GNSS positioning uncertainty prediction over a planned path considering signal attenuation for urban autonomous driving

Abstract

Autonomous driving requires path planning to choose an optimal path to destination. Most works on path planning focus on the shortest travelling time, but neglect positioning condition of a path while positioning accuracy is significant for autonomous driving. In this work, we predict GNSS protection level (PL), which is positioning uncertainty upper bound, over planned paths to help choose the optimal path. We propose an improved stochastic model that considers the signal attenuation caused by trees and buildings using laser scanning point cloud. An adaptive strategy is proposed to linearly correlate the improved model and the carrier-to-noise-density ratio to make our stochastic model more practical. Finally, pseudorange-based RTD PL is calculated by advanced receiver autonomous integrity monitoring (ARAIM) using the planned path information and GNSS ephemeris. The experiments demonstrate that our method reduces the misleading rate by over 90% both in horizontal and vertical directions compared to the original stochastic model, with the lowest false alarm rates.