[LDCA/LDML] Lane Detection

[a-0-dev]

Objective: Lane Detection for our CC20 Car

The lane Detection is an important task in autonomous driving.

Goal

• Stable and fast line detection for trajectory generation
• detect the current lane and identify the different lines and line types (outer left, middle, double, solid, dashed)
• Robustness against missing line parts (Broken lines are part of the cup)
• Robustness against obstacles and ramp
• handling for curves
• handling of crossings and intersections with priority lines.
• Pedestrian island and zebra crossings

Setup:

Our previous line detection can be found:
https://github.com/tum-phoenix/drive_ros_image_recognition/tree/polynom
You can try different computer vision approaches or machine learning approaches.

• Machine learning - line detection
  For the machine learning approach there is also a different issue:
  Lane Detection using Deep Learning drive_ml#19
• Hough lines
• Road segmentation
• ...

A template with basic functions can be found under:
tum-phoenix/drive_ml#19

Tasks:

• Implement one or more line detection approaches (for comparison)
• Focus on line detection and trajectory at first and check the stability of the trajectory
• Extract polynomial for the trajectory and the environment model
• Detect the current lane and identify the different lines and line types
• Detect Crossings and special street parts, like islands
• Estimate the curvation radius
• Compare different approaches

[xXBasti]

Maybe that can be helpful:
https://github.com/tatsuyah/Lane-Lines-Detection-Python-OpenCV