TY  - JOUR
PY  - 2021//
TI  - Collision-warning system integrated with merging behaviour prediction model based on multi-sensor fusion
JO  - International journal of vehicle design
A1  - Xu, Guoyan
A1  - Xiong, Yiwei
A1  - Niu, Huan
A1  - Yu, Guizhen
A1  - Zhou, Bin
SP  - 143
EP  - 161
VL  - 86
IS  - 1/2/3/4
N2  - 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>
LA  - en
SN  - 0143-3369
UR  - http://dx.doi.org/10.1504/IJVD.2021.122257
ID  - ref1
ER  -