Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey

TL;DR

This survey reviews how knowledge-based methods are integrated into autonomous vehicle systems for improved prediction and planning, highlighting recent advances, challenges, and future directions.

Contribution

It provides a comprehensive categorization and analysis of knowledge integration approaches in autonomous driving, including symbolic, hybrid, and emerging technologies.

Findings

Emerging importance of interpretable AI in autonomous systems

Increasing role of formal verification for safety-critical applications

Growing trend towards hybrid knowledge and machine learning approaches

Abstract

This comprehensive survey examines the integration of knowledge-based approaches in autonomous driving systems, specifically focusing on trajectory prediction and planning. We extensively analyze various methodologies for incorporating domain knowledge, traffic rules, and commonsense reasoning into autonomous driving systems. The survey categorizes and analyzes approaches based on their knowledge representation and integration methods, ranging from purely symbolic to hybrid neuro-symbolic architectures. We examine recent developments in logic programming, foundation models for knowledge representation, reinforcement learning frameworks, and other emerging technologies incorporating domain knowledge. This work systematically reviews recent approaches, identifying key challenges, opportunities, and future research directions in knowledge-enhanced autonomous driving systems. Our analysis reveals emerging trends in the field, including the increasing importance of interpretable AI, the role of formal verification in safety-critical systems, and the potential of hybrid approaches that combine traditional knowledge representation with modern machine learning techniques.