G NET Accurate Lane Detection Model for Autonomous Vehicle

Lane detection is an essential task in autonomous driving. A good lane detection model should achieve many objectives, such as high accuracy, rapid detection, and low memory. In this article, a grid-based network (G-NET) is designed to realize the aforementioned goals. In G-NET, the traditional pixel-level semantic segmentation is replaced with the area-level grid segmentation to release the detection burden. Then, a position vector is introduced to indicate where lane key point is in the grid. Meanwhile, the novel rolling convolution layer following with the down-sampling and up-sampling convolution layer has been designed for good feature extraction, ensuring each feature grid perceives all other grid features in the feature map. Then, an adaptive hyperparameter branch is introduced to calculate the binary threshold effectively. Finally, the detected lane points are classified into different lanes by introducing distance-based quaternion. G-NET is extensively evaluated on three most widely datasets: TuSimple, CULane, and CurveLanes. The results show that G-NET has a state-of-the-art performance. Meanwhile, field tests are conducted.