Accurate Lane Detection with Atrous Convolution and Spatial Pyramid Pooling for Autonomous Driving Lane detection is a fundamental capability for autonomous driving. Many effective lane detection algorithms based on traditional computer vision and recent deep learning technologies have been proposed. However, the current stateoftheart lane detection accuracy is still not satisfactory for realizing fully autonomous driving. Thus, this paper proposes a new lane detection network using atrous convolution and spatial pyramid pooling techniques to improve the lane detection accuracy. We address the detection problem with pixelwise semantic segmentation. Our network consists of one encoder and two decoders, which outputs a binary segmentation map and an embedded feature map, respectively. The embedded feature map is employed for clustering algorithms to separate segmented lane pixels into different lanes. The experimental results on the public Tusimple dataset show that our network outperforms the stateofthearts.

Semantic Segmentation Model for Road Scene Based on EncoderDecoder Structure Semantic segmentation as a pixelwise segmentation task provides rich object information, which is an important research topic in robotic perception. It has been widely applied in many fields, such as autonomous driving and robot navigation. In the application of understanding road scene, the semantic segmentation model should accurately describe the appearance and shape of different categories of objects. In addition, the semantic segmentation model need to understand the spatial relationships between different categories. In order to improve the performance of semantic segmentation model for road scene, we present a model based on encoderdecoder structure with dilated convolution. We apply this model on the Cityscapes dataset and compare it with other classical models. To assess performance, we rely on the standard Jaccard Index IoU (Intersection over Union) and mIoU (mean Intersection over Union). The experimental results verify that this model can effectively improve the performance of semantic segmentation and meet the requirements for road scene.