Browse Topic: Driver behavior

Items (702)
Find
Smart Fleet Management for Mechanical Tillage Implements2025-28-0312To be published on 11/06/2025
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
AI in Off-Highway Fleet Management: Driving Towards a Smarter Future2025-28-0335To be published on 11/06/2025
AI in Off-Highway Fleet Management: Driving Towards a Smarter Future Managing fleets of off-highway vehicles presents unique challenges that significantly impact operational efficiency, safety, and sustainability. These vehicles, often used in industries such as mining, construction, and agriculture, face several issues like maintenance and repair, reduced fuel efficiency, fleet tracking and monitoring. Tackling these challenges is crucial for enhancing the efficiency, safety, and sustainability of off-highway vehicle fleet management, and this paper presents an AI-driven solution to address these issues. AI systems leverage real-time data from telematics devices, sensors, and GPS tracking to optimize various aspects of fleet management. Key areas of impact include: Predictive Maintenance: AI algorithms analyse vehicle data to predict potential failures before they occur, reducing downtime and maintenance costs. By monitoring indicators such as engine performance and battery health
Selvaraj, Ajith Kumar, Nashipudi, Sandesh, Ramanathan, Vadivel, Durairaj, Manikandan
Strategies for Improving Energy Consumption for Electric Vehicles.2025-32-0092To be published on 11/03/2025
The global transition toward sustainability and decarburization has positioned Electric Vehicles (EVs) at the forefront of future mobility. While EVs inherently offer lower emissions compared to internal combustion engine (ICE) vehicles, optimizing their energy efficiency remains a critical challenge to ensure their viability, affordability, and widespread adoption. With global EV sales surpassing 10 million units in 2022 representing over 14% of the global vehicle market. Energy efficiency has emerged as a cornerstone in addressing key concerns such as range anxiety, charging infrastructure, operational costs and overall environmental impact. This paper embarks on a comprehensive journey, beginning with the historical evolution of EVs and identifying the multifaceted energy losses that occur during vehicle operation. By mapping the energy distribution within a typical EV, the study lays a foundational understanding of where and how inefficiencies arise. It then explores current
Jain, Gaurav, Premlal, P, Pathak, Rahul, Gore, Pandurang
An SVM-based approach to develop a new control module for H2-powered engine using driver behaviour classification2025-32-0042To be published on 11/03/2025
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 vehicle 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 system2025-28-0373To be published on 10/30/2025
Brake failure is a major cause of road accidents, contributing significantly to vehicle-related fatalities and injuries across the globe. To address this issue, this project focuses on the development of an automatic brake failure detection and integration of electromagnetic braking system to enhance vehicle safety and reliability. The system continuously monitors brake functionality by detecting anomalies in pedal free play and oil pressure levels within the braking system. If an irregularity is detected, a signal is transmitted to a microcontroller, which triggers a dashboard warning light to alert the driver, once the driver sees this light he must pull towards the service lane. If correct action is not taken within five seconds, the auxiliary electromagnetic braking system engages automatically, ensuring a controlled deceleration of the vehicle. The integration of electromagnetic brakes provides an additional layer of safety by acting as a backup braking mechanism in case of
Raja, Selvakumar
Numerical study to predict the Impact of Geometric Modification on Aero-Acoustic behaviour of a Commercial Vehicle2025-28-0426To be published on 10/30/2025
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 behaviour 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
Real Driving Database for Automotive Drive System Development2025-01-0378To be published on 10/07/2025
Knowledge of real-world driving behavior is fundamental to the development of drive systems. An application example would be the derivation of representative requirements or driving cycles based on information about the use case-specific vehicle use. The profiles are additionally enriched with underlying information such as distance, standstill times, average accelerations or a customer driving style estimation. This information could be used, for example, for drive system design. In addition, the real-world data can be used for regulatory purposes such as the definition of utility factors or the definition of real driving emission cycles. In a project funded by FVV e.V., we have developed an universal database software including data storage, user interface and general data plausibility functions for real driving data. The database contains on component and vehicle level detailed time series measurement data such as torque and speed of electric motors and internal combustion engines
Sander, Marcel, Sturm, Axel Wolfgang, Martínez Medina, Óscar, Henze, Roman, Kühne, Ulf, Eilts, Peter
The impact of inter-vehicular distance on CO₂ emissions in on-road driving hybrid vehicles2025-01-0404To be published on 10/07/2025
A major challenge for internal combustion engine vehicles is reduction of CO₂ emissions. Hybrid vehicle demand has recently increased as a countermeasure. However, in hybrid vehicles, the frequency of motor and engine usage varies depending on the driver's driving style, even when driving the same vehicle on the same route. As a result, CO₂ emissions can differ significantly between drivers. Analyzing the impact of driving characteristics on CO₂ emissions can contribute to improving the efficiency of engine and motor control in vehicles, leading to further reductions in CO₂ emissions. Therefore, this paper examines the impact of different driver behaviors on CO₂ emissions in hybrid vehicles. In this study, two drivers were asked to follow a preceding vehicle on real roads, and various data were collected during these drives. Conducting the experiments on actual roads allowed us to obtain results that closely reflect real-world driving conditions, thereby enhancing the relevance of the
Hosogi, Takafumi, Imamura, Kotaro, Sato, Susumu
Adapted Intelligent Driver Model for Improved Vehicle Following and Assessment of Energy Impacts2025-01-0387To be published on 10/07/2025
Central to predicting the impacts of individual vehicle operations within microscopic traffic simulation is the driver model. A driver model determines a vehicle’s velocity profile in various driving scenarios and interactions with other vehicles. Characteristics including driver behavior and interactions with stop signs, traffic signals, and interactions with a lead vehicle can be modeled and assessed with a representative driver model. This paper presents the application of an existing intelligent driver model (IDM) with an adaptation for vehicle following dynamics and the interaction with the lead vehicle to be more representative of driver assist systems concerning the relative distance between the lead and simulated ego vehicle. The method uses an additional control term to augment the existing IDM and reduce inter-vehicle distance to the time gap. The impact on vehicle dynamics is compared and validated with real-world ego vehicle data recorded through driver assist systems. The
Udipi, Anirudh, Jadhav, Shreeprasad, Bhure, Mayur, Hegde, Bharatkumar, Poovalappil, Aman, Robare, Andrew, Apostol, Peter, Bahramgiri, Mojtaba, Naber, Jeffrey
Lifting the Key Functional Figures Concerning Performance and Efficiency for 2nd Gen. H 2 Engines for On- and Off-Highway Installations by Tailored Engineering Measures2025-24-004909/07/2025
To curb global warming and meet stricter greenhouse gas emission standards all over the globe, it is essential to minimize the carbon footprint of applications in the mobility and transport segment. The demands on mobility, transportation and services are constantly increasing in line with worldwide population growth and the corresponding need for economic prosperity. This ongoing trend will lead to a significant increase in energy requirements for mobility-related applications in the upcoming time, despite all efficiency improvements. The timely introduction and accelerated spread of low-carbon/carbon-neutral energy sources is therefore of crucial importance. In addition to the switch to electric propulsion systems, particularly in the light-duty vehicle sector, the use of advanced and optimized hydrogen (H2)-powered internal combustion engines (ICE) represents a parallel, compatible technical option, as these applications will also meet the most stringent requirements in terms of
Koerfer, Thomas, Zimmer, Pascal, Li, Zhengling, Pischinger, Stefan, Lückerath, Moritz
An Eco-Charging Strategy for Heavy-Duty Electric Vehicles via Graph Optimization2025-24-011609/07/2025
An optimization framework for trip and charging planning for electric heavy-duty vehicles is proposed in this paper. Building upon and extending previous work on light-duty vehicles, our approach models energy-aware routing by constructing a state-augmented graph that jointly captures geographic position and battery state-of-charge. We refine the route model to include detailed vehicle dynamics and speed constraints specific to heavy-duty vehicles, and introduce an alternative graph construction method that avoids the computational complexity of lexicographic products by generating only feasible nodes. The resulting framework enables efficient trip planning that accounts for driving behavior, road characteristics, and charging infrastructure. Simulation results demonstrate the effectiveness of the approach in reducing energy consumption and ensuring operational feasibility for long-haul freight transport.
Zonetti, Daniele, Sciarretta, Antonio, De Nunzio, Giovanni
Influence of MPC Based Driver Types on the Energy Consumption of Battery Electric Vehicles2025-24-011309/07/2025
Electric vehicles are increasingly important for emission reduction and the promotion of sustainable mobility. Despite their advantages over conventional vehicles, the energy consumption of electric vehicles is heavily influenced by various factors such as driving behavior, elevation profile, and environmental conditions. In particular, the driving style plays a crucial role in determining range and energy consumption. This influence is also observed in the context of the Interreg project FreeE-Bus. This project focuses on the development of optimized charging management for electric buses in the public transport system of the Lake Constance region. Due to strict data protection regulations that prevent a detailed analysis of driver data, assessing the impact of driving styles is difficult. This paper addresses this issue by developing an innovative driver model that simulates different driver types and analyzes their effects on energy consumption. The driver model employs a Model
Konzept, Anja, Reick, Benedikt, Miller, Marius, Rautenberg, Philip, Störzer, Martin
Use of Model-Based Datasets and Neural-Networks to Support Real-Time Driving Suggestions System for Components Preservation in Battery Electric Vehicles2025-24-011509/07/2025
Nowadays, Battery Electric Vehicles (BEVs) are considered an attractive solution to support the transition towards more sustainable transportation systems. Although their well-known advantages in terms of overall propulsion efficiency and exhaust emissions, the diffusion of BEVs on the market is still reduced by some technical bottlenecks. Among those, the uncertainty about the expected durability of the vehicle's onboard battery packs plays a key role in affecting customer choice. In this context, this paper proposes the use of model-based datasets for training a driving support system based on machine learning techniques to be installed on board. The objective of this system is to acquire vehicle, environmental, and traffic information from sensor’ networks and provide real-time smart suggestions to the driver to preserve the remaining useful life of vehicle components, with particular reference to the battery pack and brakes. For the generation of the training dataset, first, a set
Bernardi, Mario Luca, Capasso, Clemente, Iannucci, Luigi, Sequino, Luigi
Reducing Range Prediction Uncertainty in Heavy-Duty Electric Vehicles Using a Route-Informed Transformer-Based Model2025-24-012209/07/2025
Electrification of heavy-duty on-road trucks used for regional freight transportation is a viable option for fleets to reduce operation and maintenance costs and lower their carbon footprint. However, there is considerable uncertainty in projecting their daily range because highly variable payload mass, among other factors, confounds battery state of charge (SOC) prediction algorithms. Previous work by the authors proposed an electric vehicle range prediction model based on two parallel recurrent neural networks (RNNs). The first RNN used mean-variance estimation to output a predicted mean and variance, and the second used bounded interval estimation to provide bounds on the SOC required to complete a trip. The dual RNN approach resulted in estimating the remaining range and error bands of the SOC over the route. The previous work was limited because it did not incorporate driving conditions, like road type and ambient temperature, that affect driver behavior and energy consumption
Jayaprakash, Bharat, Eagon, Matthew, Northrop, William F.
S.I.S.T.E.R.: An Integrated Approach to Non-Exhaust Emissions2025-24-009109/07/2025
As electric mobility spreads and evolves, non-exhaust Particulate Matter (PM) sources are gaining more attention for total vehicular emissions. A holistic approach for studying the involved phenomena is necessary to identify the parameters that have the greatest impact on this portion of emissions. To achieve this, it is necessary to develop a new platform capable of both creating testing methodologies for future regulations and enabling the parallel development of advanced tyres and brakes that meet these standards, by correlating vehicle dynamics, driving style, tyre and brake characteristics, and the resulting emissions. Here the authors present the Sustainable Integrated System for Total non-Exhaust Reduction (S.I.S.T.E.R.) project, funded by the Italian Centro Nazionale per la Mobilità Sostenibile (MOST), that aims to develop an integrated approach to study tyre/brake-related emissions from the initial stages of compound development to outdoor vehicle tests, allowing actions to be
Genovese, Andrea, De Robbio, Roberta, Lenzi, Emanuele, Caiazza, Antonio, Lippiello, Felice, Costagliola, Maria Antonietta, Marchitto, Luca, Serra, Antonio, Arimondi, Marco, Bardini, Perla
A New Methodology to Evaluate In-Service Emissions of Used Vehicles at a Large Scale Using a SEMS and Digital Tools2025-24-009009/07/2025
Air quality is an increasing concern, particularly in densely populated urban areas. Indeed, large European cities have seen pollutant concentrations exceed World Health Organization thresholds, with a significant portion of NOx emissions originating from road transportation. Studies have shown that less than five percent of the vehicle fleet, often including vehicles with defective after-treatment systems, is responsible for a disproportionate share of these emissions. This highlights the importance of not solely relying on the gradual renewal of vehicle fleets to mitigate health risks associated with air pollution. This research, funded by the French Agency for the Ecological Transition (ADEME), introduces an experimental methodology aimed at controlling emissions from vehicles already in circulation. Aramis Group, a European specialist of refurbishment and online sales of used cars, provided several refurbished used vehicles for testing, directly taken from its workflow. These
Carlos Da Silva, Daniel, Kermani, Joseph, Farcot, Fabrice, Gaie, Fabien
SAE TOMORROW TODAY - How Simulation is Shaping the Future of Motorsports1352707/24/2025
From driver training to vehicle engineering and strategy development, simulation is no longer just a tool -- it's a competitive advantage. Top racing teams are using simulators to optimize car setups, dramatically cutting down on testing time and maximizing on-track performance. But the impact of simulation goes far beyond the racetrack. With cutting-edge realism and high-bandwidth systems, Dynisma is helping elite drivers stay sharp while accelerating product development for automotive manufacturers. Dynisma Motion Generators (DMGs) create detailed 3-D environments that include driver behavior and environmental factors, and accurately model the physical characteristics of vehicles, such as tire performance and surface interactions. To learn more, we sat down with Simon Holloway, Commercial Director, to discuss how Dynisma's data-driven engineering is revolutionizing simulation in both the motorsport and automotive sectors to enhance performance, safety, and efficiency. We'd love to
Patterson, Lori
Study on Driver-Dependent Stabilization Capabilities for Safe Chassis Control with Steer By Wire2025-01-027607/02/2025
Steer-by-wire actuators represent a transformative advancement in chassis control, opening up new potential for optimizing driving behavior across the entire range of driving dynamics - including driver-dependent automatic counter steering in critical driving situations. However, from a functional safety perspective, the increased potential also introduces new risks with respect to possible system failures. To mitigate these risks, sophisticated monitoring functions are essential to ensure vehicle controllability at all times. Current research approaches for monitoring functions use safe driving envelopes. This set of safe driving states is often found by open-loop simulations, which provide a phase portrait of the nonlinear system under control and from which stability limits can be derived. However, it remains open how these open-loop stability limits correspond to the stabilization capability of a real human driver in the loop. And secondly, how these closed-loop stability limits
Birkemeyer, Janick, Naidu P.M, Tarun, Borkowski, Lukas, Müller, Steffen
Data-Driven Analysis of Vehicle Lateral Control in the Country Road Domain2025-01-026807/02/2025
The optimization and further development of automated driving functions offer significant potential for reducing the driver's workload and increasing road safety. Among these functions, vehicle lateral control plays a critical role, especially with regard to its acceptance by end customers. Significant development efforts are required to ensure the effectiveness and reliability of this aspect in real-world conditions. This work focuses on analyzing lateral vehicle control using extensive measurement data collected from a dedicated vehicle fleet at the Institute of Automotive Engineering at the Technical University of Braunschweig. Equipped with state-of-the-art measurement technology, the fleet has driven several hundred thousand kilometers, allowing for the collection of detailed information on vehicle trajectories under various driving conditions. A total of 93 participants, aged between 20 and 43 years, contributed to the dataset. These measurements have been classified into
Iatropoulos, Jannes, Panzer, Anna, Arntz, Martin, Prueggler, Adrian, Henze, Roman
Exploring the Correlation between Physiological States and Driving Behavior on Highways with Integrated ECG Measurements2025-01-029207/02/2025
Human driver errors, such as distracted driving, inattention, and aggressive driving, are the leading causes of road accidents. Understanding the underlying factors that contribute to these behaviors is critical for improving road safety. Previous studies have shown that physiological states, like raised heart rates due to stress and anxiety, can influence driving behavior, leading to erratic driving and an increased risk of accidents. In this study, we conducted on-road tests using a measurement system based on the Driver-Driven vehicle-Driving environment (3D) method. We collected physiological signals, specially electrocardiography (ECG) data, from human drivers to examine the relationship between physiological states and driving behaviors. The aim was to determine whether ECG can serve as an indicator of potential risky driving behaviors, such as sudden acceleration and frequent steering adjustments. This information enables automated driving (AD) systems to intervene in dangerous
Ji, Dejie, Flormann, Maximilian, Bollmann, Julian, Henze, Roman, Deserno, Thomas M.
Early Detection of the Loss of Control of Motorsport Vehicles10-09-03-002906/30/2025
The article investigates how to detect as quickly as possible whether the driver will lose control of a vehicle, after a disturbance has occurred. Typical disturbances refer to wind gusts, obstacle avoidance, a sudden steer, traversing a pothole, a kick by another vehicle, and so on. The driver may be either human or non-human. Focus will be devoted to human drivers, but the extension to automated or autonomous cars is straightforward. Since the dynamic behavior of vehicle and driver is described by a saddle-type limit cycle, a proper theory is developed to use the limit cycle as a reference trajectory to forecast the loss of control. The Floquet theory has been used to compute a scalar index to forecast stable or unstable motion. The scalar index, named degree of stability (DoS), is computed very early, in the best case, in a few milliseconds after the disturbance has ended. Investigations have been performed at a dynamic driving simulator. A 14 DoF vehicle model, virtually driven by
Della Rossa, Fabio, Fontana, Matteo, Giacintucci, Samuele, Gobbi, Massimiliano, Mastinu, Giampiero, Previati, Giorgio
Enhanced Equivalent Consumotion Minimization Strategy Based on Bayesian Optimization2025-01-019406/16/2025
The Equivalent Consumption Minimization Strategy (ECMS) is an effective approach for managing energy flow in hybrid electric vehicles (HEVs), balancing the use of electric energy and fuel consumption. The strategy’s performance depends heavily on the Equivalent Factor (EF), which governs this trade-off. However, the optimal EF varies under different driving conditions and is influenced by the inherent randomness in factors such as traffic, road gradients, and driving behavior, making it challenging to determine through traditional methods. This paper introduces Bayesian Optimization (BO) as a solution to address the stochastic nature of the EF parameter tuning process. By using a probabilistic model, BO efficiently navigates the complex, uncertain performance landscape to find the optimal EF parameters that minimize fuel consumption and emissions across variable conditions. Simulation results under WLTP cycles show that the proposed method reduces fuel consumption by 0.9% and improves
Zhang, Cetengfei, Zhou, Quan, Jia, Yiqi, Xiong, Lu
Driving Level Skill CertificationJ3300_202505 (Current)05/07/2025
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 Models2025-01-002005/05/2025
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 CertificationJ3300/2_202504 (Current)04/28/2025
This Recommended Practice is intended to establish a procedure to certify the trailer towing 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. This document provides directions for obtaining certification through Probitas Authentication®1 and associated trailer towing driving skill examination requirements. This document is a supplement to SAE J3300, providing information specific to the trailer towing driving skill certification and clarifying the application of the rules set forth in SAE J3300 to the trailer towing certification. While the references, definitions, rules, and guidelines presented in SAE J3300 Sections 1 through 5 apply to the trailer towing certification, they are not repeated in this document.
Driving Skills Standards Committee
A Path Tracking Control Method Considering Vehicle Stability and Road Curvature2025-01-803804/01/2025
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
Research on Dangerous Driving Behavior Recognition Algorithm Based on LSTM2025-01-820404/01/2025
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
Recommendation of Adaptive Safe Speed Limits for Different Driver Profiles Using Deep Learning2025-01-810604/01/2025
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
Integrated Multimodal System for Real-Time Driver Fatigue Detection and Cognitive Load Assessment2025-01-807604/01/2025
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 Style2025-01-820504/01/2025
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
Driver Distraction Recognition Based on the Information Bottleneck Theory and Graph Convolutional Networks2025-01-811204/01/2025
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
Driving under the Influence: A Review of Fitness to Drive, Drug-Induced Impairments, and Simulation Testing2025-01-866604/01/2025
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
Real World Emissions in a High-Altitude City: A Comparison between LPG Taxis and Gasoline Cars2025-01-849404/01/2025
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
Design and Evaluation of an Adjustable Ackermann Rate Steering System for Formula Student Racing Cars2025-01-800004/01/2025
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
A Comparative Analysis of Acceleration and Deceleration Profiles for Aggressive Driving Styles and Fuel Economy Test Cycles2025-01-860504/01/2025
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
Modeling Vehicle Behavior in Highway On-Ramps: A Game Theory and Deep Reinforcement Learning Method12-08-04-003703/28/2025
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)09-13-01-000503/26/2025
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 Responsibilities12-08-02-002103/24/2025
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 Nepal09-13-01-000203/03/2025
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 Behavior09-13-01-000302/28/2025
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 Infrastructure12-08-02-002002/24/2025
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
Analysis of Left-Turn Mixed Traffic Flow Dynamics at Y-Shaped Signalized Intersections Using Cellular Automata2025-01-720402/21/2025
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
Chinese Ride-Hailing Drivers’ Adoption Intention of New Risk Visualization System: A Questionnaire Analysis Based on the Improved UTAUT2 Model2025-01-712202/21/2025
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
Study on the Minimum Diversion Decision Distance of Autonomous Vehicle on Freeway Based on Lane-Changing Trajectory Modelling2025-01-717002/21/2025
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 Data2025-01-721002/21/2025
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
Reviews on Traffic Flow Models for Autonomous Driving: The Artificial Intelligence and Cellular Automata Based Models2025-01-712702/21/2025
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
Continuous Trust Estimation Method Based on Kalman Filtering in Human-Machine Co-Driving2025-01-718602/21/2025
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
Route-Level Energy Efficiency Analysis of Pure Electric Buses: Key Influencing Factors and Optimized Suggestions2025-01-718402/21/2025
Currently, the adoption rate of pure electric buses is continuously increasing across cities nationwide, and their energy consumption costs have become an important component of urban bus operating expenses. The aim of this study is to explore significant factors related to energy savings for a bus route, which can help bus operators improve route energy efficiency and make resource allocation more reasonable. This study selects per capita energy consumption per thousand kilometers (PKEK) as the energy efficiency indicator and constructs a regression model with robust standard errors and a hierarchical clustering model using GPS operation data, total daily energy consumption data, and card swiping data from electric buses on eight routes in the same operational area of Nanjing from April 1 to June 10, 2021. The research results confirm the existence of significant variables affecting energy efficiency, primarily including: average speed, proportion of high-speed intervals, vehicle age
Li, Muyang, Sun, Huayang, Li, Hong, Qian, Xiyou, Wang, Weining, Chen, Xuewu
Testing Methods for Advanced Intelligent Driving Systems Based on Natural Driving Behavior Characteristics2025-01-714702/21/2025
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 Simulator12-08-02-001902/12/2025
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
Items per page:
1 – 50 of 702