A Survey on Deep-Learning Approaches for Vehicle Trajectory Prediction in Autonomous Driving
This is a survey paper, so it is incremental, summarizing existing methods for researchers in autonomous driving.
The paper reviews and categorizes deep-learning approaches for vehicle trajectory prediction in autonomous driving, and provides a public implementation of Target-driveN Trajectory Prediction, noting its outstanding performance.
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.