Performing safe trajectory planning that matches perception capabilities is critical for autonomous vehicles (AV). It remains a challenge to handle uncertainty including the epistemic the aleatoric uncertainty in environmental perception in order to plan safe and accurate trajectories. We propose an integrated perception-prediction-planning algorithm for autonomous vehicles that quantifies and transfers DL-based perception uncertainties during prediction and performs prediction evaluation with uncertainty. The novelties of the approach are: 1) quantifying and transferring perceptual uncertainty to the downstream planning decision phase, which is partially extended using quantified uncertainty incorporated into a Rapidly-exploring Random Tree; 2) combining uncertainty analysis with an implicit scenario context-aware trajectory prediction framework that utilizes perceptual uncertainty as part of the implicit scenario context information; 3) integrating the proposed uncertainty-environment-aware trajectory predictor with a planning-based feasible candidate trajectory generator to capture dynamically changing perceptual states and output accurate predictions. Experimental results based on a driving behavioral dataset show that the proposed method further reduces the detour proportion of the path while ensuring safety.
Autonomous Vehicle Path Planning Strategy Considering Uncertainty in Environmental Perception
Lect. Notes Electrical Eng.
International Conference on Artificial Intelligence and Autonomous Transportation ; 2024 ; Beijing, China December 06, 2024 - December 08, 2024
The Proceedings of 2024 International Conference on Artificial Intelligence and Autonomous Transportation ; Chapter : 53 ; 524-532
2025-03-28
9 pages
Article/Chapter (Book)
Electronic Resource
English
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