Sensors and Perception for Autonomous Vehicle Development
C2403
- Content
This four-week virtual-only experience, conducted by leading experts in the autonomous vehicle industry and academia, provides an in-depth look at the most common sensor types used in autonomous vehicle applications. By reviewing the theory, working through examples, viewing sensor data, and programming movement of a Turtlebot, you will develop a solid, hands-on understanding of the common sensors and data provided by each.
This course consists of asynchronous videos you will work th
- Content
- At the end of this course, learners will be able to:
- Explain the purpose of each common sensor type in AV applications
- Describe why redundancy is needed when it comes to real-life AV sensing.
- Summarize the basics of LiDAR and how to obtain data from a laser scanner, and perform data filtering and perform basic AEB from distance measured from laser scanner from a solved exercise
- Summarize the basics of camera sensors and how to extract images in ROS and manipulate images through OpenCV library
- Perform simple line following (solved exercise)
- Interpret the logic behind extracting geometric features from images and perform lane keeping (solved exercise)
- Explain neural networks and showcase a ROS wrapper for simple models
- Complete an object detection exercise using YOLO v3
- Content
Participants will be recent graduates or new to/newly hired mechanical, electrical, and computer science engineers joining industry to support autonomous vehicle system development. The program is designed for working engineers. We also encourage those who are interested in learning more about the field of autonomous vehicles.
- Content
Prerequisites for this course include a Bachelor of Science in mechanical, software, or electrical engineering, or computer science, interest in working on autonomous vehicles, some coding ability in C or Python, and coursework in linear algebra and statistics.
- CEU
- 3