@article{ref1,
title="Collision-warning system integrated with merging behaviour prediction model based on multi-sensor fusion",
journal="International journal of vehicle design",
year="2021",
author="Xu, Guoyan and Xiong, Yiwei and Niu, Huan and Yu, Guizhen and Zhou, Bin",
volume="86",
number="1/2/3/4",
pages="143-161",
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.<p /> <p>Language: en</p>",
language="en",
issn="0143-3369",
doi="10.1504/IJVD.2021.122257",
url="http://dx.doi.org/10.1504/IJVD.2021.122257"
}