Abstract:

Roadside perception is a component of cloud control cooperative vehicle infrastructure perception. This paper proposed a fusion method for roadside multi-sensors based on measurement noise Adaptive Extended Kalman Filter (AEKF) to improve the perception accuracy and stability of roadside sensors. The fusion of target-level data from heterogeneous sensors was realized based on the sensing results of roadside cameras, lidars, and millimeter-wave radars. An online acquisition method of measurement noise was used to detect the stability of the sensor measurement value, to generate correction coefficients for the measurement noise, and to adjust adaptively the measurement noise. This method was tested in a real vehicle. The results show that this multi-sensor fusion method improves the accuracy of lateral distance estimation by 9.7%, the longitudinal distance estimation accuracy by 5.4%, and the speed estimation accuracy by 26.6%, compared with a single sensor; With the estimation accuracy of the traditional Extended Kalman Filter (EKF) algorithm being improved by 44.9% in the horizontal distance, by 21.3% in the longitudinal distance, and by 64.4% in the speed; Therefore, the AEKF algorithm estimation accuracy of this paper is higher than that of the traditional EKF algorithm.

Key words: autonomous vehicles, roadside perception, multi-sensor fusion, adaptive filters, extended Kalman filter (EKF)