# Integrating Legal and Logical Specifications in Perception, Prediction, and Planning for Automated Driving: A Survey of Methods

arXiv: https://arxiv.org/abs/2510.25386 · HTML: https://mlsanity.com/paper/2510.25386/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 37 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 14 of 14)

- [Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks](https://mlsanity.com/paper/1902.09355/) (2019)
- [ROAD-R: The Autonomous Driving Dataset with Logical Requirements](https://mlsanity.com/paper/2210.01597/) (2022)
- [Rule-based Optimal Control for Autonomous Driving](https://mlsanity.com/paper/2101.05709/) (2021)
- [Formalizing Traffic Rules for Machine Interpretability](https://mlsanity.com/paper/2007.00330/) (2020)
- [No driver, No Regulation? --Online Legal Driving Behavior Monitoring for Self-driving Vehicles](https://mlsanity.com/paper/2212.04156/) (2022)
- [TR2MTL: LLM based framework for Metric Temporal Logic Formalization of Traffic Rules](https://mlsanity.com/paper/2406.05709/) (2024)
- [Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer](https://mlsanity.com/paper/1906.02859/) (2019)
- [Exploiting T-norms for Deep Learning in Autonomous Driving](https://mlsanity.com/paper/2402.11362/) (2024)
- [Smart Automotive Technology Adherence to the Law: (De)Constructing Road Rules for Autonomous System Development, Verification and Safety](https://mlsanity.com/paper/2109.02956/) (2021)
- [A Mathematical Framework and a Suite of Learning Techniques for Neural-Symbolic Systems](https://mlsanity.com/paper/2407.09693/) (2024)
- [Towards a Digital Highway Code using Formal Modelling and Verification of Timed Automata](https://mlsanity.com/paper/2209.14036/) (2022)
- [MOD-CL: Multi-label Object Detection with Constrained Loss](https://mlsanity.com/paper/2403.07885/) (2024)
- [Traffic Regulation-aware Path Planning with Regulation Databases and Vision-Language Models](https://mlsanity.com/paper/2503.09024/) (2025)
- [Predictive Traffic Rule Compliance using Reinforcement Learning](https://mlsanity.com/paper/2503.22925/) (2025)

## Abstract (arXiv)

This survey provides an analysis of current methodologies integrating legal and logical specifications into the perception, prediction, and planning modules of automated driving systems. We systematically explore techniques ranging from logic-based frameworks to computational legal reasoning approaches, emphasizing their capability to ensure regulatory compliance and interpretability in dynamic and uncertain driving environments. A central finding is that significant challenges arise at the intersection of perceptual reliability, legal compliance, and decision-making justifiability. To systematically analyze these challenges, we introduce a taxonomy categorizing existing approaches by their theoretical foundations, architectural implementations, and validation strategies. We particularly focus on methods that address perceptual uncertainty and incorporate explicit legal norms, facilitating decisions that are both technically robust and legally defensible. The review covers neural-symbolic integration methods for perception, logic-driven rule representation, and norm-aware prediction strategies, all contributing toward transparent and accountable autonomous vehicle operation. We highlight critical open questions and practical trade-offs that must be addressed, offering multidisciplinary insights from engineering, logic, and law to guide future developments in legally compliant autonomous driving systems.