LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty. (arXiv:2209.11422v1 [cs.LG])

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 …

arxiv attention driving planning uncertainty