TP-FRL: An Efficient and Adaptive Trajectory Prediction Method Based on the Rule and Learning-Based Frameworks Fusion

Effective trajectory prediction is of great significance for the design of intelligent driving systems. To overcome the problems of low algorithm efficiency and insufficient scenario adaptation in urban environments, this article proposes a trajectory prediction framework based on the rule and learning-based frameworks fusion (TP-FRL). By augmenting the observed trajectory under the guidance of a physically possible trajectory set, the proposed framework improves algorithmic adaptation in the presence of disturbed trajectory observations. Meanwhile, rule-based spatio-temporal semantic corridors are used to vectorize agent trajectory-related regions, which improves the rationality of each agent region division and enhances the adaptability in different scenarios. Finally, based on the trajectory-related region modeling of each agent, the interest region of the central agent is learned by a graph convolutional network, and only the scenario elements in the interest region are encoded to improve the trajectory prediction efficiency of the transformer multi-head attention network. Traffic data from Argoverse and ApolloScape datasets are used to evaluate the performance of the TP-FRL model. Specifically, the TP-FRL model shows efficient performance and strong adaptability on datasets from two countries, which shows the promise of predicting trajectories under various scenarios.