Self-driving car experience

Part 1: Computer Vision and Deep Learning

• Finding lane lines
  • Workflow for lane detection pipeline:
    • Load video -> Greyscale transform ->Gaussian smoothing -> Canny edge detection -> select the region of interest -> Hough transform -> Draw lines on original video

• Traffic sign classifier
  • Datasets: German Traffic Sign Recognition Benchmark
  • Three layers neural network including a fully connected layer
  • Test accuracy: 0.935

• Advanced lane finding
  • Workflow:
    • Compute the camera calibration matrix and distortion coefficients given a set of chessboard images.
    • Apply a distortion correction to raw images.
    • Use color transforms, gradients, etc., to create a thresholded binary image.
    • Apply a perspective transform to rectify binary image (“birds-eye view”).
    • Detect lane pixels and fit to find the lane boundary.
    • Determine the curvature of the lane and vehicle position with respect to center.
    • Warp the detected lane boundaries back onto the original image.
    • Output visual display of the lane boundaries and numerical estimation of lane curvature and vehicle position.

1. Vehicle detection and tracking

Part 2: Sensor fusion, Localization, and control

Extended Kalman filter

1. Uncented Kalman Filter
2. Kidnapped vehicle
3. PID controller

1. Model predictive control

Part 3: Path planning, concentrations, and system integration

1. Path planning
2. Semantic segmentation
3. Functional safety
4. Systemintegration