用自然语言自动调整自动驾驶行为参数，让车更懂用户需求。
Language-Driven Cost Optimization for Autonomous Driving
- 用大模型理解用户描述，自动生成运动规划成本函数参数。
- 仿真验证可精准实现用户意图的驾驶行为改变。
- 支持人机交互反馈，适合非技术用户定制车辆行为。
自动驾驶车辆的行驶行为通常由运动规划器的成本函数控制，该函数编码了速度跟踪、平滑性、车道保持和避撞等目标。然而，调节塑造该成本函数的参数是一项需要专业技术的挑战，限制了车辆对动态交通场景或用户偏好的适应能力。本文提出一种语言驱动的自适应成本设计框架。大型语言模型（LLM）解析结构化场景描述和自然语言用户查询，生成应用于风险感知模型预测路径积分（MPPI）控制器的参数。系统包含人机协同验证环节，提出的行车行为变化以非技术语言描述并经确认后才部署。用户可在部署前后提供反馈，实现行为的迭代优化。在多种真实驾驶场景下评估该框架的有效性，仿真结果表明，该方法能以直观方式实现与用户意图一致的行为改变，有效弥合智能车辆控制系统与终端用户之间的鸿沟。
原文摘要 · Abstract (English)
The driving behavior of autonomous vehicles is typically governed by the cost function of their motion planner, which encodes objectives such as speed tracking, smoothness, lane keeping, and collision avoidance. However, tuning the parameters that shape this cost function is a challenging task that requires technical expertise, limiting the vehicle's ability to adapt to evolving traffic scenarios or end-user preferences. This work presents a language-driven framework for adaptive cost design in autonomous driving. A Large Language Model (LLM) interprets structured scenario descriptions and natural language user queries to generate the parameters applied to a risk-aware Model Predictive Path Integral (MPPI) controller. The system incorporates a human-in-the-loop validation stage in which the proposed behavioral changes are described in non-technical language and confirmed prior to deployment. Users may additionally provide feedback either before or after deployment, enabling iterative refinement of the vehicle's motion behavior. The framework is evaluated across multiple queries in realistic driving scenarios to assess its effectiveness. Simulation results demonstrate that the method successfully induces behavioral changes that align with the intended requirements in an intuitive manner, thereby bridging the gap between intelligent vehicle control systems and end users.
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