<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

Budapest University of Technology and Economics

Production Engineering Archives

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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...

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

Publié en ligne: 14 nov. 2018

Pages: 43 - 47

Reçu: 07 mai 2018

Accepté: 22 juin 2018

DOI: https://doi.org/10.30657/pea.2018.19.09

Mots clés
classification, traffic signal system, anomalies, autonomous vehicles, test environment

© 2018 Henrietta Lengyel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Publié en ligne: 14 nov. 2018

Pages: 43 - 47

Reçu: 07 mai 2018

Accepté: 22 juin 2018

DOI: https://doi.org/10.30657/pea.2018.19.09

Mots clésclassification, traffic signal system, anomalies, autonomous vehicles, test environment

© 2018 Henrietta Lengyel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
classification, traffic signal system, anomalies, autonomous vehicles, test environment