Citation

Xu G, Xiong Y, Niu H, Yu G, Zhou B. Int. J. Veh. Des. 2021; 86(1/2/3/4): 143-161.

Copyright

(Copyright © 2021, Inderscience Publishers)

DOI

10.1504/IJVD.2021.122257

PMID

unavailable

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.

Language: en