Boxes-Based Representation and Data Sharing of Road Surface Friction for CAVs
Boxes-Based Representation and Data Sharing of Road Surface Friction for CAVs
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DOI：
10.1007/s42421-023-00071-0
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发表时间：
2023-06
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影响因子：
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通讯作者：
Liming Gao;Juliette Mitrovich;C. Beal;Wushuang Bai;S. Maddipatla;Cindy Chen;Kshitij Jerath;H. Haeri;Lorina Sinanaj;Sean Brennan
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作者：
Liming Gao;Juliette Mitrovich;C. Beal;Wushuang Bai;S. Maddipatla;Cindy Chen;Kshitij Jerath;H. Haeri;Lorina Sinanaj;Sean Brennan
当遇到不可预见的危险道路摩擦条件时，车辆很容易意外失去控制。随着自动化和连接性越来越多地帮助驾驶员，车辆性能可以从道路摩擦预览图中显着受益，特别是确定车辆前方的摩擦力可能在何处以及如何突然减小。尽管许多技术使车辆能够测量在表面上行驶时的局部摩擦力，但这些遭遇限制了车辆在遇到低摩擦表面之前减速的能力。利用联网自动驾驶车辆 (CAV) 的连接性，可以通过汇总车辆信息来创建全球道路摩擦力地图。创建这些全局摩擦力地图的一个挑战是所涉及的数据量非常大，并且填充地图的测量结果是由不均匀覆盖网格的车辆轨迹生成的。本文提出了一种道路摩擦图生成策略，该策略将沿着 CAV 的各个轨迹测量的道路轮胎摩擦系数聚合到路面网格中。此外，通过进一步对摩擦网格进行聚类，这项工作的一个见解是，摩擦图可以用矩形框来紧凑地表示，矩形框由空间中的一对角坐标、摩擦值和框内的置信区间定义。为了演示该方法，提出了一个模拟，该模拟集成了交通模拟、车辆动力学和车载摩擦估计器以及高速公路路面，其中摩擦力在空间上发生变化，特别是在桥段上。实验结果表明，通过收集和聚合来自CAV的摩擦数据可以有效地测量道路摩擦分布。
Vehicles can easily lose control unexpectedly when encountering unforeseen hazardous road friction conditions. With automation and connectivity increasingly available to assist drivers, vehicle performance can significantly benefit from a road friction preview map, particularly to identify where and how friction ahead of a vehicle may be suddenly decreasing. Although many techniques enable the vehicle to measure the local friction as driving upon a surface, these encounters limit the ability of a vehicle to slow down before a low-friction surface is already encountered. Using the connectivity of connected and autonomous vehicles (CAVs), a global road friction map can be created by aggregating information from vehicles. A challenge in the creation of these global friction maps is the very large quantity of data involved, and that the measurements populating the map are generated by vehicle trajectories that do not uniformly cover the grid. This paper presents a road friction map generation strategy that aggregates the measured road-tire friction coefficients along the individual trajectories of CAVs into a road surface grid. In addition, through clustering the friction grids further, an insight of this work is that the friction map can be represented compactly by rectangular boxes defined by a pair of corner coordinates in space, a friction value, and a confidence interval within the box. To demonstrate the method, a simulation is presented that integrates traffic simulations, vehicle dynamics and on-vehicle friction estimators, and a highway road surface, where friction is changing in space, particularly over a bridge segment. The experimental results indicate that the road friction distribution can be measured effectively by collecting and aggregating the friction data from CAVs.