Autonomous Vehicle Projects

Specialized knowledge and experience in self-driving car technologies is crucial to building successful autonomous vehicles. As a graduate of Udacity’s highly selective Self-Driving Car Nanodegree, I’ve completed numerous studies and projects which provide me with the skills to build safe, efficient, and comfortable self-driving car software platforms.

All of my completed projects, including full source code, are available for viewing and cloning on my Github account. Please click through any project to learn more about my work!

Perception – Detection using Computer Vision and Deep Learning

Detection systems estimate the state of the surrounding environment including landmarks, vehicles, pedestrians, and other objects.

Grayscale Traffic Signs

Final Lane Detection

Final Bounding Boxes

Simulated center lane driving

  • Transfer Learning to adapt cutting-edge deep learning research for self-driving car applications

Transfer Learning

  • Semantic Segmentation to facilitate scene understanding through high-performance identification of image regions

Road Identified 3

Kalman filter tracking

Perception – Localization

Localization systems compare the model of the environment to a known map, to understand where the vehicle is.

Particle filter localization

Path Planning

Path planning systems chart a trajectory for the vehicle, using the environmental model, the map, and vehicle location.

  • Path Planning for determining valid, safe, optimal, and comfortable routing for vehicles to a destination

Path Planning

Control Systems

Control systems apply actuators in vehicle hardware to follow the trajectory created the the path planner.

  • PID Control for basic control of steering, throttle, braking, and other systems

PID simulator

MPC simulator

System Integration

  • System Integration for putting all of these techniques together in a real life Lincoln MKZ

SDC Visualization RQT

SDC Real Test