arXiv:2602.08326cs.RO2026-02

根据用户问卷设计安全距离偏好,实现个性化自动驾驶规划。

Personalized Autonomous Driving via Optimal Control with Clearance Constraints from Questionnaires

  • 通过问卷收集用户对周围车辆距离的偏好,并作为最优控制约束。
  • 将复杂场景分解为多个固定场景子问题,实现并行求解与实时决策。
  • 适用于注重驾驶舒适性、追求个性化体验的智能汽车研究者。

不考虑用户对周围车辆安全距离偏好的驾驶行为可能引发不适。为此,我们提出一种规划框架,明确引入用户对周围车辆期望安全间距的偏好。设计了一项针对性问卷,用于捕捉与交互相关的关键因素,包括周围车辆的大小、速度、位置及动态行为,以及自身车辆的动作。用户对特定场景的偏好反馈被转化为最优控制问题中的约束条件。然而,在单一最优控制问题中涵盖所有可能出现的驾驶场景在实际中不可行,因其带来的计算复杂度导致无法实时执行。为此,我们通过将原问题分解为多个固定场景的子问题进行近似处理,各子问题并行求解,并基于原始问题的成本函数选择最优解。为验证方法有效性,我们在不同用户问卷响应下进行仿真,通过衡量偏好一致率评估本规划器相较于无偏好基线方法对用户偏好的反映能力。

原文摘要 · Abstract (English)

Driving without considering the preferred separation distance from surrounding vehicles may cause discomfort for users. To address this limitation, we propose a planning framework that explicitly incorporates user preferences regarding the desired level of safe clearance from surrounding vehicles. We design a questionnaire purposefully tailored to capture user preferences relevant to our framework, while minimizing unnecessary questions. Specifically, the questionnaire considers various interaction-relevant factors, including the surrounding vehicle's size, speed, position, and maneuvers of surrounding vehicles, as well as the maneuvers of the ego vehicle. The response indicates the user-preferred clearance for the scenario defined by the question and is incorporated as constraints in the optimal control problem. However, it is impractical to account for all possible scenarios that may arise in a driving environment within a single optimal control problem, as the resulting computational complexity renders real-time implementation infeasible. To overcome this limitation, we approximate the original problem by decomposing it into multiple subproblems, each dealing with one fixed scenario. We then solve these subproblems in parallel and select one using the cost function from the original problem. To validate our work, we conduct simulations using different user responses to the questionnaire. We assess how effectively our planner reflects user preferences compared to preference-agnostic baseline planners by measuring preference alignment.

自动驾驶个性化最优控制用户偏好

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