Autonomous vehicles (AV) must be driven hundreds of millions of miles to demonstrate their reliability and safety. There is a need for alternative methods to supplement real-world testing, including virtual testing and simulations, mathematical modeling and analysis, and scenario and behavior testing. To support our automotive customers with addressing these pain points, Amazon Web Services (AWS) created a reference architecture for an advanced driver-assistance systems (ADAS) data lake. The Autonomous Driving Data Framework (ADDF) now industrializes the reference solution. This course introduces this solution.
- Course Level: Fundamental
- Duration: 45 minutes
Activities
This course includes the following: online materials and videos
Course objectives
In this course, you will learn to do the following:
- Understand autonomous driving challenges and the advantages of an AWS ADDF.
- Understand the core concepts of a data lake and data gathering.
- Understand architectural considerations.
- Understand how AWS can help solve autonomous challenges related to scene detection, search, visualization, sensor extraction, and object detection.
Intended audience
This course is intended for the following people:
- Individuals in the automotive industry who work in roles related to system architecture and business
Prerequisites
We recommend that attendees of this course have the following experience:
- Familiarity with cloud computing and the AWS Cloud
Course outline
Section 1: Introduction
- Lesson 1: How to Use This Course
- Lesson 2: Course Overview
- Lesson 3: Solution Introduction
Section 2: Key AWS Services
- Lesson 4: Data Lakes
- Lesson 5: Data Storage
- Lesson 6: Compute
- Lesson 7: Orchestration
Section 3: ADDF on AWS
- Lesson 8: ADDF on AWS Architecture
- Lesson 9: Sensor Extraction and Object Detection
- Lesson 10: Scene Detection
- Lesson 11: Search
- Lesson 12: Visualization
Section 4: Course Summary
- Lesson 13: For More Information
- Lesson 14: Contact Us
