Deep Learning-Based Train Obstacle Detection Technology: Application and Testing in Metros

With the rapid development of urban rail transit, unmanned train driving technology is also advancing rapidly. Automatic obstacle detection is particularly crucial and plays a vital role in ensuring train operation safety. This paper focuses on train obstacle detection technology and testing methods. First, we review existing obstacle detection systems and their testing methods, analyzing their technical principles, application status, advantages, and limitations. In the experimental section, the Intelligent Train Eye (ITE) system is used as a case study. Black-box testing is conducted in the level high-precision (LH) mode, with corresponding test cases designed based on various scenarios that may arise during train operations. White-box testing is performed in the level exploration (LE) mode, where the test results are meticulously recorded and analyzed. The test cases in different modes comprehensively cover the testing requirements for train operations. The results indicate that the ITE system successfully passes most of the test cases and meets the primary functional requirements.