The SGC-Informer-Based Deep Learning Framework: Managing the Efficiency-Accuracy Trade-Off in Vehicle Trajectory Prediction

Accurate and efficient trajectory prediction of surrounding vehicles is critical for ensuring the safety of autonomous driving systems, as it provides vital input for subsequent decision-making and planning modules. Relying solely on historical trajectory information is insufficient to accurately predict, especially in complex traffic scenarios with interwoven roads such as ramps, intersections, and roundabouts. Therefore, predicting future trajectories requires consideration not only of the historical trajectories of vehicles but also of the interaction among various entities of the environmental context. However, existing prediction methods suffer from expensive rendering costs and entity information loss, unnecessary complexity and redundant computation, and all-to-all interaction modeling and high computational costs, facing the challenge of the trade-off of efficiency and accuracy. To this end, we propose the SGC-Informer-based deep learning framework for fast and accurate trajectory prediction, called FESIG. Specifically, a comprehensive explicit entity vectorized representation is constructed based on Feature Engineering for the agent's trajectories and High-Definition (HD) map information, avoiding expensive rendering costs and entity information loss. Then, to mitigate excess complexity, we introduce the Simple Graph Convolution (SGC) network to effectively extract the local feature of each entity, which can remove the nonlinear transformation of the information propagation. Additionally, the Informer mechanism is employed to automatically sort and select the entities in the scenario, and then capture the complex interactions between the target vehicle and these entities that significantly impact it, thereby reducing computation costs. Experiments performed on the Argoverse forecasting dataset illustrate that the FESIG achieves the trade-off between accuracy and efficiency in the vehicle trajectory prediction task compared with the existing methods.