Lidar and radar fusion for real-time road-objects detection and tracking

Author:

Farag Wael12

Affiliation:

1. College of Engineering and Technology, American University of the Middle East, Kuwait

2. Electrical Engineering Department, Cairo University, Egypt

Abstract

In this paper, based on the fusion of Lidar and Radar measurement data, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. The lidar and radar devices are installed on the ego car, and a customized Unscented Kalman Filter (UKF) is used for their data fusion. Lidars are accurate in determining objects’ positions but significantly less accurate on measuring their velocities. However, Radars are more accurate on measuring objects velocities but less accurate on determining their positions as they have a lower spatial resolution. Therefore, the merits of both sensors are combined using the proposed fusion approach to provide both pose and velocity data for objects moving in roads precisely. The Grid-Based Density-Based Spatial Clustering of Applications with Noise (GB-DBSCAN) clustering algorithm is used to detect objects and estimate their centroids from the lidar and radar raw data. Then, the estimation of the object’s velocity as well as determining its corresponding geometrical shape is performed by the RANdom SAmple Consensus (RANSAC) algorithm. The proposed technique is implemented using the high-performance language C+⁣+ and utilizes highly optimized math and optimization libraries for best real-time performance. The performance of the UKF fusion is compared to that of the Extended Kalman Filter fusion (EKF) showing its superiority. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians.

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Reference37 articles.

1. Traffic signs classification by deep learning for advanced driving assistance systems;Farag;Intelligent Decision Technologies, IOS Press,2019

2. Road lane-lines detection in real-time for advanced driving assistance systems;Farag;Intern Conf on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT’18), Bahrain,2018

3. A comprehensive real-time road-lanes tracking technique for autonomous driving;Farag;International Journal of Computing and Digital Systems (IJCDS),2020

4. Behavior cloning for autonomous driving using convolutional neural networks;Farag;Intern Conf on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT’18), Bahrain,2018

5. Recognition of traffic signs by convolutional neural nets for self-driving vehicles;Farag;International Journal of Knowledge-based and Intelligent Engineering Systems, IOS Press,2018

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