  Uncertainty on human behaviors poses a significant challenge to autonomous
driving in crowded urban environments. The partially observable Markov decision
processes (POMDPs) offer a principled framework for planning under uncertainty,
often leveraging Monte Carlo sampling to achieve online performance for complex
tasks. However, sampling also raises safety concerns by potentially missing
critical events. To address this, we propose a new algorithm, LEarning
Attention over Driving bEhavioRs (LEADER), that learns to attend to critical
human behaviors during planning. LEADER learns a neural network generator to
provide attention over human behaviors in real-time situations. It integrates
the attention into a belief-space planner, using importance sampling to bias
reasoning towards critical events. To train the algorithm, we let the attention
generator and the planner form a min-max game. By solving the min-max game,
LEADER learns to perform risk-aware planning without human labeling.
