Robust Lane Detection for Autonomous Vehicles Using Deep Learning Techniques in the UDACITY Simulator

This paper presents a deep learning based solution for real-time lane detection in autonomous vehicles using UDACITY self-driving car simulator. Our goal is to navigate the car on a road safely and independently by training the images captured from the three cameras mounted on a virtual car using the Convolutional Neural Networks (CNN). These images are paired with steering angles and used to teach the model for responding to different road edges and lane positions. The model is trained in training mode and then implemented to drive on its own in the autonomous mode of simulator. We have applied several pre-processing techniques in order to enhance the performance and robustness of the system. The results show that the system can make accurate driving decisions even in tough scenarios, highlighting the power of computer vision and deep learning in building smarter self-driving technologies.