# Focus on Point: Parallel Multiscale Feature Aggregation for Lane Key Points Detection
## 2 Related Work  
### Lane Detection  
In the field of autonomous driving, lane detection is a significant research topic. In order to meet the requirements of practical application, the detection algorithm must not only have a powerful and fast detection performance, but must also have robustness to adapt to various environments. Refs. [2; 3; 4; 5; 6; 7; 8; 9] achieved lane detection through various hand-crafted features and mathematical models, and performed well in simple and clear scenarios. However, when there are extreme road conditions, these algorithms show limitations.  
At present, deep learning has become the dominant method in lane detection research. The detection methods based on deep learning can be grouped into two categories: segmentation-based methods and detection-based methods. Segmentation-based methods focus on networks: [PERSON]. [13] proposes a spatial CNN, which passes information between adjacent rows or columns within feature mapping to improve the performance of CNN in detecting long thin shape structures. On the basis of [13], [PERSON]. [11] proposed the recurrent feature-shift aggregator (RESA) to capture the spatial relations of pixels between rows and columns and enrich lane features. Ref. [14] divided lane detection into two stages and proposed a network architecture (LaneNet) that combines semantic segmentation and feature embedding. The network has a shared encoder and two decoders: one of the decoders performs a binary segmentation of lane pixels and background pixels, and the other classifies these lane pixel instances. Ref. [12] proposes a self-attention distillation detection method that forces shallow layers to learn a rich context feature extracted from deep layers, and obtains substantial improvement through self-learning. Ref. [10] predicts the existence and location of lane line markers in the image and groups together instances of lane pixels belonging to the same lane. Ref. [15] proposes a driving scene semantic segmentation method based on embedded loss (EL-GAN), in which, lane markers are segmented based on an input image generator, and segmentation results are judged by a shared weight discriminator.  
Detection-based methods focus on outputs of networks. Based on the RPN of faster RCNN, Ref. [19] proposed a line proposal unit (LPU) to predict the horizontal offset of the fixed vertical axis of the proposed line. Ref. [18] proposed a detection model based on anchor points that uniformly sampled pixels of lane along the vertical axis, predicted the offset between sampling points and anchor lines to carry out intensive regression and, finally, applied non-maximum suppression (NMS) to eliminate overlapping predictions and select the lane pixels with the highest score. Ref. [23] defined the lane detection problem from the perspective of multi-key point estimation and association, and used bottom-up local geometric modeling to predict the global structure. Ref. [20] predicted the horizontal axis value of the fixed vertical axis of each lane line, as well as the starting point and end point of the curve, and modeled the shape of lanes based on these predicted values. Ref. [16] predicts a specific feature map and classifies the pixels of each row of the feature map to mark the most likely position of lanes in each row.