Motion Forecasting with Unlikelihood Training in Continuous Space

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. Code is avaliable at https://github.com/Vision-CAIR/UnlikelihoodMotionForecasting