通过主动感知学习遮挡下的决策，提升自动驾驶安全性与泛化能力。
Learning Occlusion-aware Decision-making from Agent Interaction via Active Perception
- 用向量表征环境，结合语义运动基元实现高效主动感知探索。
- 在动态和静态遮挡场景下，闭环评估表现优于多个强基线模型。
- 融合预测与强化学习，实现风险感知和安全保证，适合自动驾驶研究者。
由于各种遮挡带来的高不确定性，遮挡感知决策对自动驾驶至关重要。现有方法存在计算复杂度高、场景可扩展性差或依赖有限专家数据等问题。借助探索随机化自动生成数据，我们发现强化学习（RL）在遮挡感知决策中具有潜力。然而，以往的遮挡感知强化学习在扩展至多样动态与静态遮挡场景时效率低、缺乏预测能力。为此，我们提出Pad-AI，一种通过主动感知自强化学习遮挡感知决策的框架。Pad-AI采用向量表示高效建模遮挡环境，并基于语义运动基元进行高层主动感知探索。同时，将预测与强化学习统一整合，实现风险感知学习与安全保证。该框架在动态与静态遮挡的复杂场景中测试，闭环评估表现优于多个强基线模型，展现出高效的感知-决策协同能力。
原文摘要 · Abstract (English)
Occlusion-aware decision-making is essential in autonomous driving due to the high uncertainty of various occlusions. Recent occlusion-aware decision-making methods encounter issues such as high computational complexity, scenario scalability challenges, or reliance on limited expert data. Benefiting from automatically generating data by exploration randomization, we uncover that reinforcement learning (RL) may show promise in occlusion-aware decision-making. However, previous occlusion-aware RL faces challenges in expanding to various dynamic and static occlusion scenarios, low learning efficiency, and lack of predictive ability. To address these issues, we introduce Pad-AI, a self-reinforcing framework to learn occlusion-aware decision-making through active perception. Pad-AI utilizes vectorized representation to represent occluded environments efficiently and learns over the semantic motion primitives to focus on high-level active perception exploration. Furthermore, Pad-AI integrates prediction and RL within a unified framework to provide risk-aware learning and security guarantees. Our framework was tested in challenging scenarios under both dynamic and static occlusions and demonstrated efficient and general perception-aware exploration performance to other strong baselines in closed-loop evaluations.
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