カーネル法に基づく自動運転車の評価・テストの汎用的手法
A Versatile Approach to Evaluating and Testing Automated Vehicles based on Kernel Methods
カーネル法を用いて希少な故障を効率的にサンプリングする分布を構築し、自動運転車の評価時間を大幅に短縮する手法を提案した。
著者: Zhiyuan Huang, Yaohui Guo, Henry Lam, Ding Zhao
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Evaluation and validation of complicated control systems are crucial to guarantee usability and safety. Usually, failure happens in some very rarely encountered situations, but once triggered, the consequence is disastrous. Accelerated Evaluation is a methodology that efficiently tests those rarely-occurring yet critical failures via smartly-sampled test cases. The distribution used in sampling is pivotal to the performance of the method, but building a suitable distribution requires case-by-case analysis. This paper proposes a versatile approach for constructing sampling distribution using kernel method. The approach uses statistical learning tools to approximate the critical event sets and constructs distributions based on the unique properties of Gaussian distributions. We applied the method to evaluate the automated vehicles. Numerical experiments show proposed approach can robustly identify the rare failures and significantly reduce the evaluation time.
