Learning Side-by-Side Navigation for Personal Mobility Vehicles from Human Drivers
作者: Luis Yoichi Morales, Florent Ferreri
分类: cs.RO
发布日期: 2026-10-27
💡 一句话要点
提出一种新方法实现个人移动车辆的并行导航
🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture)
关键词: 个人移动车辆 并行导航 社交队形 模型预测控制 逆强化学习 智能交通 安全层
📋 核心要点
- 现有的个人移动车辆导航方法往往缺乏有效的社交队形规划，导致乘客体验不佳。
- 论文提出了一种基于模型预测控制的协同规划方法，车辆通过观察彼此的里程计信息进行决策。
- 实验结果显示，该方法在障碍物环境中保持对话队形的时间显著高于人类驾驶者，且速度匹配良好。
📝 摘要（中文）
本文提出了一种实现个人移动车辆（PMVs）并行自主运动的方法。给定全局路径，两辆PMVs协同规划，以保持社交队形，使乘客能够进行对话，同时确保安全和平稳的行驶。每辆车运行自己的基于采样的模型预测规划器，规划器仅交换各自观察到的里程计信息。行为参数通过对十对人类驾驶PMVs的409分钟驾驶数据进行逆强化学习进行拟合。实验结果表明，在闭环仿真中，车辆在障碍物条件下保持对话队形的时间显著高于人类驾驶者。
🔬 方法详解
问题定义：本文旨在解决个人移动车辆在并行导航时的社交队形问题，现有方法未能有效考虑乘客的社交互动需求和安全性。
核心思路：通过协同规划和模型预测控制，车辆仅依赖彼此的里程计信息进行决策，确保乘客能够进行对话并保持安全行驶。
技术框架：整体架构包括两个主要模块：1) 车辆的模型预测规划器，负责路径规划；2) 安全层，确保车辆在任何情况下都能安全制动。
关键创新：该方法的创新之处在于使用逆强化学习从人类驾驶数据中提取行为参数，并通过动态调整车辆间的社交队形来提升乘客体验。
关键设计：关键设计包括使用基于采样的模型预测控制器，行为参数通过对409分钟的驾驶数据进行逆强化学习拟合，安全层确保车辆在遇到障碍时能够安全反应。
🖼️ 关键图片
📊 实验亮点
实验结果显示，在闭环仿真中，车辆在障碍物环境中保持对话队形的时间分别为60.5%和68.9%，而人类驾驶者仅为34.0%和36.0%。在清晰通道中，保持对话队形的时间更是达到81.2%和91.5%。
🎯 应用场景
该研究的潜在应用领域包括智能交通系统、共享出行服务和老年人或残疾人辅助移动设备。通过提升个人移动车辆的社交互动能力，可以显著改善乘客的出行体验，推动智能交通技术的发展。
📄 摘要（原文）
This paper presents an approach to achieve side-by-side autonomous motion with a pair of personal mobility vehicles (PMVs). Given a global path, two PMVs plan cooperatively so as to hold a social formation that lets the passengers hold a conversation, while keeping the ride safe and smooth. Each vehicle runs its own sampling-based model-predictive planner; the planners exchange nothing but the odometry each vehicle could observe of the other, and the joint decision is expressed as a pair of utility matrices over pairs of candidate trajectories, resolved by a common rule. The behavioural parameters are fitted by inverse reinforcement learning from 409 minutes of driving by ten pairs of people in human-driven PMVs in an indoor environment, and the lanes the pair can use are derived offline from the corridor itself rather than fixed by hand. A safety layer the cost cannot override executes a move only if the vehicle can still brake clear, measured from its whole body, and a vehicle that is stuck reverses along its own path and continues. In closed-loop simulation over 24 runs in the conditions the drivers were recorded in and 24 in four generated obstacle layouts, no vehicle entered a wall or a cone. The pair holds conversational formation for 60.5 and 68.9 percent of the time among obstacles (anticlockwise and clockwise) and 81.2 and 91.5 percent in a clear aisle, against the drivers' 34.0 and 36.0 percent, and 52.9 and 56.0 percent, and it matches their speed. It keeps one lateral formation where the drivers alternated between a close diagonal and a wide abreast one, and it varies far less. Experimental results are based on a differential drive kinematic simulator running on ROS 2.