Research on obstacle detection between train tracks based on multi-sensor fusion

The requirement for intelligent trains to enable real-time sensing of multi-source information throughout the entire operational process has become vital as the government aggressively encourages the digitalization, scalability, intensification, and synergistic development of rail transportation. This work introduces a fusion method that combines millimeter-wave radar and cameras in order to accurately detect obstacles inside restricted zones and anticipate their direction in real-time. First, during the millimeter-wave radar data collecting process, the threshold approach is used to filter out extraneous obstacle data outside the restricted area. This makes it possible to extract speed and distance data pertaining to obstacles located within the restricted area in front of the train's operation. The Kalman filter algorithm is subsequently used to track the obstacles and forecast their motion. Second, the information obtained by the camera and millimeter-wave radar is combined in time and space. The data is then transformed into a standardized coordinate system after the fusion procedure. It is suggested to use an algorithm based on data fusion to make target matching for the same detected barrier easier. Finally, numerous trials are carried out in various settings and with various lighting levels. Comparisons using only one sensor are part of these experiments. The outcomes show that implementing the millimeter-wave radar and camera fusion method considerably raises the rate of success of obstacle identification in front of the train while it is operating.