Bayesian Active Learningを用いた車両アダプティブクルーズコントロール性能の自動評価法

抄録

Adaptive cruise control (ACC) is one of the critical elements of vehicle performance. To ensure the quality of ACC performance, comprehensive evaluations that control both complex test scenarios that reproduce market conditions and vehicle behavior is required. However, it is difficult to evaluate all combinations of test scenarios using real test vehicles within limited development resources. Furthermore, it is necessary to determine Electrical Control Unit (ECU) parameters while considering multiple performance trade-offs. This paper proposes a new automatic screening and exploration system for ACC, incorporating Bayesian active learning (BAL). This system consists of two automated elements: an automatic evaluation system and an automatic exploration system. In the automatic evaluation system, the behavior of ACC is automatically evaluated in real-time simulation using Real Car Simulation Bench (RC-S). In the automatic exploration system, the worst condition screening evaluation of ACC performance and the exploration of the feasible region of design space for ECU parameters using BAL are conducted. As a result, it becomes possible to make the evaluation process more efficient through closed-loop evaluation, thereby improving ACC performance. In this study, an example of data comparison between RC-S and a real vehicle driving on a test course is demonstrated to show the effectiveness of the proposed system.