決定アルゴリズム評価のためのマルチモーダル安全クリティカルシナリオ生成
Multimodal Safety-Critical Scenarios Generation for Decision-Making Algorithms Evaluation
フローベースの生成モデルでマルチモーダルな安全クリティカル交通シナリオを生成し、強化学習ベースの自動運転アルゴリズムの頑健性を評価した研究。
著者: Wenhao Ding, Baiming Chen, Bo Li, Kim Ji Eun, Ding Zhao
分類: cs.LG, cs.RO, stat.ML
原文アブストラクト
Existing neural network-based autonomous systems are shown to be vulnerable against adversarial attacks, therefore sophisticated evaluation on their robustness is of great importance. However, evaluating the robustness only under the worst-case scenarios based on known attacks is not comprehensive, not to mention that some of them even rarely occur in the real world. In addition, the distribution of safety-critical data is usually multimodal, while most traditional attacks and evaluation methods focus on a single modality. To solve the above challenges, we propose a flow-based multimodal safety-critical scenario generator for evaluating decisionmaking algorithms. The proposed generative model is optimized with weighted likelihood maximization and a gradient-based sampling procedure is integrated to improve the sampling efficiency. The safety-critical scenarios are generated by querying the task algorithms and the log-likelihood of the generated scenarios is in proportion to the risk level. Experiments on a self-driving task demonstrate our advantages in terms of testing efficiency and multimodal modeling capability. We evaluate six Reinforcement Learning algorithms with our generated traffic scenarios and provide empirical conclusions about their robustness.
