Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility

• URL: http://arxiv.org/abs/2606.15251v1
• Date: Sat, 13 Jun 2026 11:15:04 GMT
• Title: Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility
• Abstract summary: Trajectory Compliance-Shaping (TraCS) is a neuro-symbolic framework that augments existing black-box motion prediction backbones with interpretable and probabilistic first-order logic.<n>TraCS employs a reactive data-streaming inference engine that maintains and efficiently updates compliance landscapes.<n>We demonstrate on the Argoverse 2 benchmark how TraCS consistently improves state-of-the-art prediction backbones.
• Score: 32.603991884549096
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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