Cloud-assisted collaborative estimation for next-generation automobile sensing

This research proposes a unique cloud-assisted collaborative estimating system for on-road sensors like potholes, ice, and road profile to develop next-generation automobiles that are safer, more efficient, and more comfortable. Conventional road information finding methods for a single vehicle are prone to model uncertainty and measurement mistakes. The proposed system iteratively improves estimate performance using many heterogeneous vehicles. Each vehicle merges its onboard measurements with pseudomeasurements from previous participating cars and transmits the resulting local onboard estimate back to the cloud for updating. The system uses a Noisy Input Gaussian Process (NIGP) approach to manage uncertain readings. The study includes the road segment length of 40 m, 2 sensors for sprung mass and suspension displacements, time span of 1.5 s, a sampling time of 0.01 s, and 151 estimation points per Kalman Filter (KF). Additionally, the Root Mean Squared Error (RMSE) was calculated for onboard estimation performance, and training was conducted using data from one vehicle and 10 vehicles for the cloud-based NIGP performance evaluation. Using pseudo-measurements from preceding vehicles significantly improves performance. Comparing the standard GP regression, the NIGP regression and a benchmark based on KF averaging estimations. The NIGP provides better variance and reduces uncertainty. Moreover, the pseudo-measurement strategy iteratively improves onboard performance when using NIGP pseudo-measurements. A detailed experimental plan is described to measure the performance of the proposed collaborative estimating method while considering fleet heterogeneity, measurement noise, and model uncertainty.