Contents
- 1 Recognizing Lane Using Image Processing
- 2 Vehicle Detection
- 3 Recognizing Traffic Signs Using Deep Learning
- 4 Training Deep Learning Model for Vehicle Steering and Accelerator
- 5 Vehicle State Estimation with Kalman Filter and Particle Filter
- 6 Control of Vehicle Steering and Accelerator with Model Predictive Control and PID Control
- 7 Control of Vehicle on Highway on 3D simulator
- 8 Control of Udacity’s Real Vehicle on Test Course
- 9 About The Course
Recognizing Lane Using Image Processing
The assignment for detecting lane with noise reduction, transforming perspective, binarizing and fitting with polynomials. At last of the assignment, I needed to detect lane of a video of driveway. The video has different color road and shadow, so tuning of algorithms was tough.Vehicle Detection
The assignment to detect vehicles from a video of driveway using HOG (Histogram of oriented gradients) (an image feature data) and Support Vector Machine (a mode of machine learning).
Recognizing Traffic Signs Using Deep Learning
The assignment for training Convolutional Neural Network and recognizing traffic signs from images. The following image is not provided by the course, but training and test image like following.
Training Deep Learning Model for Vehicle Steering and Accelerator
The assignment for handling a vehicle on 3D simulator using Deep Learning model trained by vehicle handling data on 3D simulator by human. My result was the following.Vehicle State Estimation with Kalman Filter and Particle Filter
The assignment to estimate a vehicle state on 2D simulator with Kalman Filter and Particle Filter. 2D simulator is like following image.
Control of Vehicle Steering and Accelerator with Model Predictive Control and PID Control
The assignment to control a vehicle on 3D simulator with Model Predictive Control and PID Control. The following GIF image shows Model Predictive Control result.
