Lectures:
1. Car navigation using GPS, RDS-TMC.
2. Introduction to autonomous driving, definition of basic concepts.
3. Sensors for autonomous driving, cameras, LIDAR, ultrasound.
4. Sensor data fusion.
5. Machine learning tools in autonomous driving.
6. Detecting objects around the vehicle.
7. Space mapping around the vehicle.
8. Methods of localization in known environment - particle filter.
9. Methods of localization in unknown environment - SLAM.
10. Motion models of other objects around vehicles.
11. Route planning, shortest path algorithms.
12. Search for possible route paths.
13. Predicting the route of other objects.
14. Behavioral planning, trajectory generation.
Exercises:
1. Working with the GPS geolocation system and radio transmission of traffic information using RDS-TMC.
2. Working with cameras, setting parameters and storing data.
3. Work with LIDAR sensor and ultrasonic sensors.
4. Processing of sensor data and their fusion into subsequent analysis.
5. Introduction to the software framework of machine learning.
6. Detection of interest objects around a vehicle using machine learning.
7. Creating map data based on sensor data.
8. Use of particle filter to locate a vehicle in a known environment.
9. SLAM techniques to locate a vehicle in an unknown environment.
10. Motion models of other objects around a vehicle.
11. Implement algorithms for finding the shortest path.
12. Methods of searching space of possible routes and their visualization.
13. Methods of predicting the route of other objects and their visualization.
14. Behavioral planning, trajectory generation.
Project:
In the project, students will implement the selected problem of autonomous vehicle driving using available data.
1. Car navigation using GPS, RDS-TMC.
2. Introduction to autonomous driving, definition of basic concepts.
3. Sensors for autonomous driving, cameras, LIDAR, ultrasound.
4. Sensor data fusion.
5. Machine learning tools in autonomous driving.
6. Detecting objects around the vehicle.
7. Space mapping around the vehicle.
8. Methods of localization in known environment - particle filter.
9. Methods of localization in unknown environment - SLAM.
10. Motion models of other objects around vehicles.
11. Route planning, shortest path algorithms.
12. Search for possible route paths.
13. Predicting the route of other objects.
14. Behavioral planning, trajectory generation.
Exercises:
1. Working with the GPS geolocation system and radio transmission of traffic information using RDS-TMC.
2. Working with cameras, setting parameters and storing data.
3. Work with LIDAR sensor and ultrasonic sensors.
4. Processing of sensor data and their fusion into subsequent analysis.
5. Introduction to the software framework of machine learning.
6. Detection of interest objects around a vehicle using machine learning.
7. Creating map data based on sensor data.
8. Use of particle filter to locate a vehicle in a known environment.
9. SLAM techniques to locate a vehicle in an unknown environment.
10. Motion models of other objects around a vehicle.
11. Implement algorithms for finding the shortest path.
12. Methods of searching space of possible routes and their visualization.
13. Methods of predicting the route of other objects and their visualization.
14. Behavioral planning, trajectory generation.
Project:
In the project, students will implement the selected problem of autonomous vehicle driving using available data.