Incomprehensible But Intelligible Human Logics: Toward a Data-Knowledge-Driven Trajectory Prediction Model

With the rapid advancement of autonomous driving technology, the achievement of complex trajectory prediction for human-like driving behaviors has become a critical research focus. Traditional data-driven models exhibit substantial limitations in replicating human driving logic and cognitive processes, constraining their adaptability and robustness across diverse driving scenarios. This study proposes and validates a novel Data-knowledge-driven Human-like logic Trajectory Prediction model (DHTP) using a bidirectional hybrid modeling approach. It incorporates an attention mechanism, memory reasoning, and autonomous evolution modules. The performance is assessed using multiple quantitative metrics and experimentally validated in real-world driving scenarios, including the urban expressway and highway weaving areas. The experimental results show that the DHTP model significantly outperforms the baseline model, showcasing enhanced accuracy and robustness across diverse driving conditions. Additionally, it rapidly converges to the global optimal solution, particularly in highly dynamic environments. The results indicate that optimizing the attention mechanism and autonomous evolution module allows the DHTP model to successfully simulate human driving logic and behavioral patterns. This study can help to facilitate AV-HV interaction and supports cognitive module advancement toward autonomy.