Safe autonomous driving in mixed traffic requires a unified understanding of multimodal interactions and dynamic planning under uncertainty. Existing learning-based methods often fail to capture rare but safety-critical behaviors, while rule-based systems lack adaptability in complex interactions. To address these limitations, we propose CogDrive, a cognition-driven multimodal prediction–planning fusion framework that integrates explicit modal reasoning with safety aware decision optimization. The prediction module introduces cognitive representations of interaction modes based on topological motion semantics and nearest-neighbor relational encoding. By incorporating a differentiable modal loss and multimodal Gaussian decoding, CogDrive effectively learns sparse and unbalanced interaction behaviors, improving long-tail trajectory prediction accuracy. The planning module builds upon an emergency-response concept and develops a safety-stabilized trajectory tree optimization. Short-term consistent root trajectories ensure safety within replanning cycles, while long-term branches provide smooth and collision-free avoidance under low-probability or rapidly switching modes. Experiments on Argoverse2 and INTERACTION datasets show that CogDrive achieves state-of-the-art performance, reducing minADE and miss rate while maintaining smoothness. Closed-loop simulations further confirm stable and adaptive behavior across strong-interaction scenarios such as merging and intersections. By coupling cognitive multimodal prediction with safety-oriented planning, CogDrive establishes an interpretable and reliable paradigm for safe autonomy in complex traffic.
Huang et al. (Sun,) studied this question.
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