SA-TP$^{2}$: A Safety-Aware Trajectory Prediction and Planning Model for Autonomous Driving

Trajectory prediction and planning remain key challenges for autonomous vehicles, particularly in complex and dynamic environments. Existing methods, typically based on static safety metrics like time-to-collision, fail to account for the evolving nature of risk in real-world traffic. This article proposes a novel safety-aware trajectory prediction and planning (SA-TP$^{2}$) model, which introduces an adaptive driver risk field to simulate human-like risk perception and decision-making. By dynamically modeling risk as a continuous variable, SA-TP$^{2}$ adjusts vehicle trajectories in real time, accounting for interactions with other agents, road conditions, and environmental uncertainties. The model integrates imitation learning, rule-based strategies, and physics-informed neural networks to ensure safe, efficient, and human-compatible behavior. A Linformer-based architecture and temporal hypergraph convolution network are introduced to optimize computational efficiency, enabling real-time operation in resource-constrained environments. Experimental results on benchmark datasets including next generation simulation (NGSIM), highway drone dataset (HighD), Macao connected autonomous driving (MoCAD), and NuScenes demonstrate that SA-TP$^{2}$ achieves the state-of-the-art performance in trajectory prediction. In addition, extensive closed-loop testing on the NuPlan and CommonRoad platforms further confirms that SA-TP$^{2}$ outperforms existing baselines, paving the way for safer navigation of autonomous driving systems.