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Introduction: Drivers' emotions have been widely investigated in transportation due to their significant effects on driving behaviors and traffic accidents. Appraisal theory posits that emotional reactions are influenced by individuals' attitudes toward current circumstances and events, thereby shaping their driving attitudes and styles. However, In the study of emotional driving, research often focuses on the impact of single factors such as age, gender, and personality, while the interplay between these multiple factors is a challenge. This study aims to explore the impact of age, personality, and driving experience on driving behaviors, and to investigate the interaction effect between these factors, particularly the role of emotional arousal. Method: Using moderated moderation and mediated moderation analyses, we examined how these individual factors interact and influence driving behaviors, including acceleration, speed stability, and steering performance. Data were collected from a driving simulation experiment involving 40 Chinese participants in various emotional states. Results: Our findings revealed that higher-age drivers and experienced drivers displayed lower maximum acceleration and better speed stability. Extraversion significantly mediated the relationship between age and driving behaviors, with this relationship being moderated by arousal states. Additionally, Neuroticism moderated the relationship between driving experience and driving behaviors. Conclusion: This study highlights how individual factors influence the trajectory of personality development in relation to driving behaviors. These findings have practical implications for improving traffic safety and driver education programs by incorporating emotional and personality-based interventions. Further long-term and individualized studies are needed to better understand these interactions and develop targeted interventions. [ABSTRACT FROM AUTHOR]

A typical classification of driving style from a human driver is conducted via self-assessment, which begs the question of the possibility of bias from the respondents. Although some research has been carried out validating the questionnaire, no controlled studies have yet to be reported to validate the Malaysian driving style. This study aimed to validate the Malaysian driver using the Multidimensional Driving Style Inventory (MDSI) with five-factor driving styles (careful, risky, angry, anxious, and dissociative) in on-road situations. Forty-one respondents completed the experiment on two designated routes recorded over 45 min of driving. A modest correlation existed between the MDSI and the score retrieved from the on-road observation assessment. The result showed a low-to-medium correlation collected from acceleration in longitudinal directions compared with correlation analysis utilizing the MDSI scale. Exploring such latent traits is essential for precisely classifying human driver styles without bias. [ABSTRACT FROM AUTHOR]

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Abstract This paper proposes a control strategy for the variable boost characteristics of electronic hydraulic brake (EHB) systems based on the driving style in response to the nonlinear challenges faced by the EHB systems in intelligent driving under complex personalized requirements. Initially, the working principle of the active braking of the EHB-booster was analyzed, and equivalent dynamic models and Karnopp friction models were established. Subsequently, by identifying the displacement and velocity parameters of the brake pedal, three types of variable boost characteristics-sporty, comfortable, and standard-were designed to satisfy the requirements of different driving styles. Then, to address the nonlinear disturbances caused by the variable boost characteristics, a variable-gain multiclosed-loop control strategy that considers nonlinear friction and inertia compensation was developed. Finally, the proposed control strategy was tuned and verified through the AMESim and Simulink cosimulation platform and vehicle tests. The results demonstrate that the strategy exhibits excellent control performance under various braking conditions that match driving styles, with steady-state control errors within 0.1 Mpa, providing a feasible solution for the complex nonlinear problems faced by personalized implementation in higher-order intelligent driving.

The shared control system has been a key technology framework and trend, with its advantages in overcoming the performance shortage of safety and comfort in automated vehicles. Understanding human drivers' driving capabilities and styles is the key to improving system performance, in particular, the acceptance by and adaption of shared control vehicles to human drivers. In this research, personalized shared control considering drivers' main human factors is proposed. A simulated scenario generation method for human factors was established. Drivers' driving capabilities were defined and evaluated to improve the rationality of the driving authority allocation. Drivers' driving styles were analyzed, characterized, and evaluated in a field test for the intention-aware personalized automated subsystem. A personalized shared control framework is proposed based on the driving capabilities and styles, and its evaluation criteria were established, including driving safety, comfort, and workload. The personalized shared control system was evaluated in a human-in-the-loop simulation platform and a field test based on an automated vehicle. The results show that the proposed system could achieve better performances in terms of different driving capabilities, styles, and complex scenarios than those only driven by human drivers or automated systems. [ABSTRACT FROM AUTHOR]

Driving behavior is a unique driving habit of each driver, and it has a significant impact on road safety. Classifying driving behavior and introducing policies based on the results can reduce the severity of crashes on the road. Roundabouts are particularly interesting because of the interconnected interaction between different road users at the roundabouts, in which different driving behavior is hypothesized. This study investigated driving behavior at roundabouts in a mixed traffic environment using data-driven unsupervised machine learning to classify driving behavior using a data set from three roundabouts in Germany. We used a data set of vehicle kinematics for a group of different vehicles and vulnerable road users (VRUs) at roundabouts and classified them into three categories (i.e., conservative, normal, and aggressive). The results showed that most drivers proceeding through a roundabout can be classified into two driving styles—conservative, and normal—because traffic speeds in roundabouts are relatively lower than at other signalized and unsignalized intersections. The results also showed that about 77% of drivers who interacted with pedestrians or cyclists were classified as conservative drivers, compared with about 42% of drivers who did not interact with pedestrians or cyclists, and about 51% of all drivers. Drivers tend to behave abnormally when they interact with VRUs at roundabouts, which increases the risk of crashes when an intersection is multimodal. The results of this study could help to improve the safety of roads by allowing policymakers to determine effective and suitable safety countermeasures. The results also will be beneficial for advanced driver-assistance systems (ADAS) as the technology is deployed in a mixed traffic environment. [ABSTRACT FROM AUTHOR]

This paper proposes a control strategy for the variable boost characteristics of electronic hydraulic brake (EHB) systems based on the driving style in response to the nonlinear challenges faced by the EHB systems in intelligent driving under complex personalized requirements. Initially, the working principle of the active braking of the EHB-booster was analyzed, and equivalent dynamic models and Karnopp friction models were established. Subsequently, by identifying the displacement and velocity parameters of the brake pedal, three types of variable boost characteristics-sporty, comfortable, and standard-were designed to satisfy the requirements of different driving styles. Then, to address the nonlinear disturbances caused by the variable boost characteristics, a variable-gain multiclosed-loop control strategy that considers nonlinear friction and inertia compensation was developed. Finally, the proposed control strategy was tuned and verified through the AMESim and Simulink cosimulation platform and vehicle tests. The results demonstrate that the strategy exhibits excellent control performance under various braking conditions that match driving styles, with steady-state control errors within 0.1 Mpa, providing a feasible solution for the complex nonlinear problems faced by personalized implementation in higher-order intelligent driving. [ABSTRACT FROM AUTHOR]

At present, achieving efficient, sustainable, and safe transportation has led to increasing attention on driving behavior recognition and advancements in autonomous driving. Identifying diverse driving styles and corresponding types is crucial for providing targeted training and assistance to drivers, enhancing safety awareness, optimizing driving costs, and improving autonomous driving systems responses. However, current studies mainly focus on specific driving scenarios, such as free driving, car-following, and lane-changing, lacking a comprehensive and systematic framework to identify the diverse driving styles. This study proposes a novel, data-driven approach to driving-style recognition utilizing naturalistic driving data NGSIM. Specifically, the NGSIM dataset is employed to categorize car-following and lane-changing groups according to driving-state extraction conditions. Then, characteristic parameters that fully represent driving styles are optimized through correlation analysis and principal component analysis for dimensionality reduction. The K-means clustering algorithm is applied to categorize the car-following and lane-changing groups into three driving styles: conservative, moderate, and radical. Based on the clustering results, a comprehensive evaluation of the driving styles is conducted. Finally, a comparative evaluation of SVM, Random Forest, and KNN recognition indicates the superiority of the SVM algorithm and highlights the effectiveness of dimensionality reduction in optimizing characteristic parameters. The proposed method achieves over 97% accuracy in identifying car-following and lane-changing behaviors, confirming that the approach based on naturalistic driving data can effectively and intelligently recognize driving styles. [ABSTRACT FROM AUTHOR]

Driving style is the main cause of left-turn accidents at traffic intersections, and the study of the speed change of left-turning vehicles facing different driving styles of straight vehicles can effectively explain the causes of accidents. Therefore, field observations were made at three traffic intersections in Beijing's Changping, Haidian, and Pinggu districts, and the steering angles, speeds, and other indexes of the left-turning vehicles as well as the speed change of the straight vehicles were recorded by video. The observation time was selected as peak and off-peak hours on seven weekdays and four holidays. The driving styles of left-turning vehicles were categorized by steering angle and speed and compared with their effects on the speed of straight vehicles. The results show that when the left-turning vehicles are aggressive, most of the straight vehicles will keep the original speed on weekdays, which will easily lead to accidents; on holidays, most of them will choose to slow down. When facing conservative driving style, most drivers will choose to keep the original speed on weekdays, and still do so on holidays. It can be seen that under the influence of time, aggressive driving style may induce aggressive behaviors in interacting vehicles; while conservative driving induces aggressive behaviors in interacting vehicles under any time period. The results of the study may inform the prediction of potential consequences of mixed traffic, which may lead to better integration of self-driving cars into mixed traffic roads in the future. [ABSTRACT FROM AUTHOR]

Copyright of Automotive Engineer (1674-6546) is the property of Auto Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Decision-making is a key part of autonomous driving systems. Human-like decision-making in complex scenarios is important for enhancing drivers' trust in and acceptance of autonomous driving systems. In order to improve their human-like features and their adaptability to complex scenarios, this paper proposes a comprehensive preview decision method for direction and speed based on rules and learning. First, a decision-making structure including maneuvering ability judgment, motion trajectory prediction, safety judgment, legitimacy judgment, comprehensive performance evaluation, and parameters learning is presented. Second, a learning method for the longitudinal safety distance threshold based on a BP neural network is introduced. Third, inverse reinforcement learning (IRL) is used to learn the comprehensive performance evaluation weight coefficient for different driving styles. Finally, the DJI AD4CHE dataset is processed and used to train the parameters. Car-following and lane-changing scenarios are simulated to verify the effectiveness of decision-making. The simulation results show that the proposed method can reflect human-like decision-making in multiple scenarios. [ABSTRACT FROM AUTHOR]

Previous studies have focused on the impact of individual factors on lane departure warning (LDW) utility during driving. However, comprehensive analysis has not been considered based on multiple variables, such as driver characteristics. This paper aims to propose a methodology in exploring the utility of LDW under varied warning timing situations, focusing on changes in driving style and distraction level to obtain the optimal warning timing matching relationship. A driving simulator experiment with a mixed 4 × 3 factor design was conducted. The design matrix includes four level of secondary task (ST) conditions and three warning timings situations for drivers with various driving styles. To estimate the utility of the LDW system, lane departure duration (LDD) was selected as a time-based measure of utility. Both the Kaplan-Meier method and COX model were applied and compared. Combined with questionnaire results, the results indicate that both driving style and distraction state are significant influence factors. Generally, the results suggest that the more aggressive drivers lead to the more severe lane departure behavior and they preferred late warning. In terms of distraction state, the LDD increases with the level of ST remarkably. This implies that the earlier warning timing should be given for the higher-level distraction state condition. It was also observed that adaptive warning timing is needed based on the analysis of the interactive effect among multiple variables. The results provide empirical data for the optimization of LDW system design. [ABSTRACT FROM AUTHOR]

To analyze the car-following behavior accurately, this paper takes the drivers' psychological factors into the consideration based on the psychological field theory. The vehicle dynamics indexes are extracted through vehicle history trajectories and the driving styles are clustered by k-means methods. After that the perception coefficient, reaction coefficient, and driving style correction coefficient are obtained and integrated into the psychological field for characterizing the drivers' driving styles. The psychological car-following model which considers the driving styles is built based on the Full Velocity Difference (FVD) model. Finally, the model is validated under the MATLAB/Simulink environment and the result reveals that the psychological field car-following model achieves higher accuracy of characterizing car-following behavior compared with the FVD model and the interaction potential model. [ABSTRACT FROM AUTHOR]

• The subjective assessment of compliance with traffic rules in Bulgaria is investigated in two different studies. • Study 1 finds that anxiety, sensation seeking, driving anger, and aggressive and risky driving behaviour significantly predict compliance with traffic rules. • Study 2 highlights the role of driving style revealing risky, irrational, distress-reduction, and patient and careful driving styles as significant predictors of this assessment. • Significant differences are found according to age, sex, driving experience, and negative driving outcomes. It is generally accepted that driving is safer when the rules are obeyed. Therefore, the pursuit of greater compliance with traffic rules is often seen as an intermediate goal in ensuring road safety. The article presents two studies that aim to investigate the subjective assessment of traffic rules compliance and the role of personality and behavioural factors such as anxiety, sensation seeking, driving anger, aggressive driving, and risky driving (Study 1; N=1433) on the one hand, and driving style (Study 2; N=456) on the other hand. The studies also examine the differences in traffic rules compliance by age, sex, driving experience, and negative driving outcomes such as registered violations, driving licence suspensions, and involvement traffic accidents. Self-report methods are used to assess personality factors, driving style, traffic rules compliance, and negative driving outcomes. The results show a generally positive assessment of compliance with traffic rules among the participants, with the majority reporting compliance in their daily driving behaviour. Study 1 indicates that all personality factors investigated are significant predictors of traffic rules compliance. Study 2 demonstrates that the risky style, the irrational style, the distress-reduction style, and the patient and careful style are significant predictors of traffic rule compliance. Demographic differences are also observed, with women being significantly more likely than men to perceive themselves as compliant drivers. In terms of negative driving outcomes, both studies demonstrate that individuals with a record of driving violations in the last three years, a suspended driving licence, and involvement in a traffic accident are significantly less likely to rate themselves as compliant. The results can contribute to the development of prevention programmes and road safety strategies to promote safer driving behaviour. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

IntroductionDrivers' emotions have been widely investigated in transportation due to their significant effects on driving behaviors and traffic accidents. Appraisal theory posits that emotional reactions are influenced by individuals' attitudes toward current circumstances and events, thereby shaping their driving attitudes and styles. However, In the study of emotional driving, research often focuses on the impact of single factors such as age, gender, and personality, while the interplay between these multiple factors is a challenge. This study aims to explore the impact of age, personality, and driving experience on driving behaviors, and to investigate the interaction effect between these factors, particularly the role of emotional arousal.MethodUsing moderated moderation and mediated moderation analyses, we examined how these individual factors interact and influence driving behaviors, including acceleration, speed stability, and steering performance. Data were collected from a driving simulation experiment involving 40 Chinese participants in various emotional states.ResultsOur findings revealed that higher-age drivers and experienced drivers displayed lower maximum acceleration and better speed stability. Extraversion significantly mediated the relationship between age and driving behaviors, with this relationship being moderated by arousal states. Additionally, Neuroticism moderated the relationship between driving experience and driving behaviors.ConclusionThis study highlights how individual factors influence the trajectory of personality development in relation to driving behaviors. These findings have practical implications for improving traffic safety and driver education programs by incorporating emotional and personality-based interventions. Further long-term and individualized studies are needed to better understand these interactions and develop targeted interventions.

The ability to recognize different driving styles of surrounding vehicles is crucial to determine the safest and most efficient driving decisions, prevent accidents, and analyze the causes of traffic accidents. Understanding if the surrounding vehicle is aggressive or cautious can greatly assist in the decision making of vehicles in terms of whether and when it is appropriate to make particular maneuvers. A driver's driving style usually changes with the environment, which brings great challenges to the current research. To this end, a dynamic driving-style analysis framework, in which drivers' interactions with other vehicles are considered, is proposed in this article. First, by analyzing common traffic scenarios, five surrounding vehicles are selected as the environmental vehicles to be considered. Time headway (THW) and time to collision (TTC) that can consider the relative speed and position with the ego vehicle are selected as the clustering indicators. Then, a Bayesian nonparametric learning method based on a hierarchical Dirichlet-process hidden semi-Markov model (HDP-HSMM) is introduced to extract primitive driving patterns from time-series driving data without prior knowledge of the number of these patterns. Then the driving pattern is scored according to the risk degree. A driver's aggressiveness is scored and drivers are divided into different styles based on the frequency distribution of driving patterns. The effectiveness of the proposed method is demonstrated on a real-world vehicle trajectory data set where results show that driving pattern switches and more complex driving behaviors can be better captured and understood semantically. [ABSTRACT FROM AUTHOR]

To solve the problem that the existing driver dissatisfaction model did not consider driving style and thus made lane-changing decision difficult to adapt to different types of drivers, the driver dissatisfaction model based on the preceding vehicle speed was improved, and the minimum safe distance model based on the elliptical vehicle model was combined to establish lane-changing decision model based on driving style and driver dissatisfaction. Fifteen questions were selected as the driver behavior questionnaire (DBQ) survey content, and the driving styles were divided into three categories of aggressive, cautious and ordinary by principal component analysis and K-means clustering. The driving simulator test was designed to collect the lane-changing data of drivers with different driving styles, and the driver dissatisfaction thresholds of different driving styles were obtained. The lane-changing decision model was verified by MATLAB/Simulink and Carsim simulation platforms. The results show that the lane-changing decision model can correctly generate lane-changing intentions according to driver dissatisfaction and ensure the safety of the lane-changing process to adapt to the drivers with different driving styles and make the lane-changing decision model based on driver dissatisfaction more in line with the actual driving habits of drivers. [ABSTRACT FROM AUTHOR]

Aiming at the problems that, based on data statistical characteristics, the classification and recognition method of driving style was easy to ignore the diversity of driving style during driv¬ing, a classification and recognition method of driving style was proposed based on driving events, pectral clustering and random forest. Experiments were designed to collect driving data, and the data were preprocessed to extract turning events and braking events. After standardization and dimension¬lity reduction, the spectral clustering algorithm was used to cluster the driving style of turning vents and braking events respectively. The entropy weight method was used to obtain the driving tyle weights of each driver, and the accuracy of five machine learning algorithms was compared for riving style recognition. Results show that the accuracy of driving style recognition is as 92.73% ased on random forest, which significantly improves the accuracy of driving style recognition. [ABSTRACT FROM AUTHOR]

Mountain highways are linearly complex, with extensive curves and high accident injury rates, how to improve driving safety is the key to traffic safety management on mountain highways, and it also meets the need for harmonious and sustainable development of the society. Therefore, this study investigates the effects of different guardrail color configurations on the driving behavior of different styles of drivers when driving on mountainous curves from the perspective of improving road aids – guardrails. A virtual reality experiment was designed using a driving simulator and VR technology, and 64 subjects were recruited to participate and complete the experiment. Drivers with non-adaptive driving styles (Reckless, Angry, Anxious) traveled at significantly higher speeds than subjects with adaptive driving styles (Cautious) on mountainous roads; drivers with Cautious styles had better lane-keeping ability when passing through different radii of curves as compared to non-adaptive drivers; and the red and yellow guardrails were more effective in decreasing the speeds at which drivers passed and in increasing the stability of lane-keeping. The results of the study show that the effectiveness of red and yellow guardrails is better, which provides a reference for the traffic management department to propose a standardized color setting of guardrails in mountainous areas, which is conducive to the development of more precise traffic management measures to reduce the occurrence of traffic accidents. [ABSTRACT FROM AUTHOR]

Thirty-four interviews were carried out with drivers in four countries to elicit their understanding about pollutants, specifically nitrogen dioxide (NOX) and particulate matter (PM). The results showed that most of the participants knew that cars emitted carbon dioxide (CO2), but they were less aware of the emission of NOx and PM. Also, being aware of the negative impacts of pollutants did not necessarily lead to eco-friendly vehicle choices. Most of the drivers were aware of pollutant friendly behaviours such as avoiding harsh acceleration/deceleration and maintaining smooth speed but were unaware of behaviours such as efficient gear use, avoiding engine idling, or anticipation of traffic ahead. Only a few mentioned pre-trip or strategic level behaviours (e.g., vehicle size/weight or the selection of appropriate routes and avoidance of traffic congestion). The results could be used to design educational material to raise awareness and provide drivers with tips to reduce their pollutant emissions. [ABSTRACT FROM AUTHOR]

Connected Autonomous Vehicles (CAVs) have the potential to revolutionize traffic systems by autonomously handling complex maneuvers such as freeway ramp merging. However, the unpredictability of manual-driven vehicles (MDVs) poses a significant challenge. This study introduces a novel decision-making approach that incorporates the uncertainty of MDVs' driving styles, aiming to enhance merging efficiency and safety. By framing the CAV-MDV interaction as an incomplete information static game, we categorize MDVs' behaviors using a Gaussian Mixture Model–Support Vector Machine (GMM-SVM) method. The identified driving styles are then integrated into the flexible merging decision process, leveraging the concept of pure-strategy Nash equilibrium to determine optimal merging points and timing. A deep reinforcement learning algorithm is employed to refine CAVs' control decisions, ensuring efficient right-of-way acquisition. Simulations at both micro and macro levels validate the method's effectiveness, demonstrating improved merging success rates and overall traffic efficiency without compromising safety. The research contributes to the field by offering a sophisticated merging strategy that respects real-world driving behavior complexity, with potential for practical applications in urban traffic scenarios. [ABSTRACT FROM AUTHOR]

• An experimental test track study explored the impact of three deceleration approaches and a non-driving activity on passenger comfort in diverse driving scenarios. • A linear deceleration profile was compared to two stepwise profiles derived from a trained chauffeur's driving data. • The linear deceleration was preferred for stops at Stop Signs, while smoother stepwise profiles were favoured overall. • Passenger engagement in a non-driving activity didn't affect comfort or profile preferences. • Participants reported perceiving a lower intensity of longitudinal vehicle movements when visually distracted during the drive. As automated vehicles advance and become more widespread, it is increasingly important to ensure optimal driving comfort for passengers. Recent research has focused on developing driving styles for automated vehicles that are perceived to be most comfortable. However, there is still little understanding of whether, and how, possible driving styles need to be adjusted for specific traffic scenarios. In this study, 36 participants experienced three different deceleration profiles (a linear deceleration profile 'One-Step', and two versions of stepwise deceleration profiles 'Two-Step V1 and V2') across different driving scenarios (deceleration before curves, approaching a speed-limit sign, and a stop sign). Deceleration profiles were rated by participants and the impact of non-driving related activities on driving comfort was investigated. Results showed a positive rating for all deceleration profiles in terms of comfort. For decelerations to a standstill at a Stop Sign, participants seemed to prefer the One-Step approach, in which there is a continuous, and constant deceleration. However, participants described the Two-Step V1 as a gentle and calmer approach and ranked it more frequently as a personal favourite than the One-Step profile or the Two-Step V2 profile. The visual distraction of the passenger through a non-driving activity had no impact on passenger comfort or profile preferences for the scenarios tested within this study. Nonetheless, participants reported perceiving a lower intensity of longitudinal vehicle movements when visually distracted during the drive. The results of the study provide insights into the design and implementation of comfortable deceleration profiles. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To improve the accuracy of lane-change intention prediction and analyze the influence of driving styles on prediction outcomes, the T-Encoder-Sequence model is proposed in this paper. It integrates the Transformer’s encoder module with various recurrent neural network (RNN) models and introduces a multimodal fusion input structure. Building on this, a risk indicator model, capable of reflecting driver stress, is established to calculate the model’s input parameters. Consequently, the model can simultaneously capture global information and consider the impact of vehicle classes on drivers. Furthermore, the k-means++ algorithm is employed to categorize vehicle trajectories into conservative, conventional, and aggressive types for further analysis. The results demonstrate that training the model with risk indicator parameters markedly enhances prediction performance. Under identical input parameters, the T-Encoder-Sequence model exhibits notably superior prediction efficacy compared to the original model. The T-Encoder-Sequence model, trained with risk indicator parameters, demonstrates substantial advantages compared to other studies. [ABSTRACT FROM AUTHOR]

Drivers with aggressive driving style driving electric city buses with rapid response and high acceleration performance characteristics are more prone to have traffic accidents in the starting stage. It is of great importance to accurately identify the drivers with aggressive driving style for preventing traffic accidents of city buses. In this article, a starting driving style recognition method of electric city bus is firstly proposed based on deep learning with in-vehicle Controller Area Network (CAN) bus data. The proposed model can automatically extract the deep spatiotemporal features of multi-channel time series data and achieve end-to-end data processing with higher accuracy and generalization ability. The sample data set of driving style is established by pre-processing the collected in-vehicle CAN bus data including the status of driving and vehicle motion, the data pre-processing method includes data cleaning, normalization and sample segmentation. Data set is labelled with subjective evaluation method. The starting driving style recognition method based on Convolutional Neural Network (CNN) model is constructed. Multiple sets of convolutional layers and pooling layers are used to automatically extract the spatiotemporal characteristics of starting driving style hidden in the data such as velocity and pedal position etc. The fully connected neural network and incentive function Softmax are applied to establish the relationship mapping between driving data characteristics and the starting driving styles, which are categorized as cautious, normal and aggressive. The results show that the proposed model can accurately recognize the starting driving style of electric city bus drivers with an accuracy of 98.3%. In addition, the impact of different model structures on model performance such as accuracy and F1 scores was discussed, and the performance of the proposed model was also compared with Support Vector Machine (SVM) and random forest model. The method can be used to accurately identify drivers with aggressive starting driving style and provide references for driver's safety education, so as to prevent accidents at the starting stage of electric city bus and reduce crash accidents. [ABSTRACT FROM AUTHOR]

The estimation of energy consumption under real-world driving conditions is a prerequisite for optimizing bus scheduling and meeting the requirements of route operation, thereby promoting the large-scale application of battery electric buses. However, the limitation of data accuracy and the uncertainty of many factors, such as weather conditions, traffic conditions, and driving styles, etc. make accurate energy consumption estimation complicated. In response to these challenges, a new method for estimating the energy consumption of battery electric buses (BEBs) is proposed in this research. This method estimates the speed profiles of different driving styles and the energy consumption extremes using real-world driving data. First, this research provides the constraints on speed formed by environmental factors including weather conditions, route characteristics, and traffic characteristics. On this basis, there are two levels of estimation for energy consumption. The first level classifies different driving styles and constructs the corresponding speed profiles with the time interval (10 s), the same as real-world driving data. The second level further constructs the speed profiles with the time interval of 1 s by filling in the first-level speed profiles and estimating the energy consumption extremes. Finally, the estimated maximum and minimum value of energy consumption were compared with the true value and the results showed that the real energy consumption did not exceed the extremes we estimated, which proves the method we proposed is reasonable and useful. Therefore, this research can provide a theoretical foundation for the deployment of battery electric buses. [ABSTRACT FROM AUTHOR]

Copyright of Automobile Technology is the property of Automobile Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study provides an approach to identify driving style of truck drivers by using GPS, load condition, and in-vehicle monitoring data and investigates the association of driving styles with risky driving behaviors from macro and micro perspectives. The naturalistic driving data used in this study were collected from 4,357 trucks in Hangzhou, China over three months in 2021. Six driving volatility parameters and six warning parameters were used to characterize the driving styles. Then, three driving styles under the two load conditions were identified using k-means clustering methods and principal component analysis. Finally, one-way MANOVA and ANOVA were used to analyze the relationship between driving styles and driving risk. It was found that truck drivers have different thresholds for aggressive and cautious driving style under different load conditions. Truck drivers who exhibited aggressive driving under both load conditions exhibited high driving risk. Although most truck drivers exhibited safe or normal driving under both conditions, the few who exhibited aggressive driving contribute to a disproportionate driving risk. These results can help distinguish differences in truck drivers' driving styles under different load conditions, thus providing a more comprehensive safety assessment of truck drivers' performance for monitoring purposes. [ABSTRACT FROM AUTHOR]

Although technical improvements to engines and aftertreatment systems have the greatest impact on pollutant emissions, there is also potential for reducing emissions through driver behavior. This potential can be realized in the very short term, while better emission-control technologies only take effect once they have penetrated the market. In addition to a change in driving style, the vehicle owner's choice of vehicle technology and size class will also have an impact on the future emissions of the vehicle fleet. The effects of different driving styles, the tire choice, the vehicle size class, and propulsion technologies on energy consumption and tailpipe and non-exhaust emissions are analyzed in this paper for different traffic situations and start temperatures for cars with petrol and diesel combustion engines and for battery electric vehicles. The analysis is completed with the corresponding upstream emissions from fuel and electricity production. The analysis is based on a vehicle simulation using the Passenger car and Heavy-duty Emission Model (PHEM), which is based on a large database of vehicles created using measurements of real driving conditions. For the assessment of the driving style, a novel method was developed in an H2020 project, which reproduces a measured trip with a virtual eco-driver. Carbon dioxide equivalent emissions (CO2eq) increase with increasing vehicle size, but can be reduced by around 20% for conventional vehicles and 17% for battery electric vehicles (BEVs) through an environmentally conscious driving style. On average, BEVs have around 50% lower CO2eq emissions than conventional vehicles, if the emissions from vehicle production are also taken into account. On an average journey of 35 km, the cold start of modern diesel vehicles accounts for around half of the total NOx emissions, while the proportion of cold starts for petrol vehicles is around 25%. Tire and brake wear together generate a similar amount of PN23 emissions as the exhaust gases from new cars. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Railway Science & Engineering is the property of Journal of Railway Science & Engineering Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Modern life is strongly associated with the use of cars, but the increase in acceleration speeds and their maneuverability leads to a dangerous driving style for some drivers. In these conditions, the development of a method that allows you to track the behavior of the driver is relevant. The article provides an overview of existing methods and models for assessing the functioning of motor vehicles and driver behavior. Based on this, a combined algorithm for recognizing driving style is proposed. To do this, a set of input data was formed, including 20 descriptive features: About the environment, the driver's behavior and the characteristics of the functioning of the car, collected using OBD II. The generated data set is sent to the Kohonen network, where clustering is performed according to driving style and degree of danger. Getting the driving characteristics into a particular cluster allows you to switch to the private indicators of an individual driver and considering individual driving characteristics. The application of the method allows you to identify potentially dangerous driving styles that can prevent accidents. [ABSTRACT FROM AUTHOR]

In complex traffic environment, it is still a great challenge for Autonomous Vehicles (AVs) to understand the surrounding drivers’ Lane-Changing Decision (LCD) intention accurately. The LCD intention is affected by Driver’s Psychology (DP) and Driving Style (DS). But few LCD studies considered DP and DS simultaneously. We previously proposed a LCD model, by combing DP and DS, termed as DP&DS-LCD model. Nevertheless, there are some factors not fully considered in this model, including the driver’s Visual Attention (VA) in DP quantification and the influence of driving state on DS. Therefore, an enhanced LCD Model is developed, by integrating the DP under VA (DPVA) and DS Layering (DSL), named as DPVA&DSL-LCD model. In the model, a psychological field model coupling driver’s VA mechanism is established to represent the surrounding vehicles’ influence on the driver. Then, a DSL framework is proposed by adding the influence of driving state on DS. The Gaussian Mixture Model (GMM) clustering and Support Vector Machine (SVM) classifier are respectively adopted in training and recognition phases to identify the current driving style. Finally, integrating the DPVA and DSL, the Light Gradient Boosting Machine (LightGBM) algorithm is used to train the LCD model. In experiments, the open I-80 database from Next Generation Simulation (NGSIM) is adopted to train the DPVA&DSL-LCD. And compared with other three LCD models, the prediction performance of DPVA&DSL-LCD model achieved the best. Therefore, the DPVA&DSL-LCD model is effective and could provide support for the decision-making of AVs by predicting surrounding vehicles’ LCD intention.