šŸš˜ Advanced Driver Assistance Systems

āœ… Lane Detection and Tracking

In this project, I developed a cutting-edge real-time lane detection and tracking system specifically tailored for autonomous vehicles. The primary aim was to ensure precise lane marker detection and robust lane tracking, enabling safe and efficient autonomous navigation.

Key features of the project include:

  1. Custom Hough Transform with Gradient Information [1][2]: To achieve improved lane marker detection accuracy, I devised a custom Hough Transform algorithm that efficiently extracted lane markings from video streams. By incorporating Gradient Information, the system could accurately identify lane boundaries, even under challenging lighting and road conditions.

  2. Kalman Filter for Robust Lane Tracking: This allowed for robust lane tracking.

šŸ§° Tools Used:

  • Robot Operating System (ROS): I leveraged the powerful capabilities of ROS to develop and integrate various components of the lane detection and tracking system. ROS enabled seamless communication between different modules and facilitated the efficient deployment of the solution.

šŸ‘Øā€šŸŽ“ Learnings:

  • Sensor Fusion: One of the key takeaways from this project was the integration of multiple sensors to obtain a comprehensive understanding of the vehicle's environment. The fusion of data from cameras, LiDAR, radar, and GPS allowed for more robust and accurate lane detection and tracking.

Through this project, I gained invaluable experience in developing advanced algorithms for autonomous vehicles, particularly in the critical areas of lane detection and tracking.

šŸ“œ References

  1. An Extension to Hough Transform Based on Gradient Orientation
  2. Extension: Incorporating image gradients