Chapter
Aug 12, 2020

Exploring Driver’s Deceleration Behavior in Car-Following: A Driving Simulator Study

Publication: CICTP 2020

ABSTRACT

Rear-end collisions are very common, usually occurring when a lead vehicle brakes suddenly and a following driver responds too slowly. To reduce collision rates, collision avoidance systems (CASs) have been adopted. The CAS uses a series of algorithms to trigger an alert and/or brake control to help drivers avoid collisions. However, key variables have been considered fixed values in algorithms. Detecting vehicles’ deceleration might also take seconds, extending the judgement time of CASs. The study explored following drivers’ deceleration behavior patterns. A driving simulator experiment with forty-six participants was conducted in which a leading vehicle decelerated at different rates. Results showed that change of deceleration rate was a key variable representing drivers’ deceleration urgency, correlating with the leading vehicle’s deceleration rate. More than 90% of drivers reached a maximum change of deceleration rate within 0.5 s. The study supports using change of deceleration rate as an indicator in CAS.

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Go to CICTP 2020
CICTP 2020
Pages: 3817 - 3829

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Published online: Aug 12, 2020

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1MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]
Xuedong Yan [email protected]
2MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]
3MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]
Xiaomeng Li [email protected]
4Centre for Accident Research and Road Safety-Queensland (CARRS-Q), Institute of Health and Biomedical Innovation (IHBI), Queensland Univ. of Technology, 130 Victoria Park Rd., Kelvin Grove, QLD 4059, Australia. Email: [email protected]
Jingsi Yang [email protected]
5MOT Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong Univ., Beijing 100044, PR China. Email: [email protected]

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