Vehicular localisation at high and low estimation rates during gnss outages: A deep learning approach

Abstract

Road localisation of autonomous vehicles is reliant on consistent accurate GNSS (Global Navigation Satellite System) positioning information. Commercial GNSS receivers usually sample at 1 Hz, which is not sufficient to robustly and accurately track a vehicle in certain scenarios, such as driving on the highway, where the vehicle could travel at medium to high speeds, or in safety-critical scenarios. In addition, the GNSS relies on a number of satellites to perform triangulation and may experience signal loss around tall buildings, bridges, tunnels and trees. An approach to overcoming this problem involves integrating the GNSS with a vehicle-mounted Inertial Navigation Sensor (INS) system to provide a continuous and more reliable high rate positioning solution. INSs are however plagued by unbounded exponential error drifts during the double integration of the acceleration to displacement. Several deep learning algorithms have been employed to learn the error drift for a better positioning prediction. We therefore investigate in this chapter the performance of Long Short-Term Memory (LSTM), Input Delay Neural Network (IDNN), Multi-Layer Neural Network (MLNN) and Kalman Filter (KF) for high data rate positioning. We show that Deep Neural Network-based solutions can exhibit better performances for high data rate positioning of vehicles in comparison to commonly used approaches like the Kalman filter.