Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge

• URL: http://arxiv.org/abs/2006.04767v2
• Date: Wed, 13 Jan 2021 20:41:54 GMT
• Title: Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge
• Authors: Freddy A. Boulton and Elena Corina Grigore and Eric M. Wolff
• Abstract summary: We build on classification-based approaches to motion prediction by adding an auxiliary loss that penalizes off-road predictions. This auxiliary loss can easily be pretrained using only map information, which significantly improves performance on small datasets. Our final contribution is a detailed comparison of classification and ordinal regression on two public self-driving datasets.
• Score: 3.0938904602244355
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Predicting the future motion of vehicles has been studied using various techniques, including stochastic policies, generative models, and regression. Recent work has shown that classification over a trajectory set, which approximates possible motions, achieves state-of-the-art performance and avoids issues like mode collapse. However, map information and the physical relationships between nearby trajectories is not fully exploited in this formulation. We build on classification-based approaches to motion prediction by adding an auxiliary loss that penalizes off-road predictions. This auxiliary loss can easily be pretrained using only map information (e.g., off-road area), which significantly improves performance on small datasets. We also investigate weighted cross-entropy losses to capture spatial-temporal relationships among trajectories. Our final contribution is a detailed comparison of classification and ordinal regression on two public self-driving datasets.

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