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

arXiv: https://arxiv.org/abs/2606.15251 · HTML: https://mlsanity.com/paper/2606.15251/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 31 of its 50 references that are in the map.
- First posted on arXiv 13 Jun 2026.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Dataset](https://mlsanity.com/paper/2104.10133/) (2021)
- [Neural Probabilistic Logic Programming in DeepProbLog](https://mlsanity.com/paper/1907.08194/) (2019)
- [Multi-shot ASP solving with clingo](https://mlsanity.com/paper/1705.09811/) (2017)
- [Abstract Syntax Networks for Code Generation and Semantic Parsing](https://mlsanity.com/paper/1704.07535/) (2017)
- [NeurASP: Embracing Neural Networks into Answer Set Programming](https://mlsanity.com/paper/2307.07700/) (2023)
- [Scallop: A Language for Neurosymbolic Programming](https://mlsanity.com/paper/2304.04812/) (2023)
- [HPNet: Dynamic Trajectory Forecasting with Historical Prediction Attention](https://mlsanity.com/paper/2404.06351/) (2024)
- [Leveraging Large Language Models to Generate Answer Set Programs](https://mlsanity.com/paper/2307.07699/) (2023)
- [FEND: A Future Enhanced Distribution-Aware Contrastive Learning Framework for Long-tail Trajectory Prediction](https://mlsanity.com/paper/2303.16574/) (2023)
- [Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction](https://mlsanity.com/paper/2403.18447/) (2024)
- [WOMD-LiDAR: Raw Sensor Dataset Benchmark for Motion Forecasting](https://mlsanity.com/paper/2304.03834/) (2023)
- [Trust, but Verify: Cross-Modality Fusion for HD Map Change Detection](https://mlsanity.com/paper/2212.07312/) (2022)
- [Navigating Shortcuts, Spurious Correlations, and Confounders: From Origins via Detection to Mitigation](https://mlsanity.com/paper/2412.05152/) (2024)
- [ReasonPlan: Unified Scene Prediction and Decision Reasoning for Closed-loop Autonomous Driving](https://mlsanity.com/paper/2505.20024/) (2025)
- [DeMo: Decoupling Motion Forecasting into Directional Intentions and Dynamic States](https://mlsanity.com/paper/2410.05982/) (2024)
- [Scalable Neural-Probabilistic Answer Set Programming](https://mlsanity.com/paper/2306.08397/) (2023)
- [LLASP: Fine-tuning Large Language Models for Answer Set Programming](https://mlsanity.com/paper/2407.18723/) (2024)
- [Motion Forecasting for Autonomous Vehicles: A Survey](https://mlsanity.com/paper/2502.08664/) (2025)
- [Answer Set Networks: Casting Answer Set Programming into Deep Learning](https://mlsanity.com/paper/2412.14814/) (2024)
- [Interactive and Incremental Learning of Spatial Object Relations from Human Demonstrations](https://mlsanity.com/paper/2305.09551/) (2023)
- [Relative Position Matters: Trajectory Prediction and Planning with Polar Representation](https://mlsanity.com/paper/2508.11492/) (2025)
- [The Constitutional Filter: Bayesian Estimation of Compliant Agents](https://mlsanity.com/paper/2412.18347/) (2024)
- [StaR Maps: Unveiling Uncertainty in Geospatial Relations](https://mlsanity.com/paper/2412.18356/) (2024)
- [Reactive Knowledge Representation and Asynchronous Reasoning](https://mlsanity.com/paper/2602.05625/) (2026)
- [TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation](https://mlsanity.com/paper/2605.10020/) (2026)

## Abstract (arXiv)

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.