Self-Driving Car Engineer ND
·Projects from Udacity Self-Driving Car Engineer Nanodegree that I completed during my work in the Formula Student team.
Projects
- Behavioral cloning - end-to-end Deep Learning approach to control car’s steering angle based on images from cameras (training CNN with a demonstration of center-line driving, data augmentation, Keras).
- Traffic sign classifier - traffic sign classification using CNN implemented in Tensorflow (data preprocessing, designing model architecture).
- Particle filter - Particle Filter implementation for estimating the position of the robot based on landmark map, landmark detections and robot’s control information (C++).
- Extended Kalman Filter - KF and EKF implementation for estimating the state of a moving object based on noisy lidar and radar measurements (C++, Eigen).
- Path planning - this project consisted of two parts: Behavior Planner - controlling the car’s speed and changing lanes to overtake other cars and Trajectory Planner - using splines to create a smooth trajectory based on sparse path points (C++).
- Lane line detection - road lane line detection using OpenCV in Python (Canny Edge detection, Hough transform).
- Advanced lane line detection - road lane line detection using OpenCV in Python (camera calibration, distortion correction, edge detection with Sobel operator, birds-eye view transform, fitting second-degree polynomial).
- PID - PID controller for controlling the car’s steering angle and throttle (C++).
- Capstone - Generating trajectory with desired speeds and sending controls to the simulated car (ROS).