基于事件驱动的车道线识别算法研究

Compared with the traditional color cameras, the dynamic vision sensor, a type of event based sensor, has higher time resolution, dynamic range, lower power consumption and lower bandwidth requirements. It has good application prospects in the field of automatic driving, which attracts more and more researchers' attention. However, event driven data is asynchronous and lacks a unified representation. At the same time, in the complex traffic scenario, the traditional semantic segmentation model is difficult to be applied to the event driven data based traffic scene segmentation task, for instance, the lane detection task. In view of the above problems, our study proposes a three channel encoding method for event data, which is successfully used as the input of convolution neural network by considering the spatio temporal characteristics of event data comprehensively. This paper also proposes a lane segmentation algorithm based on encoding decoding model, which is superior to the traditional event based lane line segmentation. On the DET data set, with mIoU(mean Intersection over Union) as the evaluation index, this paper reaches 58.76%, which is 4.4% higher than the benchmark.