Joint Transportation Research Program, Purdue University, West Lafayette, IN 47907, USA.
Sensors (Basel). 2022 Sep 22;22(19):7187. doi: 10.3390/s22197187.
The United States has over three trillion vehicle miles of travel annually on over four million miles of public roadways, which require regular maintenance. To maintain and improve these facilities, agencies often temporarily close lanes, reconfigure lane geometry, or completely close the road depending on the scope of the construction project. Lane widths of less than 11 feet in construction zones can impact highway capacity and crash rates. Crash data can be used to identify locations where the road geometry could be improved. However, this is a manual process that does not scale well. This paper describes findings for using data from onboard sensors in production vehicles for measuring lane widths. Over 200 miles of roadway on US-52, US-41, and I-65 in Indiana were measured using vehicle sensor data and compared with mobile LiDAR point clouds as ground truth and had a root mean square error of approximately 0.24 feet. The novelty of these results is that vehicle sensors can identify when work zones use lane widths substantially narrower than the 11 foot standard at a network level and can be used to aid in the inspection and verification of construction specification conformity. This information would contribute to the construction inspection performed by agencies in a safer, more efficient way.
美国每年有超过 3 万亿英里的车辆行驶在超过 400 万英里的公共道路上，这些道路需要定期维护。为了维护和改善这些设施，机构通常会根据施工项目的范围，临时关闭车道、重新配置车道几何形状或完全关闭道路。施工区域的车道宽度小于 11 英尺会影响高速公路的通行能力和事故率。事故数据可用于确定可以改进道路几何形状的位置。然而，这是一个手动过程，无法很好地扩展。本文介绍了使用生产车辆上的车载传感器数据测量车道宽度的研究结果。在印第安纳州的 US-52、US-41 和 I-65 上测量了超过 200 英里的道路，使用车辆传感器数据并与移动 LiDAR 点云进行了比较，作为地面实况，其均方根误差约为 0.24 英尺。这些结果的新颖之处在于，车辆传感器可以识别网络层面上工作区使用的车道宽度明显小于 11 英尺标准的情况，并可用于辅助检查和验证施工规范的一致性。这些信息将以更安全、更高效的方式为机构进行的施工检查做出贡献。
