考虑感知、运动和通信不确定性，实现多车协同实时规划。
Multi-Uncertainty Aware Autonomous Cooperative Planning
- 通过正则化模型预测控制，融合多源不确定性建模。
- 在CARLA仿真中实现实时协作编队，性能优于基准方法。
- 适合复杂交通环境下对鲁棒性要求高的自动驾驶系统。
自主协同规划（ACP）是提升未来智能交通系统中多车交互效率与安全性的关键技术。然而，由于感知、运动和通信不确定性叠加，实现鲁棒的ACP仍具挑战。本文提出一种多不确定性感知的自主协同规划（MUACP）框架，通过正则化协同模型预测控制（RC-MPC）同时考虑多种不确定性。感知、运动和通信的正则项与约束分别依据置信度、天气条件和中断概率构建。在Car Learning to Act（CARLA）仿真平台上的评估表明，所提方法能高效实现实时协同编队，在环境知识不完整的情况下，性能优于其他基准方法。
原文摘要 · Abstract (English)
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.
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