Trajectory prediction for heterogeneous traffic-agents using knowledge correction data-driven model

There is a dilemma regarding the accuracy and reality of vehicle trajectory prediction. Balancing and predicting the effective trajectory is a topic of debate in autonomous driving. We investigated this issue using knowledge-driven and data-driven methods to estimate the performance of the two most common methods and found that improving the accuracy, in reality, is challenging. Therefore, we propose a novel trajectory prediction framework for heterogeneous traffic agents, where knowledge residuals are associated with data-driven methods and correct the results to make them more consistent with actual traffic conditions on the premise of high accuracy. Experiments on six public datasets showed that the proposed framework outperforms benchmarks. With an ablation study, we further verified that our method has a good generalisability for new scenarios and high generality in data-driven model selection.