Autonomous driving system aims for safe and social-consistent driving through the behavioral integration among interactive agents. However, challenges remain due to multi-agent scene uncertainty and heterogeneous interaction. Current dense and sparse behavioral representations struggle with inefficiency and inconsistency in multi-agent modeling, leading to instability of collective behavioral patterns when integrating prediction and planning (IPP). To address this, we initiate a topological formation that serves as a compliant behavioral foreground to guide downstream trajectory generations. Specifically, we introduce Behavioral Topology (BeTop), a pivotal topological formulation that explicitly represents the consensual behavioral pattern among multi-agent future. BeTop is derived from braid theory to distill compliant interactive topology from multi-agent future trajectories. A synergistic learning framework (BeTopNet) supervised by BeTop facilitates the consistency of behavior prediction and planning within the predicted topology priors. Through imitative contingency learning, BeTop also effectively manages behavioral uncertainty for prediction and planning. Extensive verification on large-scale real-world datasets, including nuPlan and WOMD, demonstrates that BeTop achieves state-of-the-art performance in both prediction and planning tasks. Further validations on the proposed interactive scenario benchmark showcase planning compliance in interactive cases.

翻译：自动驾驶系统旨在通过交互智能体间的行为整合实现安全且社会一致性的驾驶。然而，多智能体场景的不确定性与异构交互性带来了持续挑战。当前稠密与稀疏的行为表征方法在多智能体建模中存在效率与一致性问题，导致预测与规划整合（IPP）时集体行为模式的不稳定性。为此，我们提出一种拓扑构型作为合规行为前景来引导下游轨迹生成。具体而言，我们引入行为拓扑（BeTop）——一种关键拓扑表述，能显式表征多智能体未来共识行为模式。BeTop源自辫子理论，可从多智能体未来轨迹中提炼合规交互拓扑。通过BeTop监督的协同学习框架（BeTopNet）在预测拓扑先验中实现了行为预测与规划的一致性。借助模仿式应急学习，BeTop还能有效管理预测与规划的行为不确定性。在nuPlan、WOMD等大规模真实数据集上的广泛验证表明，BeTop在预测与规划任务中均达到最先进性能。在所提出的交互场景基准测试中的进一步验证，展示了该方法在交互案例中的规划合规性。