LEADER: Learning Attention over Driving Behaviors for Planning under UncertaintyDownload PDF

16 Jun 2022, 10:45 (modified: 29 Oct 2022, 05:33)CoRL 2022 OralReaders: Everyone
Student First Author: no
Keywords: Planning under uncertainty, Integrating planning and learning, Autonomous driving
TL;DR: We propose an algorithm that learns attention over human behaviors for planning under uncertainty.
Abstract: Uncertainty in human behaviors poses a significant challenge to autonomous driving in crowded urban environments. The partially observable Markov decision process (POMDP) offers a principled general framework for decision making under uncertainty and achieves real-time performance for complex tasks by leveraging Monte Carlo sampling. However, sampling may miss rare, but critical events, leading to potential safety concerns. To tackle this challenge, we propose a new algorithm, LEarning Attention over Driving bEhavioRs (LEADER), which learns to attend to critical human behaviors during planning. LEADER learns a neural network generator to provide attention over human behaviors; it integrates the attention into a belief-space planner through importance sampling, which biases planning towards critical events. To train the attention generator, we form a minimax game between the generator and the planner. By solving this minimax game, LEADER learns to perform risk-aware planning without explicit human effort on data labeling.
Supplementary Material: zip
Website: https://sites.google.com/view/leader-paper/home
Code: https://github.com/modanesh/LEADER
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