可靠性(半导体)
计算机科学
遗传算法
考试(生物学)
可靠性工程
场景测试
模拟
工程类
人工智能
机器学习
古生物学
功率(物理)
物理
多样性(控制论)
量子力学
生物
作者
Li Sun,Song Huang,Changyou Zheng,Tongtong Bai,Zhe Hu
标识
DOI:10.1109/qrs60937.2023.00035
摘要
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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