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.

翻译：驾驶过程中若不考虑与周围车辆的首选间距，可能导致用户不适。为克服这一局限性，本文提出一种显式纳入用户对周围车辆安全间距偏好的规划框架。我们专门设计了一套问卷，旨在捕获与本框架相关的用户偏好，同时最大限度减少冗余问题。具体而言，问卷综合考虑了多种交互相关因素，包括周围车辆的尺寸、速度、位置与机动行为，以及自车机动行为。问卷反馈反映了用户在特定场景下的偏好间距，这些数据被转化为最优控制问题的约束条件。然而，在单一最优控制问题中涵盖驾驶环境所有可能场景是不现实的，因为由此产生的计算复杂度将导致实时求解不可行。为突破此限制，我们通过将原问题分解为多个子问题来近似求解，每个子问题处理固定场景。随后并行求解这些子问题，并依据原问题的代价函数进行选择。为验证本方法，我们使用不同用户问卷反馈进行仿真实验，通过测量偏好匹配度来评估规划器相对于无偏好基线规划器的用户偏好反映效果。