Data-driven evaluation of electric vehicle energy consumption for generalizing standard testing to real-world driving

Standard energy-consumption testing, providing the only publicly available quantifiable measure of battery electric vehicle (BEV) energy consumption, is crucial for promoting transparency and accountability in the electrified automotive industry; however, significant discrepancies between standard testing and real-world driving have hindered energy and environmental assessments of BEVs and their broader adoption. In this study, we propose a data-driven evaluation method for standard testing to characterize BEV energy consumption. By decoupling the impact of the driving profile, our evaluation approach is generalizable to various driving conditions. In experiments with our approach for estimating energy consumption, we achieve a 3.84% estimation error for 13 different multiregional standardized test cycles and a 7.12% estimation error for 106 diverse real-world trips. Our results highlight the great potential of the proposed approach for promoting public awareness of BEV energy consumption through standard testing while also providing a reliable fundamental model of BEVs.

Resolving uncertainty on the fly: modeling adaptive driving behavior as active inference

Understanding adaptive human driving behavior, in particular how drivers manage uncertainty, is of key importance for developing simulated human driver models that can be used in the evaluation and development of autonomous vehicles. However, existing traffic psychology models of adaptive driving behavior either lack computational rigor or only address specific scenarios and/or behavioral phenomena. While models developed in the fields of machine learning and robotics can effectively learn adaptive driving behavior from data, due to their black box nature, they offer little or no explanation of the mechanisms underlying the adaptive behavior. Thus, generalizable, interpretable, computational models of adaptive human driving behavior are still rare. This paper proposes such a model based on active inference, a behavioral modeling framework originating in computational neuroscience. The model offers a principled solution to how humans trade progress against caution through policy selection based on the single mandate to minimize expected free energy. This casts goal-seeking and information-seeking (uncertainty-resolving) behavior under a single objective function, allowing the model to seamlessly resolve uncertainty as a means to obtain its goals. We apply the model in two apparently disparate driving scenarios that require managing uncertainty, (1) driving past an occluding object and (2) visual time-sharing between driving and a secondary task, and show how human-like adaptive driving behavior emerges from the single principle of expected free energy minimization.

Behind the wheel: Probing into personality, skills, and driving behavior's role in bus rapid transit crashes

OBJECTIVE

Personality traits and driving skills are significantly associated with driving behaviors and crashes. In the case of professional bus drivers, the relationships amongst these variables have not been sufficiently examined in terms of road crashes. Therefore, this study seeks to examine the relationship between personality traits, driving skills, driving behaviors, and crash involvement among Bus Rapid Transit (BRT) drivers.

METHODS

The study employed a comprehensive data collection strategy involving self-reported questionnaires, including the driver behavior questionnaire, driver skill inventory, and Big Five inventory, alongside Global Positioning System (GPS)-extracted speeding data from a sample of 166 drivers. To explore the relationship between variables, the study utilized the Partial Least Squares Structural Equation Model (PLS-SEM) as the analytical method.

RESULT

The findings reveal that self-reported violations and actual speeding performed by drivers were positively associated with crash involvement, whereas positive driving behavior negatively influences violation, errors, speeding and crash involvement. The study also found that the safety skills were negatively associated with violations, errors, and speeding, while higher perceptual-motor skills were associated with higher instances of speeding violations, resulting to a higher possibility of getting involved in a crash. Finally, the study reveals that certain personality traits (extraversion and neuroticism) were positively associated with violations, errors, and speeding, leading to a higher risk of getting involved in crashes, whereas certain personality traits (conscientiousness and agreeableness) were associated with safe driving.

CONCLUSION

The study findings offer valuable insights into the predictors of crashes among professional BRT drivers, which can be used to enhance driving practices, ensuring the safety of the public. Moreover, these findings provide transportation agencies with better management and decision-making capabilities to implement effective interventions to improve road safety.

The effect of breathing hypoxic gas (15% FIO2 ) on physiological and behavioral outcomes during simulated driving in healthy subjects

Hypoxia is mainly caused by cardiopulmonary disease or high-altitude exposure. We used a driving simulator to investigate whether breathing hypoxic gas influences driving behaviors in healthy subjects. Fifty-two healthy subjects were recruited in this study, approved by the Science and Engineering Ethical Committee. During simulated driving experiments, driving behaviors, breathing frequency, oxygen saturation (SpO2 ), and heart rate variability (HRV) were analyzed. Each subject had four driving sessions; a 10-min practice and three 20-min randomized interventions: normoxic room air (21% FIO2 ) and medical air (21% FIO2 ) and hypoxic air (equal to 15% FIO2 ), analyzed by repeated measures ANOVA. Driving behaviors and HRV frequency domains showed no significant change. Heart rate (HR; p < 0.0001), standard deviation of the RR interval (SDRR; p = 0.03), short-term HRV (SD1; p < 0.0001), breathing rate (p = 0.01), and SpO2 (p < 0.0001) were all significantly different over the three gas interventions. Pairwise comparisons showed HR increased during hypoxic gas exposure compared to both normoxic interventions, while SDRR, SD1, breathing rate, and SpO2 were lower. Breathing hypoxic gas (15% FiO2 , equivalent to 2710 m altitude) may not have a significant impact on driving behavior in healthy subjects. Furthermore, HRV was negatively affected by hypoxic gas exposure while driving suggesting further research to investigate the impact of breathing hypoxic gas on driving performance for patients with autonomic dysfunction.

Optimization of colored pavement considering driving behavior and psychological characteristics under dynamic low-visibility conditions related to fog-a driving simulator study

OBJECTIVE

Colored pavement is commonly used to reduce the road traffic risk and promote road traffic safety, but its performance in foggy environments has not been fully assessed. The goal of this research is to explore the effectiveness and optimization of colored pavement in a dynamic low-visibility environment.

METHODS

A driving simulation experiment is conducted. Three road risk sections in which collisions are common, including a long straight section, a sharp bend section, and a long downslope section, are considered, and three forms of colored pavement are used in five different visibility environments. The effectiveness of the colored pavement is explored by collecting and analyzing driving behavior and physiological characteristic data for 30 drivers in the established driving environment, and information is obtained through a subjective colored evaluation questionnaire. Eight evaluation indexes are selected from the perspectives of driving behavior and physiological characteristics, and the gray premium evaluation method is applied to evaluate the effectiveness of different forms of colored pavement considering the influence of visibility. Finally, the optimal colored pavement under various visibility and road alignment conditions is proposed.

RESULTS

The results show that reasonably selecting colored pavement can effectively improve drivers' behaviors and physiological characteristics under foggy conditions. For different road alignments and visibility conditions, different forms of colored pavement should be used to ensure road traffic safety.

CONCLUSIONS

The findings provide a theoretical reference for the optimization of colored pavement in foggy conditions.

Research on driver's anger recognition method based on multimodal data fusion

OBJECTIVES

This paper aims to address the challenge of low accuracy in single-modal driver anger recognition by introducing a multimodal driver anger recognition model. The primary objective is to develop a multimodal fusion recognition method for identifying driver anger, focusing on electrocardiographic (ECG) signals and driving behavior signals.

METHODS

Emotion-inducing experiments were performed employing a driving simulator to capture both ECG signals and driving behavioral signals from drivers experiencing both angry and calm moods. An analysis of characteristic relationships and feature extraction was conducted on ECG signals and driving behavior signals related to driving anger. Seventeen effective feature indicators for recognizing driving anger were chosen to construct a dataset for driver anger. A binary classification model for recognizing driving anger was developed utilizing the Support Vector Machine (SVM) algorithm.

RESULTS

Multimodal fusion demonstrated significant advantages over single-modal approaches in emotion recognition. The SVM-DS model using decision-level fusion had the highest accuracy of 84.75%. Compared with the driver anger emotion recognition model based on unimodal ECG features, unimodal driving behavior features, and multimodal feature layer fusion, the accuracy increased by 9.10%, 4.15%, and 0.8%, respectively.

CONCLUSIONS

The proposed multimodal recognition model, incorporating ECG and driving behavior signals, effectively identifies driving anger. The research results provide theoretical and technical support for the establishment of a driver anger system.

Driving behavior characterization and traffic emission analysis considering the vehicle trajectory

Based on the development of the concept of a resource-saving and environmentally friendly society, needing to develop low-carbon and sustainable urban transportation. Most of the pollutants come from the emissions of motor vehicle exhaust. Therefore, this paper analyzes the relationship between driving behavior and traffic emissions, to constrain driver behavior to reduce pollutant emissions. The GPS data are preprocessed by using Navicat for data integration, data screening, data sorting, etc., and then, the speed data are cleaned by using a combination of box-and-line plots and linear interpolation in SPSS. Second, this paper uses principal component analysis (PCA) to downsize 12 indicators such as average speed, average acceleration, and maximum speed and then adopts K-MEANS and K-MEDOIDS methods to cluster the driver's behavioral indicators, selects the aggregation method based on the clustering indexes optimally, and analyzes the driver's driving state by using the symbolic approximation aggregation method; finally, according to the above research results and combined with the MOVES traffic emission model to analyze the relationship between the driver's driving mode, driving state, and traffic emissions, the decision tree can be used to predict the unknown driving mode of the driver to estimate the degree of its emissions.

Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection

The detection of abnormal lane-changing behavior in road vehicles has applications in traffic management and law enforcement. The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors can lead to unsafe interactions with surrounding vehicles, thereby increasing traffic risks. Therefore, solely focusing on individual vehicle perspectives and neglecting the influence of surrounding vehicles in abnormal lane-changing behavior detection has limitations. To address this, this study proposes a framework for abnormal lane-changing behavior detection. Initially, the study introduces a novel approach for representing vehicle trajectories that integrates information from surrounding vehicles. This facilitates the extraction of feature parameters considering the interactions between vehicles and distinguishing between different phases of lane-changing. The Light Gradient Boosting Machine (LGBM) algorithm is then employed to construct an abnormal lane-changing behavior detection model. The results indicate that this framework exhibits high detection accuracy, with the integration of surrounding vehicle information making a significant contribution to the detection outcomes.

The short-term impact of a referendum on motor vehicle collisions casualties

OBJECTIVE

A referendum took place in Greece on the 5th of July 2015 to decide whether the suggested bailout would be accepted. Since this was the first one since 1974, the period between the referendum and the subsequent national elections was characterized by increased uncertainty and had spillover effects in many aspects of everyday life. We take advantage of this quasi-experiment to investigate the short-term impact of the referendum on vehicle collisions casualties.

METHODS

We use data from the daily number of injuries and fatalities caused by vehicle collisions in 2015 and employ a difference-in-differences approach, comparing trends before and after the referendum.

RESULTS

We reveal that the referendum had a short-term impact on road traffic casualties (4.14 more casualties per day), compared to what would have been expected in the absence of the referendum.

CONCLUSIONS

The study provides evidence that negative emotions and anxiety, due to uncertainty, could promote dangerous driving behavior. Preventive and traffic control measures may need to be considered by policy makers during periods of uncertainty.

Sensation seeking predicts risky driving behavior: The mediating role of difficulties in emotion regulation

The purpose of this study was to explore the mediating effect of difficulties in emotion regulation on the relationship between sensation seeking and driving behavior based on the dual-process model of aberrant driving behavior. A sample of 299 drivers in China completed the Difficulties in Emotion Regulation Scale, the Driver Behavior Questionnaire, and the Sensation Seeking Scale V (SSS). The relationships among sensation seeking, difficulties in emotion regulation, and driving behavior were investigated using pathway analysis. The results showed that (1) disinhibition and boredom susceptibility are positively and significantly related to difficulties in emotion regulation and risky driving behaviors; (2) difficulties in emotion regulation are positively and significantly associated with risky driving behaviors; (3) difficulties in emotion regulation mediate the effect of sensation seeking on driving behaviors, supporting the dual-process model of driving behavior; and (4) professional drivers score higher in terms of difficulties in emotion regulation and risky driving behaviors than nonprofessional drivers. The findings of this study could provide valuable insights into the selection of suitable drivers and the development of certain programs that benefit road safety.

A Hybrid Model for Vehicle Acceleration Prediction

Accurate prediction of vehicle acceleration has significant practical applications. Deep learning, as one of the methods for acceleration prediction, has shown promising applications in acceleration prediction. However, due to the influence of multiple factors on acceleration, a single data model may not be suitable for various driving scenarios. Therefore, this paper proposes a hybrid approach for vehicle acceleration prediction by combining clustering and deep learning techniques. Based on historical data of vehicle speed, acceleration, and distance to the preceding vehicle, the proposed method first clusters the acceleration patterns of vehicles. Subsequently, different prediction models and parameters are applied to each cluster, aiming to improve the prediction accuracy. By considering the unique characteristics of each cluster, the proposed method can effectively capture the diverse acceleration patterns. Experimental results demonstrate the superiority of the proposed approach in terms of prediction accuracy compared to benchmarks. This paper contributes to the advancement of sensor data processing and artificial intelligence techniques in the field of vehicle acceleration prediction. The proposed hybrid method has the potential to enhance the accuracy and reliability of acceleration prediction, enabling applications in various domains, such as autonomous driving, traffic management, and vehicle control.

Sensor-Based Classification of Primary and Secondary Car Driver Activities Using Convolutional Neural Networks

To drive safely, the driver must be aware of the surroundings, pay attention to the road traffic, and be ready to adapt to new circumstances. Most studies on driving safety focus on detecting anomalies in driver behavior and monitoring cognitive capabilities in drivers. In our study, we proposed a classifier for basic activities in driving a car, based on a similar approach that could be applied to the recognition of basic activities in daily life, that is, using electrooculographic (EOG) signals and a one-dimensional convolutional neural network (1D CNN). Our classifier achieved an accuracy of 80% for the 16 primary and secondary activities. The accuracy related to activities in driving, including crossroad, parking, roundabout, and secondary activities, was 97.9%, 96.8%, 97.4%, and 99.5%, respectively. The F1 score for secondary driving actions (0.99) was higher than for primary driving activities (0.93-0.94). Furthermore, using the same algorithm, it was possible to distinguish four activities related to activities of daily life that were secondary activities when driving a car.

STUDY OF DRIVER'S ATTITUDES TOWARDS ROAD SAFETY IN GEORGIA

Introduction

Road traffic injuries are a global public health challenges and a leading cause of death and disability. This study examines the relationships between road traffic accident involvement, driving behaviors, and drivers' attitudes towards traffic safety in Georgia.

Material and methods

Behavior of 200 Georgian drivers were reported using a self-administered questionnaire. The criteria for inclusion in the study were residency of Georgia and at least one year of driving experience.

Results

A total of 200 Georgian drivers were interviewed. 59% of study participants felt that the road safety had not improved at all over the past ten years. 94% of respondents were involved in a road traffic accident as a driver. 99% of male drivers and 84% of female drivers have been fined for speeding in the last three years. 95% of males and 51% of females have experienced driving under the influence of alcohol once, and 2% of males and 43% of females have never driven under the influence of alcohol.

Conclusions

The study demonstrated that alcohol consumption, using mobile phones while driving and speeding are very common among drivers in Georgia.

The Effects of ADAS on Driving Behavior: A Case Study

The presence of numerous sensors inside modern vehicles leads to the development of new driving assistance tools, the real usefulness of which depends, however, on the environmental context. This study proposes a procedure capable of quantifying the effectiveness of some warnings produced by an On-Board Unit (OBU) inside the vehicle in a specific environmental context, even if limited only to the considered road. The experimentation was carried out by means of a driving simulator with a sample of young users with sufficiently homogeneous characteristics. The collected data were treated by ANOVA to highlight any differentiation between a traditional driving condition, without any instrumental support, and another involving the OBU was present. The results showed that only in relation to the investigated road, the OBU ensured the advantage of sending information of interest to the driver without invalidating their performance in terms of longitudinal and transverse acceleration, speeding, and steering angle. This research could be of interest to the infrastructure managers who, in case of inappropriate use of a road, could intensify active and passive safety devices for users' safety.

Ecological Interface Design and Head-Up Displays: The Contact-Analog Visualization Tradeoff

OBJECTIVE

This study investigated how the visualization of an ecological interface affects its subjective and objective usefulness. Therefore, we compared a simple 2D visualization against a contact-analog 3D visualization.

BACKGROUND

Recently, head-up displays (HUDs) have become contact-analog and visualizations have been enabled to be merged with the real environment. In this regard, ecological interface design visualizing boundaries of acceptable performance might be a perfect match. Because the real-world environment already provides such boundaries (e.g., lane markings), the interface might directly use them. However, visual illusions and undesired interference with the environment might influence the overall usability.

METHOD

To allow for a comparison, 49 participants tested the same ecological interface in two configurations, contact-analog (3D) and two dimensional (2D). Both visualizations were shown in the car's head-up display (HUD).

RESULTS

The driving simulator experiment reveals that 3D was rated as more demanding and more disturbing, but also more innovative and appealing. However, regarding driving performance, the 3D representation decreased the accuracy of speed control by 6% while significantly increasing lane stability by 20%.

CONCLUSION

We conclude that, if we want environmental boundaries guiding our behavior, the indicator for the behavior should be visualized contact-analog. If we desire artificial boundaries (e.g., speed limits) to guide behavior, the behavioral indicator should be visualized in 2D. This is less prone to optical illusions and allows for a more precise control of behavior.

APPLICATION

These findings provide guidance to human factors engineers, how contact-analog visualizations might be used optimally.

Evaluating the effects of the route guidance variable message signs on driving behaviors-a driving simulation study

OBJECTIVE

Route guidance variable message signs (VMS) are widely applied in traffic incident management on highways by providing real-time spatiotemporal guidance information. The improper panel of route guidance VMS is likely to diminish the compliance with guidance and induce risky driving behaviors, disrupting the traffic flow or even causing crashes. This article aims to investigate the effects of route guidance VMS on driving behaviors in three aspects, vehicle operation, visual perception, and route choice.

METHODS

A driving simulation study with 32 participants was carried out to investigate the driving performance under four different VMS recognizing conditions: baseline (a typical guide sign) and three route guidance VMS panel schemes.

RESULTS

Significant differences in average speed, speed fluctuation, average acceleration, and fixation proportion were found under various VMS recognition conditions. VMS3 had the least negative effects on vehicle operation and visual perception, and the compliance rate under VMS3 was the highest. The possible reasons are as follows: VMS3 has the simplified highway network structure and highlights the road directions with an eye-catching symbol, which can increase the comprehensibility of the guidance information while driving.

CONCLUSIONS

Drivers need to take multiple actions under high-speed driving conditions while recognizing VMS, including reading the route guidance VMS, remaking route decisions, and operating the vehicle concurrently. Under such circumstances, the improper VMS panel would attract more drivers' attention and induce excessive risk compensatory behaviors, reducing drivers' compliance with guidance and situation awareness, and increasing crash risks. In addition, some VMS related traffic management strategies were proposed to improve safety and mobility of highways and further provide a basis for the formulation of related standards.

Research on Vehicle Trajectory Deviation Characteristics on Freeways Using Natural Driving Trajectory Data

Lateral driving behavior analysis is the foundation of freeway cross-section design and the focus of road safety research. However, the factors that influence vehicle lateral driving behavior have not been clearly explained. The dataset of the natural driving trajectory of freeways is used in this study to analyze vehicle lateral driving behavior and trajectory characteristics. As vehicle trajectory characteristic indicators, parameters such as preferred trajectory deviation and standard deviation are extracted. The effects of lane position, speed, road safety facilities, and vehicle types on freeway trajectory behavior are investigated. The results show that lane width and lane position significantly impact vehicle trajectory distribution. As driving speed increases, the lateral distance between vehicles in the inner lane and the guardrail tends to increase. In contrast, vehicles in the outside lane will stay away from the road edge line, and vehicles in the middle lane will stay away from the right lane dividing line when the speed increases. Statistical analysis shows that the preferred trajectory distribution of the same vehicle type in different lane positions is significantly different among groups (Cohen's d > 0.7). In the same lane, the lateral position characteristics of the center of mass of different vehicle types are basically the same (Cohen's d < 0.35). This work aims to explain what variables cause trajectory deviation behaviors and how to design traffic safety facilities (guardrail and shoulder) and lane width to accommodate various vehicle types and design speeds.

Driving Behavior during Right-Turn Maneuvers at Intersections on Left-Hand Traffic Roads

In Japan, where vehicles drive on the left side of the road, pedestrian fatal accidents caused by vehicles traveling at speeds of less than or equal to 20 km/h, occur most frequently when a vehicle is turning right. The objective of the present study is to clarify the driving behavior in terms of eye glances and driver speeds when drivers of two different types of vehicles turn right at an intersection on a left-hand traffic road. We experimentally investigated the drivers' gaze, vehicle speed, and distance on the vehicle traveling trajectory from the vehicle to the pedestrian crossing line, using a sedan and a truck with a gross vehicle weight of < 7.5 tons (a light-duty truck) during right-turn maneuver. We considered four different conditions: no pedestrian dummy (No-P), right pedestrian dummy (R-P), left pedestrian dummy (L-P), and right and left pedestrian dummies (RL-P). Regarding the gazing characteristics, there was no significant difference in the average total gaze time at each AOI between the two vehicles under different conditions, which suggests that the total gaze time was not affected by the vehicle type. All participants gazed at the pedestrian dummies in R-P, L-P, and RL-P. However, the average total gaze time at the right pedestrian dummy (0.63-0.72 s) in R-P was significantly shorter than that at the left pedestrian dummy (1.46-1.57 s) in L- P for both vehicles. The average vehicle speed at the entrance line to the intersection (L1) of the light-duty truck (16.8-18.2 km/h) was lower than that of the sedan (18.8-19.7 km/h). The average vehicle speed at the pedestrian crossing line (L0) of the light-duty truck (15.5-16.0 km/h) was lower than that of the sedan (16.0-17.8 km/h). There was no significant difference in the average vehicle speeds at L1 and L0 between them under any two conditions. We investigated the estimated time to collision (TTC), calculated from the distance on the vehicle traveling trajectory from the vehicle to the pedestrian crossing line and the vehicle speed at the moment when the drivers first gazed at the pedestrian dummies. The average TTC of the right pedestrian dummy in R-P for the sedan (3.5 s) was significantly shorter than that for the light-duty truck (4.0 s). Similarly, the average TTC of the left pedestrian dummy in L-P for the sedan (3.7 s) was significantly shorter than that for the light-duty truck (4.8 s). The driving characteristics obtained in this study may contribute to the development of advanced driver support systems, particularly for vehicles turning right at intersections.

Investigating the Effect of COVID-19 on Driver Behavior and Road Safety: A Naturalistic Driving Study in Malaysia

The spread of the novel coronavirus COVID-19 resulted in unprecedented worldwide countermeasures such as lockdowns and suspensions of all retail, recreational, and religious activities for the majority of 2020. Nonetheless, no adequate scientific data have been provided thus far about the impact of COVID-19 on driving behavior and road safety, especially in Malaysia. This study examined the effect of COVID-19 on driving behavior using naturalistic driving data. This was accomplished by comparing the driving behaviors of the same drivers in three periods: before COVID-19 lockdown, during COVID-19 lockdown, and after COVID-19 lockdown. Thirty people were previously recruited in 2019 to drive an instrumental vehicle on a 25 km route while recording their driving data such as speed, acceleration, deceleration, distance to vehicle ahead, and steering. The data acquisition system incorporated various sensors such as an OBDII reader, a lidar, two ultrasonic sensors, an IMU, and a GPS. The same individuals were contacted again in 2020 to drive the same vehicle on the same route in order to capture their driving behavior during the COVID-19 lockdown. Participants were approached once again in 2022 to repeat the procedure in order to capture their driving behavior after the COVID-19 lockdown. Such valuable and trustworthy data enable the assessment of changes in driving behavior throughout the three time periods. Results showed that drivers committed more violations during the COVID-19 lockdown, with young drivers in particular being most affected by the traffic restrictions, driving significantly faster and performing more aggressive steering behaviors during the COVID-19 lockdown than any other time. Furthermore, the locations where the most speeding offenses were committed are highlighted in order to provide lawmakers with guidance on how to improve traffic safety in those areas, in addition to various recommendations on how to manage traffic during future lockdowns.

Improvements of Warning Signs for Black Ice Based on Driving Simulator Experiments

Black ice is one of the main causes of traffic accidents in winter, and warning signs for black ice are generally ineffective because of the lack of credible information. To overcome this limitation, new warning signs for black ice were developed using materials that change color in response to different temperatures. The performance and effects of the new signs were investigated by conducting driver behavior analysis. To this end, driving simulator experiments were conducted with 37 participants for two different rural highway sections, i.e., a curve and a tangent. The analysis results of the driving behavior and visual behavior experiments showed that the conventional signs had insufficient performance in terms of inducing changes in driving behavior for safety. Meanwhile, the new signs actuated by weather conditions offered a statistically significant performance improvement. Typically, driver showed two times higher speed deceleration when they fixed eyes on the new weather-actuated warning sign (12.80 km/h) compared to the conventional old warning sign (6.84 km/h) in the curve segment. Accordingly, this study concluded that the new weather-actuated warning signs for black ice are more effective than the conventional ones for accident reduction during winters.

The effects of pedestrian gestures on driver yielding at crosswalks: A systematic replication

Applied research on decreasing pedestrian injuries often focuses on how to increase driver yielding behavior but rarely studies what pedestrians can do to increase their safety. There is a lack of empirical research focusing on how pedestrians can effectively signal their need to cross the street when there is no traffic light directing the pedestrian and oncoming traffic. As a replication and extension of Crowley-Koch et al. (2011), this study examined the effects of two pedestrian gestures, an extended arm and raised hand, on driver yielding behavior at 3 crosswalks in Oklahoma City. Research assistants implemented gestures prior to crossing the street as cars approached the crosswalk. Data were collected on the percentage of drivers yielding to the pedestrian. Both pedestrian gestures increased driver yielding across all 3 sites when compared to no gesture. Results were discussed in terms of future research and practical solutions towards increasing pedestrian safety.

Driver Behavior Profiling and Recognition Using Deep-Learning Methods: In Accordance with Traffic Regulations and Experts Guidelines

The process of collecting driving data and using a computational model to generate a safety score for the driver is known as driver behavior profiling. Existing driver profiles attempt to categorize drivers as either safe or aggressive, which some experts say is not practical. This is due to the "safe/aggressive" categorization being a state that describes a driver's conduct at a specific point in time rather than a continuous state or a human trait. Furthermore, due to the disparity in traffic laws and regulations between countries, what is considered aggressive behavior in one place may differ from what is considered aggressive behavior in another. As a result, adopting existing profiles is not ideal. The authors provide a unique approach to driver behavior profiling based on timeframe data segmentation. The profiling procedure consists of two main parts: row labeling and segment labeling. Row labeling assigns a safety score to each second of driving data based on criteria developed with the help of Malaysian traffic safety experts. Then, rows are accumulated to form timeframe segments. In segment labeling, generated timeframe segments are assigned a safety score using a set of criteria. The score assigned to the generated timeframe segment reflects the driver's behavior during that time period. Following that, the study adopts three deep-learning-based algorithms, namely, Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), to classify recorded driving data according to the established profiling procedure, and selects the most suitable one for a proposed recognition system. Various techniques were used to prevent the classification algorithms from overfitting. Using gathered naturalistic data, the validity of the modulated algorithms was assessed on various timeframe segments ranging from 1 to 10 s. Results showed that the CNN, which achieved an accuracy of 96.1%, outperformed the other two classification algorithms and was therefore recommended for the recognition system. In addition, recommendations were outlined on how the recognition system would assist in improving traffic safety.

Investigating the Effect of School Bus Stopping Process on Driver Behavior of Surrounding Vehicles Based on a Driving Simulator Experiment

School bus safety has attracted widespread attention with economic development and the improvement of overall quality of the population. However, violations of school bus regulations and school bus-related crashes often occur. Limited research has been conducted on the impact of the school bus stopping process on surrounding drivers’ behavior. This study provides a driving simulator experiment to explore drivers’ behaviors during the school bus stopping process under different traffic law awareness status, traffic volume status, and initial location status. Eight variables about behavior decision and kinetic parameters were assessed for analysis by a logistic regression model and multivariate analysis of variance (MANOVA). Results show that the mean speed decreases and the number of people complying with the regulations increases after publicizing the regulations. The proportion of surrounding vehicles in the acceleration state increases, especially under the scenario that the traffic volume is large and the initial distance is far. This indicates that the enforcement of the regulations may stimulate unsafe driving behavior. The findings of this study could help policy makers to better understand the prevalence and compliance of current school bus stopping regulations among drivers and support improvements in the practical application of the regulations.

Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method

Evaluating risks when driving is a valuable method by which to make people better understand their driving behavior, and also provides the basis for improving driving performance. In many existing risk evaluation studies, however, most of the time only the occurrence frequency of risky driving events is considered in the time dimension and fixed weights allocation is adopted when constructing a risk evaluation model. In this study, we develop a driving behavior-based relative risk evaluation model using a nonparametric optimization method, in which both the frequency and the severity level of different risky driving behaviors are taken into account, and the concept of relative risk instead of absolute risk is proposed. In the case study, based on the data from a naturalistic driving experiment, various risky driving behaviors are identified, and the proposed model is applied to assess the overall risk related to the distance travelled by an individual driver during a specific driving segment, relative to other drivers on other segments, and it is further compared with an absolute risk evaluation. The results show that the proposed model is superior in avoiding the absolute risk quantification of all kinds of risky driving behaviors, and meanwhile, a prior knowledge on the contribution of different risky driving behaviors to the overall risk is not required. Such a model has a wide range of application scenarios, and is valuable for feedback research relating to safe driving, for a personalized insurance assessment based on drivers' behavior, and for the safety evaluation of professional drivers such as ride-hailing drivers.

Investigating the Effect of Social and Cultural Factors on Drivers in Malaysia: A Naturalistic Driving Study

Road accidents are increasing every year in Malaysia, and it is always challenging to collect reliable pre-crash data in the transportation community. Existing studies relied on simulators, police crash reports, questionnaires, and surveys to study Malaysia’s drivers’ behavior. Researchers previously criticized such methods for being biased and unreliable. To fill in the literature gap, this study presents the first naturalistic driving study in Malaysia. Thirty drivers were recruited to drive an instrumented vehicle for 750 km while collecting continuous driving data. The data acquisition system consists of various sensors such as OBDII, lidar, ultrasonic sensors, IMU, and GPS. Irrelevant data were filtered, and experts helped identify safety criteria regarding multiple driving metrics such as maximum acceptable speed limits, safe accelerations, safe decelerations, acceptable distances to vehicles ahead, and safe steering behavior. These thresholds were used to investigate the influence of social and cultural factors on driving in Malaysia. The findings show statistically significant differences between drivers based on gender, age, and cultural background. There are also significant differences in the results for those who drove on weekends rather than weekdays. The study presents several recommendations to various public and governmental sectors to help prevent future accidents and improve traffic safety.

Effect of Apolipoprotein E4 on the Driving Behavior of Patients with Amnestic Mild Cognitive Impairment or Mild Alzheimer's Disease Dementia

BACKGROUND

The driving behavior of patients with mild Alzheimer's disease dementia (ADD) and patients with mild cognitive impairment (MCI) is frequently characterized by errors. A genetic factor affecting cognition is apolipoprotein E4 (APOE4), with carriers of APOE4 showing greater episodic memory impairment than non-carriers. However, differences in the driving performance of the two groups have not been investigated.

OBJECTIVE

To compare driving performance in APOE4 carriers and matched non-carriers.

METHODS

Fourteen APOE4 carriers and 14 non-carriers with amnestic MCI or mild ADD underwent detailed medical and neuropsychological assessment and participated in a driving simulation experiment, involving driving in moderate and high traffic volume in a rural environment. Driving measures were speed, lateral position, headway distance and their SDs, and reaction time. APOE was genotyped through plasma samples.

RESULTS

Mixed two-way ANOVAs examining traffic volume and APOE4 status showed a significant effect of traffic volume on all driving variables, but a significant effect of APOE4 on speed variability only. APOE4 carriers were less variable in their speed than non-carriers; this remained significant after a Bonferroni correction. To further examine variability in the driving performance, coefficients of variation (COV) were computed. Larger headway distance COV and smaller lateral position COV were observed in high compared to moderate traffic. APOE4 carriers had smaller speed COV compared to non-carriers.

CONCLUSION

The lower speed variability of APOE4 carriers in the absence of neuropsychological test differences indicates reduced speed adaptations, possibly as a compensatory strategy. Simulated driving may be a sensitive method for detecting performance differences in the absence of cognitive differences.

Fitness to drive after acquired brain injury: Results from patient cognitive screening and on-road assessment compared to age-adjusted norm values

Fitness to drive after acquired brain injury or disease is a common question in rehabilitation settings. The aim of the study was to compare age-matched norms with patient cognitive test results used to predict fitness to drive. A second aim was to analyze the contribution from an on-road assessment to a final decision on resumption of driving after an acquired brain injury. Retrospective cognitive test results from four traffic medicine units (n = 333) were compared with results from a healthy norm population (n = 410) in Sweden. Patients were dichotomized according to the final decision as fit or unfit to drive made by the traffic medicine team. The norm group had significantly better results in all age groups for all cognitive tests compared with the patients considered unfit to drive and fit to drive. A binary regression analysis for the patient group showed an explained value for fit to drive/unfit to drive of 88%, including results for the Nordic Stroke Driver Screening Assessment total score, Useful Field of View total score and the final outcome from an on-road assessment. Results from the present study illustrate the importance of using several tests, methods and contexts for the final decision regarding fitness to drive.

Cognitive Efficiency and Fitness-to-Drive along the Lifespan: The Mediation Effect of Visuospatial Transformations

The way people represent and transform visuospatial information affects everyday activities including driving behavior. Mental rotation and perspective taking have recently been found to predict cognitive prerequisites for fitness-to-drive (FtD). We argue that the relationship between general cognitive status and FtD is mediated by spatial transformation skills. Here, we investigated the performance in the Mental Rotation Test (MRT) and the Perspective-Taking Test (PT) of 175 male active drivers (aged from 18 to 91 years), by administering the Montreal Cognitive Assessment (MoCA) to measure their global cognitive functioning. All participants were submitted to a computerized driving assessment measuring resilience of attention (DT), reaction speed (RS), motor speed (MS), and perceptual speed (ATAVT). Significant results were found for the effect of global cognitive functioning on perceptual speed through the full mediation of both mental rotation and perspective-taking skills. The indirect effect of global cognitive functioning through mental rotation was only found to significantly predict resilience of attention whereas the indirect effect mediated by perspective taking only was found to significantly predict perceptual speed. Finally, the negative effect of age was found on each driving measure. Results presented here, which are limited to male drivers, suggest that general cognitive efficiency is linked to spatial mental transformation skills and, in turn, to driving-related cognitive tasks, contributing to fitness-to-drive in the lifespan.

Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

Sound Effects on Physiological State and Behavior of Drivers in a Highway Tunnel

Driving behavior in a highway tunnel could be affected by external environmental factors like light, traffic flow, and acoustic environments, significantly when these factors suddenly change at the moment before and after entering a tunnel. It will cause tremendous physiological pressure on drivers because of the reduction of information and the narrow environment. The risks in driving behavior will increase, making drivers more vulnerable than driving on the regular highways. This research focuses on the usually neglected acoustic environment and its effect on drivers' physiological state and driving behavior. Based on the SIMLAB driving simulation platform of a highway tunnel, 45 drivers participated in the experiment. Five different sound scenarios were tested: original highway tunnel sound and a mix of it with four other sounds (slow music, fast music, voice prompt, and siren, respectively). The subjects' physiological state and driving behavior data were collected through heart rate variability (HRV) and electroencephalography (EEG). Also, vehicle operational data, including vehicle speed, steering wheel angle, brake pedal depth, and accelerator pedal depth, were collected. The results indicated that different sound scenarios in the highway tunnel showed significant differences in vehicle speed (p = 0.000, η² = 0.167) and steering wheel angle (p = 0.007, η² = 0.126). At the same time, they had no significant difference in HRV and EEG indicators. According to the results, slow music was the best kind of sound related to driving comfort, while the siren sound produced the strongest driver reaction in terms of mental alertness and stress level. The voice-prompt sound most likely caused driver fatigue and overload, but it was the most effective sound affecting safety. The subjective opinion of the drivers indicated that the best sound scenario for the overall experience was slow music (63%), followed by fast music (21%), original highway tunnel sound environment (13%), and voice-prompt sound (3%). The findings of this study will be valuable in improving acoustic environment quality and driving safety in highway tunnels.

Review of Studies on Older Drivers' Behavior and Stress-Methods, Results, and Outlook

This paper presents a review on relevant studies and reports related to older drivers’ behavior and stress. Questionnaires, simulators, and on-road/in-vehicle systems are used to collect driving data in most studies. In addition, research either directly compares older drivers and the other drivers or considers participants according to various age groups. Nevertheless, the definition of ‘older driver’ varies not only across studies but also across different government reports. Although questionnaire surveys are widely used to affordably obtain massive data in a short time, they lack objectivity. In contrast, biomedical information can increase the reliability of a driving stress assessment when collected in environments such as driving simulators and on-road experiments. Various studies determined that driving behavior and stress remain stable regardless of age, whereas others reported degradation of driving abilities and increased driving stress among older drivers. Instead of age, many researchers recommended considering other influencing factors, such as gender, living area, and driving experience. To mitigate bias in findings, this literature review suggests a hybrid method by applying surveys and collecting on-road/in-vehicle data.

Driving Behavior That Limits Concentration: A Nationwide Survey in Greece

Human behavior is implicated in most road accidents. The current study examined drivers’ behavior that interferes with decision making and reaction time to an incidence. Adults (≥17 years-old) participated in a questionnaire-based survey for driver’s behavior. Dataset was weighed according to sex, age and education based on the 2011 census. Differences between groups were assessed with Chi-squared tests while logistic regression models were used to identify drivers’ characteristics for specific behaviors. A total 1601 adults participated in the survey—48% males and 52% females. Texting, Global Positioning System (GPS) setting and smoking were observed more by professional drivers and drivers of an urban area, while smoking was also dependent on social class. Drink driving was observed more by males (20% vs. 5% females), while after adjusting for age, the odds of drink driving in males were 5 times higher than females (p < 0.001). A different effect of age depending on the driver’s sex and vice versa was observed regarding phone calls. Drivers’ behavior with distractive potential differed by age, sex, social class and area of residence. Male drivers were more likely to perform drink driving, while professional drivers were more likely to use cell phone for calls and texting, set the GPS and smoke while driving.

Auditory Messages for Intersection Movement Assist (IMA) Systems: Effects of Speech- and Nonspeech-Based Cues

OBJECTIVE

The objective of this study was to assess the effects of different warning messages for an Intersection Movement Assist (IMA) based on drivers' ability to avoid a potential safety hazard.

BACKGROUND

An IMA system can detect hazards and warn drivers when it is unsafe to enter an intersection. The effects of different warning information conveyed by these systems are still unknown.

METHOD

A driving simulator study with 80 participants was conducted with a red light running (RLR) scenario using a 5 (warnings) x 2 (training) between-subject design. IMA warnings included the messages "Danger," "Brake now," "Vehicle on your left," a beep, and no IMA warning. Training was provided to half of the participants. Analysis of variance and logistic regression models were used to examine differences in drivers' avoidance behavior.

RESULTS

The analyses showed that all tested warning messages can significantly enhance drivers' avoidance performance. Significant differences were observed in crash occurrence, avoidance behavior (i.e., reaction time and speed change), and eye movements (i.e., fixation pattern and time to first fixation). The effects of training also differed given the warning message provided.

CONCLUSION

The "Brake now" message performed best in reducing crash involvement and prompted better avoidance performance. The "Danger" and "Vehicle on your left" messages improved drivers' hazard detection ability. The training showed a potential to enhance the effectiveness of nonspeech warning messages.

APPLICATION

The findings of this study can help designers and engineers better design IMA warning messages for RLR scenarios.

Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

The driving behavior of bus drivers is related to the safety of all passengers and regulation of urban traffic. In order to analyze the relevant characteristics of speed and acceleration, accurate bus trajectories and patterns are essential for driver behavior analysis and development of effective intelligent public transportation. Exploiting real-time vehicle tracking, this paper develops a platform with vehicle-mounted terminals using differential global navigation satellite system (DGNSS) modules for driver behavior analysis. The DGNSS traces were used to derive the vehicle trajectories, which were then linked to road information to produce speed and acceleration matrices. Comprehensive field tests were undertaken on multiple bus routes in urban environments. The spatiotemporal results indicate that the platform can automatically and accurately extract the driving behavior characteristics. Furthermore, the platform’s visual function can be used to effectively monitor driving risks, such as speeding and fierce acceleration, in multiple bus routes. The details of the platform’s features are provided for intelligent transport system (ITS) design and applications.

The Influence of Individual Differences on Diverging Behavior at the Weaving Sections of an Urban Expressway

Urban expressway weaving sections suffer from a high crash risk in urban transportation systems. Studying driving behavior is an important approach to solve safety and efficiency issues at expressway weaving sections. This study aimed to investigate the influence of drivers’ individual differences on diverging behavior at expressway weaving sections. First, a k-means cluster analysis of 650 questionnaires was performed, to classify drivers into three categories: aggressive, conservative and normal. Then, the driving behavior of 45 drivers from the three categories was recorded in a driving simulator and analyzed by an analysis of variance. The results show that different types of drivers have different driving behaviors at weaving sections. Aggressive drivers have a higher mean speed and mean longitudinal deceleration, followed by normal and conservative drivers. Significant differences in the range of lane-change positions were found between 100, 150 and 200 m of weaving length for the same type of drivers, and the duration of weaving for aggressive drivers was significantly smaller than for normal and conservative drivers. A significant correlation was found between lane-change position and weaving duration. These results can help traffic engineers to propose effective control strategies for different types of drivers, to improve the safety of weaving sections.

Effects of advanced warning information systems on secondary crash risk under connected vehicle environment

This study evaluated the impact of an optimal in-vehicle advanced warning information service in a connected vehicle (CV) environment to prevent secondary crashes. Driving simulation experiments were designed and performed to analyze driving behavior. The forward crash situation was reproduced in a simulated highway environment, and the safety effects were assessed based on simulation data from a driving simulator (DS). To explore and analyze the effectiveness of crash notifications from the advanced warning information system (AWIS) for preventing secondary crashes, this study utilized repeated measures of multivariate analysis of variance (MANOVA), repeated measures of ANOVA, paired t-test, and Wilcoxon signed rank test. The results from this paper indicate that a warning information system was effective to prevent secondary crash risks, in general.

Safety of Raised Pavement Markers in Freeway Tunnels Based on Driving Behavior

Raised pavement markers (RPMs) are among the common safety features of roads, playing an important role in preventing and reducing traffic crashes. RPMs are regarded as an effective measure for reducing the high crash rate and mortality in freeway tunnels in China. In this study, a driving simulator experiment was conducted to investigate the safety of RPMs in a freeway tunnel. Two different RPM layouts were designed and compared to a control with no RPMs, and 32 drivers participated in the driving simulator experiments. The speed, relative speed difference, lateral position, accelerator power, acceleration, and pupil area were used as indicators of the response characteristics of drivers to RPMs, and the interaction of tunnel length, tunnel zone, and RPM alternatives was discussed. The results indicate that a significant interaction effect exists between tunnel length, tunnel zone, and RPM alternatives. RPMs could help reduce driver anxiety, boredom, and fatigue caused by the dark and monotonous tunnel driving environment, and improve driver alertness and consciousness of speed. Also, the driving risk increases with increasing tunnel length (1800 m to 3500 m).

Editorial: Paving the Way Toward Improving Safety Among Drivers With Attention-Deficit/Hyperactivity Disorder

The ability to drive is important to an individual's participation in modern society, as it enhances independence and social and economic opportunity. However, motor vehicle crashes are a leading cause of injury-related death in the United States-and the leading cause of death among 15- to 24-year-olds. Thus, it is critical that we sequentially identify who may be at inherently higher crash risk and why their crash risk might be higher, with the ultimate goal of implementing comprehensive approaches to promote safe driving practices and to improve safe mobility.

Driving Accidents, Driving Violations, Symptoms of Attention-Deficit-Hyperactivity (ADHD) and Attentional Network Tasks

Background: Iran has serious problems with traffic-related injuries and death. A major reason for traffic accidents is cognitive failure due to deficits in attention. In this study, we investigated the associations between traffic violations, traffic accidents, symptoms of attention-deficit/hyperactivity disorder (ADHD), age, and on an attentional network task in a sample of Iranian adults. Methods: A total of 274 participants (mean age: 31.37 years; 80.7% males) completed questionnaires covering demographic information, driving violations, traffic accidents, and symptoms of ADHD. In addition, they underwent an objective attentional network task (ANT), based on Posner’s concept of attentional networks. Results: More frequent traffic violations, correlated with lower age and poorer performance on the attentional network tasks. Higher symptoms of ADHD were associated with more accidents and more traffic violations, but not with the performance of the attentional tasks. Higher ADHD scores, a poorer performance on attentional network tasks, and younger age predicted traffic violations. Only higher symptoms of ADHD predicted more traffic accidents. Conclusions: In a sample of Iranian drivers, self-rated symptoms of ADHD appeared to be associated with traffic violations and accidents, while symptoms of ADHD were unrelated to objectively assessed performance on an attentional network task. Poor attentional network performance was a significant predictor of traffic violations but not of accidents. To increase traffic safety, both symptoms of ADHD and attentional network performance appear to merit particular attention.

Estimating the Necessary Amount of Driving Data for Assessing Driving Behavior

The aim of this paper was to provide a methodological framework for estimating the amount of driving data that should be collected for each driver in order to acquire a clear picture regarding their driving behavior. We examined whether there is a specific discrete time point for each driver, in the form of total driving duration and/or the number of trips, beyond which the characteristics of driving behavior are stabilized over time. Various mathematical and statistical methods were employed to process the data collected and determine the time point at which behavior converges. Detailed data collected from smartphone sensors are used to test the proposed methodology. The driving metrics used in the analysis are the number of harsh acceleration and braking events, the duration of mobile usage while driving and the percentage of time driving over the speed limits. Convergence was tested in terms of both the magnitude and volatility of each metric for different trips and analysis is performed for several trip durations. Results indicated that there is no specific time point or number of trips after which driving behavior stabilizes for all drivers and/or all metrics examined. The driving behavior stabilization is mostly affected by the duration of the trips examined and the aggressiveness of the driver.

The Relationship between in-Vehicle Technologies and Self-Regulation among Older Drivers

The study sought to understand the relationship between in-vehicle technologies (IVTs) and self-regulatory behaviors among older drivers. In a large multi-site study of 2990 older drivers, self-reported data on the presence of IVTs and avoidance of various driving behaviors (talking on a mobile phone while driving, driving at night, driving in bad weather, and making left turns when there is no left turn arrow) were recorded. Self-reports were used to identify whether avoidance was due to self-regulation. Hierarchical logistic regressions were used to determine whether the presence of a particular IVT predicted the likelihood of a given self-regulatory behavior after controlling for other factors. Results suggest that the presence of Integrated Bluetooth/Voice Control systems are related to a reduced likelihood of avoiding talking on a mobile phone while driving due to self-regulation (OR= 0.37, 95% CI= 0.29-0.47). The presence of a Navigation Assistance system was related to a reduced likelihood of avoiding talking on a mobile phone while driving (OR= 0.65, 95% CI= 0.50-0.84) and avoiding driving at night due to self-regulation (OR= 0.80, 95% CI = 0.64-1.00). Present findings suggest in-vehicle technologies may differently influence the self-regulatory behaviors of older drivers.

Analysis of Driving Behavior Based on Dynamic Changes of Personality States

This study investigated the relationship between personality states and driving behavior from a dynamic perspective. A personality baseline was introduced to reflect the driver's trait level and can be used as a basic reference for the dynamic change of personality states. Three kinds of simulated scenarios triggered by pedestrian crossing the street were established using a virtual reality driving simulator. Fifty licensed drivers completed the driving experiments and filled in the Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) questionnaire to measure the drivers' personality baselines. Key indicators were quantified to characterize the five types of personality states by K-means clustering algorithm. The results indicated that the high-risk situation had a greater impact on the drivers, especially for drivers with openness and extroversion. Furthermore, for the drivers of extroverted personality, the fluctuation of personality states in the high-risk scenario was more pronounced. This paper put forward a novel idea for the analysis of driving behavior, and the research results provide a personalized personality database for the selection of different driving modes.

Efficacy of a Neurofeedback Training on Attention and Driving Performance: Physiological and Behavioral Measures

Increased attention and lower stress levels are associated with more functional and safe driving behavior, since they contribute to reduce distractibility and risk-taking at the wheel. Previous neuroscience research highlighted that NeuroFeedback (NF) training mediated by wearable devices could be effective in terms of neurocognitive strengthening and attention regulation with a direct effect on driving attentional performance. Thus, this research aims to test the effectiveness of a NF protocol on a sample of drivers, to observe its impact on attentional skills and psychophysiological levels of stress involved in driving behavior. 50 participants were randomly assigned to the experimental and active control group. The experimental condition consisted of a 21-day mindfulness NF training with incremental duration sessions. A pre- (t0) and post-treatment (t1) assessment included behavioral, psychometric, neuropsychological, and psychophysiological autonomic measures. Specifically, the Driver Behavior Questionnaire (DBQ) and the Active Box (AB) device were used to evaluate the everyday driving behavior. Results underlined an improvement in driving behavior performance and a decrease of violations at the wheel of the experimental group (EXPg) at t1 measured, respectively by AB and DBQ. About the autonomic and neuropsychological measure, an increase in heart rate (HR) and an increased accuracy at the Stroop Task were detected: a specific increase of Stroop-related HR was found for the EXPg at t1. Also, reduced reaction times were found in the Multiple Features Target Cancellation for the EXPg at t1. Overall, the EXPg displayed a physiological, behavioral and neuropsychological increased efficiency related to attention as well as a driving-related behavioral improvement after NF training.

Deceleration Planning Algorithm Based on Classified Multi-Layer Perceptron Models for Smart Regenerative Braking of EV in Diverse Deceleration Conditions

The smart regenerative braking system (SRS) is an autonomous version of one-pedal driving in electric vehicles. To implement SRS, a deceleration planning algorithm is necessary to generate the deceleration used in automatic regenerative control. To reduce the discomfort from the automatic regeneration, the deceleration should be similar to human driving. In this paper, a deceleration planning algorithm based on multi-layer perceptron (MLP) is proposed. The MLP models can mimic the human driving behavior by learning the driving data. In addition, the proposed deceleration planning algorithm has a classified structure to improve the planning performance in each deceleration condition. Therefore, the individual MLP models were designed according to three different deceleration conditions: car-following, speed bump, and intersection. The proposed algorithm was validated through driving simulations. Then, time to collision and similarity to human driving were analyzed. The results show that the minimum time to collision was 1.443 s and the velocity root-mean-square error (RMSE) with human driving was 0.302 m/s. Through the driving simulation, it was validated that the vehicle moves safely with desirable velocity when SRS is in operation, based on the proposed algorithm. Furthermore, the classified structure has more advantages than the integrated structure in terms of planning performance.

A Personalized Behavior Learning System for Human-Like Longitudinal Speed Control of Autonomous Vehicles

As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.

Driving behavior and road traffic crashes among professional and nonprofessional drivers in South Egypt

Egypt has one of the highest traffic crash rates in the world. This study aims to investigate the correlates with driving behaviors and road traffic crashes (RTCs) among professional and nonprofessional drivers in South Egypt. A total of 518 drivers (203 professional and 315 nonprofessional) were interviewed and their sociodemographic characteristics, driving habits, and RTC involvement during the preceding 2 years were documented. The Arabic version of the Driver Behavior Questionnaire (DBQ) was used to assess drivers' lapses, errors, and violations. The results showed that professional drivers had more lapses (OR 3.03, 95% CI 1.54-5.96), errors (OR 2.88, 95% CI 1.44-5.76), and violations (OR 2.04, 95% CI 1.05-3.97) compared to nonprofessional drivers and female drivers were more likely to lapse than males (OR 3.18, 95% CI 1.79-5.66). RTC involvement was associated with female sex (OR 3.27, 95% CI 1.56-6.86), age < 30 years (OR 2.31, 95% CI 1.20-4.44), illiteracy (OR 1.51, 95% CI 1.02-2.23), eating while driving (OR 2.41, 95% CI 1.43-4.06), and not using seatbelt (OR 1.89, 95% CI 1.06-3.37). Driving lapses, errors, and violations did not significantly increase the risk of RTCs.

Correlation Among Behavior, Personality, and Electroencephalography Revealed by a Simulated Driving Experiment

Drivers play the most important role in the human-vehicle-environment system and driving behaviors are significantly influenced by the cognitive state of the driver and his/her personality. In this paper, we aimed to explore the correlation among driving behaviors, personality and electroencephalography (EEG) using a simulated driving experiment. A total of 36 healthy subjects participated in the study. The 64-channel EEG data and the driving data, including the real-time position of the vehicle, the rotation angle of the steering wheel and the speed were acquired simultaneously during driving. The Cattell 16 Personality Factor Questionnaire (16PF) was utilized to evaluate the personalities of subjects. Through hierarchical clustering of the 16PF personality traits, the subjects were divided into four groups, i.e., the Inapprehension group, Insensitivity group, Apprehension group and the Unreasoning group, named after their representative personality trait. Their driving performance and turning behaviors were compared and EEG preprocessing, source reconstruction and the comparisons among the four groups were performed using Statistical Parameter Mapping (SPM). The turning process of the subjects can be formulated into two steps, rotating the steering wheel toward the turning direction and entering the turn, and then rotating the steering wheel back and leaving the turn. The bilateral frontal gyrus was found to be activated when turning left and right, which might be associated with its function in attention, decision-making and executive control functions in visual-spatial and visual-motor processes. The Unreasoning group had the worst driving performance with highest rates of car collision and the most intensive driving action, which was related to a higher load of visual spatial attention and decision making, when the occipital and superior frontal areas played a very important role. Apprehension (O) and Tension (Q4) had a positive correlation, and Reasoning (B) had a negative correlation with dangerous driving behaviors. Our results demonstrated the close correlation among driving behaviors, personality and EEG and may be taken as a reference for the prediction and precaution of dangerous driving behaviors in people with specific personality traits.

Driving Style Recognition Based on Electroencephalography Data From a Simulated Driving Experiment

Driving style is a very important indicator and a crucial measurement of a driver's performance and ability to drive in a safe and protective manner. A dangerous driving style would possibly result in dangerous behaviors. If the driving styles can be recognized by some appropriate classification methods, much attention could be paid to the drivers with dangerous driving styles. The driving style recognition module can be integrated into the advanced driving assistance system (ADAS), which integrates different modules to improve driving automation, safety and comfort, and then the driving safety could be enhanced by pre-warning the drivers or adjusting the vehicle's controlling parameters when the dangerous driving style is detected. In most previous studies, driver's questionnaire data and vehicle's objective driving data were utilized to recognize driving styles. And promising results were obtained. However, these methods were indirect or subjective in driving style evaluation. In this paper a method based on objective driving data and electroencephalography (EEG) data was presented to classify driving styles. A simulated driving system was constructed and the EEG data and the objective driving data were collected synchronously during the simulated driving. The driving style of each participant was classified by clustering the driving data via K-means. Then the EEG data was denoised and the amplitude and the Power Spectral Density (PSD) of four frequency bands were extracted as the EEG features by Fast Fourier transform and Welch. Finally, the EEG features, combined with the classification results of the driving data were used to train a Support Vector Machine (SVM) model and a leave-one-subject-out cross validation was utilized to evaluate the performance. The SVM classification accuracy was about 80.0%. Conservative drivers showed higher PSDs in the parietal and occipital areas in the alpha and beta bands, aggressive drivers showed higher PSD in the temporal area in the delta and theta bands. These results imply that different driving styles were related with different driving strategies and mental states and suggest the feasibility of driving style recognition from EEG patterns.

Fatally injured drivers in Norway 2005-2015-Trends in substance use and crash characteristics

Objective: Norway introduced a "Vision Zero" strategy in 2001, using multiple approaches, aiming toward a future in which no one will be killed or seriously injured in road traffic crashes (RTCs). Official statistics show that the number of fatally injured road users has declined substantially from 341 deaths in 2000 to 117 in 2015. In-depth crash investigations of all fatal RTCs started in Norway in 2005. The aim of this study was to investigate whether fatal crash characteristics, vehicle safety features, and prevalence of drugs and/or alcohol among fatally injured drivers and riders has changed during 2005-2015, accompanying the reduction in road fatalities. Methods: Data on all car/van drivers and motorcycle/moped riders fatally injured in RTCs during 2005-2015 were extracted from Norwegian road traffic crash registries and combined with forensic toxicology data. Results: The proportion of cars and motorcycles with antilock braking systems and cars with electronic stability control, increased significantly during the study period. The prevalence of nonuse of seat belts/helmets and speeding declined among both fatally injured drivers and riders. In addition, the prevalence of alcohol declined, though no significant change in the total prevalence of other substances was noted. Conclusion: The observed changes toward more safety installations in cars and motorcycles and lower prevalence of driver-related risk factors like alcohol use, speeding, and nonuse of seat belts/helmets among fatally injured drivers/riders may have contributed to the decrease in road traffic deaths.