Sensor system for development of perception systems for ATO

Abstract

Developing AI systems for automatic train operation (ATO) requires developers to have a deep understanding of the human tasks they are trying to replace. This paper fills this gap and translates the regulatory requirements from the context of German railways for the AI developer community. As a result, tasks such as train’s path monitoring for collision prediction, signal detection, door operation, etc. are identified. Based on this analysis, a functionally justified sensor setup with detailed configuration requirements is presented. This setup was also evaluated by a survey within the railway industry. The evaluated sensors include RGB/IR cameras, LIDARs, radars and ultrasonic sensors. Calculations and estimates for the evaluated sensors are presented graphically and included in this paper. However, the ultimate sensor setup is still a subject of research. The results of this paper also address the lack of training and test datasets for railway AI systems. It is proposed to acquire research datasets that will allow the training of domain adaptation algorithms to transform other datasets, thus increasing the number of available datasets. The sensor setup is also recommended for such research datasets.

Toward the Enhancement of Rail Sustainability: Demonstration of a Holistic Approach to Obstacle Detection in Operational Railway Environments

“…It is a critical component of modern railway systems aiming for safer, more efficient, and more reliable transport networks. The majority of OD systems in railways are deployed as on-board systems installed on trains themselves, consisting of different on-board sensors such as cameras and lidar sensors [4][5][6][7][8]. These on-board systems are designed to continuously monitor the tracks ahead and detect any obstacles or hazards that may pose a risk to the train's safe operation.…”