Driving, Fast or Slow? Neuro-Symbolic Guidance for Motion Prediction in Multi-Modal Ground Mobility
arXiv:2606.15251v1 Announce Type: cross 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, Tr
The increasing complexity of autonomous systems and the need for explainability in safety-critical applications like self-driving necessitate hybrid AI approaches marrying symbolic and neural methods.
This development offers a pathway to more reliable, auditable, and safer autonomous navigation, potentially accelerating the deployment and public acceptance of self-driving technology.
The explicit encoding of regulatory and behavioral constraints through neuro-symbolic AI shifts motion prediction from purely black-box to a more interpretable and compliant system.
- · Autonomous vehicle developers
- · AI safety researchers
- · Regulatory bodies
- · Logistics and transportation sectors
- · Companies relying solely on opaque black-box AI
- · Insurance companies facing higher liability from unexplained accidents
Improved safety and reliability metrics for autonomous ground mobility systems.
Faster regulatory approval and broader public adoption of self-driving vehicles due to enhanced interpretability and trustworthiness.
The neuro-symbolic approach could become a standard for other safety-critical AI applications beyond autonomous driving, fostering a new generation of accountable AI systems.
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Read at arXiv cs.AI