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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]

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]

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]

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]

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]

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]

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]

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.

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.

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.

Driver vigilance research focuses on the safety impacts of maintaining sustained attention in underload conditions when automatic responses are adequate to the task and responses are sparse. However, urban environments have frequent risks that appear chaotically and require high levels of engagement—a very different vigilance task. Although drivers perceive a wide range of task demands as manageable, their movement patterns hint at more active engagement as they approach overload. This condition we are calling “Attentiveness,” to distinguish it from the classic exploration of vigilance in underload conditions. To understand how the built environment impacts driving automaticity and vigilance, data from the SHRP2 Naturalistic Driving Study (NDS) was used to evaluate driving patterns within urban multimodal street sections. Data for acceleration, jerk, lane position, and speed were tabulated for ten, 5-second epochs at 200 multimodal locations and compared to the behavior for the entire length of the drive. The built environment variables that demonstrated a strong correlation and impact size for a change in driver behavior included doorway density, block length, Walkscore, corridor aspect ratio (height over width), and terminated vistas. The commonality for these variables is that each one impacts the rate that novel stimuli arrive as drivers progress sequentially along their path. Acceleration, jerk, and lane position appear to be related to the rhythm of interruptions that occur along the length of the roadway.

Objective: This study investigated users' subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. Background: Comfort and naturalness play an important role in contributing to users' acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. Method: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner, respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking questionnaire, which assessed their risk-taking propensity. Results: Participants regarded both human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. Particularly, between the two human-like controllers, the Defensive style was considered more comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. Conclusion: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. Application: Insights into how different driver groups evaluate automated vehicle controllers are important in designing more acceptable systems. [ABSTRACT FROM AUTHOR]

Objective: This study investigated drivers' subjective feelings and decision making in mixed traffic by quantifying driver's driving style and type of interaction. Background: Human-driven vehicles (HVs) will share the road with automated vehicles (AVs) in mixed traffic. Previous studies focused on simulating the impacts of AVs on traffic flow, investigating car-following situations, and using simulation analysis lacking experimental tests of human drivers. Method: Thirty-six drivers were classified into three driver groups (aggressive, moderate, and defensive drivers) and experienced HV-AV interaction and HV-HV interaction in a supervised web-based experiment. Drivers' subjective feelings and decision making were collected via questionnaires. Results: Results revealed that aggressive and moderate drivers felt significantly more anxious, less comfortable, and were more likely to behave aggressively in HV-AV interaction than in HV-HV interaction. Aggressive drivers were also more likely to take advantage of AVs on the road. In contrast, no such differences were found for defensive drivers indicating they were not significantly influenced by the type of vehicles with which they were interacting. Conclusion: Driving style and type of interaction significantly influenced drivers' subjective feelings and decision making in mixed traffic. This study brought insights into how human drivers perceive and interact with AVs and HVs on the road and how human drivers take advantage of AVs. Application: This study provided a foundation for developing guidelines for mixed transportation systems to improve driver safety and user experience. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-objective multi-item four dimensional green transportation problem in an interval-valued intuitionistic fuzzy environment. Carbon emission is a critical problem in recent years and transportation activity is one of the main sources of carbon emission. In the transportation system, the rate of carbon emission depends on several factors such as type of fuel used, type of vehicles, load of the vehicles, driving style, traffic on the road, etc. In this study, we have described the impact of driving style on the carbon emission as well as on the transportation costs. The model has been proposed for the comparison of the amount of carbon emitted under two different driving styles, i.e., right(defensive) and wrong(aggressive). Also, the parameters of real-life multi-objective transportation problems are inherently unpredictable due to variety of factors. To handle the impreciseness and vagueness, interval-valued triangular intuitionistic fuzzy numbers (IVTIFNs) are used in the proposed model. Further, the proposed problem with IVTIFNs has been converted into a crisp form by using the expected value operator. Then the three programming techniques: weighted Tchebycheff metrics programming, interval-valued intuitionistic fuzzy programming and fuzzy TOPSIS method are employed to obtain the Pareto-optimal solution of the suggested model. A comparison is drawn between the Pareto-optimal solutions extracted with respect to two driving styles. The extracted results show that the right driving style provides a better solution in both environmental and economic aspects. Finally, a numerical application is given to demonstrate the applicability of the proposed study. Conclusions with the future research scope of this study are presented at last. [ABSTRACT FROM AUTHOR]

Compared with traditional anomaly analysis, intelligent connected vehicle (ICV) data validity analysis is faced with a variety of data anomalies, including sensor anomalies, driving behavior anomalies, malicious tampering, and so on, which eventually leads to anomalies in the data. How to integrate the vehicle moving characteristics, driving style, and traffic flow conditions to provide an effective data detection method has become a new problem in the field of intelligent networked vehicles. Based on ICV data, a particle swarm optimization data validity detection algorithm (TE-PSO-SVM) was proposed by combining driving style and traffic flow theory to realize the effective detection of driving data. In addition, aiming at the problem of mixed types of anomalies in complex scenes, a model pool is constructed, and a model selection algorithm based on reinforcement learning (RLBMS) is proposed. Experiments on the real data set HighD show that RLBMS has a better detection effect in complex scenes of mixed types of anomalies. [ABSTRACT FROM AUTHOR]

Previous research on personality in the driving context mostly focused on the negative and maladaptive personality traits. The present study investigated the links between the Light Triad traits with driving style and driving anger expression. The Light Triad framework emphasizes the positive side of the personality and it consists of humanism, faith in humanity, and Kantianism. A total of 376 active drivers (50.3 % women) aged between 18 and 70 completed the online questionnaire including the Light Triad Scale (Kaufman et al., 2019), Driver Behavior Questionnaire (DBQ; Reason et al., 1990), and Driving Anger Expression Inventory (DAX, Deffenbacher et al., 2002). Ordinary violations, aggressive violations, and positive driver behaviors subscales of the DBQ were used to measure driving style, and the DAX was used to measure the aggressive and adaptive/constructive forms of driving anger expression. Hierarchical multiple regression analyses were conducted to examine the associations of the Light Triad traits with each driving style and driving anger expression dimension after controlling for age, gender, and total mileage. The findings generally supported the expected associations. That is, some traits of the Light Triad yielded negative associations with aberrant driver behaviors and aggressive forms of driving anger. On the other hand, the opposite pattern was found in the analyses with positive driver behaviors and adaptive/constructive expression of driving anger. The findings are discussed in the light of relevant literature.

Animal welfare during transport became an largely issue because of increasing demand for improved animal welfare standards. Most studies on the animal welfare during transportation have concentrated on the atmosphere and the temperature of the truck compartments. Thus, the objective of study was to collect and quantify three axis acceleration and determine the effect of bedding for transporting pigs from farm to slaughterhouse. A total of 2,840 crossbred fattening pigs with a live weight of approximately 115 kg were used. They were raised in the same commercial farms and transported to the same commercial slaughterhouse. A 3×2×2 completely randomized factorial design was used to investigate effects of rubber type bedding (bedding or non-bedding) and two levels of driving style (aggressive or normal) in three different time periods with different outside temperatures. Air temperature treatments were as follow: high temperature ([HT] higher than 24°C); low temperature ([LT] lower than 10°C); normal temperature ([NT] 10°C to 24°C). In our experiment, pigs transported under aggressive driving style showed lower (p < 0.05) pH and water holding capacity (WHC) than those transported under normal driving style. Pigs transported under normal driving style showed a lower percentage of drip loss (DL) (p < 0.05) than those transported with an aggressive driving style. Also, transported with bedding showed higher (p < 0.05) lying behavior but lower (p < 0.05) sitting behavior than those transported without bedding. Pigs transported under normal driving style showed lower (p < 0.05) cortisol level than those transported under aggressive driving style. In conclusion, aggressive driving style cause acute stress in pigs, while bedding helps alleviate acute stress in pigs during transportation in LT.

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.

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.

To enhance the lane-changing safety of autonomous vehicles, it is crucial to accurately identify the driving styles of human drivers in scenarios involving the coexistence of autonomous and human-driven vehicles, aiming to avoid encountering vehicles exhibiting hazardous driving patterns. In this study, based on the real traffic flow data from the Next Generation Simulation (NGSIM) dataset in the United States, 301 lane-changing vehicles that meet the criteria are selected. Six evaluation parameters are chosen, and principal component analysis (PCA) is employed for dimensionality reduction in the data. The K-means algorithm is then utilized to cluster the driving styles, classifying them into three categories. Finally, ant colony optimization (ACO) of a backpropagation (BP) neural network model was constructed, utilizing the dimensionality reduction results as inputs and the clustering results as outputs for the purpose of driving style recognition. Simulation experiments are conducted using MATLAB Version 9.10 (R2021a) for comparative analysis. The results indicate that the constructed ACO-BP model achieved an overall recognition accuracy of 96.7%, significantly higher than the recognition accuracies of the BP, artificial neural network (ANN), and gradient boosting machine (GBM) models. The ACO-BP model also exhibited the fastest recognition speed among the four models. Moreover, the ACO-BP model shows varied improvements in recognition accuracy for each of the three driving styles, with an increase of 13.7%, 4.4%, and 4.3%, respectively, compared to the BP model. The simulation results validate the high accuracy, real-time capability, and classification effectiveness of this model in driving style recognition, providing new insights for this field. [ABSTRACT FROM AUTHOR]

Driving styles reflect the performance and the ability of drivers to drive in a safe and protective manner. As some of them would possibly result into harmful behaviors, the recognition of these styles continue to attract intensive investigations from the transportation community. In spite of the current promising results, the existing approaches did not yet address the management of simultaneous driving behaviors that are exhibited by a driver during the same commute. They did not also explicitly investigate the legal implication of these driving styles. To this end, we argue that intelligent collaborative solutions could adequately handle the constantly changing traffic environment, prevent aberrant driving behaviors, classify driving styles, and identify the right road traffic policies to apply at the right time to the right driver. Therefore, we are proposing a new intelligent divide-and-conquer approach that aims to process concurrent driver's driving behaviors and identify the related driving styles, accordingly. Our solution relies on a four-layer Multi-Agent System (MAS) architecture, where intelligent agents execute injection, filtering, action, and feedback processing steps to ultimately generate personalized recommendations and feedback to drivers. For the sake of illustration, we collected driving data about braking and acceleration behaviors via our dedicated mobile app AWARIDE. We successfully classified the driving styles into aggressive, normal, and conservative. We also successfully identified the transitions between these styles. [ABSTRACT FROM AUTHOR]

Investigations into driver's behavior are a very important and frequently addressed topic by researchers. They allow us to understand drivers better and characterize their behavior. However, this can be difficult due to the many factors that affect the driver while driving. Therefore, many efforts are being made to improve the safety of both drivers and other road users. In this study, in order to better understand and describe the driver's driving behavior, the results of a pilot study conducted under real traffic conditions are presented. The test route includes different road types and is characterized by varying traffic conditions. Modifications in simple vehicle movement parameters are analyzed in relation to changes in the way drivers drive test routes. [ABSTRACT FROM AUTHOR]

• Driving style correlated positively with traffic violations and crashes retrospectively and prospectively. • Higher scores on risky and angry driving styles were associated with more crashes in the following 12 months. • The predictive validity of bus drivers' driving styles on crash involvement is confirmed. The Multidimensional Driving Style Inventory (MDSI, Taubman-Ben-Ari et al., 2004) is a widely used instrument that assesses private car drivers' driving styles. However, a key validity check for the MDSI is whether the measurement of driving style is associated with drivers' crash liability. A one-year follow-up study was conducted, and one thousand bus drivers were randomly recruited from three cities (Hangzhou, Beijing and Bazhong) in China. The participants were asked to complete the Chinese version of the MDSI and a demographic questionnaire, and they needed to report their traffic violation history 12 months after the survey. Violation history data only included traffic violations and crashes that were caused by the study participants rather than other road users. A confirmatory factor analysis (CFA) was conducted to assess the factorial structure of the MDSI. The relationships between the MDSI and traffic violation history over the previous 12 months and driving patterns over the 12 months immediately following the survey were investigated. CFA results show that the model fitness of the MDSI for bus drivers is acceptable. Both drivers' traffic violations and crashes over the previous 12 months and driving patterns over the following 12 months were positively correlated with dissociative, risky and angry styles and negatively correlated with a careful style. Cluster analysis results show three clusters, with one unsafe driver group characterized by higher scores on dissociative, anxious, risky and angry styles and lower scores on careful driving styles. The number of drivers with traffic violations or crashes in the unsafe cluster is significantly higher than those in the other two clusters. More importantly, drivers in the unsafe cluster experience more traffic violations and crashes over the following 12 months than the other two driver clusters. The findings of the present study not only provide a reliable and valid instrument for assessing bus drivers' driving styles but also show valid evidence for the predictive validity of the MDSI in measuring bus drivers' crash involvement. [ABSTRACT FROM AUTHOR]

In order to overcome the low long-term predictive accuracy associated with mainstream prediction models and the limited consideration of driver characteristics, this study presents an enhanced attention mechanism for human-like trajectory prediction, which is based on Long Short-Term Memory (LSTM). A novel database structure is proposed that incorporates data about driving style and driving intent, pertaining to human factors. By utilizing the convolution computation of Convolutional Social-Long Short-Term Memory (CS-LSTM) for surrounding vehicles, spatial feature extraction around the target vehicle is achieved. Simultaneously, we introduce a dynamic driving style recognition model and a human-like driving intent recognition model to fulfill the output of the human-like module. From a temporal perspective, we employ a decoder attention mechanism to reinforce the emphasis on key historical information, while refining the attention mechanism based on driving style for human-like weight assignment. Comparative analysis with other models indicates that the proposed Driving Style-based Attention-enhanced Convolutional Social-Long Short-Term Memory (DACS-LSTM) model exhibits notable advantages in predicting human-like trajectories for long-term tasks. Visualizing the predicted trajectories of both the Attention-enhanced Convolutional Social-Long Short-Term Memory (ACS-LSTM) and our proposed model, and analyzing the impact of the human-like module on the predicted trajectory, shows that our model's predicted trajectory aligns more closely with the actual one. By comparing the weight distribution of the conventional attention mechanism and the enhanced attention mechanism proposed here, and analyzing the trajectory changes in conjunction with the driving styles, it becomes evident that our proposed model offers a marked improvement. [ABSTRACT FROM AUTHOR]

Incorporating human driving style into car-following modeling is critical for achieving higher levels of driving automation. By capturing the characteristics of human driving, it can lead to a more natural and seamless transition from human-driven to automated driving. A clustering approach is introduced that utilized principal component analysis (PCA) and k-means clustering algorithm to identify driving style types such as aggressive, moderate and conservative at the timestep level. Additionally, an online driving style recognition technique is developed based on the memory effect in driving behavior, allowing for real-time identification of a driver's driving style and enabling adaptive control in automated driving. Finally, the Intelligent Driver Model (IDM) has been improved through the incorporation of an online driving style recognition strategy into car-following modeling, resulting in a human-like IDM that emulates real-world driving behaviors. This enhancement has important implications for the field of automated driving, as it allows for greater accuracy and adaptability in modeling human driving behavior and may ultimately lead to more effective and seamless transitions between human-driven and automated driving modes. The results show that the time-step level driving style recognition method provides a more precise understanding of driving styles that accounts for both inter-driver heterogeneity and intra-driver variation. The proposed human-like IDM performs well in capturing driving style characteristics and reproducing driving behavior. The stability of this improved human-like IDM is also confirmed, indicating its reliability and effectiveness. Overall, the research suggests that the proposed model has promising performance and potential applications in the field of automated driving.