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A Neuro-Symbolic Framework Combining Inductive and Deductive Reasoning for Autonomous Driving Planning

Hongyan Wei, Wael AbdAlmageed

Abstract

Existing end-to-end autonomous driving models rely heavily on purely data-driven inductive reasoning. This "black-box" nature leads to a lack of interpretability and absolute safety guarantees in complex, long-tail scenarios. To overcome this bottleneck, we propose a novel neuro-symbolic trajectory planning framework that seamlessly integrates rigorous deductive reasoning into end-to-end neural networks. Specifically, our framework utilizes a Large Language Model (LLM) to dynamically extract scene rules and employs an Answer Set Programming (ASP) solver for deterministic logical arbitration, generating safe and traceable discrete driving decisions. To bridge the gap between discrete symbols and continuous trajectories, we introduce a decision-conditioned decoding mechanism that transforms high-level logical decisions into learnable embedding vectors, simultaneously constraining the planning query and the physical initial velocity of a differentiable Kinematic Bicycle Model (KBM). By combining KBM-generated physical baseline trajectories with neural residual corrections, our approach inherently guarantees kinematic feasibility while ensuring a high degree of transparency. On the nuScenes benchmark, our method comprehensively outperforms the state-of-the-art baseline MomAD, reducing the L2 mean error to 0.57 m, decreasing the collision rate to 0.075%, and optimizing trajectory prediction consistency (TPC) to 0.47 m.

A Neuro-Symbolic Framework Combining Inductive and Deductive Reasoning for Autonomous Driving Planning

Abstract

Existing end-to-end autonomous driving models rely heavily on purely data-driven inductive reasoning. This "black-box" nature leads to a lack of interpretability and absolute safety guarantees in complex, long-tail scenarios. To overcome this bottleneck, we propose a novel neuro-symbolic trajectory planning framework that seamlessly integrates rigorous deductive reasoning into end-to-end neural networks. Specifically, our framework utilizes a Large Language Model (LLM) to dynamically extract scene rules and employs an Answer Set Programming (ASP) solver for deterministic logical arbitration, generating safe and traceable discrete driving decisions. To bridge the gap between discrete symbols and continuous trajectories, we introduce a decision-conditioned decoding mechanism that transforms high-level logical decisions into learnable embedding vectors, simultaneously constraining the planning query and the physical initial velocity of a differentiable Kinematic Bicycle Model (KBM). By combining KBM-generated physical baseline trajectories with neural residual corrections, our approach inherently guarantees kinematic feasibility while ensuring a high degree of transparency. On the nuScenes benchmark, our method comprehensively outperforms the state-of-the-art baseline MomAD, reducing the L2 mean error to 0.57 m, decreasing the collision rate to 0.075%, and optimizing trajectory prediction consistency (TPC) to 0.47 m.
Paper Structure (28 sections, 3 equations, 2 figures, 2 tables)

This paper contains 28 sections, 3 equations, 2 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. End-to-End Autonomous Driving Planning
  4. LLM-Based Planning
  5. Deductive Reasoning and Symbolic Methods in Planning
  6. Physics-Constrained Trajectory Planning
  7. Method
  8. Overall Architecture
  9. Deductive Reasoning via LLM and ASP
  10. Scene Fact Formulation.
  11. LLM-Based Rules Extraction.
  12. Symbolic Vocabulary Design.
  13. ASP Logical Arbitration.
  14. Decision-Conditioned Trajectory Generation
  15. Perception Backbone and Planning Queries.
  16. ...and 13 more sections

Figures (2)

  • Figure 1: Overall framework. The inductive stream (neural) and deductive stream (symbolic) run in parallel. The ASP engine produces discrete driving decisions which are conditioned into the Physics-Constrained Motion Decoder via dual paths: Path 1 modulates planning queries $\mathbf{Q}'_\text{plan} = \mathbf{Q}_\text{plan} \oplus \mathbf{d}$ (spatial shape), and Path 2 biases the KBM initial velocity $v_0' = v_0 + b_v$ (speed profile).
  • Figure 2: Detailed pipeline of the deductive reasoning engine. The LLM extracts semantic rules from perception facts via prompts, which are then combined with a static logical base and evaluated by the ASP solver (Clingo) to deduce final deterministic driving decisions.