Abstract
One of the unresolved challenges for autonomous vehicles is occlusion-aware decision-making under the high uncertainty of various occlusions. Recent occlusion-aware decision-making methods encounter issues such as overly conservative behavior, high computational complexity, or scenario scalability challenges.
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 riskaware learning and reliable policy optimization.
Our framework was tested in challenging scenarios under both dynamic and static occlusions and demonstrated efficient perception-aware exploration performance to other strong baselines in closedloop evaluations.
Framework
Schematic architecture: Vectorized observations from the occluded environment are encoded through a graph neural network. The actor decodes parameterized action probabilities, which are further mapped to the semantic motion primitives. A safe interaction mechanism is engaged to avoid risky motion primitive exploration and risk-aware policy learning.