Abstract
Autonomous Driver leverages advanced computer vision to revolutionize self-driving technology. By analyzing visual data captured from diverse road environments, this project develops precise lane detection systems, adaptable to various conditions. Using a three-stage pipeline—data collection, training, and implementation—the project enhances the safety and efficiency of autonomous vehicles. This work highlights the potential of computer vision and machine learning to address the challenges of navigation in self-driving cars.

Introduction
Self-driving technology represents the cutting edge of artificial intelligence, combining vision, control, and decision-making. Lane detection, a critical component of this technology, requires reliable performance under varied road and environmental conditions. Traditional approaches often struggle with adaptability, limiting their utility in real-world scenarios.
This project presents an adaptable lane detection system that:

Related Work
3.1 Lane Detection with Computer Vision
State-of-the-art lane detection systems leverage computer vision algorithms like edge detection, region of interest (ROI) selection, and perspective transformations. These methods form the foundation of real-time lane tracking.
3.2 Machine Learning for Autonomous Driving
Recent advances in machine learning have enabled systems to predict steering angles directly from image inputs. Supervised learning models trained on labeled driving data have demonstrated remarkable accuracy in this domain.

Methodology
4.1 Data Collection
The system collects data from multiple sources:

Conclusion
Autonomous Driver demonstrates the effectiveness of computer vision and machine learning in autonomous navigation. By providing a modular and adaptable lane detection system, this project contributes to safer and more efficient self-driving technology.

References

He, K. et al. (2016). Deep Residual Learning for Image Recognition.

Chen, L. et al. (2021). Rethinking Pre-trained Visual Models.