The Emergence Characteristics of Driver’s Intentions Influenced by Different Emotions

Xiaoyuan Wang, Yongqing Guo, Chenglin Bai, Quan Yuan, Shanliang Liu and Xuegang (Jeff) Ban
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Xiaoyuan Wang: College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China
Yongqing Guo: Joint Laboratory for Internet of Vehicles, Ministry of Education-China Mobile Communications Corporation, Tsinghua University, Beijing 100084, China
Chenglin Bai: School of Physics Science and Communication Engineering, Liaocheng University, Liaocheng 252000, China
Quan Yuan: State Key Laboratory of Automotive Safety and Energy, School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China
Shanliang Liu: College of Electromechanical Engineering, Qingdao University of Science & Technology, Qingdao 266000, China
Xuegang (Jeff) Ban: Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA

Sustainability, 2021, vol. 13, issue 23, 1-13

Abstract: Drivers’ behavioral intentions can affect traffic safety, vehicle energy use, and gas emission. Drivers’ emotions play an important role in intention generation and decision making. Determining the emergence characteristics of driver intentions influenced by different emotions is essential for driver intention recognition. This study focuses on developing a driver’s intention emergence model with the involvement of driving emotion on two-lane urban roads. Driver emotions were generated using various ways, including visual stimuli (video and picture), material incentives, and spiritual rewards. Real and virtual driving experiments were conducted to collect the multi-source dynamic data of human–vehicle–environment. The driver intention emergence model was constructed based on an artificial neural network, to identify the influences of drivers’ emotions on intention, as well as the evolution characteristics of drivers’ intentions in different emotions. The results show that the proposed model can make accurate predictions on driver intention emergence. The findings of this study can be used to improve drivers’ behavior, in order to create more efficient and safe driving. It can also provide a theoretical foundation for the development of an active safety system for vehicles and an intelligent driving command system.

Keywords: driver’s emotion; driving intention; emergence characteristics; artificial neural network; active safety system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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