Evaluation of Decentralized Information Matrix Fusion for Advanced Driver-Assistance Systems in Heavy-Duty Vehicles

Abstract: Advanced driver-assistance systems (ADAS) is one of the fastest growing areas of automotive electronics and are becoming increasingly important for heavy-duty vehicles. ADAS aims to give the driver the option of handing over all driving decisions and driving tasks to the vehicle, allowing the vehicle to make fully automatic maneuvers.  In order to perform such maneuvers target tracking of surrounding traffic is important in order to know where other objects are. Target tracking is the art of fusing data from different sensors into one final value with the goal to create an as accurate as possible estimate of the reality. Two decentralized information matrix fusion algorithms and a weighted least-squares fusion algorithm for target tracking have been evaluated on two simulated overtaking maneuvers performed by a single target. The first algorithm is the optimal decentralized algorithm (ODA), which is an optimal IMF filter, the second algorithm is the decentralized-minimum-information algorithm (DMIA), which approximates the error covariance of received estimates, and the third algorithm is the naïve algorithm (NA), which uses weighted-least-squares estimation for data fusion. In addition, DMIA and NA are evaluated using real sensor data from a test vehicle. The results are generated from 100 Monte Carlo runs of the simulations. The error of position and velocity as well as the their corresponding root-mean-squared-error (RMSE) are smallest for ODA followed by NA and DMIA. ODA gives consistent estimators for the first simulated overtaking but not the second. DMIA and NA are not statistically significant on a 95 % level. The robustness against sensor failures shows that ODA is robust and yields similar results to the simulations without sensor failures. DMIA and NA are sensitive to sensor failures and yield unstable results. ODA is clearly the best option to use for sensor fusion in target tracking.