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Classification of traffic signal system anomalies for environment tests of autonomous vehicles

Lengyel H., Szalay Z.

Production Engineering Archives

2018

Vol. 19

43--47

In the future there will be a lot of changes and development concerning autonomous transport that will affect all participants of transport. There are still difficulties in organizing transport, but with the introduction of autonomous vehicles more challenges can be expected. Recognizing and tracking horizontal and vertical signs can cause a difficulties for drivers and, later, for autonomous systems. Environmental conditions, deformity and quality affect the perception of signals. The correct recognition results in safe travelling for everyone on the roads. Traffic signs are designed for people that is why the recognition process is harder for the machines. However, nowadays some developers try to create a traffic sign that autonomous vehicles can use. Computer identification needs further development, as it is necessary to consider cases where traffic signs are deformed or not properly placed. In the following investigation, the advantages and disadvantages of the different perception methods and their possibilities were gathered. A methodology for the classification of horizontal and vertical traffic signs anomalies that may help in designing better testing and validation environments for traffic sign recognition systems in the future was also proposed.

Supporting autonomous vehicles by creating HD maps

Barsi A., Poto V., Somogyi A., Lovas T., Tihanyi V., Szalay Z.

Production Engineering Archives

2017

Vol. 16

43--46

Maps are constantly developing, also, the newly defined High Definition (HD) maps increase the map content remarkably. They are based on three-dimensional survey, like laser scanning, and then stored in a fully new structured way to be able to support modern-day vehicles. Beyond the traditional lane based map content, they contain information about the roads’ neighbourhood. The goal of these maps is twofold. Primarily, they store the connections where the vehicles can travel with the description of the road-environment. Secondly, they efficiently support the exact vehicle positioning. The paper demonstrates the first results of a pilot study in the creation of HD map of an urban and a rural environment. The applied data collection technology was the terrestrial laser scanning, where the obtained point cloud was evaluated. The data storage has been solved by an in-house developed information storage model with the ability to help in vehicle control processes.