Abstract—When driving a vehicle on a road, if a driver want
to change lane, he must glance the rear and side mirrors of his
vehicle and turn his head to scan the possible approaching
vehicles on the side lanes. However, the view scope by the above
behavior is limited; there is a blind spot area invisible. To avoid
the possible traffic accident during lane change, we here propose
a lane change assistance system to assist changing lane. Two
cameras are mounted under side mirrors of the host vehicle to
capture rear-side-view images for detecting approaching
vehicles. The proposed system consists of four stages: estimation
of weather-adaptive threshold values, optical flow detection,
static feature detection, and detection decision. The proposed
system can detect side vehicles with various approaching speed;
moreover, the proposed system can also adapt variant weather
conditions and environment situations. Experiment with 14
videos on eight different environments and weather conditions,
the results reveal 96 % detection rate with less false alarm.
Index Terms—Advanced driver assistance system, blind spot detection, optical flow, underneath shadow features.
Din-Chang Tseng, Chang-Tao Hsu, and Wei-Shen Chen are with the Institute of Computer Science and Information Engineering, National Central University, Jhongli, Taiwan 32001 (e-mail: email@example.com. edu.tw, firstname.lastname@example.org, email@example.com).
Cite: Din-Chang Tseng, Chang-Tao Hsu, and Wei-Shen Chen, "Blind-Spot Vehicle Detection Using Motion and Static Features," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 516-521, 2014.