Li, N.; Chen, L.; Huang, Y. Research on Specific Scenario Generation Methods for Autonomous Driving Simulation Tests. World Electr. Veh. J.2024, 15, 2.
Li, N.; Chen, L.; Huang, Y. Research on Specific Scenario Generation Methods for Autonomous Driving Simulation Tests. World Electr. Veh. J. 2024, 15, 2.
Li, N.; Chen, L.; Huang, Y. Research on Specific Scenario Generation Methods for Autonomous Driving Simulation Tests. World Electr. Veh. J.2024, 15, 2.
Li, N.; Chen, L.; Huang, Y. Research on Specific Scenario Generation Methods for Autonomous Driving Simulation Tests. World Electr. Veh. J. 2024, 15, 2.
Abstract
In this paper, we propose a method for the generation of simulation test scenarios for autonomous driving. Based on the requirements of standard regulatory test scenarios, we can generate virtual simulation scenarios and functional scenario libraries for autonomous driving, which can be used for simulation verification of different ADAS functions. Firstly, the design operation domain (ODD) of the functional scenario is selected and the weight values of the ODD elements taken are calculated; then a combination test algorithm based on parameter weights is improved to generate autonomous driving virtual simulation test cases for the ODD elements, which can effectively reduce the number of generated test cases compared with the traditional combination test algorithm; then the traffic participant elements under each test case are Then, the values of the subelements under the traffic participant element in each test case are sampled and clustered to obtain hazard specific scenarios. Finally, the specific scenarios are applied to the automatic emergency braking (AEB) system on the model-in-the-loop (MIL) testbed to verify the effectiveness of this scenario generation method.
Keywords
automated driving virtual simulation; subjective empowerment; objective empowerment; combined testing; test case generation
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
