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

2026-06-13

AI Summary

Existing motion prediction methods often rely on black-box models that struggle to explicitly incorporate traffic rules, resulting in limited interpretability and regulatory compliance. This work proposes the Trajectory Compliance Shaping (TraCS) framework, which innovatively integrates neural networks with symbolic reasoning by translating natural-language traffic rules into probabilistic first-order logic. TraCS dynamically guides prediction models toward compliant trajectories through an agent-driven code generation mechanism and a context-aware confidence decay strategy. Evaluated on the Argoverse 2 benchmark, TraCS consistently enhances the performance of diverse state-of-the-art prediction models, demonstrating the universality, efficiency, and interpretability of probabilistic symbolic reasoning in multimodal ground-vehicle trajectory forecasting.

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.

Problem

• motion prediction
• neuro-symbolic
• traffic regulations
• autonomous navigation
• compliance reasoning

Innovation

• neuro-symbolic
• motion prediction
• trajectory compliance
• probabilistic logic
• interpretable AI