Simultaneous Registration and Fusion of Multiple Dissimilar Sensors for Cooperative Driving

Abstract

The fusion of multiple sensory information plays a key role in cooperative driving for flexible platooning of automated vehicles over a couple of lanes within a short intervehicle distance. In this paper, the problem of online sensor fusion with spatially and temporally misaligned dissimilar sensors is considered. A spatial-temporal registration model for the popular intelligent vehicular sensors including radar, global positioning system, inertial navigation system, and camera is first developed for sensor alignment. An unscented Kalman filter (UKF) is proposed here to fuse and register these sensors that are installed on a platoon of vehicles simultaneously. When the road geometry information is available from a digital map database, a constrained UKF is further developed to improve the fusion accuracy. The effect of spatial-temporal sensor misalignment upon the vehicle-state estimation is also analyzed theoretically. Simulations show that the proposed UKF method not only can align the dissimilar vehicular sensors properly with both spatial and temporal biases, but can also obtain accurate fused tracks of vehicles in a platoon.