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

Abstract

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.

Introduction

Autonomous vehicles (AVs), or self-driving cars, use advanced technologies such as cameras, sensors, LiDAR, GPS, and algorithms to navigate roads safely by minimizing human error and improving traffic efficiency. A critical component of AV functionality is lane detection, which helps the vehicle maintain its position, plan paths, and ensure safety. However, lane detection faces challenges in real-world conditions due to poor or faded lane markings, weather conditions, lighting changes, and obstacles.

The research aims to develop a real-time lane detection system using computer vision and deep learning that can reliably detect lanes even under difficult conditions like bad weather or low visibility.

The paper uses the UDACITY simulator, which provides a virtual environment with two modes:

• Training Mode: The vehicle is manually driven using keyboard inputs, capturing images from cameras mounted on the car and corresponding steering angles to train deep learning models.

• Autonomous Mode: The trained model drives the vehicle by processing real-time input, receiving steering and throttle commands to navigate autonomously.

This setup facilitates training and testing of lane detection and autonomous driving capabilities in a controlled, realistic simulation.

Conclusion

We successfully developed the real-time lane detection system for autonomous vehicles using computer vision and deep learning algorithms. With the help of UDACITY simulator, we collected the image datasets and trained the Convolutional Neural Network (CNN) model to predict the steering angle based on road curves. Also implemented the key pre-processing steps like image resizing, flipping and brightness adjustment that enhanced the model robustness for different types of environment and also for various road conditions. With the help of real-time testing in the simulator, it is clear that the vehicle can navigate the edges of road and maintain the lane alignment consistently and efficiently. Based on our study, it is clear that by implementing this technique of making the vehicle autonomous can help improve road safety, decision making ability, reduce traffic congestion, effective reduce the pollution and support for smart cities enabling the data-driven urban planning and efficient public transportation. This research can be further expanded by integrating more sensors like LiDAR, GPS to enhance the perception in complex environments. Also by real-world testing on a prototype vehicle can significantly improve the system’s capabilities.

References

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Copyright

Copyright © 2025 Arnav Patil, Atharv Waghmale, Siddheya Masurkar, Vedant Gogawale. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.