DriveMCP：面向高级驾驶辅助系统的智能体AI框架
DriveMCP: An Agentic AI framework for Advanced Driver Assistance System
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中文总结 AI 辅助
DriveMCP是一个智能体AI驾驶辅助框架，通过模块化感知、合规推理和安全仲裁流程，在CARLA模拟中减少交通违规并改善危险响应时间。
中文摘要 AI 辅助
本文提出了一种智能体AI驾驶辅助框架，将感知、合规推理、车辆状态解释和安全仲裁集成到一个模块化且可审计的流程中。该架构被称为DriveMCP，包含一个类似传感器的感知栈，并以DriveLM作为视觉语言前端，生成图结构的场景理解（图视觉问答）和基于语言的驾驶信息。world_state中的关键合规要素，包括限速标志和管辖区域提示，是通过结构化解析层从DriveLM输出中推导出来的，而非作为模拟器真实值注入。一个有状态编排层协调作为模型上下文协议（MCP）服务器暴露的专门专家：（i）规则服务器，执行基于检索增强的合规推理，涉及特定管辖区的交通法规和标志惯例；（ii）天气服务器，估计牵引力风险和情境速度建议；（iii）MCP-CAN服务器，提供控制器局域网（CAN）/车载诊断（OBD）遥测和诊断上下文，用于健康感知的风险塑造。这些输出被融合以生成结构化决策，提示推荐行动方案。随后，结果进一步通过受责任敏感安全（RSS）启发的护栏过滤，该护栏在有界在线适应下仲裁“说话”与“行动”决策。在CARLA模拟中，跨多语言、跨境和动态限速场景，DriveMCP相对于VLM-Direct、VLM-Direct+RAG和VLM-Tools-NoArbiter基线减少了交通违规和超速，同时改善了危险响应时间并保持亚秒级咨询延迟。
英文摘要
An agentic AI driver-assistance framework that integrates perception, compliance reasoning, vehicle-state interpretation, and safety arbitration into a modular and auditable pipeline. The architecture, referred to as DriveMCP, incorporates a sensor-like perception stack alongside DriveLM as the vision-language front end to generate a graph-structured scene understanding (Graph Visual Question Answering) and language-grounded driving information. Key compliance elements in world_state, including posted speed limits and jurisdiction cues, are derived from DriveLM outputs through a structured parsing layer rather than being injected as simulator ground truth. A stateful orchestration layer coordinates specialized experts exposed as Model Context Protocol (MCP) servers: (i) a Rules server that performs retrieval-augmented compliance reasoning over jurisdiction-specific traffic codes and sign conventions, (ii) a Weather server that estimates traction risk and contextual speed advisories, and (iii) an MCP-CAN server that surfaces Controller Area Network (CAN)/On-Board Diagnostics (OBD) telemetry and diagnostic context for health-aware risk shaping. These outputs are fused to generate a structured decision that prompts a recommended course of action. The outcome is then further filtered by a Responsibility-Sensitive Safety (RSS)-inspired guardrail that arbitrates speak versus act decisions under bounded online adaptation. In CARLA simulation across multilingual, cross-border, and dynamic speed-limit scenarios, DriveMCP reduces traffic infractions and overspeed relative to the VLM-Direct, VLM-Direct+RAG, and VLM-Tools-NoArbiter baselines, while improving hazard response time and maintaining sub-second advisory latency.
发表机构
- Simon Fraser University（西蒙弗雷泽大学）
机构由 AI 辅助整理，请以论文原文为准。
