複数不確実性を考慮した自律協調計画
Multi-Uncertainty Aware Autonomous Cooperative Planning
知覚・運動・通信の不確実性を同時に考慮した協調モデル予測制御により、マルチビークルの協調計画を実現するフレームワークを提案し、CARLAで有効性を示した。
著者: Shiyao Zhang, He Li, Shengyu Zhang, Shuai Wang, Derrick Wing Kwan Ng, Chengzhong Xu
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Autonomous cooperative planning (ACP) is a promising technique to improve the efficiency and safety of multi-vehicle interactions for future intelligent transportation systems. However, realizing robust ACP is a challenge due to the aggregation of perception, motion, and communication uncertainties. This paper proposes a novel multi-uncertainty aware ACP (MUACP) framework that simultaneously accounts for multiple types of uncertainties via regularized cooperative model predictive control (RC-MPC). The regularizers and constraints for perception, motion, and communication are constructed according to the confidence levels, weather conditions, and outage probabilities, respectively. The effectiveness of the proposed method is evaluated in the Car Learning to Act (CARLA) simulation platform. Results demonstrate that the proposed MUACP efficiently performs cooperative formation in real time and outperforms other benchmark approaches in various scenarios under imperfect knowledge of the environment.
