Critical Scenarios Based on Graded Hazard Disposal Model of Human Drivers Tongji University

Format:

Book

Conference/Event

Author/Creator:

Fang, Xiaowei, author.

Contributor:

Ma, Zhixiong

Yin, Qi

Zhu, Xichan

Conference Name:

SAE 2023 Intelligent and Connected Vehicles Symposium (2023-09-22 : Nanchang, China)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2023

Summary:

In order to improve the efficiency of safety performance test for intelligent vehicles and construct the test case set quickly, critical scenarios based on graded hazard disposal model of human drivers are proposed, which can be used for extraction of test cases for safety performance. Based on the natural driving data in China Field Operational Test (China-FOT), the four-stage collision avoidance process of human drivers is obtained, including steady driving stage, risk judgment stage, collision reaction stage and collision avoidance stage. And there are two human driver states: general state and alert state. Then the graded hazard disposal model of human drivers is constructed. According to the parameter distribution of natural driving data, the risk perception point, risk response point and collision reaction time of deceleration scenario and cut-in scenario are obtained, and the deceleration gradient and the maximum deceleration of each collision avoidance difficulty level are determined. For deceleration scenario and cut-in scenario, the parameter range is determined to generalize logical scenarios. Then based on the graded hazard disposal model of human drivers, the critical scenarios at the preventable and unpreventable boundaries are obtained through simulation calculation. As the concrete scenarios with high value for safety extracted from the massive logical scenarios, the critical scenarios are used to construct the test case set in the safety performance test for intelligent vehicles. For deceleration scenario, 507 critical scenarios are obtained from 10,000 logical scenarios, which increases the test efficiency by 19.72 times. For cut-in scenario, 5,121 critical scenarios are obtained from 270,000 logical scenarios, which increases the test efficiency by 52.72 times

Notes:

Vendor supplied data

Publisher Number:

2023-01-7054

Access Restriction:

Restricted for use by site license