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AWS DeepRacer Evo and Sensor Kit now available for purchase

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AWS DeepRacer is a fully autonomous 1/18th scale race car powered by reinforcement learning (RL) that gives machine learning (ML) developers of all skill levels the opportunity to learn and build their ML skills in a fun and competitive way. AWS DeepRacer Evo includes new features and capabilities to help you learn more about ML through the addition of sensors that enable object avoidance and head-to-head racing. Starting today, while supplies last, developers can purchase AWS DeepRacer Evo for a limited-time, discounted price of $399, a savings of $199 off the regular bundle price of $598, and the AWS DeepRacer Sensor Kit for $149, a savings of $100 off the regular price of $249. Both are available on Amazon.com for shipping in the USA only.

AWS DeepRacer Evo and Sensor Kit now available for purchase 1

What is AWS DeepRacer Evo?

AWS DeepRacer Evo is the next generation in autonomous racing. It comes fully equipped with stereo cameras and a LiDAR sensor to enable object avoidance and head-to-head racing, giving you everything you need to take your racing to the next level. These additional sensors allow for the car to handle more complex environments and take actions needed for new racing experiences. In object avoidance races, you use the sensors to detect and avoid obstacles placed on the track. In head-to-head, you race against another car on the same track and try to avoid it while still turning in the best lap time.

Forward-facing left and right cameras make up the stereo cameras, which help the car learn depth information in images. It can then use this information to sense and avoid objects it approaches on the track. The backward-facing LiDAR sensor detects objects behind and beside the car.

The AWS DeepRacer Evo car, available on Amazon.com, includes the original AWS DeepRacer car, an additional 4 megapixel camera module that forms stereo vision with the original camera, a scanning LiDAR, a shell that can fit both the stereo camera and LiDAR, and a few accessories and easy-to-use installation tools for a quick installation. If you already own an AWS DeepRacer car, you can upgrade your car to have the same capabilities as AWS DeepRacer Evo with the AWS DeepRacer Sensor Kit.

AWS DeepRacer Evo under the hood

The following table summarizes the details of AWS DeepRacer Evo.

CAR 1/18th scale 4WD monster truck chassis
CPU Intel Atom™ Processor
MEMORY 4 GB RAM
STORAGE 32 GB (expandable)
WI-FI 802.11ac
CAMERA 2 X 4 MP camera with MJPEG
LIDAR 360 degree 12 meters scanning radius LIDAR sensor
SOFTWARE Ubuntu OS 16.04.3 LTS, Intel® OpenVINO™ toolkit, ROS Kinetic
DRIVE BATTERY 7.4V/1100mAh lithium polymer
COMPUTE BATTERY 13600 mAh USB-C PD
PORTS 4x USB-A, 1x USB-C, 1x Micro-USB, 1x HDMI
INTEGRATED SENSORS Accelerometer and Gyroscope

Getting started with AWS DeepRacer Evo

You can get your car ready to hit the track in five simple (and fun) steps. For full instructions, see Getting Started with AWS DeepRacer.

Step 1: Install the sensor kit

The first step is to set up the car by reconfiguring the sensors. The existing camera shifts to one side to allow room for the second camera to create a stereo configuration, and the LiDAR is mounted on a bracket above the battery and connects via USB between the two cameras.

AWS DeepRacer Evo and Sensor Kit now available for purchase 2

Step 2: Connect and test drive

Connect any device to the same Wi-Fi network as your AWS DeepRacer car and navigate to its IP address in your browser. After you upgrade to the latest software version, use the device console to take a test drive.

AWS DeepRacer Evo and Sensor Kit now available for purchase 3

Step 3: Train a model

Now it’s time to get hands-on with ML by training an RL model on the AWS DeepRacer console. To create a model using the new AWS DeepRacer Evo sensors, select the appropriate sensor configuration in Your Garage, train and evaluate the model, clone, and iterate to improve the model’s performance.

AWS DeepRacer Evo and Sensor Kit now available for purchase 4

Step 4: Load the model onto the device

You can download the model for the vehicle from the AWS DeepRacer console to your local computer, and then upload it to the AWS DeepRacer vehicle using the file you chose in the Models section on the AWS DeepRacer console.

AWS DeepRacer Evo and Sensor Kit now available for purchase 5

Step 5: Start racing

Now the rubber hits the road! In the Control vehicle page on the device console, you can select autonomous driving, choose the model you want to race with, make adjustments, and choose Start vehicle to shift into gear!

AWS DeepRacer Evo and Sensor Kit now available for purchase 6

Building a DIY track

Now you’re ready to race, and every race car needs a race track! For a fun activity, you can build a track for your AWS DeepRacer Evo at home.

  1. Lay down tape on one border of a straight line (your length varies depending on available space).
    AWS DeepRacer Evo and Sensor Kit now available for purchase 7
  2. Measure a width of approximately 24”, excluding the tape borders.
    AWS DeepRacer Evo and Sensor Kit now available for purchase 8
  3. Lay down a parallel line and match the length.
    AWS DeepRacer Evo and Sensor Kit now available for purchase 9
  4. Place the vehicle at one edge of the track and get ready to race!
    AWS DeepRacer Evo and Sensor Kit now available for purchase 10

After you build your track, you can train your model on the console and start racing. Try more challenging races by placing objects (such as a box or toy) on the track and moving them around.

For more information about building tracks, see AWS DeepRacer Track Design Templates.

When you have the basics down for racing the car, you can spend more time improving and getting around the track with greater success.

Optimizing racing performance

Whether you want to go faster, round corners more smoothly, or stop or start faster, model optimization is the key to success in object avoidance and head-to-head racing. You can also experiment with new strategies:

  • Defensive driver – Your car is penalized whenever its position is within a certain range to any other object
  • Blocker – When your car detects a car behind it, it’s incentivized to stay in the same lane to prevent passing

The level of training complexity and time also impact the behavior of the car in different situations. Variables like the number of botcars on the training track, whether botcars are static or moving, and how often they change lanes all affect the model’s performance. There is so much more you can do to train your model and have lots of fun!

Join the race to win glory and prizes!

There are plenty of chances to compete against your fellow racers right now! Submit your model to compete in the AWS DeepRacer Virtual Circuit and try out object avoidance and head-to-head racing. Throughout the 2020 season, the number of objects and bots on the track increases, requiring you to optimize your use of sensors to top the leaderboard. Hundreds of developers have extended their ML journey by competing in object avoidance and head-to-head Virtual Circuit races in 2020 so far.

For more information about an AWS DeepRacer competition from earlier in the year, check out the F1 ProAm DeepRacer event. You can also learn more about AWS DeepRacer in upcoming AWS Summit Online events. Sign in to the AWS DeepRacer console now to learn more and start your ML journey.


About the Author

AWS DeepRacer Evo and Sensor Kit now available for purchase 11Dan McCorriston is a Senior Product Marketing Manager for AWS Machine Learning. He is passionate about technology, collaborating with developers, and creating new methods of expanding technology education. Out of the office he likes to hike, cook and spend time with his family.

 

 

 

 



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