Browse Topic: Driver behavior

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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
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
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
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
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
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
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
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.
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
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
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
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
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
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
Dynamic Integration of Bilateral and Adaptive Cruise Control for Intuitive Vehicle Behavior and Congestion Mitigations2026-26-00371/16/2026
Bilateral Cruise Control (BCC) is a new concept that has been shown to reduce traffic congestion and enhance fuel/energy efficiency compared to Adaptive Cruise Control (ACC). BCC considers both lead and trailing vehicles to determine the ego vehicle’s acceleration, effectively damping any disturbance down the vehicle string and reducing possibilities for congestion. Despite the advantages demonstrated with BCC, one major limitation is its non-intuitive behavior, which stems from the fact that the BCC reacts not just to the lead vehicle but also to the trailing vehicle’s movement. This paper identifies key issues with BCC control and proposes solutions that retain the benefits of BCC while maintaining intuitive behavior. Specifically, a novel switching strategy is proposed to switch between ACC and BCC control modes by critically analyzing the driving conditions. The proposed system ensures acceptable driving behavior with predictable braking and acceleration, resulting in an intuitive
A, Arya, A, Aishwarya, D, Vishal Mitaran, M, Senthil Vel, Kumar, Vimal
Drive Cycle Type Identification Using Data Driven Methodologies2026-26-06491/16/2026
Identifying the type of drive cycle is crucial for analyzing customer usage, optimizing vehicle performance and emission control. Methods that rely on geographical location for drive cycle identification are limited by varying driving conditions at the same location (e.g. heavy traffic during peak hours vs. free-flowing traffic at night). This paper proposes a methodology to identify the type of drive cycle (city, interurban, highway or hybrid) using drive characteristics derived from vehicle data rather than geographical location. Real-world vehicle data from testing trucks is taken, whose drive profiles are already known. Initially, multiple characteristic features of the drive cycle are identified from literature surveys and domain experience. These features, which can be extracted from basic signal data, include gear shifts, time spent in different driving modes (acceleration, cruise, standstill), velocity distributions, and an 'aggressiveness factor' representing overall driving
Reddy, Mallangi Prashanth, Gorain, Raju, Ganguly, Gourav
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
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
Crowdsourcing Indian Traffic Scenarios for ADAS Development and Testing2026-26-00461/16/2026
In the Indian context, introduction of ADAS can play a positive role in improving road safety by assisting the driver and preventing unsafe driver behaviour. Technologies like Automated Emergency Braking (AEB), Lane Keep System, Adaptive Cruise Control, Driver Drowsiness Detection, Driver Alcohol detection etc., if deployed safely and used in a safe manner can help prevent many of the current road deaths in India. Safe deployment and safe use of such ADAS technologies require the systems to operate without failure within their operational design domains (ODD) and not surprise the drivers with sudden or unpredictable failures, to help develop their trust in the technology. As a result, identifying test scenarios remain a key step in the development of Advanced Driver Assistance Systems (ADAS). This remains a challenge due to the large test space especially for the Indian context due to the unpredictable traffic behaviour and occasional road infrastructure. In this paper, we introduce a
Serry, Hamid, Dodoiu, Tudor, Alakkad, Fadi, Zhang, Xizhe, Khastgir, Siddartha, Jennings, Paul
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
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
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
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
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
AI-Powered Intelligent Cognitive Lens for Driver Monitoring for Safe Transportation and Increased Fleet Efficiency Abstract2026-26-06341/16/2026
The rapid evolution of intelligent transportation systems has made drivers’ attentiveness and adherence to safety protocols more critical than ever. Traditional monitoring solutions often lack the adaptability to detect subtle behavioral changes in real time. This paper presents an advanced AI-powered Driver Monitoring System designed to continuously assess driver behavior, fatigue, distractions, and emotional state across various driving conditions. By providing real-time alerts and insights to vehicle owners, fleet operators, and safety personnel, the system significantly enhances road safety. The system integrates lightweight AI/ML algorithms, image processing techniques, perception models, and rule-based engines to deliver a comprehensive monitoring solution for multiple transportation modes, including automotive, rail, aerospace, and off-highway vehicles. Optimized for edge devices, the models ensure real-time processing with minimal computational overhead. Alerts are communicated
Chikhale, Shraddha, Sing, Sandip, Hivarkar, Umesh, Mardhekar, Amogh
Reverse Engineering of CAN (Controller Area Network) of an Electric Vehicle2026-26-01501/16/2026
In-vehicle communication among different vehicle electronic controller units (ECU) to run several applications (I.e. to propel the vehicle or In-vehicle Infotainment), CAN (Controller Area Network) is most frequently used. Given the proprietary nature and lack of standardization in CAN configurations, which are often not disclosed by manufacturers, the process of CAN reverse engineering becomes highly complex and cumbersome. Additionally, the scarcity of publicly accessible data on electric vehicles, coupled with the rapid technological advancements in this domain, has resulted in the absence of a standardized and automated methodology for reverse engineering the CAN. This process is further complicated by the diverse CAN configurations implemented by various Original Equipment Manufacturers (OEMs). This paper presents a manual approach to reverse engineer the series CAN configuration of an electric vehicle, considering no vehicle information is available to testing engineers. To
Kumar, Rohit, Sahu, Hemant, Penta, Amar, Bhatt, Purvish
Ocular-Behavioral Metrics for Driver State Classification for Indian Driving Contexts: KSS-Based Evaluation, Conformance and Implementation Challenges2026-26-06681/16/2026
With the growing adoption of Advanced Driver Assistance Systems (ADAS) in the Indian automotive landscape, the need for effective Driver Monitoring Systems (DMS) has become increasingly critical. This paper presents the design, development, and validation of a Driver Distraction and Attention Warning System (DDAWS) tailored to Indian driving conditions. The proposed system integrates two key modules: Driver Attention Monitoring and Drowsiness Detection, using a high-resolution driver-facing camera to analyse head pose, facial landmarks, and behavioural cues. The drowsiness module incorporates metrics such as PERCLOS and Eye Aspect Ratio (EAR), evaluated against the Karolinska Sleepiness Scale (KSS). Recognizing the limitations of self-assessed scales like KSS in dynamic driving environments, the study compares algorithmgenerated KSS values with self-reported scores to assess model accuracy. Additionally, the framework aligns with automotive safety standards such as AIS184,EU 2021/1341
Verma, Harshal, Kale, Jyoti Ganesh, Karle, Ujjwala
Leveraging Road Accident Database to Enhance ADAS Features and Testing Protocols in Context of Indian Driving Conditions2026-26-00141/16/2026
ADAS i.e. Advanced Driver Assistance Systems are pivotal towards amplifying road safety by reducing human error and assisting drivers in critical situations. Most major ADAS technologies are developed and validated using data and test scenarios that are predominantly based on the driving conditions and road environments of developed countries. However, in a country like India, where driving behavior, traffic dynamics, road infrastructure, and accident characteristics differ significantly, the ADAS technologies and test scenarios validated by different forums create a critical gap in deploying such systems on vehicles to work on Indian roads. The major aim of this study was to determine and generate India-specific ADAS test scenarios from the Road Accident Sampling System India (RASSI) database, available MoRTH reports, and data from previously executed ADAS test cases. Through this research, we propose a methodology to identify, extract, and analyze accident scenarios pertaining to the
Adhikari, Mayur, Bhagat, Ajinkya, Verma, Harshal, Kale, Jyoti Ganesh, Karle, Ujjwala, Sharma, Chinmaya
Optimizing Collision Warnings: A Shift from Time to Collide to Time to Brake2026-26-00471/16/2026
In automotive safety systems, Time to Collide (TTC) is traditionally used to trigger warnings in auto-emergency braking systems. However, TTC can lead to premature or inaccurate warnings as it is calculated based on the relative speed and distance between the ego and an obstacle. TTC does not consider the vehicle’s braking dynamics, such as brake prefill lag which varies across different vehicles, maximum deceleration, and the effectiveness of braking systems and assumes constant speed which may not always be realistic. We propose Time to Brake (TTB) as a more effective parameter for driver warnings. TTB directly relates to the action a driver needs to take—braking. It provides a clear indication of when braking should begin to avoid a collision, whereas TTC only tells us about the possibility of a collision. To calculate TTB we utilize the brake profile, which incorporates both deceleration and system jerk for improved accuracy. The proposed warning time is the sum of variable brake
Singh, Ashutosh Prakash, Kumawat, Himanshu, Gupta, Sara
Calibration Strategy to Address Abnormal Engine Noise in Cold Transient Driving Condition2026-26-05851/16/2026
Passenger vehicle users often manoeuvre their cars in diverse and unpredictable driving patterns. The vast and varied terrain of the Indian subcontinent further complicates this scenario, introducing unique challenges due to differences in driving expertise, vehicle usage, and environmental conditions. A specific challenge addressed in this paper arises during different engine temperatures and transient driving conditions—a critical phase for engine calibration to ensure optimal drivability and emissions performance. With current calibration practices, a backfire like abnormal engine noise was observed during certain transient driving patterns. This paper presents a novel calibration methodology designed to eliminate such abnormal noise. The proposed approach involves a step-by-step transient calibration refinement, making the calibration process more robust and adaptable to any driving behaviour. The paper outlines the specific challenges encountered and details the multi-level
Suna, Bhagyashree, Tyagarajan, Sethuramalingam, Pise, Chetan, Aishwarya, Amritansh
Parameterizing External Factors Influencing E-Motor Consumption2026-26-01751/16/2026
In recent times, a standard driving cycle is an excellent way to measure the electric range of EVs. This process is standardized and repeatable; however, it has some drawbacks, such as low active functions being tested in a controlled environment. This sometimes causes huge variations in the range between driving cycles and actual on-road tests. This problem of variation can be solved by on-road testing and testing a vehicle for customer-based velocity cycles. On-road measurement may be high on active functions while testing, which may give an exact idea of real-world consumption, but the repeatability of these test procedures is low due to excessive randomness. The repeatability of these cycles is low due to external factors acting on the vehicle during on-road testing, such as ambient temperature, driver behavior, traffic, terrain, altitude, and load conditions. No two measurements can have the same consumption, even if they are done on the same road with the same vehicle, due to the
Kelkar, Kshitij, Kanakannavar, Rohit
A CNN–LSTM–SVM Framework for Mapping Driving Styles to Adaptive Suspension Modes10-10-02-00111/9/2026
Personalized suspension control is pivotal for enhancing vehicle dynamics and ride comfort in intelligent driving systems. This study proposes a driver style recognition model integrating convolutional neural network (CNN) and long–short-term memory (LSTM) networks to match suspension modes with driving styles, validated via a MATLAB–Python co-simulation platform. Time-series multi-source sensor data (throttle position, steering angle, braking intensity) are processed by CNN to extract spatiotemporal features and by LSTM to capture long-term temporal dependencies, enabling accurate classification of aggressive, smooth, and conservative driving styles. A support vector machine (SVM) maps these styles to optimal suspension modes—sport, comfort, or economy—forming an end-to-end framework. Simulation results demonstrate that the CNN–LSTM model achieves an 88% classification accuracy, a 17.33% improvement over the genetic algorithm-optimized backpropagation (GA-BP) model. The SVM-based
Wang, Zhuang, Liu, Jiang, Sun, Haoyu, Yuan, Yinghao, Liu, Jianze, Chen, Xiaofei, Wang, Honglin
An Interpretable Deep Learning Decision-Making Framework for Autonomous Lane-Change Inspired by Human Driving Experiences2025-01-733912/31/2025
Lane change plays a critical role in autonomous driving and directly affects traffic safety and efficiency. Although deep learning-based lane-change decision-making frameworks have achieved promising results, they still face fundamental challenges in producing human-consistent and trustworthy behavior, mainly due to: 1) Inadequate psychology-informed personalization, as most frameworks focus on physical variables but neglect psychological factors (e.g., risk tolerance, urgency), limiting their ability to capture individual differences in lane-change motivations. 2) Limited holistic understanding of traffic context, most frameworks lack consideration of high-level and interpretable indicators (e.g., traffic pressure) in comprehensively assessing dynamic traffic scenarios, limiting their capacity for human-like contextual understanding. 3) Lack of transparent and interpretable decision logic, as many frameworks operate as black boxes with opaque reasoning processes, hindering human
Chen, Yanbo, Chen, Jiaqi, Yu, Huilong, Xi, Junqiang
Game Theory-Based Coordinated Control Strategy for Multiple Types of Vehicles in Ramp Concourse Area2025-01-734812/31/2025
Decision modeling based on game theory provides an effective means to achieve safe and efficient ramp merging. However, there are some limitations in the current research, such as previous ramp merge control only studied the interaction problem of networked autonomous vehicles, ignoring the diversity of vehicle types, which is a non-negligible problem in real life. To solve this problem, this study proposes to use different game approaches to address the merging challenge. First, a static game is used to deal with the merging problem of networked self-driving vehicles, and then a belief pool with non-cooperative game approach is used to deal with the problem of human driver’s driving style with the merging problem of self-driving vehicles with human-driven vehicles with unknown information. The simulation results show that the efficiency of on-ramp merging can be significantly improved when networked self-driving cars interact with each other; in the case of merging self-driving cars
Gao, Zhenyu, Dong, Jiuyun, Zhang, Lu, Guo, Ge
A Framework for Multi-Scenario Performance Evaluation of Modular Autonomous Driving Systems2025-01-731912/31/2025
Currently, we face the challenge that ensuring ADS safety remains the primary bottleneck to large-scale commercial deployment—while benchmarks such as the CARLA Leaderboard have spurred progress, their coarse evaluation granularity, inability to quantify procedural risks, and lack of differentiation among algorithms in complex scenarios make in-depth diagnostics and functional safety validation exceedingly difficult. To address these challenges, we propose EvalDrive, a framework that seems to offer a more comprehensive approach to multi-scenario performance evaluation for modular autonomous driving systems. Within this broader analytical framework, EvalDrive appears to provide what seems to be three key contributions. (1) It constructs what appears to represent a structured and extensible scenario library, comprising a majority of 44 interactive scenarios, 23 weather conditions, and 12 town environments, which are then systematically expanded through parameterized variations. (2) Our
Jia, Chunyu, Kong, Yan, Ma, Yao, Pei, Xiaofei
Design and Implementation of a Multimodal Driver Data Collection and Processing Apparatus for Naturalistic Driving Scenarios2025-01-734612/31/2025
As intelligent cockpit technology continues to evolve, the ways in which information is presented and interacted with within vehicle systems are becoming increasingly diverse, driving the development of driver-machine interaction toward multi-modal integration, proactive sensing, and personalized responses. As the core perception object of the intelligent cockpit, the accuracy of driver state recognition directly impacts the intelligence level of cockpit interaction and driving safety. In response to the increasing trend of task diversity and behavioral response complexity in natural driving scenarios, there is an urgent need to develop a driver multimodal data collection and processing tool with high timeliness, non-intrusiveness, and multi-source synchronization capabilities, serving as the key foundation for driver state modeling and intelligent interaction support. Based on multiple resource theory (MRT) and driver status perception mechanisms, this study designs and develops a
Chen, Ke, Li, Xinyi, Cheng, Jiahao, Guo, Gang, Li, Wenbo
Driver Personalized Lane Change Behavior Analysis Based on Attention Deep Embedding Clustering2025-01-734712/31/2025
The characteristic representation and in-depth understanding of driver personalized driving behavior are fundamental to achieving human-like autonomous driving, enhancing the rationality of autonomous driving decisions, and meeting passengers’ personalized needs. [ADDED]Personalized driving behavior refers to individual-specific patterns in vehicle operation that emerge from drivers’ unique combinations of skills, risk tolerance, and habitual responses.However, current research lacks consideration of cluster analysis in the feature representation stage and ignores the time-varying contribution degree of time series values to low-dimensional features, which inhibits further utilization and development. This study adopts deep embedding clustering method and introduces attention mechanism to investigate driver personalized high-speed lane change behavior.[ADDED] Using a comprehensive driving simulator platform, we collected 15-channel time series data from 12 drivers performing 216 lane
Dong, Haomin, Wang, Wei, Wang, Yue, Li, Lun, Yue, Yi, Tian, Jiaxiao, Han, Jiayi
Risk Identification and Accompanying Information Service Design of Long Downhill Road Section2025-99-025012/23/2025
In order to reduce traffic accidents and losses in long downhill sections of expressways, giving drivers reasonable prevention and control means of information induction can improve the safety of long downhill sections. The location of the accompanying information service of the driver's vehicle terminal and the rationality of the intervention information are worth studying. This study takes a high-speed long downhill road as an example, divides the risk level of the long downhill road based on the road safety risk index model, and verifies it with the help of driving behavior data. Secondly, three coverage schemes of sensing devices are designed according to the results of risk classification, and the HMI interface of accompanying information service is designed according to the different coverage degrees of sensing devices. Finally, a driving simulation experiment was carried out based on the driving simulator, and the speed control level, psychological comfort level, operational
Wang, Yuejia, Weng, Wenzhong, Luan, Sen, Dai, Yibo
Investigating the Impact of Social Media on Driving Safety: A Case Study in Hong Kong2025-99-025412/23/2025
Focusing on drivers in Hong Kong, this paper analyzes how social media usage contributes to inattentive driving and the associated safety consequences. Data were collected using a questionnaire-based survey and analyzed through chi-square tests, Fisher’s exact tests, and Cramér’s V effect size calculations to examine the relationships between demographic and driving-related factors—including gender, age group, education level, driving experience, and self-rated driving skills—and the level of high-risk perception. The findings reveal that gender, age, experience, and Self-assessed driving ability significantly influence drivers’ perception of high-risk situations. Furthermore, significant interaction effects were observed among these variables, indicating that they do not operate in isolation but rather interact to shape risk perception. For example, middle-aged and older female drivers with higher education levels and extensive driving experience demonstrated a heightened perception
Dong, Jinhai, Ye, Haocheng, Cui, Ziheng, Chen, Yang
Research on the Visibility Attenuation Model of Pavement Markings for Older Drivers Based on Retroreflective Luminance Coefficient2025-99-022012/23/2025
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Cheng, Lizhi, Guan, Yanyan, Cheng, Xinyu, Hu, Jiangbi, Fu, Youlei, Yang, Biyu, Song, Shousong
An AI-Based Embedded Offline Voice Assistant for Commercial Trucks: Enhancing Driver Safety and HMI Design2025-36-007812/18/2025
Commercial vehicle operation faces challenges from driver distraction associated with traditional Human-Machine Interfaces (HMIs) and inconsistent network connectivity, particularly in long-haul scenarios. This paper addresses these issues through the development and presentation of an embedded, offline AI-powered voice assistant. The system is designed to reduce driver distraction and enhance operational efficiency by enabling hands-free control of vehicle functions and access to critical information, irrespective of internet availability. The technical approach involves a three-tier architecture comprising an Android-based In-Vehicle Infotainment (IVI) unit for primary user interaction and voice processing, an Android mobile device acting as a communication bridge and processing hub, and a proprietary OBD-II dongle for CAN bus interfacing. Offline speech recognition is achieved using embedded wake word detection and speech-to-intent engines. A user-centered design methodology
De Oliveira Nelson, Rafael, De Almeida, Lucas Gomes, Arantes Levenhagen, Ivan
Eco-Driving Longitudinal Control Strategy Based on Preceding Vehicle Acceleration Prediction2025-99-044512/10/2025
Automatic driving technology can achieve precise control of the vehicle. Compared with manual driving, it can greatly avoid bad driving behaviors such as rapid acceleration, rapid deceleration, and idle driving, more stable, efficient and safer control of vehicles, thus reducing energy consumption and pollution emissions, has great potential for eco-driving. Previous research on eco-driving car-following strategy is usually based on the current vehicle state. However, the real driving scene is extremely complex and changeable, which makes the existing research easy to fall into the dilemma of local optimal solution when dealing with complex long-term planning tasks, and it is difficult to gain comprehensive insight into the path of global optimal solution. According to the literature, bad driving behaviors such as rapid acceleration and rapid deceleration have a great impact on the energy consumption and emissions of vehicles, in order to realize eco-driving, planning control method
Luo, Shije, Zhao, Qi
Multimodal Intelligent Transportation Data Fusion and Application Methods in Heavy-Duty Vehicles2025-99-013911/11/2025
Heavy-duty commercial vehicles (HDCVs) are the key mobile nodes in intelligent transportation systems (ITS). However, their complex operating conditions and the diversity of data sources (such as road conditions, driver behavior, traffic signals, and on-board sensors) present considerable difficulties for accurately estimating the state and perceiving the environment using a single modality of data. This requires effective multi-modal data fusion to enhance the control and decision-making capabilities of HDCVs. This paper addresses this need by proposing a customized multi-modal intelligent transportation data fusion framework for intelligent HDCVs. This paper presents a solution for establishing a multi-modal intelligent transportation data collection platform, including real-scene collection methods and simulation scene collection methods based on the SUMO-MATLAB joint simulation platform. Through three representative case studies, the application methods of multi-modal traffic data
Chen, Zhengxian, Wang, Shaoqi, Jiang, Huiming, Zhou, Fojin, Wang, Mingqiang, Li, Jun
Smart Fleet Management for Mechanical Tillage Implements2025-28-031211/6/2025
Tillage, a fundamental agricultural practice involving soil preparation for planting, has traditionally relied on mechanical implements with limited real-time data collection or adjustment capabilities. The lack of real-time data and implement statistics results in fleet managers struggling to track performance, driver behavior, and operational efficiency of the implements. Lack of data on vehicle performance can result in unexpected breakdowns and higher maintenance costs, ensuring compliance with regulations is challenging without proper data tracking, potentially leading to fines and legal issues. Bluetooth-enabled mechanical implements for tillage operations represent an emerging frontier in precision agriculture, combining traditional soil preparation techniques with modern wireless technology. Implement mounted battery powered BLE (Bluetooth Low Energy) modules operated by solar panel based rechargeable batteries to power microcontroller. When Implement is operational turns
Kaniche, Onkar, Rajurkar, Kartik, Gokhale, Sourabha, Vadnere, Mohan
An SVM-Based Approach to Develop a New Control Module for H2-Powered Engine using Driver Behaviour Classification2025-32-004211/3/2025
In the next years, the global hydrogen vehicle market is expected to grow at a very high rate. Consequently, it is necessary for scholars and professionals to study and test specific components in order to rise motor efficiency leveraging the new features of connectivity available in smart roads. In particular, our research is focused on the developement of an engine control module driven by evaluation of usage characteristics (e.g., driving style) and "connected-to-x" scenarios using the standard engine control approach. Moreover, the module proposed enables the implementation of "fast running" models to improve the response of vehicles and make the best possible use of H2-powered engine characteristics. That said, in this paper is proposed a new approach to implement the control module, using Support Vector Machine (SVM) as the machine learning algorithm to detect driving style, and consequently modify the parameters of the engine. We choose SVM because i) it is less prone to
Mastroianni, Michele, Merola, Simona, Irimescu, Adrian, De Santis, Marco, Esposito, Christian, Aversano, Lerina
Automatic Brake Failure Detection with Electromagnetic Braking System2025-28-037310/30/2025
Brake failures in the vehicles can cause hazardous accidents so having a better monitoring and emergency braking system is very important. So, this project consists of an autonomous brake failure detector integrated with Automatic Braking using Electromagnetic coil braking which detects the braking failure at the time and applied the combinations of the brakes, to overcome this kind of accidents. So, here the system comprises of IR sensor circuit, control unit and electromagnetic braking system. How it works: The IR sensor monitors the brake wire, and if the wire is broken, the control unit activates the electromagnetic brakes, stopping the vehicle in a safe manner. This system enhances vehicle safety by ensuring immediate braking action without driver intervention. Key advantages include real-time brake monitoring, reduced mechanical wear, quick response time, and an automatic failsafe mechanism. The system’s minimal reliance on hydraulic components also makes it suitable for harsh or
Raja, Selvakumar, John, Godwin, Siddarth, J P, Senthilkumar, Akash, Mathew, Abhay, R. S., Nakandhrakumar, Nandagopal, Sasikumar, Arumugam, Sivasankar
Numerical Study to Predict the Impact of Geometric Modification on Aero-Acoustic Behaviour of a Commercial Vehicle2025-28-042610/30/2025
To address the growing concern of increasing noise levels in urban areas, modern automotive vehicles need improved engineering solutions. The need for automotive vehicles to have a low acoustic signature is further emphasized by local regulatory requirements, such as the EU's regulation 540/2014, which sets sound level limits for commercial vehicles at 82 dB(A). Moreover, external noise can propagate inside the cabin, reducing the overall comfort of the driver, which can have adverse impact on the driving behavior, making it imperative to mitigate the high noise levels. This study explores the phenomenon of change in acoustic behavior of external tonal noise with minor geometrical changes to the A-pillar turning vane (APTV), identified as the source for the tonal noise generation. An incompressible transient approach with one way coupled Acoustics Wave solver was evaluated, for both the baseline and variant geometries. Comparison of CFD results between baseline and variant showed
Pawar, Sourabh, Sharma, Shantanu, Singh, Ramanand
A Hybrid Framework for Kinematic Vehicle Prediction: YOLOv11 with Unscented Kalman Filter12-09-02-001310/22/2025
In the context of intelligent transportation systems and applications such as autonomous driving, it is essential to predict a vehicle’s immediate future states to enable precise and timely prediction of vehicles’ movements. This article proposes a hybrid short-term kinematic vehicle prediction framework that integrates a novel object detection model, You Only Look Once version 11 (YOLOv11), with an unscented Kalman filter (UKF), a reliable state estimation technique. This study provides a unique method for real-time detection of vehicles in traffic scenes, tracking and predicting their short-term kinematics. Locating the vehicle accurately and classifying it in a range of dynamic scenarios is achievable by the enhanced detection capabilities of YOLOv11. These detections are used as inputs by the UKF to estimate and predict the future positions of the vehicles while considering measurement noise and dynamic model errors. The focus of this work is on individual vehicle motion prediction
Pahal, Sudesh, Nandal, Priyanka
Improved Continuum Model with Driver Behaviour Nonlinear Analysis2025-99-001110/17/2025
A macroscopic traffic flow model based on car-following models of aggressive and timid drivers is presented in this study. Utilizing differential equation theory, we derive the types and stability characteristics of equilibrium solutions across diverse scenarios within the model. The incorporation of a viscous component improves the system’s stability. Additionally, a branch analysis is performed on the new model to examine the emergence of Hopf and saddle-node bifurcations. Simulation results confirm that the proposed model accurately reflects intricate nonlinear phenomena in traffic flow. Notably, the numerical solutions obtained through data simulation align closely with analytical predictions. Additionally, our findings highlights the importance of incorporating branch analysis in providing complementary insights to existing traffic flow theories.
Yang, ChunFeng, Qi, LinYuan, Shi, LongYu, Tang, Qiang, Tan, LiXiang
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