一种结合归纳推理与演绎推理的神经符号框架用于自动驾驶规划
A Neuro-Symbolic Framework Combining Inductive and Deductive Reasoning for Autonomous Driving Planning
AI总结：
本文提出一种神经符号框架，结合归纳与演绎推理提升自动驾驶规划的透明性和安全性，在nuScenes基准中优于现有方法。
摘要：
现有的端到端自动驾驶模型依赖纯数据驱动的归纳推理，导致在复杂长尾场景中缺乏可解释性和绝对安全性。为克服这一瓶颈，我们提出了一种新的神经符号轨迹规划框架，将严谨的演绎推理无缝整合到端到端神经网络中。具体而言，我们的框架利用大型语言模型（LLM）动态提取场景规则，并使用答案集编程（ASP）求解器进行确定性逻辑仲裁，生成安全且可追溯的离散驾驶决策。为弥合离散符号与连续轨迹之间的差距，我们引入了决策条件解码机制，将高层逻辑决策转换为可学习的嵌入向量，同时约束规划查询和车辆可微分运动学自行车模型（KBM）的物理初始速度。通过结合BMB生成的物理基线轨迹与神经残差校正，我们的方法在保证运动学可行性的同时确保了高透明度。在nuScenes基准上，我们的方法在L2均方误差、碰撞率和轨迹预测一致性（TPC）方面均优于现有方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
