Tag: Robocar

Build an Autonomous Vehicle on AWS and Race It at the re:Invent Robocar Rally

Autonomous vehicles are poised to take to our roads in massive numbers in the coming years. This has been made possible due to advances in deep learning and its application to autonomous driving. In this post, we take you through a tutorial that shows you how to build a remote control (RC) vehicle that uses Amazon AI services.

Typically each autonomous vehicle is stacked with a lot of sensors that provide rich telemetry. This telemetry can be used to improve the driving of the individual vehicle but also the user experience. Some examples of those improvements are time saved by smart drive routing, increased vehicle range and efficiency, and increased safety and crash reporting. On AWS, customers like TuSimple have built a sophisticated autonomous platform using Apache MXNet. Recently TuSimple completed a 200-mile driverless ride.

To drive awareness of deep learning, AWS IoT, and the role of artificial intelligence (AI) in autonomous driving, AWS will host a workshop-style hackathon—Robocar Rally at re:Invent 2017. This is the first in a series of blog posts and Twitch videos for developers to learn autonomous AI technologies and to prepare for the hackathon. For more details on the hackathon, see Robocar Rally 2017.

In this tutorial we’ll leverage the open source platform project called Donkey. If you want, you can experiment with your own 1/10 scale electric vehicle. However we’ll stick to the recommended 1/16 scale RC vehicle used in the donkey project.

Here are a couple of videos that show two of the cars that we have built at AWS using the tutorial that follows.



Vehicle Build Process

The process for assembling and configuring the autonomous vehicle can be found in this repo. It also includes a full materials list with links on where to purchase the individual components. The main components are the RC Car, Raspberry Pi, Pi Cam, and Adafruit Servo HAT, the combined cost of which was less than $250. You can buy additional sensors, such as a stereo camera, LIDAR data collector, and an accelerometer, to name a few.

We recommend that you follow the steps on this Github repo to ensure a basic level of capabilities and a path to success that minimizes some undifferentiated heavy lifting.