Autonomous Ground Vehicle Path Planning in Urban Environments Using GNSS and Cellular Signals Reliability Maps: Simulation and Experimental Results

Abstract

A framework for autonomous ground vehicle (AGV) path planning using global navigation satellite systems (GNSS) signals and cellular long-term evolution (LTE) signals is evaluated through several simulations and experiments. The objective of path planning is to prescribe the optimal path for the AGV to follow to reach a desired target point. Optimality is defined as the shortest distance, while minimizing the AGV's position estimation error and guaranteeing that the uncertainty about its position is below a desired threshold. Path planning is prescribed via signal reliability maps, which provide information about regions where large errors due to cellular signal multipath or obstructed GNSS line-of-sight are expected. Simulation results are presented demonstrating that utilizing ambient cellular LTE signals together with GNSS signals 1) reduces the uncertainty about the AGV's position, 2) increases the number of feasible paths to choose from, which could be useful if other considerations arise (e.g., traffic jams and road blockages due to construction), and 3) yields significantly shorter feasible paths, which would otherwise be infeasible with GNSS signals alone. Experimental results on a ground vehicle navigating in downtown Riverside, CA, USA, are presented demonstrating a close match between the simulated and experimental results.