Recurrent Cross Attention Mechanism for Improved Robustness and Real-Time Performance in Lane Detection

Abstract

Lane detection is a key technical challenge in autonomous driving and advanced driving assistance systems. The current lane detection system has insufficient robustness and low detection accuracy problem in complex and harsh environmental conditions, and lacks research on visual cues without information. In order to accurately and effectively detect multiple lanes in challenging scenarios and meet the real-time requirements of actual driving, a lane detection method based on recurrent cross attention mechanism is proposed in this paper. Firstly, by embedding the recurrent cross attention module in the feature extraction module, the contextual information of surrounding pixels on the intersection path is obtained, and useful information is collected from other regions for accurate lane detection; Secondly, anchor point detection is selected based on the prior position of the designed lane starting position; Finally, combining mixed anchor points to represent lane coordinates, using row and column anchors to learn lane coordinates and improve the real-time performance of lane detection models. The detection model RCCLaneNet designed in this paper was evaluated on the TuSimple and CULane datasets. The experimental results showed that the F1 of this method can reach 97.05% on the TuSimple dataset, 78.5% on the CULane dataset, and the real-time inference speed index FPS can reach +150. The proposed model achieves advanced performance in lane detection tasks in complex scenes while maintaining real-time detection efficiency, and has good universality in actual driving scenarios.

Keywords

• Lane detection
• attention mechanism
• autonomous driving
• deep learning