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Vehicle Trajectory Prediction via Neural Fusion of Multiple EKF-Based Trajectory Candidates

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

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 scene graphs, and semantic maps. However, in specific motion regimes such as acceleration, deceleration, and turning, these predictors may fail to reflect physically feasible trajectories. To address this issue, this study proposes a framework that fuses the output of Trajectron++, a neural network

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