Aiming at the problem that the intersection of high beam headlights affects the visual attention of automobile drivers,so it is difficult to ensure the safety of automobile drivers driving at night,this paper studies the external environment detection meth-od of adaptive driving beam(ADB)automobile headlights based on machine vision and deep learning.The image data of external envi-ronment for the ADB automobile headlights is acquired through the CCD camera of machine vision,the data filtering method is used to eliminate the interference light source data in the collected image data,delimit the detection target area of external environment for the ADB automobile headlights according to different road conditions,detect the light source of external environment target through depth learning algorithm,and predic each target light source track in combination with extended Kalman filtering.When there is a car's headlamp source in front of vehicles,The ADB system timely adjusts the brightness of the light beads in the corresponding area of the car's high beam headlights,reducing the impact of the high beam intersection on the driver vision during high-speed driving,and ensu-ring the safe driving of cars.The experimental results show that this method can effectively eliminate all kinds of interference light sources,accurately detect the target light source,and the trajectory prediction results of the target light source are very close to the real results,which can accurately complete the external environment detection of ADB automobile headlights.
machine visiondeep learningADB headlightsexternal environment detectionimage data acquisitiondata filtering
维普
万方数据
