Brandon
AI-Based Prediction of Motion Sickness in Autonomous Vehicles Using Passenger Profiles and Driving Route Data2026-01-0154To be published on 04/07/2026
Autonomous Vehicles (AV) are expected to significantly reshape travel experiences. Drivers will no longer be responsible for operating the vehicle and will instead take on the role of occupants, free to engage in personal activities such as reading or using phones. Consequently, motion sickness arises from a mismatch between the motion perceived by the body and the motion expected by the brain. Motion sickness detection in AVs has typically been approached through sensor-driven methods that monitor vehicle dynamics as well as passenger conditions, requiring continuous streams of data for reliable predictions. However, while vehicle-mounted sensors can be seamlessly integrated into the system, passenger-oriented solutions often rely on wearable devices, which may not always be practical for wide use in everyday settings. To address this gap, we introduce a ProfileFusion model, a multimodal deep learning model providing a more user-friendly solution that requires minimal user input. It
Khosravifard, Mina, Malik, Usama Zaid, Nguyen, Trang, Grosseruschkamp PhD, Marc, Lichtenberg, Frank
From Prediction to Design: Using Context-Aware Graph Neural Networks and Explainable AI to Anticipate Transfer-of-Control2026-01-0049To be published on 04/07/2026
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 that adapts proactively to the operating context, enhances comfort, and improves safety. We present a context-aware framework that generates a unified driver–vehicle–environment representation by fusing data from videos of the in-cabin and forward road with vehicle kinematics, and driver behaviors (annotations of driver glance, hands-on-wheel, and secondary-task engagement). This representation is encoded in a hierarchical Graph Neural Network that classifies driver-initiated TOC to: (i) Manual-to-Automation (M2A) and (ii) Automation-to-Manual (A2M) and predicts 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
Zhao, Zhouqiao, Gershon, Pnina
Navigation Integrated End-to-end Path Planning Development and Vehicle Testing2026-01-0032To be published on 04/07/2026
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 and lighting conditions, and it shows improved route adherence in vehicle testing. The proposed model is validated in a comma 3x device installed on a 2025 Nissan Leaf test vehicle. The road test
Wang, Hanchen, Li, Taozhe, Hajnorouzali, Yasaman, Burch, Collin, li, Victoria, Tan, Lin, Arjmanzdadeh, Ziba, Xu, Bin
Curvature-Based End-to-End Autonomous Vehicle Path Planning at Intersections2026-01-0045To be published on 04/07/2026
Autonomous vehicle navigation requires accurate prediction of driving path curvature to ensure smooth and safe trajectory planning. This paper presents an end-to-end approach to curvature prediction using deep neural networks trained on real-world driving data. We develop a comprehensive neural network architecture that directly maps high-dimensional sensor inputs to curvature predictions, eliminating the need for intermediate feature engineering steps. The model processes multi-modal inputs including vision features, vehicle state parameters, and environmental context through a deep learning pipeline. Our end-to-end approach demonstrates the feasibility of learning complex driving behaviors directly from raw sensor data, providing a more robust and generalization solution compared to traditional rule-based methods. The neural network architecture incorporates dropout regularization and adaptive learning rate scheduling to ensure stable training and optimal performance. Results show
Hajnorouzali, Yasaman, Wang, Hanchen, Li, Taozhe, Burch, Collin, Lee, Victoria, Tan, Lin, Arjmandzadeh, Ziba, Xu, Bin
Risk analysis of occupant injuries with different lower limb postures during vehicle frontal impact2026-01-0572To be published on 04/07/2026
To investigate the biomechanical characteristics of injuries sustained by occupants with different lower limb postures during frontal impact, the human body finite element models- THUMS AM50 under standard sitting posture and sitting posutres with 100° calf bend, 90° calf bend, and crossed legs were analyzed in the frontal impact at 56 km/h. Injury biomechanical analysis demonstrates that the occupant under the sitting posture with crossed legs has the highest overall injure risk, e.g, the highest HIC (Head Injury Critirion) value, peak value of head accerelation, VC (Viscous Crterion) values, strain of the thracic organs, stress of pelvic cortical bone and knee ligments, and so on. In summary, different lower limb postures can affect the overall risk of occupant injury: the smaller the calf flexion angle and the more complex the leg posture, the higher the risk of overall injury. In the future study it is necessary to strengthen the protection of occupants in non-standard seating
Li, Dongqiang, jiang, Yejie, Tan, Chun, Li, Yanyan, HeQuan, Wu, Jiang, Binhui, Zhu, Feng
Autonomous Racing Control Using Reinforcement Learning with Racing Line Guidance2026-01-0031To be published on 04/07/2026
In recent years, with the emergence of autonomous racing competitions such as Roborace, autonomous racing cars have become a prominent research focus and have undergone rapid development. This paper establishes a reinforcement learning-based decision-making and control framework for autonomous racing cars, achieving cooperative optimization between high-speed racing and safety control under extreme operating conditions. By integrating a Control Barrier Function constrained reward function for dynamic stability and a racing line-guided path optimization, the proposed framework achieves a synergistic balance between minimum lap time and operational safety. Dynamic stability domain constraints based on a two-degree-of-freedom single-mass rigid-body model and racing line optimization based on track geometric modeling are established. The phase plane stability boundary is constructed using the yaw rate method and double-line method, upon which Control Barrier Function constraints are
peng, tao, Zhang, Weiqi, Li, Zengwen, sudong, jiang
Collision Avoidance Effectiveness of an Automated Driving System Using a Human Driver Behavior Reference Model in Reconstructed Fatal Collisions2026-01-0520To be published on 04/07/2026
Avoiding and mitigating any potential collision is dependent on (1) road user ability to avoid entering into a conflict, the (conflict avoidance effect), and (2) road user response should a conflict be entered, the (collision avoidance effect). This study examined the collision avoidance effect of an automated driving system (ADS) using a human behavior reference model. The reference model used in this study reflects the response time and evasive action (referred to as collision avoidance) of a human driver that is non-impaired, with eyes on the conflict (NIEON) - a consistently performing, always-attentive driver that does not exist in the human population. Notably, the NIEON model is a tool for evaluating collision avoidance only, as it inherits the pre-conflict behaviors (includes conflict avoidance) of the ADS that is being evaluated. Counterfactual simulations were run on responder collision scenarios based on reconstructions from a 10-year period of human fatal crashes from the
Scanlon, John M., Kusano, Kristofer D., Engstrom, Johan, Victor, Trent
Adaptive Driver-Centric ADAS Using Machine Learning for Personalized Assistance2026-01-0063To be published on 04/07/2026
Advanced Driver Assistance Systems (ADAS) are becoming increasingly integral to vehicle safety; however, most current implementations rely on static algorithms with fixed thresholds that do not account for individual driver behavior. This can lead to false positives or unnecessary alerts, thereby reducing user trust and system effectiveness. This paper presents a Driver-Adaptive ADAS framework that leverages machine learning to dynamically tailor assistance logic based on real-time driver behavior and driving style. Instead of treating all drivers uniformly, the system learns patterns such as reaction time, maneuver preferences, and braking tendencies. It adjusts key parameters like Time-To-Collision (TTC) thresholds, Forward Collision Warning (FCW) timings, and Automatic Emergency Braking (AEB) triggers accordingly. A software-based proof of concept is developed using Python and MATLAB, where simulated vehicle and driver behavior data are processed to classify driver types and adapt
Bhargav, Matavalam
Advanced Integrated Chassis Control for Improved Energy Efficiency and Driving Experience in Electric Vehicle Applications2026-01-0643To be published on 04/07/2026
Complexity of modern ground vehicles grows constantly, since car manufacturers want to provide functionality, while customers are expecting innovation and recent technologies to be integrated into the latest models released to the market. Recent advances in hard- and software opened the gates for new means of vehicle control and operation. Especially the transition to electric propulsion systems and decoupled chassis actuators offer completely new opportunities of dynamics control and manipulation. This paper presents an approach for integrated chassis and vehicle motion control in (battery) electric vehicle applications by using new and innovative controllers as well as mechatronic chassis systems. In several experiments on public roads with a fully instrumented vehicle demonstrator, that features in-wheel based rear-wheel drive and a hybrid brake-by-wire-system, the proposed control is tested under real environmental and traffic conditions with respect to aspects like energy
Heydrich, Marius, Mitsching, Thomas, Ivanov, Valentin
Game-Changing Flow-Centric Traffic Orchestration - A Novel Framework for Stabilizing Mixed-Traffic Dynamics2026-01-0099To be published on 04/07/2026
The recent rise in traffic flow instability and fatal accidents has become a central concern for transportation researchers and policy makers. Hansa Tek Netics (HTN) observes that technology-enabled vehicles - equipped with Advanced Driver Assistance Systems (ADAS) and Adaptive Cruise Control (ACC) - exhibit driving dynamics that diverge from those of human-driven vehicles. This divergence creates interaction conflicts that may amplify disturbances in mixed traffic, thereby contributing to the traffic’s metastability on freeways. To investigate this hypothesis, we developed a flow-centric analytical framework and analyzed the metastability phenomenon. Using both simulation and field observations, we examined how mismatched control philosophies between human and automated vehicles propagate disturbances across traffic streams. Results show that even small differences in dynamics cause positive feedback and trigger instabilities that spread nonlinearly through the flow. We observe that
Schlueter, Georg J.
A Cooperative AFS/DYC Control for FWDIEV Based on Dissipative Energy Method2026-01-0619To be published on 04/07/2026
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
Multi‑Level Driving Behavior Modeling and Hydrogen Refueling Analysis in Fuel Cell Vehicles Using Big Data2026-01-0467To be published on 04/07/2026
This study shows a new data-driven framework for analyzing driver behavior using high-frequency data from fuel cell vehicles (FCEVs). The proposed pipeline involves data collection and processing, trip segmentation, segment-level preprocessing, segment-level features extraction and processing, and unsupervised clustering across both dynamic and stationary behavior. PCA–UMAP–KMeans pipeline has been used on the driving behavior research by classifying drivers into different driving styles. Stationary behaviors including the hydrogen refueling characteristics is researched by integrating dynamic signal and related location date. Finally, both driving and stationary behaviors are integrated and combined as individual driver profile for FCEVs for future vehicle product development.
Chen, Jiawen, wu, guangquan, lin, chengbo, xu, lin, li, changyu
Behavioral Determinants of Electric Vehicle Battery Degradation: Evidence from Large-Scale Real-world Operations2026-01-0458To be published on 04/07/2026
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(Shawn), Qian, Xiaodong
Comparison of Ammonia Emission Characteristics Between Different Gasoline Vehicles Equipped with Three-Way Catalysts2026-01-0374To be published on 04/07/2026
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
Quantifying Accuracy and Robustness to Guide Fidelity Selection for Multi-fidelity Vehicle Dynamics Models2026-01-0149To be published on 04/07/2026
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
Development of a lightweight and quick-to-install pedal robot for unmanned chassis dynamometer testing2026-01-0197To be published on 04/07/2026
As electric vehicles become more popular, their development and testing using chassis dynamometers is also increasing. Compared to internal combustion engine vehicles, chassis dynamometer testing for electric vehicles requires several to dozens of times longer, requiring increasingly more labor. Furthermore, low-temperature testing is also required, further increasing the labor intensity of testing. Therefore, we developed and evaluated a pedal robot to facilitate unmanned and automated testing. The pedal robot developed in this study weighs only 12 kg and can be installed in a matter of minutes. It is the world's first pedal robot that mimics human driving behavior by driving with one foot and operating the accelerator and brake pedals. Unlike existing driving robots, the robot's actuators do not need to be directly connected to the vehicle pedals, making installation quick. Furthermore, it is installed on the floor of the driver's seat, eliminating the need for a seat attachment. The
Lee, Daeyup, Kang, ji myeong, Jo, Yechan, Choi, SeongUn, shin, Jaesik, Kim, Jongmin, Kang, Keonwoo, Lee, Jongtae
A Sub Optimal Fuzzy Control Strategy for Balancing Driver Demand and Actuator Acceleration in Electrified Powertrains2026-01-0451To be published on 04/07/2026
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 favour 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 SIMO systems. FLAIR integrates a 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. Unlike rigid approaches, it enables bi-directional power-flow decisions—allocating system power
Banuso, Abdulquadri, Sha, Hangxing, Shenoy, Aayush, Madireddy, Krishna Chaitanya
Multi-Storey AI Based Parking System2026-28-0129To be published on 02/12/2026
This paper presents a comprehensive design and implementation of an AI-based parking management system tailored to address the challenges of modern parking facilities. The system is designed to enhance efficiency, reduce manual intervention, and optimize operational costs while providing a seamless experience for drivers. The core architecture of the system integrates advanced technologies, including computer vision and IR sensors, thereby minimizing reliance on conventional sensors and streamlining the parking process. By leveraging computer vision, the system continuously monitors parking areas in real-time, accurately detecting vehicle occupancy status, and dynamically updating the availability of parking spaces. This real-time monitoring capability enables efficient space utilization, allowing for the optimization of parking resources. In addition to optimizing space allocation, the proposed system prioritizes security through the integration of video surveillance. Utilizing AI
N, Kalaiarasi, Gupta, Shivansh, Hajarnis, Mihir, Anand, Vikas
RDAS: A Shared Control Strategy Considering Real-Time Driving Ability in Driver Takeover Tasks10-10-02-00141/24/2026
Autonomous vehicles require drivers to assume control of the vehicle in situations where the vehicle control system cannot perform its intended task. A shared control-based approach to driving authority transfer can effectively mitigate the driving risks associated with diminished driver capability due to prolonged disengagement, but it may readily precipitate human–machine conflicts—oscillatory steering behavior, excessive driver workload, and unstable control during weight transitions. Addressing the characteristics of driver capability variations during takeover tasks, a shared control strategy incorporating real-time driving ability, termed the real-time driving ability strategy (RDAS), is proposed. Initially, a real-time capability assessment strategy based on an expected steering angle model is developed. By collecting driving data under conditions of adequate driver capability to train an adaptive neuro-fuzzy inference system (ANFIS) neural network, the expected steering angle
Qi, Zhenliang, Liu, Ping, Duan, Haotian, Zhou, Zilong, Huang, Haibo
Effect of Correlated Colour Temperature of Automotive LED Headlamps on Visibility and Safety2026-26-01301/16/2026
The objective of this study was to examine the effect of Correlated Colour Temperature (CCT) of automotive LED headlamps on driver’s visibility and comfort during night driving. The experiment was conducted on different headlamps having different correlated colour temperatures ranging from 5000K to 6500K in laboratory. Further study was conducted involving participants of different age group and genders for understanding their perception to identify objects when observed in light of different LED headlamps with different CCTs. Studies have shown that both Correlated Colour Temperature and illumination level affect driver’s alertness and performance. Further study required on headlamps with automatically varying CCT to get better solution on driver’s visibility and safety.
Patil, Mahendra G., Kirve, Jyoti, Parlikar, Padmakumar
Early Prediction of Relaxation Voltage & Detection of State of Battery for Functional Safety2026-26-01661/16/2026
During parking conditions of vehicles, the state of the battery is uncertain as it goes through the relaxation process. In such scenarios, the battery voltage may exceed the functional safety limits. If we cross the functional safety limits, it is hazardous to the driver as well as the occupant. In this case, relaxed voltage plays a crucial role in identifying the safe state of the battery. To estimate the relaxed cell voltage there are methods such as RC filter time constat modeling and relaxation voltage error method. The problem with these solutions is the waiting time and accuracy to determine the relaxation voltage. In this manuscript, a solution is proposed which ensures the above problem is reduced. To achieve the reduction of relaxation voltage estimation time, a python sparse identification of nonlinear dynamics (PySindy) is used which identifies and fits an equation model based on observing the battery characteristics at different SOC and temperatures. The implementation is
Pandey, Priyanshu, Nilajkar, Ankur, Panda, Abinash
Adapting ADAS to Indian Driving and User Needs2026-26-00451/16/2026
The penetration of ADAS in automotive markets is increasing rapidly. However, their effectiveness and acceptance are significantly influenced by regional driving behaviours and infrastructure. This study explores the interaction between naturalistic driver behaviour in India and the operational characteristics of ADAS systems (FCW, ACC, LCF and BSD) with focus on cars. Using real-world driving data collected from Indian roads, the research aims to highlight the divergence between ADAS design assumptions often based on structured Western traffic environments and the complex, dynamic nature of Indian traffic, characterized by frequent human negotiation, informal road practices, and different vehicle types. The study characterizes multiple driver’s driving pattern through naturalistic driving and ADAS systems behaviour in corresponding situations, notably how they adapt to unstructured Indian scenarios such as lane ambiguity, pedestrian unpredictability, traffic flow unpredictability and
Sankpal, Krishnath Namdev, Magar, Akshay, Khot, Ankush, Kulkarni, Alok, Perez, Marc
Representative Drive Cycle Velocity Profiles Prediction Using ML Algorithms for Energy Efficiency2026-26-06381/16/2026
Maximizing vehicle energy efficiency and its performance is a high priority for automotive industries as customers’ expectations rise. Engineers constantly face the challenge of balancing the conflicting goals of achieving superior performance and maximizing energy efficiency, all while meeting increasingly tight development timelines. Leveraging digital methods can potentially enable considerable reduction in development timelines. Driving cycles function as standardized measurement procedures for certifying vehicle fuel efficiency and driving range. Representative velocity profiles condense numerous real-life driving cycles to enable quicker energy analysis and driver feedback evaluations. This paper introduces a novel methodology for generating synthetic drive cycles, such as average velocity cycles and ideal consumption velocity cycles, based on real-life driving scenarios. In this study, the importance of creating representative drive cycles to enhance vehicle performance and
Kanakannavar, Rohit, Kelkar, Kshitij, Sadalge, Anand
Driving Performance Impact Analysis on EV Components for Efficient, Safe, and Intelligent Mobility2026-26-06551/16/2026
Electric vehicles (EVs) are the cornerstone of sustainable transportation, but their performance and component longevity are heavily influenced by driving behaviors. This study proposes a comprehensive analytical framework to assess how different driving styles affect the operational health of key EV components such as the battery pack, motor, and DC-DC converter. Various driving styles such as aggressive, moderate, and economical are discriminated against using dynamic vehicle operation signatures including acceleration and braking intensity, turning profiles, and load variations. These behavioral patterns are reflected in the electrical responses, namely current and voltage waveforms across power electronic systems. By analyzing these electrical signatures, a range of KPIs can be estimated for each component, offering insights into their operational stress and degradation trends. Experimental analysis using real-time EV datasets validates the framework’s ability to predict and
Deole, Kaushik, Kumar, Pankaj, Hivarkar, Umesh
Crash Pulse Characterization for the Passenger Vehicle Fleet in India2026-26-00291/16/2026
A crash pulse is the signature of the deceleration experienced by a vehicle and its occupants during a crash. The deceleration-time plot or crash pulse provides key insights into occupant kinematics, occupant restraints, occupant loading and efficiency of the structure in crash energy dissipation. Analysing crash pulse characteristics like shape, slope, maximum deceleration, and duration helps in understanding the impact of the crash on occupant safety and vehicle crashworthiness. This paper represents the crash pulse characterization study done for the vehicles tested at ARAI as per the ODB64 test protocol. Firstly, the classification and characterization of the crash pulses is done on the basis of the unladen masses of the vehicles. The same are further analysed for suitability of mathematical waveform models such as Equivalent Square Wave (ESW), Equivalent Triangular Wave (ETW), Equivalent Sine Wave (ESW), Equivalent Haversine Wave (EHSW) as well as EDTW (Equivalent dual trapezia
Mishra, Satish, Kulkarni, Dileep, Borse, Tanmay, Mahindrakar, Rahula Ashok, Mahajan, Rahul, Jaju, Divyan
A Comparative Analysis of Passenger Car Fuel Economy between Laboratory and Real Driving Testing2026-26-02251/16/2026
In India, fuel economy is one of the most critical factors influencing a customer's decision to own a passenger car. Beyond consumer preference, fuel consumption also plays a significant role in the nation's energy security. In line with this, the government promotes fuel-efficient vehicles and technologies through various regulations, policies, and mandates. Vehicle manufacturers, in response, focus on designing vehicles that align with both customer expectations and regulatory requirements. Fuel economy certification is typically based on standardized laboratory tests that simulate controlled environmental conditions, driving cycle (MIDC), vehicle load, and operation of electrical and electronic systems. However, actual on-road driving conditions by end user vary significantly due to factors such as traffic conditions, ambient temperature, air conditioning use, driving behavior and variable loading of the vehicle. With implementation of Bharat Stage VI, Real Driving Emission (RDE
Singh, Abhay Pratap, Bathina, Revanth Kumar, Tijare, Shantanu
Ambient Light Reflection Analysis Using Dimensional Modeling Tools to Predict Windshield View Obscuration and Improve Safety2026-26-04761/16/2026
Ambient light reflecting off internal components of the car, specifically the Head-Up Display (HUD), creates unwanted reflections on the Windshield. These reflections can obscure the driver's field of view, potentially compromising safety and reducing visual comfort. The extent of this obscuration is influenced by geometrical factors such as the angle of the HUD and the curvature of the Windshield, which need to be analyzed and managed. The primary motivation is to improve driver safety and visual comfort. This is driven by the need to address the negative impact of ambient light reflecting off Head-Up Displays (HUDs), which can impair visibility through the Windshield. There is a need for tools and methods to address this issue proactively during the vehicle design phase. This study employs a tool-based modeling method to trace the pathways of ambient light from its source, reflecting off the HUD, and onto the Windshield using a dimensional modeling tool. It focuses on: Geometrical
Muchchandi, Vinod, Akula, Satya Jayanth, Mahindrakar, Pramod, G S, Sharath
Integrated Evaluation of Pre-Crash Braking and Crash Injury Outcomes Using Human Body Models and ATDs2026-26-00191/16/2026
Traditionally, occupant safety research has centered on passive safety systems such as seatbelts, airbags, and energy-absorbing vehicle structures, all designed under the assumption of a nominal occupant posture at the moment of impact. However, with increasing deployment of active safety technologies such as Forward Collision Warning (FCW) and Autonomous Emergency Braking (AEB), vehicle occupants are exposed to pre-crash decelerations that alter their seated position before the crash. Although AEB mitigates the crash severity, the induced occupant movement leads to out-of-position behavior (OOP), compromising the available survival space phase and effectiveness of passive restraint systems during the crash. Despite these evolving real-world conditions, global regulatory bodies and NCAP programs continue to evaluate pre-crash and crash phases independently, with limited integration. Moreover, traditional Anthropomorphic Test Devices (ATDs) such as Hybrid III dummies, although highly
Pendurthi, Chaitanya Sagar, THANIGAIVEL RAJA, T, Kondala, Hareesh, Sudarshan, B.Sudarshan, Nehe, Vaibhav, Rao, Guruprakash
Driving Behavior Modelling with Graph Attention Networks2026-26-06611/16/2026
In automotive engineering, understanding driving behavior is crucial for decision on specifications of future system designs. This study introduces an innovative approach to modeling driving behavior using Graph Attention Networks (GATs). By leveraging spatial relationships encoded in H3 indices, a graph-based model constructed, which captures dependencies between various vehicle operational parameters and their operational regions using H3 indices. The model utilizes CAN signal features such as speed, fuel efficiency, engine temperature, and categorical identifiers of vehicle type and sub-type. Additionally, regional indices are incorporated to enrich the contextual information. The GAT model processes these heterogeneous features, learning to identify patterns indicative of driving behavior. This approach offers several significant advantages. Firstly, it enhances the accuracy of driving behavior modeling by effectively capturing the complex spatial and operational dependencies
Salunke, Omkar
Smart Stability: Automation of Braking Solutions, Hill Hold and Parking Brake Strategy in Tractors Using Electro-Hydraulic Actuation2026-26-02051/16/2026
Agricultural operations in hilly, uneven & slopy terrains demands high levels of operator focus, effort and skill. However, todays farming ecosystem across the globe is affected by 2 major scenarios: the aging workforce in the agricultural sector and the ever-growing problem of distraction due to mobile device and social media use. These issues compromise safety during operations such as start stop maneuvers, parking on slopes, and maneuvering in confined & narrow areas. Stringent emission norms are also being mandated across developed and developing countries as a measure to reduce Global Greenhouse house gas emissions. These measures are indeed necessary for sustainability but has increased overall tractor purchase and operating costs without improving safety & operator comfort. There has been a trend seen around the world in terms of poor sales post Emission implementation. Registration of Older tractors without these stringent emission norms were also witnessed in Developed
M, Rojer, T, Ganesan, P, Velusamy, Natarajan, Saravanan, V, Mathankumar, tripathi, Shankar, Narni, Kiran, Haldorai, Rajan, Devakumar, Kiran
Study of Drive Trace Indices and Their Correlation with CO 2 in Chassis Dynamometer2026-26-05171/16/2026
With introduction of Corporate Average Fuel Efficiency norms (hereafter referred as CAFÉ norms) in India, the manufacturers of all M1 Category vehicles (not exceeding 3,500kg GVW) must ensure that they comply with Annual Corporate average CO2 target as defined in regulation. Moreover, this target will become stricter at various stages in the coming years. Hence CO2 emissions are becoming one of the major focus parameters during vehicle development. There are several factors that can impact CO2 emissions during measurement in laboratory-based test cycles such as MIDC or WLTC. One such major factor is driving variations. Although speed and time tolerances are provided during the test (as part of AIS 137/AIS 175) to limit the variation, even within these tolerances, drive-related effects make significant contribution to test results variability. Monitoring and control of such variations is important to understand the true fuel economy potential of the vehicle. Drive Trace indices are
ER, Shivram, Rawat, Vijaypal, Khandelwal, Vineet, Kumar, Arun, Malhotra, Jitendra
Analysis of Road Accident Statistics for Females in India and Gaps in Safety Regulations2026-26-00201/16/2026
This paper investigates the current state of road safety for female occupants in India, with a particular focus on road accident statistics and the gaps in safety regulations. According to the Road Accident in India 2022 report by the Ministry of Road Transport and Highways (MoRTH), female occupants constitute 16% of passenger car fatalities. Using a extensive dataset of 596 passenger car accidents involving at least one female occupant from the Road Accident Sampling System – India (RASSI), this study evalu the severity and patterns of injuries sustained by female drivers and passengers. The analysis identifies critical shortcomings in existing safety measures, particularly in addressing anatomical differences and male-centric safety designs. Gender-sorted injury trends reveal heightened vulnerabilities for women in crash scenarios. Current regulatory frameworks bank on crash test dummies developed on average male anthropometry, neglecting female-specific biomechanical needs in
Ayyagari, Chandrashekhar, G, Santhosh Kumar, Rao, Guruprakash
Occupant Kinematics and Injuries during High Speed Side Impacts: An Analysis of ARAI Data 2016-20242026-26-00271/16/2026
The objective of the present study is to examine trends in occupant kinematics and injuries during side impact tests carried out on vehicle models over the period of time. Head, shoulder, torso, spine, and pelvis kinematic responses are analysed for driver dummy in high speed side impacts for vehicle model years, MY2016-2024. Side impact test data from the tests conducted at The Automotive Research Association of India (ARAI) is examined for MY2016-2024. The test procedure is as specified in AIS099 or UNECE R95, wherein a 950kg moving deformable barrier (MDB) impacts the side of stationary vehicle at 50km/hr. An Instrumented 50th percentile male EUROSID-2 Anthropomorphic Test Device is positioned in the driver seat on the impacting side. Occupant kinematic data, including head accelerations, Head Injury Criterion (HIC15), Torso deflections at thorax and abdominal ribs, spine accelerations at T12 vertebra, and pelvis accelerations are evaluated and compared. The “peak” and “time to
Mishra, Satish, Borse, Tanmay, Kulkarni, Dileep, Mahajan, Rahul
Vehicle Usage Digital Twins: Mobility Analysis Test Case2026-26-03941/16/2026
In the evolving landscape of the automotive industry, this study presents an innovative approach to developing digital twins for driver profiles, establishing a standardized and scalable procedure for collecting and analyzing driving data on a global scale. The proposed methodology centers on the development of a robust cloud infrastructure, including Data Lake and associated services, designed for efficient storage and processing of large volumes of data from multiple markets and vehicle types. The research introduces an adaptable procedure for data collection campaigns, applicable to diverse global markets and encompassing a wide range of vehicles, from internal combustion engines to electric and hybrid models. A key feature of this approach is the establishment of advanced data decoding protocols, enabling precise interpretation of CAN network information from vehicles of different manufacturers and models, even when the CAN structure is not previously known. The study defines
Arturo, Rubio, Marín Saltó, Anna, Diaz, Francisco, Olivencia, Sergio
Development of a Fuel Injector Driver for Accelerated Testing2026-26-05641/16/2026
This paper presents the design of a cost-effective fuel injector driver designed for accelerated testing of injectors. The driver simulates injection patterns across a wide range of vehicle operating conditions and can be programmed with injection maps for different engines, test cycles based on drawing specifications, pre-defined engine running profiles, and manual control, where the user defines PWM frequency and duty cycle. It also enables remote operation through a Wi Fi access point. An injector driver-based test setup was developed to study wear and evaluate leakage tendency in an injector design. To simulate extended field usage in a short timeframe, an accelerated operating cycle was derived using telematics data. Injector samples were tested with periodic leak rate measurements. Conducting such tests at vehicle level or on engine test bench would involve significant time and cost. This setup is an effective tool for rapid comparative analysis across supplier design, enabling
Bhatt, Pancham, Agrawal, Adheesh, Kuchhal, Abhinav
Driver-in-the-Loop Optimization of Tire-Vehicle Dynamics for Variant Application through Physical Tire Model2026-26-00991/16/2026
Today due to time to market requirements, Original Equipment Manufacturers (OEM) prefers platform modularity for Product Development in Automotive Domain. Money and time being main constraint we need to focus on single platform which can give flavors of different category just by changing Ride height and Tyre and some extra tunable. Taking this as challenge still tyre development for new variant demands lot of time and iterations which can lead to delays in time to market. This study provides a virtual development process using driver in loop Simulator and Multi body dynamics simulation which are real time capable and integrating physical tire models. The proposed alteration introduces ride height changes, weight distribution changes, and center of gravity changes from existing vehicle design. The proposed new vehicle variant also introduces tire change from highway terrain type to all-terrain type as it was intended to deliver some off-roading capabilities, thereby vehicle dynamics
Shrivastava, Apoorv, Asthana, Shivam
Method and System of Detecting Obstacle around Vehicle Door2026-26-00261/16/2026
Vehicle door-related accidents, especially in urban environments, pose a significant safety risk to pedestrians, infrastructure and vehicle occupants. Conventional rear view systems fails to detect obstacles in blind spots directly below the Outside Rear View Mirror (ORVM), leading to unintended collisions during door opening. This paper presents a novel vision-based obstacle detection system integrated into the ORVM assembly. It utilizes the monocular camera and a projection-based reference image technique. The system captures real-time images of the ground surface near the door and compares them with calibrated reference projections to detect deviations caused by obstacles such as pavements, potholes or curbs. Once such an obstacle is detected the vehicle user is alerted in the form of a chime.
Bhuyan, Anurag, Khandekar, Dhiraj, Jahagirdar, Shweta
India WLTP Vs MIDC: Comparative Analytical Study on BEV Range & Energy Consumption for Proposing iWLTP 4-Phase2026-26-02441/16/2026
In India, Currently Continuous FULL MIDC (Modified Indian Driving Cycle) is used to declare the Range & Energy consumption of BEV (Battery Electric Vehicle). AISC (Automotive Industry Standards Committee) is looking to implement Worldwide Harmonized Light-Duty Test Procedure (WLTP) in India. AISC released AIS 175 for WLTP implementation from Apr 2027. The objective of WLTP is to standardize the test procedure globally for evaluating Emission/FE/Range of Light Duty Vehicles. But the effect of AIS 175 regulation on Battery Electric Vehicles Range Declaration is very less. The Range is almost same as Full MIDC declared Range. The On-road Range BEV is always lesser than the Declared Range of vehicles because of ambient conditions. Usually, the Full MIDC declared Range will be 20% ~26% higher than actual On Road Range. The Range of BEV as per India WLTP 3-Phase was observed 18% ~ 24% higher than actual On-road range of vehicles. There is only 2% difference observed between Full MIDC Range
Shiva Kumar, Mucharla, Tentu, Kavya
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