tl;dr: We propose a model-predictive-control (MPC)-based planner (AdaptiveDriver) that makes use of world models who's agent behaviours adapt to the sequence. We compare AdaptiveDriver to state-of-art baselines (PDMC[1]).
PDMC collides with a decelerating lead vehicle (frame 20) while AdaptiveDriver avoids the collision.
PDMC collides with a decelerating lead vehicle (frame 20) while AdaptiveDriver avoids the collision.
AdaptiveDriver reaches the goal state (green square) faster than PDMC for a turn-right sequence.
AdaptiveDriver reaches the goal state (green square) faster than PDMC for a straight-road sequence.
Related Works
[1] Dauner, Daniel, et al. "Parting with misconceptions about learning-based vehicle motion planning." Conference on Robot Learning. PMLR, 2023.