Perception-Aware Path Planning for Autonomous Vehicles in Uncertain Environment
Perception-Aware Path Planning for Autonomous Vehicles in Uncertain Environment
- New search for: Tang, Chen
- New search for: Xiong, Lu
- New search for: Chen, Zhan
- New search for: Tang, Chen
- New search for: Xiong, Lu
- New search for: Chen, Zhan
Recent researches in autonomous driving mainly consider the uncertainty in perception and prediction modules for safety enhancement. However, obstacles which block the field-of-view (FOV) of sensors could generate blind areas and leaves environmental uncertainty a remaining challenge for autonomous vehicles. Current solutions mainly rely on passive obstacles avoidance in path planning instead of active perception to deal with unexplored high-risky areas. In view of the problem, this paper introduces the concept of information entropy, which quantifies uncertain information in the blind area, into the motion planning module of autonomous vehicles. Based on model predictive control (MPC) scheme, the proposed algorithm can plan collision-free trajectories while actively explore unknown areas to minimize environmental uncertainty. Simulation results under various challenging scenarios demonstrate the improvement in safety and comfort with the proposed perception-aware planning scheme.
Document information
Perception-Aware Path Planning for Autonomous Vehicles in Uncertain Environment
Sae Technical Papers
SAE 2022 Intelligent and Connected Vehicles Symposium ; 2022
2022-12-22
Conference paper
English
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Perception-Aware Path Planning for Autonomous Vehicles in Uncertain Environment
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