The modern tram is equipped with special units that allow it to communicate with the transport infrastructure and orient itself in space thanks to cameras and lidars (Light Detection And Ranging - a method for remote distance measurement). Using up-to-date and highly accurate map data, it can react to unforeseen events while driving. These include, for example, obstacles on the track or pedestrians crossing the road. Since July this year, the tram has been running on a test circuit in Pilsen.
Some elements of the tram were developed within the DIDYMOS research project. This project is partly followed by experts from the Department of Cybernetics at the Faculty of Applied Sciences (FAV), headed by Lukáš Picek. Together with colleagues and in cooperation with Škoda Digital and Škoda Transportation, they are working on the so-called Smart Depot project. The depot and polygon in the Finnish city of Tampere will be used to test automated and autonomous rolling stock. Sensor elements will be installed in the test section to automatically detect obstacles using lidar or cameras. "The project aims to design and test in a real environment methods for analyzing the safety-critical functions of AI algorithms for locating and detecting obstacles usable in autonomous tram control," explains Lukáš Picek.
Currently, a relatively large number of systems address the use and design of conventional and advanced computer vision and machine learning methods in autonomous vehicle control and robotics. In most cases, simpler types of sensors, such as radar or ultrasound, are used for obstacle detection. This is due to the complexity of AI algorithms and the vagueness of standards that certification of systems using AI algorithms requires.
Faculty of Applied Sciences |
Martina Batková |
23. 10. 2024 |