LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty

Mohamad Hosein Danesh, Panpan Cai, David Hsu

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.

Student First Author: no

Website: https://sites.google.com/view/leader-paper/home

Code: https://github.com/modanesh/LEADER