Journal articles: 'Driving Behavior'

1

Zhang, Yanning, Zhongyin Guo, and Zhi Sun. "Driving Simulator Validity of Driving Behavior in Work Zones." Journal of Advanced Transportation 2020 (June 9, 2020): 1–10. http://dx.doi.org/10.1155/2020/4629132.

Full text

Abstract:

Driving simulation is an efficient, safe, and data-collection-friendly method to examine driving behavior in a controlled environment. However, the validity of a driving simulator is inconsistent when the type of the driving simulator or the driving scenario is different. The purpose of this research is to verify driving simulator validity in driving behavior research in work zones. A field experiment and a corresponding simulation experiment were conducted to collect behavioral data. Indicators such as speed, car-following distance, and reaction delay time were chosen to examine the absolute and relative validity of the driving simulator. In particular, a survival analysis method was proposed in this research to examine the validity of reaction delay time. The result indicates the following: (1) most indicators are valid in driving behavior research in the work zone. For example, spot speed, car-following distance, headway, and reaction delay time show absolute validity. (2) Standard deviation of the car-following distance shows relative validity. Consistent with previous researches, some driving behaviors appear to be more aggressive in the simulation environment.

APA, Harvard, Vancouver, ISO, and other styles

2

He, Yi, Shuo Yang, Xiao Zhou, and Xiao-Yun Lu. "An Individual Driving Behavior Portrait Approach for Professional Driver of HDVs with Naturalistic Driving Data." Computational Intelligence and Neuroscience 2022 (January 22, 2022): 1–14. http://dx.doi.org/10.1155/2022/3970571.

Full text

Abstract:

More than 50% major road accidents are caused by risk driving behaviors from professional drivers of Heavy Duty Vehicles (HDVs). The quantitative estimation of driving performance and driving behaviors portrait for professional drivers is helpful to measure the driver’s driving risk and inherent driving style. Previous studies have attempted to evaluate risk driving behavior, but most of them rely on high-demand vehicle and driving data. However, few studies can dig into the causes and correlations behind individual driving behavior and quantify the driving behaviors portrait for professional driver based on long-term naturalistic driving. In this study, the data is from On-Board Unit (OBU) devices mounted in the HDVs in China. Based on the driving behavior pattern diagram and the frequency and ranking of drivers’ typical driving patterns, a driving behavior portrait approach is proposed by comprehensively considering the vehicle safety, driving comfort, and fuel economy indicators. The similarities and differences of different drivers’ driving behaviors are quantitatively analyzed. The high precision and sampling frequency data from vehicles are used to verify the proposed approach. The results demonstrated that the driving behavior portrait approach can digitally describe the individual driving behaviors styles and identify the potential driving behaviors with long-term naturalistic driving data. The development of this approach can help quantitatively evaluate the individual characteristic of risk driving behaviors to prevent road accidents.

APA, Harvard, Vancouver, ISO, and other styles

3

Liu, Wenlong, Hongtao Li, and Hui Zhang. "Dangerous Driving Behavior Recognition Based on Hand Trajectory." Sustainability 14, no. 19 (September 28, 2022): 12355. http://dx.doi.org/10.3390/su141912355.

Full text

Abstract:

Dangerous driving behaviors in the process of driving will produce road traffic safety hazards, and even cause traffic accidents. Common dangerous driving behavior includes: eating, smoking, fetching items, using a handheld phone, and touching a control monitor. In order to accurately identify the dangerous driving behaviors, this study first uses the hand trajectory data to construct the dangerous driving behavior recognition model based on the dynamic time warping algorithm (DTW) and the longest common sub-sequence algorithm (LCS). Secondly, 45 subjects’ hand trajectory data were obtained by driving simulation test, and 30 subjects’ hand trajectory data were used to determine the dangerous driving behavior label. The matching degree of hand trajectory data of 15 subjects was calculated based on the dangerous driving behavior recognition model, and the threshold of dangerous driving behavior recognition was determined according to the calculation results. Finally, the dangerous driving behavior recognition algorithm and neural network algorithm are compared and analyzed. The dangerous driving behavior recognition algorithm has a fast calculation speed, small memory consumption, and simple program structure. The research results can be applied to dangerous driving behavior recognition and driving distraction warning based on wrist wearable devices.

APA, Harvard, Vancouver, ISO, and other styles

4

Ni, Dingan, Fengxiang Guo, Hui Zhang, Mingyuan Li, and Yanning Zhou. "Improving Older Drivers’ Behaviors Using Theory of Planned Behavior." Sustainability 14, no. 8 (April 15, 2022): 4769. http://dx.doi.org/10.3390/su14084769.

Full text

Abstract:

The proportion of older drivers has increased with the aging population. In order to improve the driving behavior and safety of older drivers, we aim to analyze behavior differences between older and younger drivers and then study an improvement strategy based on the older drivers’ behavioral characteristics. Older drivers’ behaviors can be enhanced through training, thereby improving driving safety. Simulated scenarios for behavior analysis and training are constructed for drivers who are recruited from the general driving population. Data on the drivers’ eye movement, physiological and psychological conditions, operation behavior, and vehicle status are collected and analyzed. The theory of planned behavior is adopted to construct a driving behavior enhancement training model for older drivers. Finally, a structural equation model is developed to comprehend the relationship between training level, driver characteristics, and traffic safety. The ability and speed of older drivers to obtain traffic information is worse than those of young and middle-aged drivers, and the vehicle control capability of older drivers has a larger volatility. The driving behavior training model can improve older drivers’ driving stability and safety, as follows: the positive effect of training on driving behavioral improvement is larger than the negative effect of aging; the negative effect of training level on dangerous driving tendency is larger than the positive effect of driver’s aging. The driving behavior of older drivers should be improved for the safety and stability of driving operations through the PNE (perceived-norm-execution) model. The relationship between training level, driving behavior characteristics, and traffic safety is discussed using the structural equation model, and results show that the training can improve the effect of the drivers’ age on the characteristics of driving behavior, and that older drivers tend to decrease dangerous driving tendencies.

APA, Harvard, Vancouver, ISO, and other styles

5

Ma, Chunmei, Xili Dai, Jinqi Zhu, Nianbo Liu, Huazhi Sun, and Ming Liu. "DrivingSense: Dangerous Driving Behavior Identification Based on Smartphone Autocalibration." Mobile Information Systems 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/9075653.

Full text

Abstract:

Since pervasive smartphones own advanced computing capability and are equipped with various sensors, they have been used for dangerous driving behaviors detection, such as drunk driving. However, sensory data gathered by smartphones are noisy, which results in inaccurate driving behaviors estimations. Some existing works try to filter noise from sensor readings, but usually only the outlier data are filtered. The noises caused by hardware of the smartphone cannot be removed from the sensor reading. In this paper, we propose DrivingSense, a reliable dangerous driving behavior identification scheme based on smartphone autocalibration. We first theoretically analyze the impact of the sensor error on the vehicle driving behavior estimation. Then, we propose a smartphone autocalibration algorithm based on sensor noise distribution determination when a vehicle is being driven. DrivingSense leverages the corrected sensor parameters to identify three kinds of dangerous behaviors: speeding, irregular driving direction change, and abnormal speed control. We evaluate the effectiveness of our scheme under realistic environments. The results show that DrivingSense, on average, is able to detect the driving direction change event and abnormal speed control event with 93.95% precision and 90.54% recall, respectively. In addition, the speed estimation error is less than 2.1 m/s, which is an acceptable range.

APA, Harvard, Vancouver, ISO, and other styles

6

Zuraida, Rida, and Nike Septivani. "Risky-driving behavior and it relation with eco-driving behavior based on an adapted Manchester Driving Behavior questionnaire." IOP Conference Series: Earth and Environmental Science 195 (December 14, 2018): 012072. http://dx.doi.org/10.1088/1755-1315/195/1/012072.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Bejar, M., N. Regaieg, D. Gdoura, J. Aloulou, and O. Amami. "Anxious driving behavior among taxi drivers." European Psychiatry 64, S1 (April 2021): S184—S185. http://dx.doi.org/10.1192/j.eurpsy.2021.488.

Full text

Abstract:

IntroductionThe data suggest that anxious drivers may engage in problem behaviors that expose them and others to an increased risk of negative traffic events.ObjectivesTo study the problematic behavior taxi drivers related to anxiety in three areas exaggerated safety/caution, performance deficits, and hostile/aggressive behaviors and to determine the factors who are associated with them.MethodsThis is a cross-sectional descriptive and analytical study of 58 taxi drivers in the city of Sfax, Tunisia. We used an anonymous questionnaire that included a socio-demographic fact sheet, and a driver behavior rating scale: Driver Behavior Survey (DBS) with 21 items.ResultsThe mean age of the drivers was 40.8 ± 10.2 years. The sex ratio was 0.98. 75.9% were married. 6.9% lived alone. 53.4% were smokers and 25.9% drank alcohol. Coffee and tea consumption were 59% and 33% respectively. 67% had a pathological personal history, including osteoarticular pathologies. 17.2% had a history of serious accidents. The behavior related to anxiety among taxi drivers was 74.66 ± 13.35. The hostile behavior was 18.88 ± 8, the exaggerated safety behavior was 38.31 ± 7.3 and the deficit performance related to anxiety was 17.47 ± 7.1. The problematic behavior in our population was significantly associated with lifestyle alone, coffee consumption and with serious accidents.ConclusionsThe results of our study identified some risk factors that could lead to poorly adaptive driving behaviors among Taxi drivers. These elements reinforce us in the idea that this population requires special care with a meeting with the doctor.

APA, Harvard, Vancouver, ISO, and other styles

8

Tu, Huizhao, Zhenfei Li, Hao Li, Ke Zhang, and Lijun Sun. "Driving Simulator Fidelity and Emergency Driving Behavior." Transportation Research Record: Journal of the Transportation Research Board 2518, no. 1 (January 2015): 113–21. http://dx.doi.org/10.3141/2518-15.

Full text

APA, Harvard, Vancouver, ISO, and other styles

9

Yang, Xiao Yu, Dan Li, and Peng Jun Zheng. "Effects of Eco-Driving on Driving Performance." Applied Mechanics and Materials 178-181 (May 2012): 2859–62. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.2859.

Full text

Abstract:

This paper investigates the effects of two driving behaviors on driving performance, the driving with eco-driving support and the general driving. Through observing and analyzing these driving behaviors in a variety of situations, driving performance under conditions of with and without eco-driving was evaluated. Based on the measurements on fuel consumption, speed control and gear use, it was found that eco-driving device can guide drivers to take proper driving behavior, such as in which way to drive and how to drive in order to achieve energy saving. The paper revealed the effects of eco-driving and how to drive efficiently.

APA, Harvard, Vancouver, ISO, and other styles

10

Nachmann, Karl, Benjamin Pillot, Petrina Moore, and Eva Wiese. "Driving with Robots: Mind perception and propensity for aggressive driving." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1965–70. http://dx.doi.org/10.1177/1071181320641473.

Full text

Abstract:

Mind perception, or the tendency to ascribe agency (i.e., the ability to plan and act) and experience (i.e., the ability to sense and feel) to others, is an important design consideration for human-robot inter-action since an agent’s mind status affects how we interact with it and how we interpret its behavior. The current study examines whether observable behaviors of robot-piloted autonomous vehicles are interpreted differently, lead to different emotional reactions and trigger different behaviors of the ob-server as a function of the robot driver’s perceived mind status. We expect that aggressive behavior of robot drivers perceived to be high in agency would be interpreted as more intentional, and as such would lead to stronger negative reactions and retaliatory behaviors. Consistent with our expectations, the robot driver high in agency was perceived as more intentional and elicited more irritation in partici-pants.

APA, Harvard, Vancouver, ISO, and other styles

11

Dai, Songyin, Yuan Zhong, Cheng Xu, Hongzhe Liu, Jiazheng Yuan, and Pengfei Wang. "An Intelligent Security Classification Model of Driver’s Driving Behavior Based on V2X in IoT Networks." Security and Communication Networks 2022 (May 11, 2022): 1–12. http://dx.doi.org/10.1155/2022/6793365.

Full text

Abstract:

Traffic accidents occur frequently in Internet of Things (IoT) safety system. Traffic accidents are largely caused by drivers’ unsafe driving behaviors in the process of driving. Aiming at the problem of low safety of real-time warning in driving, this paper proposes a model to detect driver behavior. Firstly, according to the driver target detection for positioning, combined with the Pose Estimation to identify the driver in the process of driving a variety of driving behaviors, at the same time, a rating model is built to score drivers’ driving behaviors. Then, by integrating the driver behavior model and evaluation rules, the system can give timely and active warning when the driver makes unsafe behavior in the process of driving. Finally, in the V2X scenario, feedback and presentation are given to users in the form of points. The experimental results show that, in the scenario of Internet of vehicles, the driving behavior rating model can well analyze and evaluate drivers’ driving behaviors, so that drivers can more accurately understand their abnormal driving behaviors and driving scores, which plays a significant role in IoT safety management.

APA, Harvard, Vancouver, ISO, and other styles

12

Chen, Chen, Xiaohua Zhao, Ying Yao, Yunlong Zhang, Jian Rong, and Xiaoming Liu. "Driver’s Eco-Driving Behavior Evaluation Modeling Based on Driving Events." Journal of Advanced Transportation 2018 (2018): 1–12. http://dx.doi.org/10.1155/2018/9530470.

Full text

Abstract:

Eco-driving is an effective means to reduce vehicle fuel consumption. Although many researches and devices have been developed to introduce eco-driving, quantitative effects of driver behaviors on fuel consumption are still unclear, as well as quantitative eco-driving advices. To solve this problem and promote the application of eco-driving in China, a driving-events-based eco-driving behavior evaluation model was proposed in this paper. First, based on taxicab operating data, the relationship between three vehicle operating parameters (speed, acceleration, and driving mode duration) and fuel consumption was analyzed. Then, nine fuel-consumption-involved driving events (including Accelerating Sharply, Decelerating Sharply, and Long-Time Accelerating) were proposed and defined. Using the frequency of each driving event in a certain distance as independent variable and vehicle fuel consumption as dependent variable, principal component analysis (PCA) and multiple linear regression were applied to establish driver’s eco-driving behavior evaluation model. The model was proved to be highly accurate (96.72%). At last, based on the evaluation model, corresponding quantitative eco-driving advices were provided to help driver to improve their driving skills.

APA, Harvard, Vancouver, ISO, and other styles

13

Hogema, Jeroen H. "Modeling Motorway Driving Behavior." Transportation Research Record: Journal of the Transportation Research Board 1689, no. 1 (January 1999): 25–32. http://dx.doi.org/10.3141/1689-04.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Toledo, Tomer, Haris N. Koutsopoulos, and Moshe Ben-Akiva. "Integrated driving behavior modeling." Transportation Research Part C: Emerging Technologies 15, no. 2 (April 2007): 96–112. http://dx.doi.org/10.1016/j.trc.2007.02.002.

Full text

APA, Harvard, Vancouver, ISO, and other styles

15

Liu, Jing, Cheng Wang, Zhipeng Liu, Zhongxiang Feng, and N. N. Sze. "Drivers’ Risk Perception and Risky Driving Behavior under Low Illumination Conditions: Modified Driver Behavior Questionnaire (DBQ) and Driver Skill Inventory (DSI)." Journal of Advanced Transportation 2021 (November 19, 2021): 1–13. http://dx.doi.org/10.1155/2021/5568240.

Full text

Abstract:

Most road crashes are caused by human factors. Risky behaviors and lack of driving skills are two human factors that contribute to crashes. Considering the existing evidence, risky driving behaviors and driving skills have been regarded as potential decisive factors explaining and preventing crashes. Nighttime accidents are relatively frequent and serious compared with daytime accidents. Therefore, it is important to focus on driving behaviors and skills to reduce traffic accidents and enhance safe driving in low illumination conditions. In this paper, we examined the relation between drivers’ risk perception and propensity for risky driving behavior and conducted a comparative analysis of the associations between risk perception, propensity for risky driving behavior, and other factors in the presence and absence of streetlights. Participants in Hefei city, China, were asked to complete a demographic questionnaire, the Driver Behavior Questionnaire (DBQ), and the Driver Skill Inventory (DSI). Multiple linear regression analyses identified some predictors of driver behavior. The results indicated that both the DBQ and DSI are valuable instruments in traffic safety analysis in low illumination conditions and indicated that errors, lapses, and risk perception were significantly different between with and without streetlight conditions. Pearson’s correlation test found that elderly and experienced drivers had a lower likelihood of risky driving behaviors when driving in low illumination conditions, and crash involvement was positively related to risky driving behaviors. Regarding the relationship between study variables and driving skills, the research suggested that age, driving experience, and annual distance were positively associated with driving skills, while myopia, penalty points, and driving self-assessment were negatively related to driving skills. Furthermore, the differences across age groups in errors, lapses, violations, and risk perception in the presence of streetlights were remarkable, and the driving performance of drivers aged 45–55 years was superior to that of drivers in other age groups. Finally, multiple linear regression analyses showed that education background and crash involvement had a positive influence on error, whereas risk perception had a negative effect on errors; crash involvement had a positive influence, while risk perception had a negative effect on lapse; driving experience and crash involvement had a positive influence on violation; and age had a negative influence on it.

APA, Harvard, Vancouver, ISO, and other styles

16

Hu, Jie, Li Xu, Xin He, and Wuqiang Meng. "Abnormal Driving Detection Based on Normalized Driving Behavior." IEEE Transactions on Vehicular Technology 66, no. 8 (August 2017): 6645–52. http://dx.doi.org/10.1109/tvt.2017.2660497.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

David, Ruth, Sandra Rothe, and Dirk Söffker. "State Machine Approach for Lane Changing Driving Behavior Recognition." Automation 1, no. 1 (November 17, 2020): 68–79. http://dx.doi.org/10.3390/automation1010006.

Full text

Abstract:

Research in understanding human behavior is a growing field within the development of Advanced Driving Assistance Systems (ADASs). In this contribution, a state machine approach is proposed to develop a driving behavior recognition model. The state machine approach is a behavior model based on the current state and a given set of inputs. Transitions to different states occur or we remain in the same state producing outputs. The transition between states depends on a set of environmental and driving variables. Based on a heuristic understanding of driving situations modeled as states, as well as one of the related actions modeling the state, using an assumed relation between them as the state machine topology, in this paper, a crisp approach is applied to adapt the model to real behaviors. An important aspect of the contribution is to introduce a trainable state machine-based model to describe drivers’ lane changing behavior. Three driving maneuvers are defined as states. The training of the model is related to the definition/tuning of transition variables (and state definitions). Here, driving data are used as the input for training. The non-dominated sorting genetic algorithm II is used to generate the optimized transition threshold. Comparing the data of actual human driving behaviors collected using driving simulator experiments and the calculated driving behaviors, this approach is able to develop a personalized behavior recognition model. The newly established algorithm presents an easy to apply, reliable, and interpretable AI approach.

APA, Harvard, Vancouver, ISO, and other styles

18

Ma, Changxi, Wei Hao, Wang Xiang, and Wei Yan. "The Impact of Aggressive Driving Behavior on Driver-Injury Severity at Highway-Rail Grade Crossings Accidents." Journal of Advanced Transportation 2018 (October 22, 2018): 1–10. http://dx.doi.org/10.1155/2018/9841498.

Full text

Abstract:

The effect of aggressive driving behavior on driver’s injury severity is analyzed by considering a comprehensive set of variables at highway-rail grade crossings in the US. In doing so, we are able to use a mixed logit modelling approach; the study explores the determinants of driver-injury severity with and without aggressive driving behaviors at highway-rail grade crossings. Significant differences exist between drivers’ injury severity with and without aggressive driving behaviors at highway-rail grade crossings. The level of injury for younger male drivers increases a lot if they are with aggressive driving behavior. In addition, driving during peak-hour is found to be a statistically significant predictor of high level injury severity with aggressive driving behavior. Moreover, environmental factors are also found to be statistically significant. The increased level of injury severity accidents happened for drivers with aggressive driving behavior in the morning peak (6-9 am), and the probability of fatality increases in both snow and fog condition. Driving in open space area is also found to be a significant factor of high level injury severity with aggressive driving behaviors. Bad weather conditions are found to increase the probability of drivers’ high level injury severity for drivers with aggressive driving behaviors.

APA, Harvard, Vancouver, ISO, and other styles

19

Karl, I., G. Berg, F. Ruger, and B. Farber. "Driving Behavior and Simulator Sickness While Driving the Vehicle in the Loop: Validation of Longitudinal Driving Behavior." IEEE Intelligent Transportation Systems Magazine 5, no. 1 (2013): 42–57. http://dx.doi.org/10.1109/mits.2012.2217995.

Full text

APA, Harvard, Vancouver, ISO, and other styles

20

Wang, Pengwei, Song Gao, Liang Li, Shuo Cheng, and Hailan Zhao. "Research on driving behavior decision making system of autonomous driving vehicle based on benefit evaluation model." Archives of Transport 53, no. 1 (April 30, 2020): 21–36. http://dx.doi.org/10.5604/01.3001.0014.1740.

Full text

Abstract:

Autonomous driving vehicle could increase driving efficiency, reduce traffic congestion and improve driving safety, it is considered as the solution of current traffic problems. Decision making systems for autonomous driving vehicles have significant effects on driving performance. The performance of decision making system is affected by its framework and decision making model. In real traffic scenarios, the driving condition of autonomous driving vehicle faced is random and time-varying, the performance of current decision making system is unable to meet the full scene autonomous driving requirements. For autonomous driving vehicle, the division between different driving behaviors needs clear boundary conditions. Typically, in lane change scenario, multiple reasonable driving behavior choices cause conflict of driving state. The fundamental cause of conflict lies in overlapping boundary conditions. To design a decision making system for autonomous driving vehicles, firstly, based on the decomposition of human driver operation process, five basic driving behavior modes are constructed, a driving behavior decision making framework for autonomous driving vehicle based on finite state machine is proposed. Then, to achieve lane change decision making for autonomous driving vehicle, lane change behavior characteristics of human driver lane change maneuver are analyzed and extracted. Based on the analysis, multiple attributes such as driving efficiency and safety are considered, all attributes benefits are quantified and the driving behavior benefit evaluation model is established. By evaluating the benefits of all alternative driving behaviors, the optimal driving behavior for current driving scenario is output. Finally, to verify the performances of the proposed decision making model, a series of real vehicle tests are implemented in different scenarios, the real time performance, effectiveness, and feasibility performance of the proposed method is accessed. The results show that the proposed driving behavior decision making model has good feasibility, real-time performance and multi-choice filtering performance in dynamic traffic scenarios.

APA, Harvard, Vancouver, ISO, and other styles

21

Bao, Qiong, Hanrun Tang, and Yongjun Shen. "Driving Behavior Based Relative Risk Evaluation Using a Nonparametric Optimization Method." International Journal of Environmental Research and Public Health 18, no. 23 (November 26, 2021): 12452. http://dx.doi.org/10.3390/ijerph182312452.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

22

Yang, Longhai, Xiqiao Zhang, Xiaoyan Zhu, Yule Luo, and Yi Luo. "Research on Risky Driving Behavior of Novice Drivers." Sustainability 11, no. 20 (October 9, 2019): 5556. http://dx.doi.org/10.3390/su11205556.

Full text

Abstract:

Novice drivers have become the main group responsible for traffic accidents because of their lack of experience and relatively weak driving skills. Therefore, it is of great value and significance to study the related problems of the risky driving behavior of novice drivers. In this paper, we analyzed and quantified key factors leading to risky driving behavior of novice drivers on the basis of the planned behavior theory and the protection motivation theory. We integrated the theory of planned behavior (TPB) and the theory of planned behavior (PMT) to extensively discuss the formation mechanism of the dangerous driving behavior of novice drivers. The theoretical analysis showed that novice drivers engage in three main risky behaviors: easily changing their attitudes, overestimating their driving skills, and underestimating illegal driving. On the basis of the aforementioned results, we then proposed some specific suggestions such as traffic safety education and training, social supervision, and law construction for novice drivers to reduce their risky behavior.

APA, Harvard, Vancouver, ISO, and other styles

23

Hao, Ruru, Hangzheng Yang, and Zhou Zhou. "Driving Behavior Evaluation Model Base on Big Data From Internet of Vehicles." International Journal of Ambient Computing and Intelligence 10, no. 4 (October 2019): 78–95. http://dx.doi.org/10.4018/ijaci.2019100105.

Full text

Abstract:

This article attempts to evaluate whether a driving behavior is fuel-efficient. To solve this problem, a driving behavior evaluation model was proposed in this article. First, the operating data and fuel consumption data of five trucks were obtained from the vehicle networking system. Four characteristic parameters, which are closely related to fuel consumption, were extracted from 19 sets of vehicle operating data. Then, K-means clustering combined with DBSCAN was adopted to cluster the four characteristic parameters into different driving behaviors. Three types of driving behavior were labeled respectively as low, medium and high fuel consumption driving behavior after clustering analysis. The clustering accuracy rate reached 79.7%. Finally, a fuel consumption-oriented driving behavior evaluation model was established. The model was trained with the labeled samples. The trained model can evaluate the driving behavior online and gives an evaluation of whether the driving behavior is fuel-efficient. The test results show that the prediction accuracy rate of the proposed model can reach to 77.13%.

APA, Harvard, Vancouver, ISO, and other styles

24

Yang, Liu, Zhengbing He, Wei Guan, and Shixiong Jiang. "Exploring the Relationship between Electroencephalography (EEG) and Ordinary Driving Behavior: A Simulated Driving Study." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 37 (July 2, 2018): 172–80. http://dx.doi.org/10.1177/0361198118783165.

Full text

Abstract:

Driving behavior studies based on electroencephalography (EEG) have mostly investigated the relationship between various risky driving behaviors and brain activity, while only a few studies have discussed the relationship between ordinary driving behavior (drivers’ behavior in normal situations) and brain activity. To bridge the gap, we conducted a driving simulator experiment to collect data on ordinary driving behavior, including acceleration, space headway, speed, time headway, lane deviation, and amplitude of steering wheel movements. At the same time, the amplitude, log-transformed power (LTP), and power spectral density of EEG were extracted as EEG features. The quantitative relationships between ordinary driving behavior features and EEG features were investigated, where power spectrum analysis was performed to process EEG signals and Pearson correlation analysis was utilized for statistical analysis. The results indicated that ordinary driving behavior relates to all four brain regions, especially the temporal, occipital, and frontal regions. β-LTP was found to be most relevant to ordinary driving behavior. Furthermore, acceleration, speed, and space headway may have potential correlation with EEG features (e.g., β-LTP). These findings improve our understanding of the correlation between brain activity and driving behavior, and show potential for application in transportation safety, such as advanced driver assistance systems design.

APA, Harvard, Vancouver, ISO, and other styles

25

Rengifo, Carolina, Jean-Rémy Chardonnet, Hakim Mohellebi, Damien Paillot, and Andras Kemeny. "Driving simulator study of the relationship between motion strategy preference and self-reported driving behavior." SIMULATION 97, no. 9 (March 25, 2021): 619–33. http://dx.doi.org/10.1177/0037549721999716.

Full text

Abstract:

Faithful motion restitution in driving simulators normally focuses on track monitoring and maximizing the platform workspace by leaving aside the principal component—the driver. Therefore, in this work we investigated the role of the motion perception model on motion cueing algorithms from a user’s viewpoint. We focused on the driving behavior influence regarding motion perception in a driving simulator. Participants drove a driving simulator with two different configurations: (a) using the platform dynamic model and (b) using a supplementary motion perception model. Both strategies were compared and the participants’ data were classified according to the strategy they preferred. To this end, we developed a driving behavior questionnaire aiming at evaluating the self-reported driving behavior influence on participants’ motion cueing preferences. The results showed significant differences between the participants who chose different strategies and the scored driving behavior in the hostile and violations factors. In order to support these findings, we compared participants’ behaviors and actual motion driving simulator indicators such as speed, jerk, and lateral position. The analysis revealed that motion preferences arise from different reasons linked to the realism or smoothness in motion. Also, strong positive correlations were found between hostile and violation behaviors of the group who preferred the strategy with the supplementary motion perception model, and objective measures such as jerk and speed on different road segments. This indicates that motion perception in driving simulators may depend not only on the type of motion cueing strategy, but may also be influenced by users’ self-reported driving behaviors.

APA, Harvard, Vancouver, ISO, and other styles

26

Zhao, Xiaohua, Xingjian Zhang, Jian Rong, and Jianming Ma. "Identifying Method of Drunk Driving Based on Driving Behavior." International Journal of Computational Intelligence Systems 4, no. 3 (2011): 361. http://dx.doi.org/10.2991/ijcis.2011.4.3.10.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Richards, Tracy L., Jerry L. Deffenbacher, Lee A. Rosén, Russell A. Barkley, and Trisha Rodricks. "Driving Anger and Driving Behavior in Adults With ADHD." Journal of Attention Disorders 10, no. 1 (August 2006): 54–64. http://dx.doi.org/10.1177/1087054705284244.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Zhao, Xiaohua, Xingjian Zhang, Jian Rong, and Jianming Ma. "Identifying Method of Drunk Driving Based on Driving Behavior." International Journal of Computational Intelligence Systems 4, no. 3 (May 2011): 361–69. http://dx.doi.org/10.1080/18756891.2011.9727794.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Lee, Soonyeol, and Soonchul Lee. "Mediating effect of coping behavior on the relationship between driving stress and traffic accident risk." Korean Journal of Industrial and Organizational Psychology 24, no. 4 (November 30, 2011): 673–93. http://dx.doi.org/10.24230/kjiop.v24i4.673-693.

Full text

Abstract:

The present study was conducted to determine the effects of driving stress on traffic accident risk. Specifically, this study verified the effects of driving stress on drivers' coping behaviors and the aptitude of mediating models through which coping behavior types affect traffic accident risk. As a result, driving stress directly increased traffic accident risk and indirectly affected them through(good and bad) coping behavior types. This indicates that driving stress directly and indirectly affect traffic accident risk by the medium of(good and bad) coping behavior types in multilateral ways.(Commercial and leisure-purposed) driving purposes showed significant differences in the relations between driving stress and traffic accident risk. Specifically, commercial drivers were affected by driving stress, compared to leisure-purposed drivers. As they were unable to defer or abandon driving even under driving stress, commercial drivers responded to the stress more sensitively and increased traffic accident risk by selecting inappropriate(bad) coping behaviors. The results show that the mere concentration on driving stress management cannot sufficiently lower the traffic accident risks caused by driving stress. This is because driving stress have indirect influences on traffic accident risk. Hence, it will be necessary to seek how to reduce driving stress and control coping behavior types in order to lower the traffic accidents risk by the stress.

APA, Harvard, Vancouver, ISO, and other styles

30

Zakrajsek, Jennifer, Lisa Molnar, David Eby, Lidia Kostyniuk, Nicole Zanier, David J. LeBlanc, and Tina B. Sayer. "GUIDELINES FOR DEVELOPING EVIDENCE-BASED RISKY DRIVING COUNTERMEASURES THAT INCLUDE OLDER DRIVERS." Innovation in Aging 6, Supplement_1 (November 1, 2022): 164. http://dx.doi.org/10.1093/geroni/igac059.655.

Full text

Abstract:

Abstract Driver behavior will continue to play a critical role in driving safety for the foreseeable future. Utilizing behavior change theory appropriately presents opportunities to improve the effectiveness of risky driving countermeasures that have been under-utilized to date. Older drivers should not be excluded from consideration of risky behaviors. Forty-six drivers (33% age 65+) completed surveys, then drove for three weeks with data collection during all trips. The Theory of Planned Behavior guided a two-phased regression analysis approach: 1) behavioral intentions were predicted using attitudes about behaviors and demographics; 2) observed risky behavior was predicted using behavioral intentions, theory constructs, personality/psychosocial characteristics, demographics, and driving exposure. Results were synthesized and the emergent themes were used to formulate guidelines for developing theory-based education and communication risky driving countermeasures. Guidelines focused on four risky driving behaviors observed in a large proportion of participants (72% - 96%): holding/using a cellphone; eating/drinking; speeding; and tailgating. Twenty-six guidelines were developed across four categories: 1) relationships among risky behaviors; 2) characteristics or underlying dimensions of risky driving (e.g., time, location, emotion); 3) behavior change theory constructs; 4) audience and message factors. While older drivers self-reported low frequencies of risky behaviors, low intentions for future risky behaviors, and less favorable attitudes toward risky behaviors than younger drivers they were regularly observed engaging in risky behaviors: distracted behaviors in 79% of trips and 2.1 speeding events per trip. Risky driving countermeasures are appropriate for older drivers and the emergent guidelines will be presented with recommended variations for older drivers.

APA, Harvard, Vancouver, ISO, and other styles

31

Yao, Ying, Xiaohua Zhao, Hongji Du, Yunlong Zhang, and Jian Rong. "Classification of Distracted Driving Based on Visual Features and Behavior Data using a Random Forest Method." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (September 20, 2018): 210–21. http://dx.doi.org/10.1177/0361198118796963.

Full text

Abstract:

This research is to explore the relationship between a driver’s visual features and driving behaviors of distracted driving, and a random forest (RF) method is developed to classify driving behaviors and improve the accuracy of detecting distracted driving. Drivers were required to complete four distraction tasks while they followed simulated vehicles in the experiment. In data analysis, the features of distracted driving behaviors are first described, and the visual data are classified into three distraction levels based on the AttenD algorithm. Based on the collected data, this paper shows the relationship between visual features and driving behavior. Significant differences are discovered between different distraction tasks and distraction levels. Additionally, driving behavior data is used to build an RF model to classify distracted driving into three levels. Results demonstrate that this model is feasible to capture the classification of distraction and its accuracy for each distraction task is over 90%. Areas under receiver operating characteristic curve calculated through error-correcting output codes are mainly around 0.9, indicating good reliability. With this classification method, distraction levels could be classified with vehicle operation characteristics. The model established by this method could detect distractions in actual driving through the detection of driving behavior without the need of eye tracking systems.

APA, Harvard, Vancouver, ISO, and other styles

32

Fraade-Blanar, Laura, and James P. Smith. "Cognitive Change and Driving Behavior among Older Drivers." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 33 (October 18, 2018): 89–100. http://dx.doi.org/10.1177/0361198118801356.

Full text

Abstract:

Per vehicle miles traveled, older adults have a high fatal crash rate. One factor is dementia. This study aims to assess how differences in cognition affect driving behavior among older drivers. We analyzed data from the Health and Retirement Study. Our study used cognition, demographics, and driving behavior from 2006 to 2014 for respondents aged 65 and above. Three levels of driving behavior were measured: whether the individual could drive, whether they had driven in the past month, and whether they drove long distances. Cognitive function was measured through the Telephone Interview for Cognitive Status. Additionally, individuals were coded as having no diagnosis of dementia, a diagnosis within 2 years, or a diagnosis more than 2 years previously. We estimated the likelihood of each driving behavior in association with cognition using a modified Poisson regression model for binary outcomes. Among respondents ( N = 16,061), 79% could drive. Of these, 93% had driven in the past month, and of these, 64% drove long distances. Compared with no impairment, mild impairment was associated with a significant 12% decrease in probability of being able to drive, an 8% decrease in driving within the past month, and a 24% decrease in driving long distances. The decrease was larger among those with severe impairment. Results were in a similar direction and strength comparing individuals without dementia with individuals 0 to 2 years after diagnosis, and to more than 2 years after diagnosis. A strong positive association exists between lower cognition and lower driving exposure.

APA, Harvard, Vancouver, ISO, and other styles

33

Zhang, Jun, ZhongCheng Wu, Fang Li, Chengjun Xie, Tingting Ren, Jie Chen, and Liu Liu. "A Deep Learning Framework for Driving Behavior Identification on In-Vehicle CAN-BUS Sensor Data." Sensors 19, no. 6 (March 18, 2019): 1356. http://dx.doi.org/10.3390/s19061356.

Full text

Abstract:

Human driving behaviors are personalized and unique, and the automobile fingerprint of drivers could be helpful to automatically identify different driving behaviors and further be applied in fields such as auto-theft systems. Current research suggests that in-vehicle Controller Area Network-BUS (CAN-BUS) data can be used as an effective representation of driving behavior for recognizing different drivers. However, it is difficult to capture complex temporal features of driving behaviors in traditional methods. This paper proposes an end-to-end deep learning framework by fusing convolutional neural networks and recurrent neural networks with an attention mechanism, which is more suitable for time series CAN-BUS sensor data. The proposed method can automatically learn features of driving behaviors and model temporal features without professional knowledge in features modeling. Moreover, the method can capture salient structure features of high-dimensional sensor data and explore the correlations among multi-sensor data for rich feature representations of driving behaviors. Experimental results show that the proposed framework performs well in the real world driving behavior identification task, outperforming the state-of-the-art methods.

APA, Harvard, Vancouver, ISO, and other styles

34

Zhao, Yucheng, Jun Liang, Long Chen, Yafei Wang, and Jinfeng Gong. "Evaluation and prediction of free driving behavior type based on fuzzy comprehensive support vector machine." Journal of Intelligent & Fuzzy Systems 42, no. 4 (March 4, 2022): 2863–79. http://dx.doi.org/10.3233/jifs-201680.

Full text

Abstract:

Driving behavior type is a hotspot in transportation field, but there have been few studies on free driving behavior type. The factor of current driving behavior evaluation model is single, and its environmental adaptability is insufficient, and driving behavior type is difficult to predict accurately. In addition, free driving behavior as one kind of the important driving operation behaviors lacks quantitative assessment methods and models. In view of these deficiencies, evaluation and prediction of free driving behavior based on Fuzzy Comprehensive Support Vector Machine (FC-SVM) is proposed. Firstly, a variety of individual decision-making behavior data obfuscating with environmental complexity are collected. These obtained parameters were used as FC multi-factor evaluation parameters to quantitatively evaluate free driving behavior from multiple aspects, and to qualitatively derive the driver’s driving behavior type. Further, the SVM used the RBF kernel function to obtain the optimal parameters and train the SVM network, and it used the obtained SVM model for the prediction of driving behavior type in short time. The results of simulations using different methods show that the SD value of FC-SVM evaluation results is the lowest, only 1.273. Compared with other common methods, its MacroP reaches 89.2%. It is interesting to find that aggressive driving can be more distinct from other behavior types. Moreover, the mixed traffic flow composed of aggressive driver has a higher traffic efficiency in basic sections. This work is of great value for improving driving behavior, reducing road congestion and improving road traffic efficiency in the mixed intelligent traffic.

APA, Harvard, Vancouver, ISO, and other styles

35

Prato, Carlo Giacomo, Tsippy Lotan, and Tomer Toledo. "Intrafamilial Transmission of Driving Behavior." Transportation Research Record: Journal of the Transportation Research Board 2138, no. 1 (January 2009): 54–65. http://dx.doi.org/10.3141/2138-09.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Brown, Richard C., Joe M. Sanders, and S. Kenneth Schonberg. "Driving Safety and Adolescent Behavior." Pediatrics 77, no. 4 (April 1, 1986): 603–7. http://dx.doi.org/10.1542/peds.77.4.603.

Full text

Abstract:

Accidents, and mainly automotive accidents, are currently the leading cause of mortality and morbidity among young people. Understanding and addressing the issue of automotive accident prevention requires an awareness of the multiple psychodynamic, familial, and societal influences that affect the development and behavior of adolescents. Risk-taking behavior is the product of complex personal and environmental factors. As pediatricians, we have the obligation and the opportunity to improve the safety of our youth who drive and ride. This opportunity is available to us not only in our roles as counselors to youth and families, but also as we serve as role models, educators, and agents for change within our communities.

APA, Harvard, Vancouver, ISO, and other styles

37

Hoogendoorn, Raymond, Serge Hoogendoorn, and Karel Brookhuis. "Driving Behavior in Emergency Situations." Transportation Research Record: Journal of the Transportation Research Board 2316, no. 1 (January 2012): 11–19. http://dx.doi.org/10.3141/2316-02.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Sansone, Randy A., Justin S. Leung, and Michael W. Wiederman. "Driving Citations and Aggressive Behavior." Traffic Injury Prevention 13, no. 3 (May 2012): 337–40. http://dx.doi.org/10.1080/15389588.2012.654412.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Mazureck, Undine, and Jan van Hattem. "Rewards for Safe Driving Behavior." Transportation Research Record: Journal of the Transportation Research Board 1980, no. 1 (January 2006): 31–38. http://dx.doi.org/10.1177/0361198106198000106.

Full text

APA, Harvard, Vancouver, ISO, and other styles

40

Lund, Adrian K., and Brian O'Neill. "Perceived risks and driving behavior." Accident Analysis & Prevention 18, no. 5 (October 1986): 367–70. http://dx.doi.org/10.1016/0001-4575(86)90010-2.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

James, Leon J. "Moral Reasoning in Driving Behavior." Psychology and Cognitive Sciences - Open Journal 3, no. 3 (August 4, 2017): e6-e8. http://dx.doi.org/10.17140/pcsoj-3-e006.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Beeli, Gian, Susan Koeneke, Katja Gasser, and Lutz Jancke. "Brain stimulation modulates driving behavior." Behavioral and Brain Functions 4, no. 1 (2008): 34. http://dx.doi.org/10.1186/1744-9081-4-34.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Wang, Lili, Wenjie Yao, Chen Chen, and Hailu Yang. "Driving Behavior Recognition Algorithm Combining Attention Mechanism and Lightweight Network." Entropy 24, no. 7 (July 16, 2022): 984. http://dx.doi.org/10.3390/e24070984.

Full text

Abstract:

In actual driving scenes, recognizing and preventing drivers’ non-standard driving behavior is helpful in reducing traffic accidents. To resolve the problems of various driving behaviors, a large range of action, and the low recognition accuracy of traditional detection methods, in this paper, a driving behavior recognition algorithm was proposed that combines an attention mechanism and lightweight network. The attention module was integrated into the YOLOV4 model after improving the feature extraction network, and the structure of the attention module was also improved. According to the 20,000 images of the Kaggle dataset, 10 typical driving behaviors were analyzed, processed, and recognized. The comparison and ablation experimental results showed that the fusion of an improved attention mechanism and lightweight network model had good performance in accuracy, model size, and FLOPs.

APA, Harvard, Vancouver, ISO, and other styles

44

Nochajski, Thomas H., Amy R. Manning, Robert Voas, Eileen P. Taylor, Michael Scherer, and Eduardo Romano. "The impact of interlock installation on driving behavior and drinking behavior related to driving." Traffic Injury Prevention 21, no. 7 (August 12, 2020): 419–24. http://dx.doi.org/10.1080/15389588.2020.1802020.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Zhao, Dengfeng, Yudong Zhong, Zhijun Fu, Junjian Hou, and Mingyuan Zhao. "A Review for the Driving Behavior Recognition Methods Based on Vehicle Multisensor Information." Journal of Advanced Transportation 2022 (October 7, 2022): 1–16. http://dx.doi.org/10.1155/2022/7287511.

Full text

Abstract:

The frequent traffic accidents lead to a large number of casualties and large related financial losses every year; this serious state is owed to several factors; among those, driving behavior is one of the most imperative subjects to discuss. Driving behaviors mainly include behavior characteristics such as car-following, lane change, and risky driving behavior such as distraction, fatigue, or aggressive driving, which are of great help to various tasks in traffic engineering. An accurate and reliable method of driving behavior recognition is of great significance and guidance for vehicle driving safety. In this paper, the vehicle multisensor information, vehicle CAN bus data acquisition system, and typical feature extraction methods are summarized at first. And then, several driving behavior recognition models based on machine learning and deep learning are reviewed. Through a detailed analysis of the features of random forests, support vector machines, convolutional neural networks, and recurrent neural networks used to build driving behavior recognition models, the following findings are obtained: the driving behavior model constructed by traditional machine learning model is relatively mature but it is greatly affected by feature extraction, data scale, and model structure, which affects the accuracy of the final driving behavior recognition. Deep learning model based on a neural network has achieved high accuracy in identifying driving behavior, and it may gradually become the mainstream of constructing the driving behavior model with the development of big data, artificial intelligence technology, and computer hardware. Finally, this paper points out some content that needs to be further explored, to provide reference and inspiration for scholars in this field to continue to study the driving behavior recognition model in depth.

APA, Harvard, Vancouver, ISO, and other styles

46

Li, Hao, Junyan Han, Shangqing Li, Hanqing Wang, Hui Xiang, and Xiaoyuan Wang. "Abnormal Driving Behavior Recognition Method Based on Smart Phone Sensor and CNN-LSTM." International Journal of Science and Engineering Applications 11, no. 1 (January 2022): 1–8. http://dx.doi.org/10.7753/ijsea1101.1001.

Full text

Abstract:

Accurate identification of abnormal driving behavior is very important to improve driver safety. Aiming at the problem that threshold or traditional machine learning methods are mostly used in existing studies, it is difficult to accurately identify abnormal driving behavior of vehicles, a method of abnormal driving behavior recognition based on smartphone sensor data and convolutional neural network (CNN) combined with long and short-term memory (LSTM) was proposed. Smartphone sensors are used to collect vehicle driving data, and data sets of various driving behaviors are constructed by preprocessing the data. A recognition model based on a convolutional neural network combined with a long short-term memory network was constructed to extract depth features from data sets and recognize abnormal driving behaviors. The test results show that the accuracy of the model based on CNN-LSTM can reach 95.22%, and the performance indexes can reach more than 94%. Compared with the recognition model constructed only by CNN or LSTM, this model has higher recognition accuracy.

APA, Harvard, Vancouver, ISO, and other styles

47

Nicolleau, Martin, Nicolas Mascret, Claire Naude, Isabelle Ragot-Court, and Thierry Serre. "The influence of achievement goals on objective driving behavior." PLOS ONE 17, no. 10 (October 27, 2022): e0276587. http://dx.doi.org/10.1371/journal.pone.0276587.

Full text

Abstract:

Investigating psychological characteristics through self-reported measures (e.g., anger, sensation seeking) and dynamic behaviors through objective measures (e.g., speed, 2D acceleration, GPS position etc.) may allow us to better understand the behavior of at-risk drivers. To assess drivers’ motivation, the theoretical framework of achievement goals has been studied recently. These achievement goals can influence the decision-making and behaviors of individuals engaged in driving. The four achievement goals in driving are: seeking to improve or to drive as well as possible (mastery-approach), to outperform other drivers (performance-approach), to avoid driving badly (mastery-avoidance), and to avoid being the worst driver (performance-avoidance). Naturalistic Driving Studies (NDS) provide access to the objective measurements of data not accessible through self-reported measurements (i.e., speed, accelerations, GPS position). Three dynamic criteria have been developed to characterize the behavior of motorists objectively: driving events, time spent above acceleration thresholds (longitudinal and transversal), and the extent of dynamic demands. All these criteria have been measured in different road contexts (e.g., plain). The aim of this study was to examine the predictive role of the four achievement goals on these objective driving behaviors. 266 drivers (96 women, 117 men) took part in the study, and 4 242 482 km was recorded during 8 months. Simultaneously, they completed the Achievement Goals in Driving Questionnaire. The main results highlighted that mastery-approach goals negatively predicted hard braking and the extent of dynamic demands on plain and hilly roads. Mastery-approach goals seem to be the most protective goals in driving. Future research on the promotion of mastery-approach goals in driving may be able to modify the behavior of at-risk drivers.

APA, Harvard, Vancouver, ISO, and other styles

48

Bianchi, Alessandra, and Heikki Summala. "The “genetics” of driving behavior: parents’ driving style predicts their children’s driving style." Accident Analysis & Prevention 36, no. 4 (July 2004): 655–59. http://dx.doi.org/10.1016/s0001-4575(03)00087-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Yue, Lishengsa, Mohamed Abdel-Aty, and Zijin Wang. "Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle." Journal of Intelligent and Connected Vehicles 5, no. 1 (December 24, 2021): 36–45. http://dx.doi.org/10.1108/jicv-08-2021-0013.

Full text

Abstract:

Purpose This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline. Design/methodology/approach Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms. Findings Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles. Originality/value To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

APA, Harvard, Vancouver, ISO, and other styles

50

Xie, Han, Kehong Li, and Juanxiu Zhu. "Analysis of the Relationship between Vehicle Behaviors of Changing Lane and Volume of Traffic under Different Operating Ratios of Autonomous Vehicles." Journal of Advanced Transportation 2022 (September 26, 2022): 1–23. http://dx.doi.org/10.1155/2022/3142483.

Full text

Abstract:

The lane-changing behavior is one of the important causes in traffic accident in congest traffic, and many behaviors of change lane affect volume of traffic. When autonomous driving vehicles are running on road with human-driven vehicles, the effects of change lane on traffic are different. In all human-driven vehicles traffic, the vehicle behaviors of changing lane are more competent. When autonomous driving vehicles are running in mixed traffic, the behaviors of changing lane decrease and the volume of traffic increases. However, a few studies have involved the relationship between traffic volume and lane-change behavior. In a sense, the study of this relationship is good for understanding the operation mechanism of mixed traffic. In this paper, we proposed the linear regression model to describe the relationship between traffic volume and lane-change behavior. The model can be used to establish the basic graph model. Here, we used empirical, simulation, and data-driven methods to obtain data and established a multiple linear regression model. First, we empirically study the continuous traffic with all human-driven vehicles. Then, the corresponding simulation model is established, and the availability of the simulation model is proved by data comparison with empirical study. Finally, 9 rounds of simulation experiments are carried out with the established simulation model. The number of autonomous driving vehicles in each round of simulation experiment increases by 10%. Then, we analyze the data of the behaviors of changing lane and the volume of traffic from simulation experiments. The following was found: (1) an increase in autonomous driving vehicle leads to an increase in traffic volume and a slight decrease in lane changing behaviors; (2) the influence of different proportions of autonomous vehicles on the traffic volume of lanes at different locations is slightly different; and (3) the relationships among the rate of vehicles entering lane, the rate of vehicles exiting lane together, and the volume of traffic show obvious linear relationships with the increase in autonomous driving vehicles. We used multiple linear regression models to carry out description, and the obtained parameter value intervals are close under different operating ratios of autonomous vehicles. To sum up, on multilane roads, especially 4-lane urban expressways, autonomous driving vehicles join in the traffic, which can effectively increase the volume of traffic of each lane while reducing vehicle behaviors of changing lane. The relationships between vehicle behaviors of changing lane and the traffic volume in mixed traffic show linear relationships with the increase in autonomous driving vehicles. In the future, we will further study whether this relationship model can be used in discrete traffic flow.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Driving Behavior'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles