Accurate and interpretable motion prediction for heterogeneous traffic spaces, including pedestrians, bicycles, cars, and trucks, is essential for safe autonomous navigation. Nevertheless, state-of-the-art approaches remain predominantly black-box, lacking explicit encoding of the regulatory and behavioral constraints of real-world mobility. We propose Trajectory Compliance-Shaping (TraCS), a neuro-symbolic framework that augments existing black-box motion prediction backbones with interpretable and probabilistic first-order logic. To do so, TraCS employs an agentic code-generation pipeline to bridge the gap between natural-language descriptions of traffic regulations and probabilistic motion prediction. Furthermore, TraCS employs a reactive data-streaming inference engine that maintains and efficiently updates compliance landscapes as scenes evolve. To prevent TraCS from overconfidently steering the backbone's predictions in the wrong direction, we propose a neural confidence rating learned as a context-aware attenuation of the compliance signal. We demonstrate on the Argoverse 2 benchmark how TraCS consistently improves state-of-the-art prediction backbones, showing that probabilistic and symbolic compliance reasoning is a broadly applicable and computationally efficient complement to purely neural motion predictors.

翻译：异质交通空间（包括行人、自行车、轿车和卡车）中准确且可解释的运动预测对于安全自主导航至关重要。然而，现有主流方法仍以黑箱模型为主，缺乏对现实交通中规则与行为约束的显式编码。我们提出轨迹合规性塑形框架（TraCS），这是一种神经符号框架，通过可解释的概率一阶逻辑增强现有黑箱运动预测骨干网络。为此，TraCS采用自主代码生成流水线，弥合自然语言描述的交通规则与概率运动预测之间的鸿沟。此外，TraCS采用反应式数据流推理引擎，能在场景演变过程中维护并高效更新合规性态势图。为避免TraCS过度自信地将骨干网络的预测引向错误方向，我们提出一种神经置信度评分机制，该评分作为合规信号的情境感知衰减因子进行学习。通过在Argoverse 2基准上的实验，我们证明了TraCS能持续改进主流预测骨干网络，显示概率性与符号性合规推理可成为纯神经运动预测器广泛适用且计算高效的补充方案。