Knowledge Integration Strategies in Autonomous Vehicle Prediction and Planning: A Comprehensive Survey
This survey provides a systematic review of knowledge integration strategies for researchers and developers in autonomous driving, identifying key challenges and future directions in this safety-critical domain.
This survey analyzes methods for integrating domain knowledge, traffic rules, and commonsense reasoning into autonomous driving systems, specifically for trajectory prediction and planning. It categorizes approaches by knowledge representation and integration, from symbolic to neuro-symbolic, and reviews recent developments in logic programming, foundation models, and reinforcement learning.
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.