Cloud-Based Collaborative Road Profile Estimation Using Gaussian Process

Abstract:

Road profile information is used to improve vehicle suspension control and detect road irregularities such as potholes. While a great many road profile estimation approaches exist， they have been traditionally 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 new collaborative estimation framework that exploits multiple heterogeneous vehicles to iteratively improve the estimation. Specifically， each vehicle combines its onboard measurements （e.g.， accelerometers and yaw/roll rate sensors） with a crowdsourced Gaussian process （GP） estimation from the cloud into a Kalman filter （KF） to iteratively refine the estimation. The GP is trained from the crowdsourced local estimations of all prior participating vehicles， which is then sent to the latest participating vehicle as “pseudo-measurements” to enhance the onboard estimation. The resultant local onboard estimation is sent back to the cloud to update the GP. It is shown that using the GP as additional pseudo-measurements can iteratively improve the road profile estimation performance from vehicle by vehicle. Extensive simulations are performed to show the efficacy of the proposed approach.