Scalable and Efficient SSD-Based Lane Detection Model for Autonomous Vehicle Systems

The ADAS (Advanced Driver Assistance System) is a crucial component of autonomous cars that ensures the safety of both drivers and passengers. ADAS components include Adaptive Cruise Control, Automatic Braking/Steer Away, Lane-Keeping System, Blind Spot Assist, Lane Departure Warning System, and Lane Detection. The lane detection system utilizes a camera to provide information about the geometric characteristics of lane line structures to the intelligent system of the vehicle. This enables the system to accurately determine the position of the lane on the road. This paper presents an enhanced Single Shot MultiBox Detector (SSD) model tailored for lane detection tasks in autonomous driving. By integrating multi-scale feature maps and default boxes, the SSD framework efficiently handles object detection in a single pass through the network. The approach leverages a TuSimple dataset comprising 600 high-resolution images, carefully annotated to capture diverse road conditions and lane Lines. Post-processing techniques, including the Hough Transform, are incorporated to refine the lane detection accuracy. The model achieves a remarkable 93% accuracy, demonstrating its robustness and efficacy in real-world scenarios. Key innovations include the use of VGG16 as the backbone network for feature extraction, multi-scale feature maps for detecting lanes at various scales, and advanced non-maximum suppression (NMS) techniques for filtering overlapping bounding boxes. The localization and confidence loss functions are finely tuned to optimize the detection performance. The results indicate significant improvements over traditional methods, making this model a viable solution for real-time lane detection in autonomous vehicles. Future work will focus on expanding the dataset and incorporating additional contextual information to further enhance detection accuracy.