A Railway Accident Prevention System Using an Intelligent Pilot Vehicle

Railway transportation, as a pillar of modern civilization, unavoidably suffers from external risk factors such as natural disasters, track breakages, and train collisions, which lead to substantial loss of life and property. Therefore, there is an urgent need to design a mechanism for warning and preventing railway accidents in order to diminish costs. We propose an add-on solution to the current system, which equips a train with a multifunctional pilot vehicle in the front: the vehicle pilots its mother train, warning it of impending danger, and stopping it if required. Specifically, the pilot vehicle is equipped with a wireless communication device to converse with the mother train, a ranging device for measuring the real-time distance from the mother train, a camera to capture the railway conditions ahead and recognize anomaly situations, and other sensors (e.g., collision detector and tiltmeter) to monitor its own conditions. Based on the above equipment, an efficient autonomous driving method is designed for the pilot vehicle to adjust the distance from the train. The autonomous driving problem can be formulated into a multi-objective functional optimization, where the objective is to minimize the total energy consumption and the experienced jerk of the pilot vehicle, and the decision is a continuous-time function that represents the traction or braking force imposed on the pilot vehicle. Additionally, a vision-based deep learning method is devised to automatically detect the mentioned railway anomalies using the ego-view camera of the pilot vehicle. To control the operational and maintenance costs, we propose to deploy pilot vehicles only for trains running in potentially dangerous environments, e.g., mountainous areas during rainy days. By implementing the proposed scheme, we anticipate a reduction in accident rates within railway systems.