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利用车道保持辅助传感器数据对道路路面标线维护进行优先级排序。

Prioritizing Roadway Pavement Marking Maintenance Using Lane Keep Assist Sensor Data.

机构信息

Joint Transportation Research Program, College of Engineering, Lyles School of Civil Engineering, Purdue University, West Lafayette, IN 47907, USA.

Ford Motor Company, MD 3135, 2101 Village Road Dearborn, Dearborn, MI 48121, USA.

出版信息

Sensors (Basel). 2021 Sep 8;21(18):6014. doi: 10.3390/s21186014.

Abstract

There are over four million miles of roads in the United States, and the prioritization of locations to perform maintenance activities typically relies on human inspection or semi-automated dedicated vehicles. Pavement markings are used to delineate the boundaries of the lane the vehicle is driving within. These markings are also used by original equipment manufacturers (OEM) for implementing advanced safety features such as lane keep assist (LKA) and eventually autonomous operation. However, pavement markings deteriorate over time due to the fact of weather and wear from tires and snowplow operations. Furthermore, their performance varies depending upon lighting (day/night) as well as surface conditions (wet/dry). This paper presents a case study in Indiana where over 5000 miles of interstate were driven and LKA was used to classify pavement markings. Longitudinal comparisons between 2020 and 2021 showed that the percentage of lanes with both lines detected increased from 80.2% to 92.3%. This information can be used for various applications such as developing or updating standards for pavement marking materials (infrastructure), quantifying performance measures that can be used by automotive OEMs to warn drivers of potential problems with identifying pavement markings, and prioritizing agency pavement marking maintenance activities.

摘要

美国拥有超过四百万英里的道路,进行维护活动的地点的优先级通常依赖于人工检查或半自动化专用车辆。路面标记用于划定车辆行驶的车道边界。原始设备制造商 (OEM) 也使用这些标记来实现先进的安全功能,例如车道保持辅助 (LKA),最终实现自动驾驶。然而,由于天气和轮胎及吹雪机作业的磨损,路面标记会随着时间的推移而恶化。此外,它们的性能还取决于照明(白天/晚上)和表面状况(湿/干)。本文介绍了印第安纳州的一个案例研究,在该案例中,研究人员驾驶了超过 5000 英里的州际公路,并使用 LKA 对路面标记进行了分类。2020 年和 2021 年的纵向比较表明,检测到两条线的车道比例从 80.2%增加到 92.3%。这些信息可用于各种应用,例如为路面标记材料(基础设施)制定或更新标准,量化可用于汽车 OEM 警告驾驶员潜在的路面标记识别问题的性能指标,并为机构的路面标记维护活动确定优先级。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/534a/8473332/ead066dcde28/sensors-21-06014-g001.jpg

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