Abstract: Motion forecasting is essential for making safe and intelligent decisions in robotic applications such as autonomous driving. Existing methods often formulate it as a sequence-to-sequence prediction problem, solved in an encoder-decoder framework with a maximum likelihood estimation objective. State-of-the-art models leverage contextual information including the map and states of surrounding agents. However, we observe that they still assign a high probability to unlikely trajectories resulting in unsafe behaviors including road boundary violations. Orthogonally, we propose a new objective, unlikelihood training, which forces predicted trajectories that conflict with contextual information to be assigned a lower probability. We demonstrate that our method can improve state-of-art models' performance on challenging real-world trajectory forecasting datasets (nuScenes and Argoverse) by avoiding up to 56% context-violated prediction and improving up to 9% prediction accuracy.
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