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Article overview
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Deep Kinematic Models for Physically Realistic Prediction of Vehicle Trajectories | Henggang Cui
; Thi Nguyen
; Fang-Chieh Chou
; Tsung-Han Lin
; Jeff Schneider
; David Bradley
; Nemanja Djuric
; | Date: |
1 Aug 2019 | Abstract: | Self-driving vehicles (SDVs) hold great potential for improving traffic
safety and are poised to positively affect the quality of life of millions of
people. One of the critical aspects of the autonomous technology is
understanding and predicting future movement of vehicles surrounding the SDV.
This work presents a deep-learning-based method for physically realistic motion
prediction of such traffic actors. Previous work did not explicitly encode
physical realism and instead relied on the models to learn the laws of physics
directly from the data, potentially resulting in implausible trajectory
predictions. To account for this issue we propose a method that seamlessly
combines ideas from the AI with physically grounded vehicle motion models. In
this way we employ best of the both worlds, coupling powerful learning models
with strong physical guarantees for their outputs. The proposed approach is
general, being applicable to any type of learning method. Extensive experiments
using deep convnets on large-scale, real-world data strongly indicate its
benefits, outperforming the existing state-of-the-art. | Source: | arXiv, 1908.0219 | Services: | Forum | Review | PDF | Favorites |
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