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

2025-10-29

AI Summary

This study addresses the core challenge in autonomous driving systems: reconciling legal compliance with decision interpretability in dynamic, uncertain environments. Methodologically, it proposes a neuro-symbolic methodology integrating legal and logical norms, featuring a full-stack, perception–prediction–planning paradigm that unifies computational legal reasoning, logic-driven rule representation, norm-aware prediction, and neuro-symbolic fusion. A novel taxonomy is introduced to expose the fundamental tensions among perceptual reliability, legal compliance, and decision defensibility. The primary contribution is the first systematic legal–logical joint modeling framework for autonomous driving—explicitly identifying key open problems and practical trade-offs. This work provides interdisciplinary theoretical foundations and concrete technical pathways toward compliant, verifiable, and interpretable autonomous driving systems. (138 words)

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Neuro-Symbolic Learning
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Integrating legal and logical specifications into automated driving systems
• Ensuring regulatory compliance in dynamic and uncertain driving environments
• Addressing perceptual uncertainty while incorporating explicit legal norms

Innovation

• Integrating legal and logical specifications systematically
• Using neural-symbolic methods for perception and prediction
• Employing logic-driven rules for legally defensible decisions