Abstract
In this paper, a decision support system is proposed to assist an analyst in updating the highway roadside asset inventory. The feasibility of the system is tested with assets along an 8 km section of the A27 highway on the south coast of England, UK. Survey data from a vehicle equipped with a single forward-facing camera and a GPS-enabled inertial measurement unit, aerial imagery of the highway, and the asset inventory are fused to develop the system. The camera on the vehicle is calibrated so that assets may be automatically located within the survey images. The assets are then classified by a state-of-the-art convolutional neural network. Therefore, those assets recorded correctly in the inventory and those needing further manual inspection are automatically identified. Three different asset types are considered (traffic signs, matrix signs, and reference marker posts), and overall 91% of the assets in a withheld test set are verified automatically. Thus the analyst is presented with a much smaller set of assets for which the inventory is incorrect and which require further inspection. We therefore demonstrate the value in fusing multiple data sources to develop decision support systems for transportation asset monitoring.
Highlights
In this paper, a decision support system is proposed to assist an analyst in updating the highway roadside asset inventory
We test the method by considering those assets in the test set that are verified, and those identified for further manual inspection
The results show that the system performs well for all the asset types considered— of the 21 assets identified for further manual inspection, 13 were the result of an incorrect classification by the convolutional neural networks (CNNs) or an impeded view of the asset
Summary
A decision support system is proposed to assist an analyst in updating the highway roadside asset inventory. The assets are classified by a state-of-the-art convolutional neural network Those assets recorded correctly in the inventory and those needing further manual inspection are automatically identified. We demonstrate the value in fusing multiple data sources to develop decision support systems for transportation asset monitoring. Over 70% of U.S state agencies survey the highway to collect data on assets such as the pavement, signs, guardrails, and lighting units [2]. For roadside assets, an analyst usually inspects the survey data, and updates the asset inventory manually. There is a requirement for tools and systems to assist the analyst in their decision-making when monitoring roadside assets. Rich data sources concerning the assets already exist to develop and test the tools
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