Problem
Research questions and friction points this paper is trying to address.
end-to-end driving system
safety risks
crowded scenes
unstructured obstacles
risk-aware occupancy
Risk-Aware Occupancy for Safety-Oriented End-to-End Autonomous Driving
🤖 AI Summary
本文提出一种风险感知占用方法，通过统一的BEV图编码环境信息，并设计ROIDrive网络生成安全导向的行驶轨迹，以减少自动驾驶中的碰撞风险。
📝 Abstract
Sparse representation formulates the environment perception for the end-to-end driving system as a set of discrete elements like objects and lane lines. This formulation meets safety risks in crowded, occluded scenes dealing with unstructured obstacles, uncertain regions, and intricate interactions. In this paper, we propose a dense representation, risk-aware occupancy, to characterize planning-relevant risks in an explicit and uniform manner. It jointly encodes global scene occupancy, map-derived traffic constraints, and future dynamic agent occupancy into a unified BEV map. The unified BEV map captures the risk evidence for trajectory planning in both spatial and temporal dimensions. We design an E2E network, ROIDrive, to realize risk-aware occupancy. It predicts risk-aware occupancy with an independent branch and injects it into planning queries for safety-oriented trajectory generation. In addition, to quantify the safety problem, we introduce RiskOcc4D-nuScenes built upon nuscenes and occ3d-nuscenes. Our risk-aware occupancy yields relative open-loop collision reductions of 52.9% under the UniAD metric and 35.0% under the ST-P3 metric on nuScenes.
Innovation
Methods, ideas, or system contributions that make the work stand out.
risk-aware occupancy
unified BEV map
ROIDrive
end-to-end driving system
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