A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving
Abstract
With the rapid development of machine learning, autonomous driving has become a hot issue, making urgent demands for more intelligent perception and planning systems. Self-driving cars can avoid traffic crashes with precisely predicted future trajectories of surrounding vehicles. In this work, we review and categorize existing learning-based trajectory forecasting methods from perspectives of representation, modeling, and learning. Moreover, we make our implementation of Target-driveN Trajectory Prediction publicly available at https://github.com/Henry1iu/TNT-Trajectory-Predition, demonstrating its outstanding performance whereas its original codes are withheld. Enlightenment is expected for researchers seeking to improve trajectory prediction performance based on the achievement we have made.
- Publication:
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arXiv e-prints
- Pub Date:
- October 2021
- DOI:
- 10.48550/arXiv.2110.10436
- arXiv:
- arXiv:2110.10436
- Bibcode:
- 2021arXiv211010436L
- Keywords:
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- Computer Science - Robotics
- E-Print:
- Accepted by ROBIO2021