Title: Collision-warning system integrated with merging behaviour prediction model based on multi-sensor fusion

Authors: Guoyan Xu; Yiwei Xiong; Huan Niu; Guizhen Yu; Bin Zhou

Addresses: School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology Beihang University (Shahe Campus), 9 Nansan Street, Shahe Higher Education Park, Changping, Beijing, 102206, China ' School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology Beihang University (Shahe Campus), 9 Nansan Street, Shahe Higher Education Park, Changping, Beijing, 102206, China ' School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology Beihang University (Shahe Campus), 9 Nansan Street, Shahe Higher Education Park, Changping, Beijing, 102206, China ' School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology Beihang University (Shahe Campus), 9 Nansan Street, Shahe Higher Education Park, Changping, Beijing, 102206, China ' School of Transportation Science and Engineering, Key Laboratory of Autonomous Transportation Technology for Special Vehicles, Ministry of Industry and Information Technology Beihang University (Shahe Campus), 9 Nansan Street, Shahe Higher Education Park, Changping, Beijing, 102206, China

Abstract: One of the most dangerous situations on roads is that drivers choose to merge into traffic without warning. This paper presents a real-time collision warning system in merging scenario and our approach mainly focuses on the forward vehicle in different lane. First, multi-sensor is used to detect the distance and speed information of forward vehicles. Based on the detection result, a neural network is designed to predict whether they are going to merge into ego lane or not. The prediction model correctly classifies 92% of merging behaviour in our test dataset. Then, a collision warning algorithm is proposed to cope with different merging manoeuvres. The algorithm is tested on a real road on our embedded platform and the results show that the system can effectively alert drivers to brake when collision threats are posed.

Keywords: collision warning; multi-sensor; merging behaviour prediction; perception system; deep learning; convolution neural networks; object detection; lane detection; neural network.

DOI: 10.1504/IJVD.2021.122257

International Journal of Vehicle Design, 2021 Vol.86 No.1/2/3/4, pp.143 - 161

Received: 12 Jul 2020
Accepted: 12 Mar 2021

Published online: 14 Apr 2022 *

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