发表机构
Netherlands Organisation for Applied Scientific Research; Eindhoven University of Technology; Radboud University（荷兰应用科学研究组织; 埃因霍温理工大学; 拉德堡德大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种结合神经网络与模型预测控制的双过程自动驾驶框架，通过元认知组件和知识图谱进行风险推理，在CARLA测试中减少89%碰撞并提升规则遵守。
AI 中文摘要
在自动驾驶系统部署到公共道路之前，这些系统必须符合安全标准、交通规则和社会规范，这一点至关重要。尽管在大量驾驶数据上训练的神经网络在常规驾驶任务中表现良好，但这些模型在面对数据中未充分体现的新颖情境时往往力不从心。在这项工作中，我们提出了一种新颖框架，该框架将用于常规驾驶任务中直觉式、基于学习的规划的神经网络，与用于陌生情境中基于推理的规划的模型预测控制相结合，其灵感来源于双过程理论。我们设计了一个元认知组件，利用知识图谱，基于明确的感知信息以及相关的交通规则和社会规范，对情境风险进行推理，从而在两种规划方式之间进行切换。情境风险通过风险场来表示，既指导元认知组件中的切换机制，也指导基于推理的规划器对安全标准、交通规则和社会规范的遵守。我们在CARLA中测试了该框架的有效性，针对涉及（紧急）车辆闯红灯的典型分布外情境的多种变体。我们表明，与仅使用神经网络的规划器相比，该新颖架构将场景中的碰撞次数减少了89%，并提高了对特殊优先通行权规则的遵守程度。
英文摘要
Before autonomous driving systems can be deployed on public roads, it is vital that these systems comply with safety standards, traffic rules, and social norms. Although neural networks trained on large amounts of driving data perform well in routine driving tasks, these models often struggle in novel situations that are not well-represented in the data. In this work, we propose a novel framework that combines a neural network for intuitive, learning-based planning in routine driving tasks with model predictive control for reasoning-based planning in unfamiliar situations, inspired by Dual Process Theory. A meta-cognitive component is designed to switch between the two, using a knowledge graph to reason about contextual risk based on explicit perceptual information and relevant traffic rules and social norms. Contextual risk is represented through risk fields, guiding both the switching mechanism in the meta-cognitive component and compliance with safety standards, traffic rules, and social norms in the reasoning-based planner. The effectiveness of our framework is tested in CARLA for variations of a typical out-of-distribution situations involving (emergency) vehicles running a red light. We show that the novel architecture reduces the number of collisions in the scenarios by 89% and improves compliance with the special right-of-way rules, compared to the NN-only planner.
Comments8 pages, 9 figures, Accepted for publication at the IEEE International Conference on Intelligent Transportation Systems (ITSC), 2026
