Machine Learning Methods for Vehicle Positioning in Vehicular Ad-Hoc Networks

Abstract

Unambiguous vehicular sensing is one of the most important aspects in autonomous driving in vehicular ad-hoc networks. The conventional techniques such as communication-based technologies (e.g., GPS) or the reflection-based technologies (e.g., RADAR, LIDAR) have various limitations in detecting concealed vehicles in dense urban areas without line of sight which may trigger serious accidents for autonomous vehicles. To address this issue, this paper proposed a machine learning method based on stochastic Gaussian process regression (SGP) to position vehicles in a distributed vehicular system with received signal vector (RSV) information. To estimate the test vehicle position and respective position errors, the proposed SGP method records the RSV readings at neighboring locations with continuous approximation of the vehicle-to-vehicle (V2V) distance, angle of arrival (AoA), and path delay. Then, the subsequent averaging of the training RSVs minimizes the effects of the shadowing noise and multipath fading. The prediction performance of the proposed learning approach is measured in terms of the root mean square prediction error (RMSE) in a realistic environment. Finally, the prediction performance of the proposed learning method is compared with other existing fingerprinting methods for error-free location estimation of the vehicular network.