This SAE Recommended Practice is intended to establish a procedure to certify the fundamental driving skill levels of professional drivers. This certification can be used by the individual driver to qualify their skills when seeking employment or other professional activity. These certification levels may also be used by test facilities or other organizations when seeking test or professional drivers of various skills. The associated family of documents listed below establish driving skill criteria for various specific categories. SAE J3300: Driving level SAE J3300/1: Low mu/winter driving SAE J3300/2: Trailer towing SAE J3300/3: Automated driving Additional certifications to be added as appropriate. This main document provides: (1) common definitions and general guidance for using this family of documents, (2) directions for obtaining certification through Probitas Authentication®1, and (3) driving level examination requirements.

Driving Skills Standards Committee

Leveraging User Feedback to Optimize the Tuning of EVSE Models

2025-01-0020

05/05/2025

The implementation of active sound design models in vehicles requires precise tuning of synthetic sounds to harmonize with existing interior noise, driving conditions, and driver preferences. This tuning process is often time-consuming and intricate, especially facing various driving styles and preferences of target customers. Incorporating user feedback into the tuning process of Electric Vehicle Sound Enhancement (EVSE) offers a solution. A user-focused empirical test drive approach can be assessed, providing a comprehensive understanding of the EVSE characteristics and highlighting areas for improvement. Although effective, the process includes many manual tasks, such as transcribing driver comments, classifying feedback, and identifying clusters. By integrating driving simulator technology to the test drive assessment method and employing machine learning algorithms for evaluation, the EVSE workflow can be more seamlessly integrated. But do the simulated test drive results

Hank, Stefan, Kamp, Fabian, Gomes Lobato, Thiago Henrique

Trailer Towing Driving Skill Certification

J3300/2_202504 (Current)04/28/2025

Driving Skills Standards Committee

Research on Dangerous Driving Behavior Recognition Algorithm Based on LSTM

2025-01-8204

04/01/2025

To address the issue of high accident rates in road traffic due to dangerous driving behaviors, this paper proposes a recognition algorithm for dangerous driving behaviors based on Long Short-Term Memory (LSTM) networks. Compared with traditional methods, this algorithm innovatively integrates high-frequency trajectory data, historical accident data, weather data, and features of the road network to accurately extract key temporal features that influence driving behavior. By modeling the behavioral data of high-accident-prone road sections, a comprehensive risk factor is consistent with historical accident-related driving conditions, and assess risks of current driving state. The study indicates that the model, in the conditions of movement track, weather, road network and conditions with other features, can accurately predict the consistent driving states in current and historical with accidents, to achieve an accuracy rate of 85% and F1 score of 0.82. It means the model can

Huang, Yinuo, Zhang, Miaomiao, Xue, Ming, Jin, Xin

A Path Tracking Control Method Considering Vehicle Stability and Road Curvature

2025-01-8038

04/01/2025

The current research landscape in path tracking control predominantly focuses on enhancing tracking accuracy, often overlooking the critical aspect of passenger comfort. To address this gap, we propose a novel path tracking control method that integrates vehicle stability indicators and road curvature variations to elevate passenger comfort. The core contributions are threefold: firstly, we conduct comprehensive vehicle dynamics modeling and analysis to identify key parameters that significantly impact ride comfort. By integrating human comfort metrics with vehicle maneuverability indices, we determine the optimal range of dynamics parameters for maximizing passenger comfort during driving. Secondly, inspired by human driving behavior, we design a path tracking controller that incorporates an anti-saturation algorithm to stabilize tracking errors and a curvature optimization algorithm to mimic human driving patterns, thereby enhancing comfort. Lastly, comparative simulations with two

Lu, Jun, Zeng, Dequan, Hu, Yiming, Wang, Xiaoliang, Liu, Dengcheng, Jiang, Zhiqiang

Design and Evaluation of an Adjustable Ackermann Rate Steering System for Formula Student Racing Cars

2025-01-8000

04/01/2025

FSAE is a competition designed to maximize car performance, in which the steering system is a key subsystem, and the steering system performance directly affects the cornering performance of the car. The driver relies on the steering system for effective handling, which is also crucial for cornering and achieving faster lap times. Therefore, while improving the performance of the steering system, it is crucial to match the vehicle design to the driver's habits. Traditionally, steering systems typically use an Ackermann rate between 0% and 100% to offset the slip angle caused by tire deformation, thus achieving the purpose of reducing tire wear. Calculations have shown that a 40-60% Ackermann rate provides a similar compensation effect with little difference in tire wear. The traditional steering design method also does not consider the driver's driving habits and feedback, which is not conducive to the improvement of the overall performance of the car. In FSAE's figure-of-eight loops

Wu, Hailin, Li, Mingyuan

Recommendation of Adaptive Safe Speed Limits for Different Driver Profiles Using Deep Learning

2025-01-8106

04/01/2025

Personalization is a growing topic in the automotive space, where Artificial Intelligence can be used to deliver a customized experience in features like seat positioning and climate control. Considering that the leading cause of accidents is driving at an inappropriate speed, personalizing the speed limit for a driver can greatly improve vehicle safety. Current speed limits apply to all drivers, irrespective of skill, including special speed limits when there are adverse weather conditions. As these speed limits do not consider an individual’s skill and capabilities, the limit could still be inappropriate for a given driver in that specific driving context. Therefore, we propose a system that can profile the driver’s style to recommend a personalized speed limit, based on both the environmental context and their skill in that environment. The system uses a neural network to classify the driver’s behavior in specific environments by monitoring the vehicle data and the environmental

Perumal, Rathapriya, Chouhan, Madhvendra, Rangarajan, Rishi

Real World Emissions in a High-Altitude City: A Comparison between LPG Taxis and Gasoline Cars

2025-01-8494

04/01/2025

To study the real driving emission characteristics of light-duty vehicles fueled with liquefied petroleum gas (LPG) and gasoline in a high-altitude city, experimental investigations were performed on two LPG taxis and three gasoline passenger cars in Lhasa using a portable emission measurement system (PEMS). The results reveal that the emission factors of CO2, CO, NOx, and HC of LPG taxis are 159.19±11.81, 18.38±9.73, 1.53±0.46, and 1.27±0.99 g/km, and those of gasoline cars are 223.51±23.1, 1.51±0.68, 0.27±0.16, and 0.06±0.04 g/km, respectively. The emissions show strong relationships with driving mode, which is considerably affected by driving behavior. Furthermore, as vehicle speed increases, the emission factors of both LPG taxis and gasoline cars decrease. The emission rates of both types of vehicles are low and change slightly at a vehicle specific power (VSP) of 0 kW/t or below; After that, the rates slowly increase initially and then increase rapidly with increasing VSP. These

Lyu, Meng, Xu, Yan, Huang, Meihong, Wang, Yunjing

A Comparative Analysis of Acceleration and Deceleration Profiles for Aggressive Driving Styles and Fuel Economy Test Cycles

2025-01-8605

04/01/2025

Efficient and sustainable transportation in urban environments depends on understanding driving behaviors, and their implications. This study explores into the distinction between aggressive and non-aggressive driving patterns, leveraging an on-road driving dataset provided by an automotive company. By contrasting this data with established Fuel Economy cycles from United States Environmental Protection Agency (EPA) and employing curve-fitting techniques, the research not only reveals driving patterns but also predicts potential behaviors in unfamiliar scenarios. Results show significantly different acceleration profile patterns between different driving behaviors which has serious impact in fuel economy and environmental wellness. The findings highlights the environmental impact of driving behaviors, paving the way for environmentally responsible policy recommendations and sustainable driving practices.

Padmanaban, Gandhimathi, Feng, Fred, Dai, Edward, Saini, Ankit, Hu, Guopeng, Zhao, Yanan

Driving under the Influence: A Review of Fitness to Drive, Drug-Induced Impairments, and Simulation Testing

2025-01-8666

04/01/2025

This literature review examines the concept of Fitness to Drive (FTD) and its impairment due to drug consumption. Using a Systematic Literature Review (SLR) methodology, the paper analyzes literature from mechanical engineering and related fields to develop a multidisciplinary understanding of FTD. Firstly, the literature is analysed to provide a definition of FTD and collect methods to assess it. Secondly, the impact of drug use on driving performance is emphasized. Finally, driving simulators are presented as a valid possibility for analysing such effects in a safe, controlled and replicable environment. Key findings reveal a lack of a comprehensive taxonomy for FTD, with various assessment protocols in use. Only static simulators are employed for drug evaluation, limiting realism and result reliability. Standard Deviation of Lane Position (SDLP) emerges as a gold-standard measure for assessing driver performance. Future research should focus on developing standard definitions for

Uccello, Lorenzo, Nobili, Alessandro, Pasina, Luca, Novella, Alessio, Elli, Chiara, Mastinu, Gianpiero

Driver Distraction Recognition Based on the Information Bottleneck Theory and Graph Convolutional Networks

2025-01-8112

04/01/2025

Driver distraction remains a leading cause of traffic accidents, making its recognition critical for enhancing road safety. In this paper, we propose a novel method that combines the Information Bottleneck (IB) theory with Graph Convolutional Networks (GCNs) to address the challenge of driver distraction recognition. Our approach introduces a 2D pose estimation-based action recognition network that effectively enhances the retention of relevant information within neural networks, compensating for the limited data typically available in real-world driving scenarios. The network is further refined by integrating the CTR-GCN (Channel-wise Topology Refinement Graph Convolutional Network), which models the dynamic spatial-temporal relationships of human skeletal data. This enables precise detection of distraction behaviors, such as using a mobile phone, drinking water, or adjusting in-vehicle controls, even under constrained input conditions. The IB theory is applied to optimize the trade

Zhang, Ji, Bai, Yakun

Integrated Multimodal System for Real-Time Driver Fatigue Detection and Cognitive Load Assessment

2025-01-8076

04/01/2025

As human drivers' roles diminish with higher levels of driving automation (SAE L2-L4), understanding driver engagement and fatigue is crucial for improving safety. We developed an integrated hardware and software system to analyze driver interaction with automated vehicles, with a particular focus on cognitive load and fatigue assessment. The system includes three submodules; namely the Driver Behavior Measurement (DBM), Vehicle Dynamics Measurement (VDM), and the Driver Physiological Measurement (DPM). The DBM module uses electro-optical (EO) and infrared (IR) camera to track a number of facial features such as eye aspect ratio (EAR), mouth aspect ratio (MAR), pupil circularity (PUC), and mouth to eye aspect ratio (MOE). Although determining these metrics from images of the driver’s face in conditions such as low light or with sunglasses is challenging, the paper showed that fusion of EO and IR image analysis produces robust performance. The VDM module utilizes an Inertial Measurement

Jirjees, Abdullah, Rahman, Taufiq, Farhani, Ghazal, Singh, Daniel, Charlebois, Dominique

A Novel Hybrid Approach for Segmenting Drivers Based on Their Driving Style

2025-01-8205

04/01/2025

Drivers present diverse landscapes with their distinct personalities, preferences, and driving habits influenced by many factors. Though drivers' behavior is highly variable, they can exhibit clear patterns that make sorting them into one category or another possible. Discrete segmentation provides an effective way to categorize and address the differences in driving style. The segmentation approach offers many benefits, including simplification, measurement, proven methodology, customization, and safety. Numerous studies have investigated driving style classification using real-world vehicle data. These studies employed various methods to identify and categorize distinct driving patterns, including naturalist differences in driving and field operational tests. This paper presents a novel hybrid approach for segmenting driver behavior based on their driving patterns. We leverage vehicle acceleration data to create granular driver segments by combining event and trip-based methodologies

Chavan, Shakti Pradeep, Chinnam, Ratna Babu

Modeling Vehicle Behavior in Highway On-Ramps: A Game Theory and Deep Reinforcement Learning Method

12-08-04-0037

03/28/2025

With the improvement of autonomous driving technology, the testing methods for traditional vehicles can no longer meet autonomous driving needs. The simulation methods based on virtual scenario have become a current research hotpot. However, the background vehicles are often pre-set in most existing scenarios, making it difficult to interact with the tested autonomous vehicles and generate dynamic test scenarios that meet the characteristics of different drivers. Therefore, this study proposes a method combining game theory and deep reinforcement learning, and uses a data-driven approach to realistically simulate personalized driving behavior in highway on-ramps. The experimental results show that the proposed method can realistically simulate the speed change and lane-change actions during vehicle interaction. This study can provide a dynamic interaction test scenario with different driver style for autonomous vehicle virtual test in highway on-ramps and a more realistic environment

Qiu, Fanke, Wang, Kan, Li, Wenli

Longitudinal Analysis of Forward Blind Zone Changes in Popular Vehicle Models (1997–2023)

09-13-01-0005

03/26/2025

Background. In 2022, vulnerable road user (VRU) deaths in the United States increased to their highest level in more than 40 years. At the same time, increasing vehicle size and taller front ends may contribute to larger forward blind zones, but little is known about the role that visual occlusion may play in this trend. Goal. Researchers measured the blind zones of six top-selling light-duty vehicle models (one pickup truck, three SUVs, and two passenger cars) across multiple redesign cycles (1997–2023) to determine whether the blind zones were getting larger. Method. To quantify the blind zones, the markerless method developed by the Insurance Institute for Highway Safety was used to calculate the occluded and visible areas at ground level in the forward 180° arc around the driver at ranges of 10 m and 20 m. Results. In the 10-m forward radius nearest the vehicle, outward visibility declined in all six vehicle models measured across time. The SUV models showed up to a 58% reduction

Epstein, Alexander K., Brodeur, Alyssa, Drake, Juwon, Englin, Eric, Fisher, Donald L., Zoepf, Stephen, Mueller, Becky C., Bragg, Haden

Improving Safety of Autonomous Vehicles: A Verifiable Method for Graceful Degradation of Decision and Control Responsibilities

12-08-02-0021

03/24/2025

Developing safe and reliable autonomous vehicles is crucial for addressing contemporary mobility challenges. While the goal of autonomous vehicle development is full autonomy, up to SAE Level 4 and beyond, human intervention remains necessary in critical or unfamiliar driving scenarios. This article introduces a method for gracefully degrading system functionality and seamlessly transferring decision-making and control between the autonomous system and a remote safety operator when needed. This transfer is enabled by an onboard dependability cage, which continuously monitors the vehicle’s performance during its operation. The cage communicates with a remote command control center, allowing for remote supervision and intervention by a safety driver. We assess this methodology in both lab and test field settings in a case study of last-mile parcel delivery logistics and discuss the insights and results obtained from these evaluations.

Aniculaesei, Adina, Aslam, Iqra, Zhang, Meng, Buragohain, Abhishek, Vorwald, Andreas, Rausch, Andreas

Analysis of Speeding and Crashes on Bridges in Mugling to Narayanghat Highway Segment in Nepal

09-13-01-0002

03/03/2025

Driving speed affects road safety, impacting crash severity and the likelihood of involvement in accidents on highway bridges. However, their impacts remain unclear due to inconsistent topography and consideration of crash types. This study aimed to identify the status of accidents and factors associated with accidents occurring on bridges along the Mugling to Narayanghat highway segment in Nepal. The study area involves the selected highway segment stretching from Aptari junction (CH: 2+42) to Mugling junction (CH: 35+677). Spanning 33.25 km, the road traverses through both hilly and Terai regions. The study employs descriptive and correlation statistics to analyze crash data from 2018 to 2023, aiming to achieve its research objectives. The study reveals overspeeding as the primary cause of crashes, notably head-on and rear-end collisions. Two-wheelers frequently exceed the speed limit of 40 km/h limit (29–88 km/h), and four-wheelers do similarly (18–81 km/h), leading to overspeeding

Giri, Om Prakash, Shahi, Padma Bahadur, Kunwar, Deepak Bahadur

Effects of Emergency Vehicle Warning Lighting System Characteristics on Driver Perception and Behavior

09-13-01-0003

02/28/2025

Secondary crashes, including struck-by incidents are a leading cause of line-of-duty deaths among emergency responders, such as firefighters, law enforcement officers, and emergency medical service providers. The introduction of light-emitting diode (LED) sources and advanced lighting control systems provides a wide range of options for emergency lighting configurations. This study investigated the impact of lighting color, intensity, modulation, and flash rate on driver behavior while traversing a traffic incident scene at night. The impact of retroreflective chevron markings in combination with lighting configurations, as well as the measurement of “moth-to-flame” effects of emergency lighting on drivers was also investigated. This human factors study recruited volunteers to drive a closed course traffic incident scene, at night under various experimental conditions. The simulated traffic incident was designed to replicate a fire apparatus in the center-block position. The incident

Bullough, John D., Parr, Scott, Hiebner, Emily, Sblendorio, Alec

Toward a Methodology for Safety—Performance Trade-Off Analysis for Connected Automated Vehicles Supported by a Smart Infrastructure

12-08-02-0020

02/24/2025

Introducing connectivity and collaboration promises to address some of the safety challenges for automated vehicles (AVs), especially in scenarios where occlusions and rule-violating road users pose safety risks and challenges in reconciling performance and safety. This requires establishing new collaborative systems with connected vehicles, off-board perception systems, and a communication network. However, adding connectivity and information sharing not only requires infrastructure investments but also an improved understanding of the design space, the involved trade-offs and new failure modes. We set out to improve the understanding of the relationships between the constituents of a collaborative system to investigate design parameters influencing safety properties and their performance trade-offs. To this end we propose a methodology comprising models, analysis methods, and a software tool for design space exploration regarding the potential for safety enhancements and requirements

Fornaro, Gianfilippo, Törngren, Martin, Gaspar Sánchez, José Manuel

Chinese Ride-Hailing Drivers’ Adoption Intention of New Risk Visualization System: A Questionnaire Analysis Based on the Improved UTAUT2 Model

2025-01-7122

02/21/2025

Advanced Driver Assistance Systems (ADAS) have achieved significant progress worldwide, with the primary goals of enhancing driving safety, improving operational efficiency, and supporting vehicle automation. These systems are increasingly dependent on intelligent connected technologies, which enhance drivers' awareness and capacity to recognize and respond to potential road hazards in real-time. Within ADAS, risk visualization systems have become especially crucial, as they provide immediate alerts, thereby promoting safer driving behavior and enabling drivers to make more informed decisions on the road. This study expands upon existing frameworks by investigating the adoption of advanced risk visualization systems among Chinese ride-hailing drivers through an improved Unified Theory of Acceptance and Use of Technology (UTAUT2) model. The improved model introduces two novel constructs: Technology Trust and Perceived Risk, addressing critical gaps in understanding safety-critical

Zhang, Jiayan, Li, Linheng, Pan, Yan, Qu, Xu, Ran, Bin

Reviews on Traffic Flow Models for Autonomous Driving: The Artificial Intelligence and Cellular Automata Based Models

2025-01-7127

02/21/2025

Cellular Automata (CA) has emerged as a powerful computational model that has been widely applied in the field of traffic flow simulation, effectively capturing the complex dynamic behaviors of traffic systems and variable environmental conditions. With the rapid advancements in autonomous driving technology, traditional CA traffic flow simulation models for human-driving condition are updating, especially adapting to the Artificial Intelligence (AI) integrated driving behavior of autonomous vehicle (AV). This paper conducts an analysis on the existing explorations of CA-based traffic flow modelling for AVs. First, this paper utilizes the knowledge graph analysis tool “VOSviewer” to visually represent the relations among the state of art studies. The keyword clustering helps to reveal current research hotspots and developmental trajectories. Subsequently, the paper classifies how CA models are improved to adapt the AVs, from the view of the car-following, lane-changing, AV platoon, and

Li, Tianyi, He, Shanglu, Chen, Meng, Lu, Chunyi, Cao, Congyong

Analysis of Left-Turn Mixed Traffic Flow Dynamics at Y-Shaped Signalized Intersections Using Cellular Automata

2025-01-7204

02/21/2025

Through the method of on-site video observation, this study divides the intersection area into three parts according to the road traffic characteristics of the Y-shaped signalized intersections, and at the same time obtains the relevant parameters. These parameters include the left-turn speed and traffic density of motor vehicles within both the internal and exit areas, the frequency of lane-changing and queuing behaviors of non-motorized vehicles in the internal area, and the left-turn speed and traffic density of non-motorized vehicles in both the internal and exit areas. The data extraction and analysis of the parameters provide strong data support for further analysis of the subsequent mixed traffic flow. A cellular automaton model is developed using the intersection’s exit area as the scenario. The exit area is divided into three lanes based on the queuing patterns of mixed traffic. Corresponding traffic rules are established according to the traffic density of motorized and non

Yuan, Li, Liu, Xiaowei

Study on the Minimum Diversion Decision Distance of Autonomous Vehicle on Freeway Based on Lane-Changing Trajectory Modelling

2025-01-7170

02/21/2025

Most autonomous vehicles employ a relatively conservative lane-changing strategy in freeway system. In the diversion areas, autonomous vehicles typically initiate lane-changing to curb lanes at lower speeds at a considerable distance from the diversion point, resulting in a decrease in the overall traffic efficiency within the diversion areas. However, lane-changing decision points excessively close to exit ramps can exacerbate the urgency of the lane-changing process, prompting irrationally forced lane-changing and increasing the collision risk. To provide decision-making references for the safe and rapid diverging of autonomous vehicles in freeway diversion areas, this study proposes a minimum diversion decision distance (MDDD) model for autonomous vehicles through microscopic lane-changing trajectory data. Specifically, the lane-changing process was divided into waiting for the acceptable gap stage and executing the lane-changing stage in this model. Subsequently, UAV aerial

Li, ZhenFa, Luo, BaoGuo, Yang, Qi, Chen, Xu, Pan, BingHong

Truck Overload Warning and Risk Assessment Method Based on In-Vehicle System Data

2025-01-7210

02/21/2025

Overloading of trucks will not only damage road infrastructure, lead to exhaust pollution, and even cause serious traffic accidents, resulting in huge losses of life and property. However, most of the methods to evaluate truck overloading are limited by environmental factors, so it is impossible to monitor truck overloading in real time. In order to solve this problem, a truck overload detection method based on real-time vehicle diagnosis big data is proposed in this paper. The method comprehensively considers multiple factors affecting the actual power of trucks through mathematical modeling. It based on the effects of overload on fuel combustion efficiency, harmful gas emission, exhaust temperature, and vehicle power loss, The truck overload evaluation model is constructed to judge whether the truck is overloaded or not in real time. Based on the truck overload assessment and truck accident risk factor extraction , a real-time operation risk assessment model based on fault tree

Chen, Yuguang, Lin, Honghao, Wang, Yanan

Continuous Trust Estimation Method Based on Kalman Filtering in Human-Machine Co-Driving

2025-01-7186

02/21/2025

Subjective trust and active takeover behavior characteristics are two important aspects of trust performance in human-machine co-driving cars. However, trust is a subjective, abstract concept that changes over time and is difficult to measure directly. At present, there is a lack of quantitative research on objective trust and dynamic estimation of continuous trust under the influence of different independent variables, which inhibits its further use and development. This study adopts a continuous objective trust estimation method based on driving behavior, which mathematically describes the continuous measurement problem of objective trust and extracts driving behavior indicators in different traffic event research segments. The objective trust state space equation is established, and the objective trust estimation model is constructed based on the Kalman filter algorithm. Through model parameter definition and model verification, the estimation results and subjective trust are

Lin, Qingyang, Huang, Jun, Wang, Xinpeng, Lyu, Nengchao

Testing Methods for Advanced Intelligent Driving Systems Based on Natural Driving Behavior Characteristics

2025-01-7147

02/21/2025

The technology of advanced intelligent driving systems equipped in intelligent and connected vehicles evolves rapidly, while the corresponding testing and evaluation standards lag behind. To propose new testing methods for advanced intelligent driving systems, this study first identifies typical intelligent driving functions through extensive research. Based on the distribution characteristics of natural driving scenarios, a series of test cases for these typical functions are developed. By employing simulation methods and the "logical scenario-critical scenario-experimental scenario" mechanism, the generalized test cases are filtered, enabling the rapid extraction of high-value test cases for real-world field testing and validation. Finally, the typical intelligent driving functions of advanced driving systems are analyzed and evaluated from the perspectives of perception and decision-making, as well as control operations. The experimental results demonstrate that the proposed testing

Mo, Jingyue, Wu, Biao, Ma, Zhixiong, Ren, Hongze, He, Jiacan

Validating Human Factors-Based Classification Models for Safe and Efficient Mixed-Autonomy Interactions at Intersections Using a Virtual Simulator

12-08-02-0019

02/12/2025

The introduction of autonomous vehicles (AVs) promises significant improvements to road safety and traffic congestion. However, mixed-autonomy traffic remains a major challenge as AVs are ill-suited to cooperate with human drivers in complex scenarios like intersection navigation. Specifically, human drivers use social cooperation and cues to navigate intersections while AVs rely on conservative driving behaviors that can lead to rear-end collisions, frustration from other road users, and inefficient travel. Using a virtual driving simulator, this study investigates the use of a human factors-informed cooperation model to reduce AV reliance on conservative driving behaviors. Four intersection scenarios, each involving a left-turning AV and a human driver proceeding straight, were designed to obfuscate the right-of-way. The classification models were trained to predict the future priority-taking behavior of the human driver. Results indicate that AVs employing the human factors-informed

Ziraldo, Erika, Oliver, Michele

Real-Time Driver Drowsiness Detection Using YOLOv8 with Whale Optimization Algorithm

09-13-01-0001

02/10/2025

Driver fatigue and drowsiness portray an integral role in the frequency of road accidents. Putting in place policies intended to alert drivers is imperative for averting accidents and saving lives. This work aims to improve road safety by devising a real-time driver drowsiness detection system. To accomplish this, drowsiness is detected using YOLOv8 algorithm optimized with the whale optimization algorithm (WOA). Key facial cues such as eye closure and yawning frequency are monitored to analyze driving behavior by the suggested approach. YOLOv8 model optimized with WOA processes video streams in real time and sets off an alarm on the graphical user interface (GUI) dashboard based on the output. The proposed approach was investigated using two datasets namely UTA-RLDD and D3S. A 640 × 640 pixel image with a frame rate of 50 fps was used in the investigation. The mAP at 0.5 (mean average precision at 0.5 IoU (intersection over union) threshold) of drowsiness detection system using UTA

Nandal, Priyanka, Pahal, Sudesh, Sharma, Tripti, Omesh, Omesh

Advanced Driver Assistance Systems for Driver Health and Fatigue Monitoring Using Camera, Biometric Sensors, Telematics and Machine Learning

2025-28-0195

02/07/2025

Automotive industries focus on driver safety leading to raising improvements and advancements in Advanced Driver Assistance Systems (ADAS) to avoid collisions and provide safety and comfort to the drivers. This paper proposes a novel approach toward Driver health and fatigue monitoring systems that uses cabin cameras and biometric sensors communicating continuously with vehicle telematics systems to enhance real-time monitoring and predictive intervention. The data from the camera and biometric sensors is sent to the machine learning algorithm (LSBoost) which processes the data and if anything is wrong concerning the driver's behavior then immediately it communicates with vehicle telematics and sends information to the emergency services. This approach enhances driver safety and reduces accidents caused due to health-related driver impairment. This system comprises several sensors and fusion algorithms are applied between different sensors like cabin camera and biometric sensors, all

Bhargav, Matavalam

Research on Fuel Cell Vehicle Performance Based on PEMFC Stack Degradation

2025-01-7072

01/31/2025

The degradation of vehicle performance resulting from powertrain degradation throughout the lifecycle of alternative energy vehicles (AEVs) has consistently been a focal issue among scholars and consumers. The purpose of this paper is to utilize a one-dimensional vehicle simulation model to analyze the changes in power performance and economy of fuel cell vehicles as the Proton Exchange Membrane Fuel Cell (PEMFC) stack degrades. In this study, a simulation model was developed based on the design parameters and vehicle architecture of a 45kW fuel cell vehicle. The 1D model was validated for accuracy using experimental data. The results indicate that as the stack performance degrades, the attenuation rate of the fuel cell engine is further amplified, with a degradation of up to 13.6% in the system's peak output power at the End of Life (EOL) state after 5000 hours. Furthermore, the level of economic performance degradation of the complete vehicle in the EOL state is dependent on the

Li, You, Du, Jing, Guo, Donglai, Wang, Kai, Wang, Yupeng

Emission and Economic Characteristics of Diesel/PODE Blended Fuel in Actual Road Driving

2025-01-7115

01/31/2025

Diesel/Polymethoxy Dimethyl Ether (PODE) blend fuel can significantly reduce emissions from diesel engines. However, emission levels often vary due to high transients during real-world driving conditions. To evaluate the emission and economic performance of diesel/PODE blend fuel, this study analyzed the real-world driving behavior of heavy tractors using different blend ratios (0%, 20%, 30%) across urban, suburban, and expressway road sections, in compliance with the national VI emission standard. Based on Vehicle Specific Power (VSP) bins, the study compared carbon monoxide, carbon dioxide, nitrogen oxide, particulate matter, and fuel consumption rates between pure diesel and blended fuels, providing insights into their performance under varying driving conditions. In addition, specific emissions of pollutants, effective fuel consumption, and effective thermal efficiency for urban, suburban, and expressway sections, as well as for the entire test process, are analyzed to quantify the

Liu, He, Yang, Yajing, Farooq, Muhammad Shahid, Liu, Shenghua, Wei, Yanju

Physical and Cognitive Driver Reactions Characterization in Response to Active Safety Systems: A Preliminary Study

2024-36-0136

12/20/2024

Advanced Driver Assistance Systems (ADAS) are technologies that automate, facilitate, and improve the vehicle’s systems. Indeed, these systems directly interfere with braking, acceleration, and drivability of driving operations. Thus, the use of ADAS directly reflects the psychology behind driving a vehicle, which can have an automation level that varies from fully manual (Level 0) to fully autonomous (Level 5). Even though ADAS technologies provide safer driving, it is still a challenge to understand the complexity of human factors that influence and interact with these new technologies. Also, there has been limited exploration of the correlation between the physical and cognitive driver reactions and the characteristics of Brazilian roads and traffic. Therefore, the present work sought to establish a preliminary investigation into a method for evaluating the driving response profile under the influence of ADAS technologies, such as Lane Centering and Forward Collision Warning, on

Castro, Gabriel M., Silva, Rita C., Miosso, Cristiano J., Oliveira, Alessandro B. S.

Trajectory Planning and Tracking Control for Single Lane Changing with Different Driving Styles of Intelligent Vehicles Based on Seventh-Degree Polynomial

12-08-03-0028

12/14/2024

Single lane changing is one of the typical scenarios in vehicle driving. Planning an appropriate lane change trajectory is crucial in autonomous and semi-autonomous vehicle research. Existing polynomial trajectory planning mostly uses cubic or quintic polynomials, neglecting the lateral jerk constraints during lane changes. This study uses seventh-degree polynomials for lane change trajectory planning by considering the vehicle lateral jerk constraints. Simulation results show that the utilization of the seventh-degree method results in a 41% reduction in jerk compared to the fifth-degree polynomial. Furthermore, this study also proposes lane change trajectory schemes that can cater to different driving styles (e.g., safety, efficiency, comfort, and balanced performance). Depending on the driving style, the planned lane change trajectory ensures that the vehicle achieves optimal performance in one or more aspects during the lane change process. For example, with the trajectory that

Lai, Fei, Huang, Chaoqun

A Driving Behavior Decision Method Based on Gradient Boosting Tree Algorithm

2024-01-7030

12/13/2024

In order to reduce the incidence of traffic accidents and improve passengers’ driving experience, intelligent driving technology has attracted more and more attention. The core content of intelligent driving technology includes environment perception, behavior decision-making and control follow-up. Simulating driver’s behavior decision-making based on multi-source heterogeneous environment information is the key to liberate drivers and become the focus and difficulty of intelligent driving technology. Aiming at this key problem, this paper presents a design method of driving behavior decision maker based on machine learning after fuzzy classification of historical data. Firstly, 1000 sets of driving environment-decision results database are generated randomly according to driving rules and driving state. A fuzzy classification rule is established to classify driving environment information such as speed and relative distance. Then, a driving behavior decision maker is designed based on

Li, Hongluo, Xia, Hongyang, Huang, Yongxian, Xu, You, Xu, Wei

Driving Behavior Modeling Based on Inverse Reinforcement Learning

2024-01-7029

12/13/2024

With the advancement of intelligent driving technology, today’s smart vehicles must not only make accurate and safe driving decisions but also exhibit high human-likeness to ensure better acceptance from people. Developing vehicle behavior models with increased human-likeness has become a significant industry focus. However, existing vehicle behavior models often struggle to balance human-likeness and interpretability. While some researchers use inverse reinforcement learning (IRL) to model vehicle behavior, ensuring both human-likeness and a degree of interpretability, challenges such as reward function design difficulties and low human-likeness in background vehicle modeling persist. This study addresses these issues by focusing on highway scenarios without on-ramps, specifically following and lane-changing behaviors, using the CitySim dataset. IRL is employed to create a vehicle behavior model with improved human-likeness, utilizing a linear reward function to capture driver

Xu, Xiaobin, Han, Wei, Leng, Bo, Xiong, Lu

Interaction-Aware Deep Reinforcement Learning Approach Based on Hybrid Parameterized Action Space for Autonomous Driving

2024-01-7035

12/13/2024

Learning-based motion planning methods such as reinforcement learning (RL) have shown great potential of improving the performance of autonomous driving. However, comprehensively ensuring safety and efficiency remain a challenge for motion planning technology. Most current RL methods output discrete behavioral action or continuous control action, which lack an intuitive representation of the future motion and then face the problems with unstable or reckless driving behavior. To address these issues, this work proposes an interaction-aware reinforcement learning approach based on hybrid parameterized action space for autonomous driving in lane change scenario. The proposed method can output high-level feasible trajectory and low-level actuator control command to control the vehicle’s motion together. Meanwhile, the reward functions for the local traffic environment are designed to evaluate the effect of the interaction between ego vehicle and surrounding vehicles. The contributions of

Li, Zhuoren, Jin, Guizhe, Yu, Ran, Leng, Bo, Xiong, Lu

Human-like Behavior Strategy for Autonomous Vehicles Considering Driving Styles

2024-01-7046

12/13/2024

Autonomous driving technology plays a crucial role in enhancing driving safety and efficiency, with the decision-making module being at its core. To achieve more human-like decision-making and accommodate drivers with diverse styles, we propose a method based on deep reinforcement learning. A driving simulator is utilized to collect driver data, which is then classified into three driving styles—aggressive, moderate, and conservative—using the K-means algorithm. A driving style recognition model is developed using the labeled data. We then design distinct reward functions for the Deep Q-Network (DQN), Proximal Policy Optimization (PPO), and Soft Actor-Critic (SAC) algorithms based on the driving data of the three styles. Through comparative analysis, the SAC algorithm is selected for its superior performance in balancing comfort and driving efficiency. The decision-making models for different styles are trained and evaluated in the SUMO simulation environment. The results indicate that

Shen, Chuanliang, Zhang, Longxu, Shi, Bowen, Ma, Xiaoyuan, Li, Yi, Hu, Hongyu

Customized Vehicle Feature Control through Driver Recognition Technology

2024-28-0219

12/05/2024

This paper presents a novel approach for customizing vehicle features through driver recognition technology. The system combines Cultural Adaptive Face Recognition (CAFR) using FaceNet and Contrastive Language-Image Pretraining (CLIP) models, along with OpenCV, to recognize drivers and customize vehicle feature control. To identify a driver, the system compares their features against a pre-existing database using FaceNet, which generates efficient face embeddings. The driver image and contextual information collected is processed by OpenAI’s CLIP to generate CLIP embeddings which leverages multimodal learning. FaceNet and CLIP embeddings’ fusion is done and are stored in the Qdrant search database for efficient retrieval and similarity searches. Once the driver is recognized, the system adjusts vehicle features such as temperature settings, music selections, and seat adjustments according to the driver's preferences. Additionally, the system implements optical character recognition

Marimuthu, Ranjithkumar

Validation Methodology of Different Driver Monitoring Systems Using a Modular Motion Drive Simulator

2024-28-0263

12/05/2024

In India, Driver Drowsiness and Attention Warning (DDAW) system-based technologies are rising due to anticipation on mandatory regulation for DDAW. However, readiness of the system to introduce to Indian market requires validations to meet standard (Automotive Industry Standard 184) for the system are complex and sometimes subjective in nature. Furthermore, the evaluation procedure to map the system accuracy with the Karolinska sleepiness scale (KSS) requirement involves manual interpretation which can lead to false reading. In certain scenarios, KSS validation may entail to fatal risks also. Currently, there is no effective mechanism so far available to compare the performance of different DDAW systems which are coming up in Indian market. This lack of comparative investigation channel can be a concerning factor for the automotive manufactures as well as for the end-customers. In this paper, a robust validation setup using motion drive simulator with 3 degree of freedom (DOF) is

Raj, Prem raj Anand, Selvam, Dinesh Kumar, Thanikachalam, Ganesh, Sivakumar, Vishnu

Evaluating the Impact of Velocity Advisory Systems on Fuel Economy of Passenger Vehicles: A Driver Simulator Study

2024-01-4275

11/05/2024

With increasing emphasis on sustainable mobility and efficient energy use, advanced driver assistance systems (ADAS) may potentially be utilized to improve vehicles’ energy efficiency by influencing driver behavior. Despite the growing adoption of such systems in passenger vehicles for active safety and driver comfort, systematic studies examining the effects of ADAS on human driving, in the context of vehicle energy use, remain scarce. This study investigates the impacts of a driver speed advisory system on energy use in a plug-in hybrid electric vehicle (PHEV) through a controlled experiment using a driving simulator. A mixed urban highway driving environment was reconstructed from digitalizing a real-world route to observe the human driver’s behavior with and without driving assistance. The advisory system provided drivers with an optimized speed profile, pre-calculated for the simulated route to achieve maximum energy efficiency. Participants were instructed to navigate the

Telloni, Marcello, Farrell, James, Mendez, Luis, Ozkan, Mehmet Fatih, Chrstos, Jeffrey, Canova, Marcello, Stockar, Stephanie

Solutions for Making Group Travel More Connected and Easy

2024-28-0029

10/17/2024

The integration of Vehicle-to-Everything (V2X) communication technologies holds immense potential to revolutionize the automotive industry by enabling vehicles to communicate with each other (V2V) and with infrastructure (V2I). This paper investigates the feasibility of V2X and V2I communication, exploring available communication methods for vehicles to communicate. Many a times people like to travel together and it involves more than one vehicle travelling together, in such cases they often get lost the information about fellow vehicles due to the traffic condition and different driving behaviors of the individual driver. In such cases they communicate over phones to get to know the location of fellow vehicle or keep sharing their live locations. In such cases they don’t just follow the destination in maps also they should be continuously monitoring their fellow vehicles position. It is important for vehicles travelling in group to have communication and be connected so that they know

Barre, Deva Harshitha

A Driver Safety System Using Raspberry Pi and Computer Vision

2024-28-0030

10/17/2024

Road safety remains a critical concern globally, with millions of lives lost annually due to road accidents. In India alone, the year 2021 witnessed over 4,12,432 road accidents resulting in 1,53,972 fatalities and 3,84,448 injuries. The age group most affected by these accidents is 18-45 years, constituting approximately 67% of total deaths. Factors such as speeding, distracted driving, and neglect to use safety gear increases the severity of these incidents. This paper presents a novel approach to address these challenges by introducing a driver safety system aimed at promoting good driving etiquette and mitigating distractions and fatigue. Leveraging Raspberry Pi and computer vision techniques, the system monitors driver behavior in real-time, including head position, eye blinks, mouth opening and closing, hand position, and internal audio levels to detect signs of distraction and drowsiness. The system operates in both passive and active modes, providing alerts and alarms to the

Ganesh, Katta, Prasad, Gvl

Personalized Design of Variable Transmission Ratio and Selection Switching Strategies Considering Drivers’ Steering Characteristics

10-08-04-0028

09/14/2024

In order to meet the driving characteristics and needs of different types of drivers and to improve driving comfort and safety, this article designs personalized variable transmission ratio schemes based on the classification results of drivers’ steering characteristics and proposes a switching strategy for selecting variable transmission ratio schemes in response to changes in driver types. First, data collected from driving simulator experiments are used to classify drivers into three categories using the fuzzy C-means clustering algorithm, and the steering characteristics of each category are analyzed. Subsequently, based on the steering characteristics of each type of driver, suitable speed ranges, steering wheel travel, and yaw rate gain values are selected to design the variable transmission ratio, forming personalized variable transmission ratio schemes. Then, a switching strategy for variable transmission ratio schemes is designed, using a support vector machine to build a

Chen, Chen, Zheng, Hongyu, Zong, Changfu

Comprehensive Study on Challenges to Introduce Autonomous Emergency Braking (AEBS) Feature in Indian Perspective

2024-01-3050

09/08/2024

India is a diverse country in terms of road conditions, road maintenance, traffic conditions, traffic density, quality of traffic which implies presence of agricultural tractors, bullock carts, autos, motor bikes, oncoming traffic in same lane, vulnerable road users (VRU) walking in the same lanes as vehicles, VRU’s crossing roads without using zebra crossings etc. as additional traffic quality deterrents in comparison to developed countries. The braking capacity of such vivid road users may not be at par with global standards due to their maintenance, loading beyond specifications, driver behavior which includes the tendency to maintain a close gap between the preceding vehicle etc. which may lead to incidents specifically of rear collisions due to the front vehicle going through an emergency braking event. The following paper provides a comprehensive study of the special considerations or intricacies in implementation of Autonomous Emergency Braking (AEBS) feature into Indian traffic

Kartheek, Nedunuri, Khare, Rashmita, Sathyamurthy, Sainathan, Manickam, Praveenkumar, Kuchipudi, Venkata Sai Pavan

Virtual Identification of Driver-Specific Characteristics for Assisted Lateral Control

2024-01-5090

09/02/2024

Advanced driver assistance systems (ADAS) have become an integral part of today’s vehicle development. These systems are designed to provide secondary support to the driver, but the driver is primarily responsible for the driving task, e.g., lane-keeping assist (LKA). The driving setup and testing of these LKA systems is very time-consuming and usually applied in the car, based on experiences and subjective evaluation. This results in a cost-intensive calibration of the system. An objective-based calibration procedure can increase efficiency. For a targeted calibration of the system, it is necessary to define and identify key performance indicators (KPIs), which are able to describe the secondary support in sufficient detail. Usually, subjective feelings are used to derive KPIs. Vice versa, there are no results on how to design an LKA without any subjective assessment, before the calibration. With this in mind, this paper is focused on filling this unknown aspect by using virtual

Baumann, Benjamin, Iatropolous, Jannes, Panzer, Anna, Henze, Roman

Driving Intention Recognition Model for Highway Ramp-Merging Scene

12-08-02-0016

07/24/2024

Driving safety in the mixed traffic state of autonomous vehicles and conventional vehicles has always been an important research topic, especially on highways where autonomous driving technology is being more widely adopted. The merging scenario at highway ramps poses high risks with frequent vehicle conflicts, often stemming from misperceived intentions [1]. This study focuses on autonomous and conventional vehicles in merging scenarios, where timely recognition of lane-changing intentions can enhance merging efficiency and reduce accidents. First, trajectory data of merging vehicles and their conflicting vehicles were extracted from the NGSIM open-source database in the I-80 section. The segmented cubic polynomial interpolation method and Savitzky–Golay filtering are utilized for data outlier removal and noise reduction. Second, the processed trajectory data were used as input to a hybrid Gaussian hidden Markov (GMM-HMM) model for driving intention classification, specifically lane

Ren, You, Wang, Xiyao, Song, Jiaqi, Lu, Wenyang, Li, Penglong, Li, Shangke

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