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Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge | | Freddy A. Boulton
; Elena Corina Grigore
; Eric M. Wolff
; | | Date: |
8 Jun 2020 | | 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 emph{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. | | Source: | arXiv, 2006.4767 | | Services: | Forum | Review | PDF | Favorites | |
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