Driving Anger States Detection Based on Incremental Association Markov Blanket and Least Square Support Vector Machine

Ping Wan, Chaozhong Wu, Yingzi Lin and Xiaofeng Ma

Discrete Dynamics in Nature and Society, 2019, vol. 2019, 1-17

Abstract:

Driving anger, known as “road rage”, has gradually become a serious traffic psychology issue. Although driving anger identification is solved in some studies, there is still a gap in driving anger grading which is helpful to take different intervening measures for different anger intensity, especially in real traffic environment. The main objectives of this study are: (1) explore a novel driving anger induction method based on various elicitation events, e.g., traffic congestion, vehicles weaving/cutting in line, jaywalking and red light waiting in real traffic environment; (2) apply incremental association Markov blanket (IAMB) algorithm to select typical features related to driving anger states; (3) employ least square support vector machine (LSSVM) to identify different driving anger states based on the selected features. Thirty private car drivers were enrolled to perform field experiments on a busy route selected in Wuhan, China, where drivers’ anger could be induced by the elicitation events within limited time. Meanwhile, three types of data sets including driver physiology, driving behaviors and vehicle motions, were collected by multiple sensors. The results indicate that 13 selected features including skin conductance, relative energy spectrum of β band of electroencephalogram, standard deviation (SD) of pedaling speed of gas pedal, SD of steering wheel angle rate, vehicle speed, SD of speed, SD of forward acceleration and SD of lateral acceleration have significant impact on driving anger states. The IAMB-LSSVM model achieves an accuracy with 82.20% which is 2.03%, 3.15%, 4.34%, 7.84% and 8.36% higher than IAMB using C4.5, NBC, SVM, KNN and BPNN, respectively. The results are beneficial to design driving anger detecting or intervening devices in intelligent human-machine systems.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnddns:2745381

DOI: 10.1155/2019/2745381

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