Browse Topic: Human Factors and Ergonomics

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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
Numerical Investigation on Thorax Rib Deflections of 50th Male in Vehicle Side Oblique Pole Impact Using Human Body Model2026-01-0566To be published on 04/07/2026
The WorldSID-50M dummy is an advanced side impact dummy that has been adopted extensively in regulatory testing and third party testing such as Euro-NCAP and C-NCAP. However, with increased testing modalities, excessive upper thorax rib deflection was observed in some vehicle side impact tests. It is believed that interaction between arm and thorax rib causes excessive deflection of upper thorax rib in side oblique pole impact with a large side airbag, which has better protection to the shoulders of an occupant. The objectives of the study was as following: (1) Excessive upper thoracic rib deflection of WorldSID-50M dummy are presented in the side oblique pole crash tests while this observation was not found in side impact tests with the barrier. (2) The effect of the interaction between arm and thorax of a 50th male occupant on upper thorax rib deflection was studied using Human Body Models (HBMs). The study indicates that the interaction between arm and thorax ribs has minor effect
Zhou, DY, Chen, Shaopeng, Yan, Li, Wu, Jing, Liu, Chong, Lv, Xiaojiang, Yang, Heping
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
Bias and uncertainty in the time, position, and speed data of consumer-grade GPS devices2026-01-0544To be published on 04/07/2026
The goal of this study is to quantify the accuracy and uncertainty of position and speed measurements acquired by a wide range of consumer-grade GPS-enabled devices while road cycling. We acquired position and speed data simultaneously from 13 consumer devices (e.g., phones, watches, bicycle computers) at their maximum sampling rate (typically 1 Hz) during 10 hours (thirty 20 minute sessions) and 150 km of road cycling in a suburban environment. The position data from these devices were compared to real-time kinematic (RTK) data acquired using a differential GPS system and the speed data from these devices were compared to speed data acquired with high-resolution wheel rotation sensors (23,040 pulses per revolution) synchronized with the RTK data. Based on these comparisons, we generated probability distributions for the errors in the position and speed measured by each of the consumer devices. This study provides foundational data needed to quantify the accuracy and uncertainty across
Booth, Gabrielle R., Mitchell, Alan L., Siegmund, Gunter P.
Target Cascading Kinematics and Compliance Tables to Suspension Design2026-01-0584To be published on 04/07/2026
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
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
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
Virtual Sensor Development for Real-Time Estimation of Transient NOx Emissions for Internal Combustion Engine2026-01-0370To be published on 04/07/2026
Accurately measuring NOx emissions under transient engine conditions is becoming increasingly important with upcoming Euro 7 and EPA 2027 regulations. Traditional physical sensors often struggle with cost and response time, especially with aging of sensors in dynamic operation. This paper introduces a machine-learning–based virtual NOx sensor that can provide real-time emission estimates while reducing reliance on hardware sensors. The approach uses multiple machine-learning methods (Random Forest, Bootstrap Aggregating, Adaptive Boosting, Gradient Boosting, Extreme Gradient Boosting) and selected best one to establish correlations between engine operating parameters, measured steady-state data, and transient duty cycle NOx emissions. Validation across different duty cycles has shown strong alignment with physical sensor readings, with R² values above 0.9995 for known data sets and above 0.9534 for unseen conditions. The model needs to be trained with larger training samples to further
Kumar, Chandan, Dahodwala, Mufaddel, Thawrani, Kiran
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
ProGuard: A Real-time Preemptive Artificial Intelligence-Based Anti-Pinch System for Bus Entryways on Low-Power Edge Devices2026-01-0100To be published on 04/07/2026
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
Analysis of the Impact on Ride Comfort from Splitting the Unsprung Mass in Vehicle Modeling2026-01-0212To be published on 04/07/2026
The study was conducted to investigate the differences in ride comfort analysis between treating the unsprung mass as a whole and modeling it separately. A classical two-degree-of-freedom single-wheel vehicle vibration model and a three-degree-of-freedom single-wheel vehicle vibration model with split unsprung mass were established, with their state-space descriptions determined. The fundamental vibration response quantities of both models were identified, and time-domain simulations under random road excitation were performed using Matlab/Simulink. The results indicate that the two modeling approaches exhibit minimal differences in ride comfort analysis for the sprung mass, but there are certain differences for the unsprung mass. Additionally, for the three-degree-of-freedom single-wheel vehicle vibration model with split unsprung mass, the axle-to-wheel mass ratio was introduced to analyze the changes in the fundamental vibration response quantities when the unsprung mass increases
Jie, Li, Wei, Deng
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
Using Unreal Engine Software for Driving Studies: Developing Documentation to Support Collaboration2026-01-0517To be published on 04/07/2026
Context: A wide range of applications already support driving simulator studies, including CARLA, miniSim, VI-GRADE, SCANeR, and others, each with distinct strengths and limitations. However, many academic institutions are turning to free and accessible gaming platforms such as Unreal Engine and Unity. In the Human Factors Group at the University of Michigan Transportation Research Institute (UMTRI), a multidisciplinary team of 19 students is developing an Unreal Engine-based driving simulator as a research tool to investigate the difficulty of driving roads and for other purposes. Problem: Until recently, the absence of consistent documentation has created major obstacles, including errors in code management and steep learning curves for new contributors. To address the issue, the team initiated a development freeze and established documentation standards tailored to Unreal Engine’s Blueprint framework, a graphical node-based programming language that poses organizational and learning
Erturk, Selay, Stimec, Luka, Xin, Jared Hongyi, Tasmaan, Ashley, Mitani, Kate, Zhang, Luya, Green, Paul
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
A Frequency-Aware Reinforcement Learning Framework for Suspension Control with Integrated Temporal Features and Expert Guidance2026-01-0209To be published on 04/07/2026
Active suspension systems play a crucial role in improving vehicle ride comfort and handling stability. However, most existing studies focus on the low-frequency range below 20 Hz, leaving the suppression of high-frequency vibrations within 50–500 Hz largely unexplored, even though these vibrations strongly affect in-cabin noise and ride quality. To address this gap, this study introduces a quarter-car suspension model incorporating both bushing dynamics and a rigid-ring tire within a reinforcement learning (RL) framework. A major challenge for RL-based suspension control is its degradation in high-frequency performance. To overcome this issue, we design an innovative training framework that integrates multiple synergistic strategies. First, frequency-domain rewards are incorporated as auxiliary signals to explicitly guide policy optimization in the high-frequency band. Second, long short-term memory (LSTM) networks are embedded in both the Actor and Critic to capture the sequential
zhu, Zhehui, Zhang, Lijun, Meng, Dejian, Hu, Xingyu
Data-driven Study on Chassis Suspension Performance Degradation2026-01-0582To be published on 04/07/2026
The performance of chassis suspension mechanisms critically affects vehicle handling, ride comfort, and safety. Under severe operating conditions, health monitoring of chassis suspension mechanisms can detect potential problems in time, thereby reducing safety risks and optimizing maintenance costs. With the rapid development of sensing and data analytics technologies, data-driven approaches are increasingly used in health monitoring. This study aims to achieve real-time monitoring of chassis suspension performance degradation using a deep neural network (DNN). First, a semi-vehicle suspension model is constructed to simulate the vehicle's degraded state under bumpy road conditions, generating a dataset of key suspension parameters. Subsequently, a DNN model comprising three hidden layers is developed to assess suspension performance degradation. To optimize model performance, the effects of different numbers of neurons and hidden layers on model accuracy are explored. Experimental
Liao, Yinsheng, Lei, Yisong, Su, Ailin, Wang, Zhenfeng
Modified Hybrid A* Collision-Free Path-Planning for Parking a Vehicle in Tight Spaces2026-01-0046To be published on 04/07/2026
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
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
Sensorless Estimation of In-Cabin CO2 Concentration for Model-Based Air Recirculation Control2026-01-0130To be published on 04/07/2026
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
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
Research on Intelligent Vehicle Fleet Longitudinal Control Based on Fuzzy Model Predictive Control2026-01-0040To be published on 04/07/2026
Addressing the challenge of balancing tracking efficiency and stability in longitudinal cooperative control of intelligent vehicle platoons, this paper proposes a hierarchical control strategy integrating fuzzy logic with Model Predictive Control (MPC). The upper-layer controller, based on the platoon's longitudinal dynamics model, formulates an optimization objective function that considers both tracking performance and ride comfort. A fuzzy logic system is introduced to adaptively adjust the weighting coefficients within the MPC objective function in real-time based on the prevailing tracking state, outputting an optimal desired acceleration. The lower-layer controller, utilizing an inverse longitudinal dynamics model incorporating the engine's universal characteristic map and a brake system model, accurately translates the desired acceleration into throttle opening or brake pressure via a Proportional Integral Derivative (PID) control algorithm. To validate the control strategy, a
Li, Ye
Advanced Damping Force Modeling Using Machine Learning for Next-Generation Electric Vehicle Suspensions2026-01-0586To be published on 04/07/2026
The rapid evolution of electric vehicles (EVs) has necessitated innovative approaches to optimize ride comfort, handling, and overall suspension performance. Unlike conventional internal combustion engine vehicles, EVs introduce unique challenges due to their distinct weight distribution, powertrain dynamics, and noise characteristics. This paper presents an advanced damping force modeling methodology leveraging machine learning (ML) techniques to enhance the suspension design process for next-generation EVs. The study employs data-driven ML algorithms, such as Gradient Boosting, Random Forest, and Neural Networks, to model the nonlinear and frequency-dependent behavior of dampers under various operational conditions. A comprehensive dataset, generated through simulation and experimental testing, captures the effects of road profiles, vehicle dynamics, and damping settings. The proposed ML-based approach predicts damping forces with high accuracy, outperforming traditional analytical
Hazra, Sandip, Tangadpalliwar, Sonali, Khan, Arkadip
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
A Shared Autonomy Architecture: Utilizing an Advanced Personalized Control System (APeCS) and Shared Autonomy Arbiter (SAB)2026-01-0147To be published on 04/07/2026
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 Torque Vector Control for Distributed Drive Intelligent Electric Vehicles Using Model and Data Driven Approaches2026-01-0628To be published on 04/07/2026
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 roughness and adhesion is proposed, which optimizes the comprehensive performance of vehicle chassis. First, combined with the characteristic of the unbalance dynamic force caused by the air gap between the stator and
Wang, Zhenfeng, Zhao, Gaoming, Zhang, Zhijie
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
HaloBus: Edge Computing‑Enabled Real‑Time Boarding and Exit Detection for Enhanced Transportation Safety Using Lightweight AI2026-01-0555To be published on 04/07/2026
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 are forced upon the individual households with their own methods. Common methods include applications on a personal device or a small tracker. However, not everyone can afford these options, and every parent deserves to feel like their child is safe. That is why HaloBus was invented. The system employs YOLOv5u—an Ultralytics‑enhanced, anchor‑free, split‑head architecture that offers a superior accuracy–speed tradeoff. By providing real-time, on-device alerts, HaloBus enables immediate intervention to prevent a student from being left behind, thereby shifting the focus from reactive post-incident
Getz, Grayson, Zadeh, Mehrdad, TAN, Teik-Khoon
Impact of User-Adjustable Vehicle-Dynamics Tuning on Vehicle Durability2026-01-0194To be published on 04/07/2026
Software-defined vehicles offer customers a greater degree of customization on vehicle operation. One such feature is a user-adjustable tuning of vehicle ride and handling, where customers can vary the ride height, the active damper stiffness (affecting ride comfort), front-rear torque balance (affecting ride handling), and other vehicle dynamics features. While promising a great customer experience, such a feature can expose the vehicle to a wider range of structural loads than before, particularly when such tuning is extended to cover spirited “sport” mode driving, offroad 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
Demiri, Albion, White, Dylan, Ramakrishnan, Sankaran, Borton, Zackery, Khapane, Prashant
A Cooperative Hand Gesture Recognition System for Human Machine Interface to Control Robotic Arms2026-01-0252To be published on 04/07/2026
In the context of Industry 5.0, effective collaboration between humans and machines demands seamless and natural interaction. Hand-Gesture Recognition (HGR) is a promising technology for creating intuitive human-machine interfaces (HMIs) that allow users to control robots without physical devices or wearables. This research introduces a real-time HGR system designed to control a 6-Degree-of-Freedom (DoF) robotic arm using YOLOv10, a state-of-the-art deep learning model for hand gesture recognition. While YOLOv10 offers high accuracy, its computational demands exceed the capabilities of edge devices typically attached to robotic arms, posing a significant hardware challenge. To overcome this, we propose a cooperative client-server architecture that distributes the workload between the edge device and a more powerful remote server. An RGB camera mounted on the robotic arm captures hand gesture images and sends image data to the server using User Datagram Protocol (UDP). The server
DeHaven, Aaron Lee, Park, Jungme
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
A Novel Admission Scheme for Connection-Less IoT Devices2026-01-0166To be published on 04/07/2026
This paper introduces a novel framework for enabling ultra-low latency, connectionless communication among IoT devices, particularly in emergency and non-emergency scenarios where traditional network connectivity is unavailable or delayed. The system leverages a Layer 2 IoT protocol exempt from admission control, allowing devices to discover and access services based on contextual awareness—such as location, time, and urgency—without requiring prior network association. Key innovations include pre-association service discovery, dynamic admission classification, and a connectionless service model that enables IoT devices to transmit critical data through nearby facilitator devices (e.g., vehicles) to emergency call services. A machine learning-based proximity estimation engine ranks closed devices based on their functional capabilities, mobility, and historical performance, ensuring optimal service delivery. The system architecture also features a classification manager for zone-based
Giusto, Paolo, Chmeiseh, Mustafa H., Sergakis, John, Neishaboori, Azin, Thanayankizil, Lakshmi, Fernandes, Richard, Vemuri, Vikas
Machine Learning based Meta-Analysis of IIHS Moderate Overlap Frontal Crash Test for Rear Seat Occupant Safety Performance2026-01-0579To be published on 04/07/2026
A machine learning (ML)-based meta-analysis was conducted to evaluate rear seat occupant safety performance in the Insurance Institute for Highway Safety (IIHS) Moderate Overlap Frontal (MOF) 2.0 crash test. ML models were trained on historical IIHS crash test data to predict rear passenger injury metrics using vehicle architecture, restraint system characteristics, crash pulse parameters, and vehicle kinematics as input features. The models demonstrated high predictive accuracy and were subsequently used in a Sobol sensitivity analysis to identify critical design parameters influencing injury outcomes. The analysis revealed that rear passenger injury metrics were most sensitive to restraint system parameters. Specifically, crash pulse magnitude was the dominant factor for head injury metrics, pretensioner activation time for neck tension force, and lap belt force for the Neck Injury Criterion (Nij). For chest-related metrics—sternum deflection, dynamic belt position, and maximum belt
Lalwala, Mitesh, Kim, Wonhee, Furton, Lisa, Song, Jay
Quasi-Static Head Restraint Pull-Tests to Assess Retention Performance: Maximum Load and Damage Characteristic2026-01-0564To be published on 04/07/2026
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
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
Development and Application of SAE J3260 & J3290 Recommended Practice for Minimizing Spread of Infections in Motor Vehicles2026-01-0128To be published on 04/07/2026
In response to COVID pandemic, and at the persistent urging of the late Ward Atkinson, long-time SAE leader in Interior Climate Control Committees, the SAE Cabin Disinfection Practices Committee was formed in May 2021. Towards that end, two SAE J-standards have been produced by this committee: SAE J3260, Identification of Acceptable Cleaning and Disinfection Products for Vehicle Interior Surfaces and Exterior Touch Points, and SAE J3290, Selection and Use of Technologies and Methods for Vehicle Cabin Air Disinfection. This SAE paper will unpack the background and application of these J-Standards to minimize the probability of spreading of infection during the operation of motor vehicles while in normal operation, before and after vehicle servicing, and when in use as paid-for-hire operation - for both ‘normal’ environmental conditions, and during times of pandemic emergencies. Additionally, recent advances in air and surface disinfection technologies—including methods relevant to SARS
Schaeber, Steven, Mathur, Gursaran, Taylor, Dwayne
Optimization of Active–Passive Integrated Evaluation Weighting for Pedestrian Protection2026-01-0577To be published on 04/07/2026
Pedestrians, as the most vulnerable road users, largely determine the severity of traffic accidents through the extent of their injuries. Traditional vehicle safety evaluations have primarily emphasized passive safety. However, with the widespread implementation of active safety technologies such as Automatic Emergency Braking (AEB), passive-only assessments no longer adequately reflect a vehicle’s true pedestrian protection performance.This study employed finite element models of a sedan and an SUV to analyze changes in pedestrian head and lower limb injury metrics before and after AEB intervention, thereby quantifying the associated injury-mitigation benefits. The results show that AEB reduced the maximum head injury value (HIC15) by about 50%, lowering the proportion of severe head injuries from 0.217 to 0.118. Conversely, braking-induced pitch effects increased the proportions of severe femur and tibia bending moment injuries to 0.235 and 0.294, respectively, making them higher
shi, Xinxin, YE, BIN, Liu, Yu, Zhan, Zhenfei, Sun, Bowen, Zhao, yuanyi, zhu, lei
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
Electric Vehicles & the Grid - Synergy or Strain?2026-01-0398To be published on 04/07/2026
As the demand for electrical power has surged over recent years due to the increasing popularity of data centers for Artificial Intelligence (AI) and Electric Vehicles (EVs), it is becoming evident that the aging electrical grid infrastructure is struggling to keep up. Some of the problems this aging infrastructure has resulted in include frequent blackouts due to weather related events, reduced efficiency resulting in higher maintenance costs and outdated communication systems causing poor monitoring and response times. Modernization of the grid in conjunction with integration of the transportation sector with the grid is essential to ensure the reliability and resiliency of the grid. Electric vehicles have dramatically increased in popularity, with most vehicle manufacturers offering at least one electric option in their lineups. Looking at recent developments in vehicle-to-grid (V2G) technology, a new possibility becomes evident; instead of straining the power grid, the electric
Dahlmann, Alexander Drake, Lele, Sneha
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 Research on Driving Performance of Vehicle Body Control using vehicle height adjustment system2026-01-0626To be published on 04/07/2026
Recently, premium cars include a device in the suspension that can adjust the ride height. The main function of this system is to help the vehicle pass through uneven roads by keeping the vehicle body high so that the lower part of the vehicle does not contact the road surface. In particular, the device of the air spring type improves riding comfort by adjusting the vehicle's wheel rate. The proposed system was designed to adjust the ride height by adjusting the position of the spring lower seat up and down. By modeling the machinery topology of the proposed system in detail using a dynamics tool, we would like to investigate the feasibility of implementing driving performance by controlling not only the height adjustment function but also the vehicle body Posture of the vehicle during driving.
Park, Jaeyong, Sang Hoon, Lee, Jong Min, Kim, Choi, Jang Han
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
Effects of Child Anthropometry, Seatback Angle, Seat Rotation Angle, Seatbelt Force Limiting, and Collision Type on Injury Risk of Child Occupants in Highly Automated Vehicles2026-01-0570To be published on 04/07/2026
This study aimed to evaluate the influence of child anthropometry, seat posture (recline and rotation), seatbelt force limiting, and frontal collision scenarios on the kinematic response and injury risk to children in highly automated vehicles. The TUST IBMs 6YO-O model was conducted the frontal collisions in sled tests. This simulation matrix includes five percentiles six-year-old occupants (P3, P25, P50, P75, and P97), three seatback angles (20°, 30°, and 45°), four seat rotation angles (0°, 90°, 180°, and 270°), three seatbelt force limiting (2.6 kN, 3.6 kN, and 4.6 kN), and three collision types. Injury risks were assessed including the child occupant's head, neck, chest/abdomen, and lumbar region in each simulation (n=540). The results indicate that the child anthropometry, the seatback angle, and the seat rotation angle have a significant influence on the motion responses. Statistically significant differences between all the groups within each independent variable category were
Wang, Yanxin, Zhao, Hongqian, Li, Haiyan, He, Lijuan, Cui, Shihai, Lv, Wenle
Optimization of Ride Comfort and Handling Using Advanced Control Strategies for Semi-Active Suspension Systems2026-01-0211To be published on 04/07/2026
Semi-Active suspensions are decisive for both comfort and handling. This study develops a quarter‑vehicle model that captures pitch and roll and compares control strategies under realistic road inputs. The controller employs an adaptive control suggested design that updates online to reject road‑induced disturbances and parameter drift, while a bio‑inspired optimizer sharpens the gains for robust, deployment‑oriented performance. The framework evaluates seat travel, body and passenger accelerations, suspension deflections, tire deformation at each wheel, and body pitch/roll, with sensor demand kept deliberately low. Across representative road profiles, time‑ and frequency‑domain assessments in MATLAB/Simulink show clear and consistent superiority of the adaptive design—and even stronger performance for the optimized variant—over the conventional sliding‑mode baseline. Vibration is decisively suppressed, suspension and tire‑load excursions are tightly contained, and overall stability
M.faragallah, Mohamed, Metered, Hassan, Abdelaziz, Taha, Mohamed, M.A.
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