Road edge drop-off has well known impacts on the safety of the traveling public and roadway service life. Continuous inspection of roadsides is therefore needed to monitor their condition. Autonomous vehicles (AVs) are increasing in number and have the potential to be used for road and roadside condition data collection. This study explored the use of AVs to assess road edge drop-off. To this end, the ability and accuracy of a research grade autonomous vehicle platform built on a passenger car was used to determine road edge drop-off. Data were collected for the roadside along a state highway in Ohio using the different sensors of the vehicle platform, including a Light Detection and Ranging (lidar) sensor. A 160-m (525-ft) long section of the highway was selected and surveyed using a high accuracy stationary terrestrial laser scanner to obtain the topographic map of the highway and its sides. The lidar data were analyzed using fully automated deep learning methods to determine the edge drop-off severity along the selected section. The analyzed lidar data were compared with those obtained using the high accuracy stationary terrestrial laser scanner. The results of this study showed that the autonomous vehicle platform can be used successfully to assess the road edge drop-off with excellent accuracy, particularly when using end-to-end deep learning methods for analyzing the collected data.

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