Planning Reliability Assurance Tests for Autonomous Vehicles Based on Disengagement Events Data
针对自动驾驶车辆可靠性测试规划缺乏统计方法的问题，基于加州车辆管理局的脱离事件数据，利用齐次和非齐次泊松过程开发了两种测试规划策略，并平衡多个目标以提供实用建议。
Artificial intelligence (AI) technology has become increasingly prevalent and transforms our everyday lives. One important application of AI technology is the development of autonomous vehicles (AVs). However, the reliability of an AV needs to be carefully demonstrated via an assurance test so that the product can be used with confidence in the field. To plan for an assurance test, one needs to determine how many AVs need to be tested for how many miles and the standard for passing the test. Existing research has made great efforts in developing reliability demonstration tests in the other fields of applications for product development and assessment. However, statistical methods have not been utilized in AV test planning. This paper aims to fill in this gap by developing statistical methods for planning AV reliability assurance tests based on recurrent events data. We explore the relationship between multiple criteria of interest in the context of planning AV reliability assurance tests. Specifically, we develop two test planning strategies based on homogeneous and non-homogeneous Poisson processes while balancing multiple objectives with the Pareto front approach. We also offer recommendations for practical use. The disengagement events data from the California Department of Motor Vehicles AV testing program is used to illustrate the proposed assurance test planning methods.