Indoor localization of vehicles using Deep Learning
Modern vehicles are equipped with numerous driver assistance and telematics functions, such as Turn by Turn navigation. Most of these systems rely on precise positioning of the vehicle. While Global Navigation Satellite Systems (GNSS) are available outdoors, these systems fail in indoor environments such as a car park or a tunnel. Alternatively, the vehicle can localize itself with landmark based positioning and internal car sensors, yet this is not only costly but also requires precise knowledge of the enclosed area. Instead, our approach is to use infrastructure based positioning. Here, we utilize off the shelf cameras mounted in the car park and Vehicle to Infrastructure Communication to allow all vehicles to obtain an indoor position given from an infrastructure based localization service. Our approach uses a Convolutional Neural Network (CNN) with Deep Learning to identify and localize vehicles in a car park. We thus enable position based Driver Assistance Systems (DAS) and telematics in an underground facility. We compare the novel Deep Learning classifier to a conventional classifier using Haar like features.
2016 IEEE 17th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM)