Monocular Vision-Based Vehicle Distance Prediction utilizing Number Plate

To reduce road traffic accidents, maintaining a safe distance between vehicles can play an important role. Advanced driver assistance systems (ADAS) primarily focus on collision avoidance technologies. This article introduces two vehicle distance detection methods based on monocular vision. Parameters extracted from the number plate are utilized to predict the distance. Two different parameters are considered for the two methods, one is the weight-height, and another one is the digit size of the number plate in pixels. To calculate these parameters, the number plate must first be detected, for which YOLOv5 is employed. Finally, the distance is predicted using either a regression model or an ANN model that takes the extracted parameters from the number plate as input. As the number plate is focused rather than the whole car size, the posture of the vehicle on the road doesn’t affect the prediction much. Furthermore, different car models do not require separate trained models. Despite the fact that number plates also vary, which is mostly dependent on region, they may generally be more standardized than automobile types.