FiM predicts future trajectories by first learning a reward distribution over a grid world via inverse reinforcement learning, then rolling out intention plans that condition a Mamba-enhanced trajectory decoder.
Multimodal trajectory predictions for autonomous driving using deep convolutional networks
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Foresight in Motion: Reinforcing Trajectory Prediction with Reward Heuristics
FiM predicts future trajectories by first learning a reward distribution over a grid world via inverse reinforcement learning, then rolling out intention plans that condition a Mamba-enhanced trajectory decoder.