按需协作感知提升城市自动驾驶决策安全与效率
Situation-aware Autonomous Driving Decision Making with Cooperative Perception on Demand
- 仅在需要时激活协作感知，降低通信负担
- 基于部分可观测马尔可夫决策过程在线求解决策
- 实测证明在城市场景下安全高效运行
本文研究了协作感知对城市道路自动驾驶决策的影响。协作感知带来的扩展感知范围可有效应对车辆间的隐含依赖关系，从而提升车辆决策性能。同时，考虑到无线通信的固有局限，提出一种按需协作感知（CPoD）策略，仅在扩展感知范围对情境感知必要时才激活协作。将带CPoD的情境感知决策建模为部分可观测马尔可夫决策过程（POMDP），并在线求解。评估结果表明，所提方法可在城市道路自动驾驶中安全高效运行。
原文摘要 · Abstract (English)
This paper investigates the impact of cooperative perception on autonomous driving decision making on urban roads. The extended perception range contributed by the cooperative perception can be properly leveraged to address the implicit dependencies within the vehicles, thereby the vehicle decision making performance can be improved. Meanwhile, we acknowledge the inherent limitation of wireless communication and propose a Cooperative Perception on Demand (CPoD) strategy, where the cooperative perception will only be activated when the extended perception range is necessary for proper situation-awareness. The situation-aware decision making with CPoD is modeled as a Partially Observable Markov Decision Process (POMDP) and solved in an online manner. The evaluation results demonstrate that the proposed approach can function safely and efficiently for autonomous driving on urban roads.
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