From reducing traffic congestion to improving transportation, autonomous vehicles have immense potential in enhancing productivity and quality of life. As a safety-critical system, autonomous vehicles must undergo extensive testing before being deployed on public roads to ensure their safety and reliability. Given the complexity and high dimensionality of testing scenarios for autonomous driving, this paper proposes a test case generation method based on an improved genetic algorithm. The LGSVL simulator is used to conduct simulation tests on the Baidu Apollo system. The experimental results demonstrate that the test cases generated by this method can effectively test various safety violations of autonomous vehicles and improve the efficiency of generating effective test cases.
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Test Case Generation for Autonomous Driving Based on Improved Genetic Algorithm
Semantic Scholar · Engineering · 2023
Abstract
From reducing traffic congestion to improving transportation, autonomous vehicles have immense potential in enhancing productivity and quality of life. As a safety-critical system, autonomous vehicles must undergo extensive testing before being deployed on public roads to ensure their safety and reliability. Given the complexity and high dimensionality of testing scenarios for autonomous driving, this paper proposes a test case generation method based on an improved genetic algorithm. The LGSVL simulator is used to conduct simulation tests on the Baidu Apollo system. The experimental results demonstrate that the test cases generated by this method can effectively test various safety violations of autonomous vehicles and improve the efficiency of generating effective test cases.