  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.
