Deep Learning-Based Robust Visible Light Positioning for High-Speed Vehicles

Abstract

Robustness is a key factor for real-time positioning and navigation, especially for high-speed vehicles. While visible light positioning (VLP) based on LED illumination and image sensors is widely studied, most of the VLP systems still suffer from the high positioning latency and the image blurs caused by high-speed movements. In this paper, a robust VLP system for high-speed vehicles is proposed based on a deep learning and data-driven approach. The proposed system can significantly increase the success rate of decoding VLP-LED user identifications (UID) from blurred images and reduce the computational latency for detecting and extracting VLP-LED stripe image regions from captured images. Experimental results show that the success rate of UID decoding using the proposed BN-CNN model could be higher than 98% when that of the traditional Zbar-based decoder falls to 0, while the computational time for positioning is decreased to 9.19 ms and the supported moving speed of our scheme can achieve 38.5 km/h. Therefore, the proposed VLP system can enhance the robustness against high-speed movement and guarantee the real-time response for positioning and navigation for high-speed vehicles.