Cloud-Assisted Collaborative Road Information Discovery with Gaussian Process: Application to Road Profile Estimation

TL;DR

This paper introduces a cloud-assisted collaborative framework using Gaussian processes to improve road profile estimation by leveraging multiple vehicles' measurements, effectively handling uncertainties and enhancing accuracy.

Contribution

It presents a novel collaborative estimation method that combines vehicle measurements with cloud-based Gaussian processes, explicitly managing measurement uncertainties for better road information discovery.

Findings

Significant improvement in road profile estimation accuracy.

Effective handling of vehicle heterogeneity and measurement noise.

Iterative enhancement of estimation performance across vehicles.

Abstract

There is an increasing popularity in exploiting modern vehicles as mobile sensors to obtain important road information such as potholes, black ice and road profile. Availability of such information has been identified as a key enabler for next-generation vehicles with enhanced safety, efficiency, and comfort. However, existing road information discovery approaches have been predominately performed in a single-vehicle setting, which is inevitably susceptible to vehicle model uncertainty and measurement errors. To overcome these limitations, this paper presents a novel cloud-assisted collaborative estimation framework that can utilize multiple heterogeneous vehicles to iteratively enhance estimation performance. Specifically, each vehicle combines its onboard measurements with a cloud-based Gaussian process (GP), crowdsourced from prior participating vehicles as "pseudo-measurements", into a local estimator to refine the estimation. The resultant local onboard estimation is then sent back to the cloud to update the GP, where we utilize a noisy input GP (NIGP) method to explicitly handle uncertain GPS measurements. We employ the proposed framework to the application of collaborative road profile estimation. Promising results on extensive simulations and hardware-in-the-loop experiments show that the proposed collaborative estimation can significantly enhance estimation and iteratively improve the performance from vehicle to vehicle, despite vehicle heterogeneity, model uncertainty, and measurement noises.