A Comprehensive Method to Evaluate Ride Comfort of Autonomous Vehicles under Typical Braking Scenarios: Testing, Simulation and Analysis

Binshuang Zheng, Zhengqiang Hong (), Junyao Tang (), Meiling Han, Jiaying Chen and Xiaoming Huang
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Binshuang Zheng: School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Zhengqiang Hong: China State Construction Engineering (Hong Kong) Limited, Hong Kong 999077, China
Junyao Tang: School of Transportation, Southeast University, Nanjing 211189, China
Meiling Han: School of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Jiaying Chen: China Construction America, CCA Headquarters, 525 Washington Blvd., 31st Floor, Jersey City, NJ 07310, USA
Xiaoming Huang: School of Transportation, Southeast University, Nanjing 211189, China

Mathematics, 2023, vol. 11, issue 2, 1-23

Abstract: To highlight the advantages of autonomous vehicles (AVs) in modern traffic, it is necessary to investigate the sensing requirement parameters of the road environment during the vehicle braking process. Based on the texture information obtained using a field measurement, the braking model of an AV was built in Simulink and the ride comfort under typical braking scenarios was analyzed using CarSim/Simulink co-simulation. The results showed that the proposed brake system for the AV displayed a better performance than the traditional ABS when considering pavement adhesion characteristics. The braking pressure should be controlled to within the range of 4 MPa~6 MPa on a dry road, while in wet road conditions, the pressure should be within 3 MPa~4 MPa. When steering braking in dry road conditions, the duration of the “curve balance state” increased by about 57.14% compared with wet road conditions and the recommended curve radius was about 100 m. The slope gradient had a significant effect on the initial braking speed and comfort level. Overall, the ride comfort evaluation method was proposed to provide theoretical guidance for AV braking strategies, which can help to complement existing practices for road condition assessment.

Keywords: autonomous vehicles; texture information; ride comfort; multiple logistic regression analysis; braking scenarios (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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