Vehicle Trajectories Prediction via Nearest Neighbor Historical Behavior

Abstract

Trajectory prediction is crucial in enabling autonomous driving systems to make informed decisions, plan appropriate paths, and enhance traffic safety and efficiency. It remains an immensely challenging task due to complex interaction between vehicles, the difficulty of extracting traffic rules information, and the dynamic changes in traffic flow. Existing methods model the interactions among vehicles or extract traffic flow density features, but overlook the effects of neighboring vehicles’ movements and interactions, which contain traffic rules and the influence of surrounding traffic conditions. To achieve this, we propose a new method taking into account neighboring vehicles’ dynamic behaviors and history, allowing for a more comprehensive understanding of the traffic environment. Firstly, a novel dual-stream nearest vehicle attention mechanism method is proposed that leverages the historical state and position of the neighbors’ vehicle and captures its features. Secondly, in order to effectively encode these features, we recombine them by the multi-head attention mechanism. Lastly, in order to fusion these features and other inputs, we extract and combine the relationships between them by a self-attention mechanism. Our approach not only outperforms other methods in evaluation metrics but also demonstrates excellent results in real-world scenarios.