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.

翻译：自动驾驶车辆的驾驶行为通常由其运动规划器的代价函数控制，该函数编码了速度跟踪、平滑性、车道保持和避障等目标。然而，调整该代价函数的参数是一项需要专业技术知识的挑战性任务，这限制了车辆适应不断变化的交通场景或最终用户偏好的能力。本文提出了一种面向自动驾驶自适应代价设计的语言驱动框架。大语言模型（LLM）通过解读结构化的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。该系统引入了一个人机交互验证阶段，在该阶段中，拟议的行为变化会以非技术性语言描述，并在部署前进行确认。用户还可以在部署前后提供反馈，从而实现对车辆运动行为的迭代优化。该框架在多个真实驾驶场景的查询中进行了评估，以检验其有效性。仿真结果表明，该方法能够以直观的方式成功诱导出符合预期需求的行为变化，从而弥合智能车辆控制系统与最终用户之间的鸿沟。