ADSAP: An Adaptive Speed-Aware Trajectory Prediction Framework with Adversarial Knowledge Transfer

Accurate trajectory prediction of surrounding vehicles is a fundamental challenge in autonomous driving, requiring sophisticated modeling of complex vehicle interactions, traffic dynamics, and contextual dependencies. This paper introduces Adaptive Speed-Aware Prediction (ADSAP), a novel trajectory prediction framework that advances the state of the art through innovative mechanisms for adaptive attention modulation and knowledge transfer. At its core, ADSAP employs an adaptive deformable speed-aware pooling mechanism that dynamically adjusts the model’s attention distribution and receptive field based on instantaneous vehicle states and interaction patterns. This adaptive architecture enables fine-grained modeling of diverse traffic scenarios, from sparse highway conditions to dense urban environments. The framework incorporates a sophisticated speed-aware multi-scale feature aggregation module that systematically combines spatial and temporal information across multiple scales, facilitating comprehensive scene understanding and robust trajectory prediction. To bridge the gap between model complexity and computational efficiency, we propose an adversarial knowledge distillation approach that effectively transfers learned representations and decision-making strategies from a high-capacity teacher model to a lightweight student model. This novel distillation mechanism preserves prediction accuracy while significantly reducing computational overhead, making the framework suitable for real-world deployment. Extensive empirical evaluation on the large-scale NGSIM and highD naturalistic driving datasets demonstrates ADSAP’s superior performance. The ADSAP framework achieves an 18.7% reduction in average displacement error and a 22.4% improvement in final displacement error compared to state-of-the-art methods while maintaining consistent performance across varying traffic densities (0.05–0.85 vehicles/meter) and speed ranges (0–35 m/s). Moreover, ADSAP exhibits robust generalization capabilities across different driving scenarios and weather conditions, with the lightweight student model achieving 95% of the teacher model’s accuracy while offering a 3.2× reduction in inference time. Comprehensive experimental results supported by detailed ablation studies and statistical analyses validate ADSAP’s effectiveness in addressing the trajectory prediction challenge. Our framework provides a novel perspective on integrating adaptive attention mechanisms with efficient knowledge transfer, contributing to the development of more reliable and intelligent autonomous driving systems. Significant improvements in prediction accuracy, computational efficiency, and generalization capability demonstrate ADSAP’s potential ability to advance autonomous driving technology.