Publication
Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility
Simon Kohaut; Felix Divo; Julius Hahnewald; Benedict Flade; Julian Eggert; Kristian Kersting; Devendra Singh Dhami
In: Computing Research Repository eprint Journal (CoRR), Vol. abs/2606.15251, Pages 1-24, arXiv, 2026.
Abstract
Accurate and interpretable motion prediction for heterogeneous traf-
fic spaces, including pedestrians, bicycles, cars, and trucks, is essential for safe
autonomous navigation. Nevertheless, state-of-the-art approaches remain pre-
dominantly 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 predic-
tion. Furthermore, TraCS employs a reactive data-streaming inference engine that
maintains and efficiently updates compliance landscapes as scenes evolve. To pre-
vent TraCS from overconfidently steering the backbone’s predictions in the wrong
direction, we propose a neural confidence rating learned as a context-aware atten-
uation 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