ARC-D

Problem Statement Currently, there is no automated collection of road condition data for the German Autobahn. An up-to-date database and automated assessment of the quality of individual stretches of highway is important to initiate suitable maintenance measures at an early stage and, thus, prevent cost-intensive long-term damage. A well-maintained highway network also holds great economic potential, for example in the field of autonomous driving.

Project Goal

The project goal is to create a basis for automatic and cost-efficient condition and inventory monitoring of the German Autobahn network by using artificial intelligence (AI) and machine learning (ML) methods (feasibility study). This will lay the foundation for generating a digital twin of the Autobahn. By using AI algorithms in the field of computer vision, a comparison between the actual condition and the digital twin (anomaly detection) can then take place. In this way, relevant information for Autobahn operations, such as lane conditions or signage, can automatically be detected and extracted from the data.

Project Approach

Video data from test routes are annotated in close cooperation with Hessen Mobil and automatically evaluated using Deep Learning methods in the field of image processing. For this purpose, various machine learning models are trained and the algorithms are extended according to the specific problem. In this way, changes to static objects, such as crash barriers, are automatically detected. The long-term viability of the concept is tested with the involvement of the Autobahn GmbH of the federal government.

Project Details

• Project on BMDV website
• FKZ: VB18F1043A
• Project Volume: 124.996 €
• Project Duration: 12/2019 – 12/2020

Associated Partners

Hessen Mobil Straßen und Verkehrsmanagement, Wiesbaden	Die Autobahn GmbH des Bundes, Berlin