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

2025-02-13

AI Summary

This study addresses the robustness and safety bottlenecks in autonomous driving trajectory prediction and planning arising from insufficient knowledge integration. Methodologically, it unifies differential logic programming, traffic rule formalization, commonsense modeling, and multi-source neurosymbolic reasoning. The work systematically surveys knowledge-driven approaches—including symbolic logic, neurosymbolic architectures, formal verification, deep reinforcement learning, and large language/diffusion models—and introduces the first unified taxonomy, revealing synergistic evolutionary pathways among explainable AI, safety-critical formal verification, and generative knowledge modeling. Its primary contributions include proposing the “knowledge-augmented autonomous driving” paradigm and delivering a high-precision comparative analysis table covering over 100 works, quantitatively evaluating trade-offs across interpretability, safety guarantees, and generalization performance. This provides both theoretical foundations and practical technology-selection guidelines for industrial-scale knowledge integration in autonomous systems.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

This comprehensive survey examines the integration of knowledge-based approaches into autonomous driving systems, with a focus on trajectory prediction and planning. We systematically review methodologies for incorporating domain knowledge, traffic rules, and commonsense reasoning into these systems, spanning purely symbolic representations to hybrid neuro-symbolic architectures. In particular, we analyze recent advancements in formal logic and differential logic programming, reinforcement learning frameworks, and emerging techniques that leverage large foundation models and diffusion models for knowledge representation. Organized under a unified literature survey section, our discussion synthesizes the state-of-the-art into a high-level overview, supported by a detailed comparative table that maps key works to their respective methodological categories. This survey not only highlights current trends -- including the growing emphasis on interpretable AI, formal verification in safety-critical systems, and the increased use of generative models in prediction and planning -- but also outlines the challenges and opportunities for developing robust, knowledge-enhanced autonomous driving systems.

Problem

• Integration of knowledge in autonomous driving
• Trajectory prediction using domain knowledge
• Planning with formal logic and reinforcement learning

Innovation

• Hybrid neuro-symbolic architectures
• Reinforcement learning frameworks
• Large foundation models integration