Autonomous Vehicle
The course is about:
The system and the components of the autonomous vehicles
The algorithms and techniques
About the Course
Autonomous vehicle is capable of sensing its environment and moving safely with no human input. In this course, students will learn the system, mechanism, and components of the autonomous vehicles. The focus is on the AI algorithms and techniques behind the autonomous vehicles.
Main Contents
Introduction to Autonomous Driving
- How a self-driving car works
- Vehicle Levels of automation
Computer Vision for Self-Driving Cars
- Perception tasks
- Object detection
- Segmentation
- Pose estimation
- 3D point clouds and 3D perception
Sensing in Self-Driving Cars
- Radar
- LiDAR and 3D LiDAR perception
- Camera and 2D camera data
- Sensor Fusion
Localization
- Overview
- Simultaneous Localization and Mapping (SLAM)
- Improvements to basic SLAM
Planning
- Autonomous vehicle planning systems
- Mission planning
- Behavioral planning
- Motion planning
Control
- Classical control
- Model predictive control
- Trajectory generation and tracking
Driver State
- Driver state detection
- Driver glance region classification
- Driver pose estimation
- Driver emotion
VoxelNet for Self-Driving Cars
- Architecture
- Feature learning network
- Region proposal network
- Average precision
- Model training
Complex YOLO for Self-Driving Cars
- Architecture
- Average precision
- Model training
FaF for Self-Driving Cars
- Architecture
- Average precision
- Model training
LidCamNet for Self-Driving Cars
- Architecture
- Average precision
- Model training