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Vehicle Trajectory Prediction via Neural Fusion of Multiple EKF-Based Trajectory Candidates
Predicting the future trajectories of surrounding vehicles in autonomous driving is important for collision risk assessment and safe ego-vehicle path planning. Conventional neural network-based trajectory predictors typically achieve strong prediction performance by exploiting agent history, dynamic
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