Authors: Wael Farag
Addresses: College of Engineering and Technology, American University of the Middle East, Kuwait; Electrical Engineering Department, Cairo University, Egypt
Abstract: In this paper, a real-time road-object detection and tracking (LR_ODT) method for autonomous driving is proposed. The method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customised unscented Kalman filter (UKF) is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose-estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilises highly optimised math and optimisation libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians.
Keywords: sensor fusion; Kalman filter; object detection and tracking; advanced driving assistance systems; ADASs; autonomous driving.
International Journal of Computational Science and Engineering, 2021 Vol.24 No.5, pp.517 - 529
Received: 13 May 2020
Accepted: 09 Jan 2021
Published online: 12 Oct 2021 *