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11. Study on the Stability Control Strategy for Distributed Driving Electric Vehicle

Author

Hai, Deng, Xie, Xianyi, Jin, Lisheng, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wuhong, editor, Baumann, Martin, editor, and Jiang, Xiaobei, editor

Published
2020
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12. A Novel Pedestrian Orientation Estimation Method for Autonomous Driving

Author

Gao, Ming, Jin, LiSheng, Jiang, Yuying, Guo, Baicang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Wang, Wuhong, editor, Baumann, Martin, editor, and Jiang, Xiaobei, editor

Published
2020
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19. Effect of Cognitive Distraction on Physiological Measures and Driving Performance in Traditional and Mixed Traffic Environments

Author

Hua, Qiang, Jin, Lisheng, Jiang, Yuying, Guo, Baicang, and Xie, Xianyi

Subjects
Traffic accidents -- Comparative analysis -- Physiological aspects, Transportation industry
Abstract

Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents., Author(s): Qiang Hua [1]; Lisheng Jin (corresponding author) [1,2]; Yuying Jiang [3]; Baicang Guo [1]; Xianyi Xie [2] 1. Introduction The World Health Organization claims that traffic accidents are the [...]

Published
2021
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30. Analysis on the Influencing Factors of Driving Behaviours Based on Theory of Planned Behaviour

Author

Baicang Guo, Jin Lisheng, Yuying Jiang, and Hua Qiang

Subjects
050210 logistics & transportation, Article Subject, Road traffic safety, 05 social sciences, Perspective (graphical), Applied psychology, Theory of planned behavior, Latent variable, Engineering (General). Civil engineering (General), Structural equation modeling, Multidisciplinary approach, 0502 economics and business, Survey data collection, TA1-2040, Psychology, Path analysis (statistics), 050203 business & management, Civil and Structural Engineering
Abstract

Driving behaviour is a complex and multidisciplinary research domain, and bad driving behaviours that threaten the safety of road users should be refrained. In order to better educate, manage, and restrain driver’s behaviours, from the perspective of human factors and psychology, the present study deconstructed driving behaviours based on theory of planned behaviour (TPB) into five categories: mistakes (Mis), lapses and slips (LaS), violations (Vio), driving experience (Exp), and safety attitude and awareness (SAA). According to today’s practical traffic rules and conditions in China, a driving behaviour questionnaire was built as an analysing tool and the survey data were collected in accordance with the demographic of Chinese drivers. Furthermore, a driving behaviour analysis model contains the aforementioned categories was established by using the structural equation model (SEM). Through the path analysis results among latent variables and manifested variables, it was found that Exp has an impact on Vio and LaS, and better SAA can inhibit Vio and Mis. In conclusion, the prime aim of improving road traffic safety is to reduce Vio by means of educating and improving the drivers’ Exp and SAA. Moreover, drivers’ LaS and Mis are transition processes which should be corrected timely and prevented from continuing to evolve into Vio.

Published
2021

39. Effect of Cognitive Distraction on Physiological Measures and Driving Performance in Traditional and Mixed Traffic Environments

Author

Xianyi Xie, Hua Qiang, Jin Lisheng, Baicang Guo, and Yuying Jiang

Subjects
050210 logistics & transportation, Economics and Econometrics, Data collection, TA1001-1280, Article Subject, Computer science, Strategy and Management, Mechanical Engineering, Cognitive distraction, 05 social sciences, Steering wheel, Fixation (psychology), Standard deviation, Computer Science Applications, Transportation engineering, Distraction, 0502 economics and business, Automotive Engineering, Distracted driving, 0501 psychology and cognitive sciences, Entropy (energy dispersal), Transportation and communications, 050107 human factors, Simulation, HE1-9990
Abstract

Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents.

Published
2021

40. A risky prediction model of driving behaviors: especially for cognitive distracted driving behaviors

Author

Jin Lisheng, Guo Baicang, Shi Jian, and Zhang Shunran

Subjects
Computer science, Process (engineering), Test set, Task analysis, Distracted driving, Cognition, Sample (statistics), Simulation, Driving safety, Data modeling
Abstract

The non-driving related operation behavior in driving process has a significant impact on road traffic status and driving safety, but there is less systematic study on the main characteristics and influence mechanism of such behaviors. Aiming at this problem, four types of typical behaviors of normal and abnormal driving are monitored and recorded by real vehicle test. The cognitive distracted driving behavior is taken as the research object, and the influence mechanism and prediction method of distracted driving are studied by using the driver's physiological state and vehicle running state. This paper focuses on the changes and statistical characteristics of driver's physiological state parameters and vehicle running state parameters during distracted driving, and then explores the influence mechanism of different types of distracted driving tasks with different loads on driver's state. This paper analyzes the influence mechanism from two aspects of human and vehicle. Based on the comparison of behavior criterion and load criterion, the parameter system of cognitive distracted driving behavior considering driving load is obtained after cross analysis. The prediction model is established as the training sample of LSTM model, and the model is tested with the data collected from real vehicle test After 100000 iterations, the training accuracy is 90.2% on the training set and 74% on the test set. The results showed that the cross-comparison method is scientific and reasonable, and the prediction model of distracted driving behavior based on physiological state and vehicle running state has good accuracy.

Published
2020

47. Stacking‐based ensemble learning method for cognitive distraction state recognition for drivers in traditional and connected environments.

Author

Jin, Lisheng, Hua, Qiang, Zhang, Shunran, and Guo, Baicang

Subjects
INTELLIGENT transportation systems, TRAFFIC safety, ANALYSIS of variance, STANDARD deviations, SUPPORT vector machines
Abstract

Compared with the traditional environment, a connected environment in which multiple types of intelligent vehicles share roads will be more complicated. However, do changes in the traffic environment affect the driver's distraction level and distraction recognition? Relatively few studies have addressed these questions. This study constructs traditional and connected environments and conducts driving simulation experiments. Distracted driving data for 60 drivers were collected in each environment, and the one‐way repeated analysis of variance (ANOVA) method was used to select the distraction indices with significant differences as the input indices. An independent t‐test was applied to analyse changes in the distraction indices in the two environments. The results show that the distraction indices, including the standard deviation (SD) of the horizontal fixation angles, blink frequency, mean pupil diameter, mean saccade speed, SD of the steering wheel angle, mean speed and SD of the lane acceleration, differ in the two environments; therefore, the distraction recognition model in the traditional environment is no longer suitable for connected environments. A distraction state recognition model developed with a stacking‐based ensemble learning algorithm composed of a support vector machine (SVM), extreme gradient boosting (XGBoost), long short‐term memory (LSTM), and bi‐directional LSTM with an attention mechanism (AT‐Bi‐LSTM) was proposed for each environment. The recognition accuracy reaches 95.71% in the traditional environment, and the accuracy rate reaches 97.92% in the connected environment. Compared with traditional algorithms, the proposed approach yields greater recognition accuracy and can be applied in intelligent vehicle distraction early warning assistance systems to improve road safety. [ABSTRACT FROM AUTHOR]

Published
2022
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