Category: news

Join the AI Driving Olympics, 5th edition, starting now!

Posted on | by Ivano Marocchi

Compete in the 5th AI Driving Olympics (AI-DO)

The 5th edition of the Artificial Intelligence Driving Olympics (AI-DO 5) has officially started!

The AI-DO serves to benchmark the state of the art of artificial intelligence in autonomous driving by providing standardized simulation and hardware environments for tasks related to multi-sensory perception and embodied AI.

Duckietown hosts AI-DO competitions biannually, with finals events held at machine learning and robotics conferences such as the International Conference on Robotics and Automation (ICRA) and the Neural Information Processing Systems (NeurIPS). 

 The AI-DO 5 will be in conjunction with NeurIPS 2020 and have two leagues: Urban Driving and Advanced Perception. 

Urban driving league challenges

This year’s Urban League includes a traditional AI-DO challenge (LF) and introduces two new ones (LFP, LFVM).

Lane Following (LF)



Get the LF kit

The most traditional of AI-DO challenges: have a Duckiebot navigate a road loop without intersection, pedestrians (duckies) or other vehicles. The objective is traveling the longest path in a given time while staying in the lane.

Lane following with Pedestrian (LFP)



Get the LFP kit

The LFP challenge is new to AI-DO. It builds upon LF by introducing static obstacles (duckies) on the road. The objectives are the same as for lane following, but do not hit the duckies! 

Lane Following with Vehicles, multi-body (LFVM)



Get the LFVM kit

In this traditional AI-DO challenge, contestants seek to travel the longest path in a city without intersections nor pedestrians, but with other vehicles on the road. Except this year there’s a twist. In this year’s novel multi-body variant, all vehicles on the road are controlled by the submission.

Getting started: the webinars

We offer a short webinar series to guide contestants through the steps for participating: from running our baselines in simulation as well as deploying them on hardware. All webinars are 9 am EST and free!

Introduction

Learn about the Duckietown project and the Artificial Intelligence Driving Olympics.




  • Nov. 9, 2020

ROS baseline

How to run and build upon the “traditional” Robotic Operation System (ROS) baseline.




  • Nov. 11, 2020

Local development

On the workflow for developing and deploying to Duckiebots, for hardware-based testing.




  • Nov. 13, 2020

RL baseline

Learn how to use the Pytorch template for reinforcement learning approaches.




  • Nov. 16, 2020

IL baseline

Introduction to the Tensorflow template, use of logs and simulator for imitation learning.




  • Nov. 18, 2020






Synch Webinar Calendar

Advanced sensing league challenges

Previous AI-DO editions featured: detection, tracking and prediction challenges around the nuScenes dataset.

For the 5th iteration of AI-DO we have a brand new lidar segmentation challenge.

The challenge is based on the recently released lidar segmentation annotations for nuScenes and features an astonishing 1,400,000,000 lidar points annotated with one of 32 labels.

We hope that this new benchmark will help to push the boundaries in lidar segmentation. Please see https://www.nuscenes.org/lidar-segmentation for more details.

Furthermore, due to popular demand, we will organize the 3rd iteration of the nuScenes 3d detection challenge. Please see https://www.nuscenes.org/object-detection for more details.

AI-DO 5 Finals event

The AI-DO finals will be streamed LIVE during 2020 edition of the Neural Information Processing Systems (NeurIPS 2020) conference in December.

Learn more about the AI-DO here.



Participate in AI-DO 5 now



Get your AI-DO hardware kit



Join our Slack Community

Thank you to our generous sponsors!

The Duckietown Foundation is grateful to its sponsors for supporting this fifth edition of the AI Driving Olympics!



IROS2020: Watch The Workshop on Benchmarking Progress in Autonomous Driving

Posted on | by Ivano Marocchi

What a start for IROS 2020 with the "Benchmarking Progress in Autonomous Driving" workshop!

The 2020 edition of the International Conference on Intelligent Robots and Systems (IROS) started great with the workshop on “Benchmarking Progress in Autonomous Driving”.

The workshop was held virtually on October 25th, 2020, using an engaging and concise format of a sequence of four 1.5-hour moderated round-table discussions (including an introduction) centered around 4 themes.

The discussions on the methods by which progress in autonomous driving is evaluated, benchmarked, and verified were exciting. Many thanks to all the panelists and the organizers!  

Here are the videos of the various sessions. 

Opening remarks

Theme 1: Assessing progress for the field of autonomous vehicles (AVs)

Moderator: Andrea Censi

Invited Panelists:

Theme 2: How to evaluate AV risk from the perspective of real world deployment (public acceptance, insurance, liability, …)?

Moderator: Jacopo Tani

Invited Panelists:

Theme 3: Best practices for AV benchmarking

Moderator: Liam Paull

Invited Panelists:

Theme 4: Do we need new paradigms for AV development?

Moderator: Matt Walter

Invited Panelists:

Closing remarks

You can find additional information about the workshop here.

The Workshop on Benchmarking Progress in Autonomous Driving at IROS 2020

Posted on | by Liam Paull

The IROS 2020 Workshop on Benchmarking Autonomous Driving

Duckietown has also a science mission: to help develop technologies for reproducible benchmarking in robotics.  

The IROS 2020 Workshop on Benchmarking Autonomous Driving provides a platform to investigate and discuss the methods by which progress in autonomous driving is evaluated, benchmarked, and verified.

It is free to attend.

The workshop is structured into 4 panels around four themes. 

  1. Assessing Progress for the Field of Autonomous Driving
  2. How to evaluate AV risk from the perspective of real world deployment (public acceptance, insurance, liability, …)?
  3. Best practices for AV benchmarking
  4. Algorithms and Paradigms

The workshop will take place on Oct. 25, 2020 starting at 10am EDT. 

Invited Panelists

We have  a list of excellent invited panelists from academia, industry, and regulatory organizations. These include: 

  • Emilio Frazzoli (ETH Zürich / Motional)
  • Alex Kendall (Wayve)
  • Jane Lappin (National Academy of Sciences)
  • Bryant Walker Smith (USC Faculty of Law)
  • Luigi Di Lillo (Swiss Re Insurance), 
  • John Leonard (MIT)
  • Fabio Bonsignorio (Heron Robots)
  • Michael Milford (QUT)
  • Oscar Beijbom (Motional)
  • Raquel Urtasun (University of Toronto / Uber ATG). 

Please join us...

Please join us on October 25, 2020 starting at 10am EST for what should be a very engaging conversation about the difficult issues around benchmarking progress in autonomous vehicles.  

For full details about the event please see here.

Round 3 of the the AI Driving Olympics is underway!

Posted on | by Liam Paull

The AI Driving Olympics (AI-DO) is back!

We are excited to announce the launch of the AI-DO 3, which will culminate in a live competition event to be held at NeurIPS this Dec. 13-14.

The AI-DO is a global robotics competition that comprises a series of events based on autonomous driving. This year there are three events, urban (Duckietown), advanced perception (nuScenes), and racing (AWS Deepracer).  The objective of the AI-DO is to engage people from around the world in friendly competition, while simultaneously benchmarking and advancing the field of robotics and AI. 

Check out our official press release.

  • Learn more about the AI-DO competition here.

If you've already joined the competition we want to hear from you! 

 Share your pictures on facebook and twitter. 

Duckietown Workshop at RoboCup Junior

Posted on | by Kirsten Bowser

Duckietown Workshop at RoboCup Junior

In collaboration with the RoboCup Federation, the Duckietown Foundation will be offering workshops at RoboCup 2019 in Sydney, Australia, providing a hands-on introduction to the Duckietown platform.

We will be hosting three one-day workshops as part of RoboCup 2019 from July 4-6, 2019  for teachers, students, and independent learners who are interested in finding out more about the Duckietown platform. Attendance is completely free and everyone is welcome to apply, even if you are not participating in RoboCup. There are no formal requirements, though basic familiarity with GNU/Linux and shell usage is recommended. 

If you would like to apply to attend a workshop, please complete this form. 

We will have Duckiebots and Duckietowns for participants to use. However, you are more than welcome to bring your own Duckiebots, available for purchase at https://get.duckietown.org. 

Congratulations to the winners of the second edition of the AI Driving Olympics!

Posted on | by Kirsten Bowser

Team JetBrains came out on top on all 3 challenges

It was a busy (and squeaky) few days at the International Conference on Robotics and Automation in Montreal for the organizers and competitors of the AI Driving Olympics. 

The finals were kicked off by a semifinals round, where we the top 5 submissions from the Lane Following in Simulation leaderboard. The finalists (JBRRussia and MYF) moved forward to the more complicated challenges of Lane Following with Vehicles and Lane Following with Vehicles and Intersections. 

Results from the AI-DO2 Finals event on May 22, 2019 at ICRA

If you couldn’t make it to the event and missed the live stream on Facebook, here’s a short video of the first run of the JetBrains Lane Following submission.

Thanks to everyone that competed, dropped in to say hello, and cheered on the finalists by sending the song of the Duckie down the corridors of the Palais des Congrès. 

A few pictures from the event

Don't know much about the AI Driving Olympics?

It is an accessible and reproducible autonomous car competition designed with straightforward standardized hardware, software and interfaces.

Get Started

Step 1: Build and test your agent with our available templates and baselines

Step 2: Submit to a challenge

https://challenges.duckietown.org

Check out the leaderboard

View your submission in simulation

Step 3: Run your submission on a robot

https://get.duckietown.org/

in a Robotarium

AI-DO 2 Validation and Testing Registration

Posted on | by Kirsten Bowser

We are in the final countdown to AI-DO 2 at ICRA!

Now is the time to let us know if you will be using the validation and testing facilities at the Duckietown competition ground. Please register below!

Round 2 of the the AI Driving Olympics is underway!

Posted on | by Kirsten Bowser

The AI-DO is back!

We are excited to announce that we are now ready to accept submissions for AI-DO 2, which will culminate in a live competition event to be held at ICRA 2019 this May 20-22.

The AI Driving Olympics is a global robotics competition that comprises a series of challenges based on autonomous driving. The AI-DO provides a standardized simulation and robotics platform that people from around the world use to engage in friendly competition, while simultaneously advancing the field of robotics and AI. 

Check out our official press release.

The finals of AI-DO 1 at NeurIPS, December 2018

We want to see your classical robotic and machine learning based algorithms go head to head on the competition track. Get started today!

Want to learn more or join the competition? Information and get started instructions are here.

If you've already joined the competition we want to hear from you! 

 Share your pictures on facebook and twitter. 

 Get involved in the community by:

asking for help

offering help

AI-DO 1 at NeurIPS report. Congratulations to our winners!

Posted on | by Kirsten Bowser

The winners of AIDO-1 at NeurIPS

duckie-only-transparent

There was a great turnout for the first AI Driving Olympics competition, which took place at the NeurIPS conference in Montreal, Canada on Dec 8, 2018. In the finals, the submissions from the top five competitors were run from  five different locations on the competition track. 

Our top five competitors were awarded $3000 worth of AWS Credits (thank you AWS!) and a trip to one of nuTonomy’s offices for a ride in one of their self-driving cars (thanks APTIV!) 

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WINNER

Team Panasonic R&D Center Singapore & NUS

(Wei Gao)


Check out the submission.

The approach: We used the random template for its flexibility and created a debug framework to test the algorithm. After that, we created one python package for our algorithm and used the random template to directly call it. The algorithm basically contains three parts: 1. Perception, 2. Prediction and 3. Control. Prediction plays the most important role when the robot is at the sharp turn where the camera can not observe useful information.

2nd Place

Jon Plante


Check out the submission.

The approach:  “I tried and imitate what a human does when he follows a lane. I believe the human tries to center itself at all times in the lane using the two lines as guides. I think the human implicitly projects the two lines into the horizon and where they intersect is where the human directs the vehicle towards.”

 

3rd Place

Vincent Mai


Check out the submission.

The approach: “The AI-DO application I made was using the ROS lane following baseline. After running it out of the box, I noticed a couple of problems and corrected them by changing several parameters in the code.”

 

 

Jacopo Tani - IMG_20181208_163935

4th Place

Team JetBrains

(Mikita Sazanovich)


Check out the submission.

The approach: “We used our framework for parallel deep reinforcement learning. Our network consisted of five convolutional layers (1st layer with 32 9×9 filters, each following layer with 32 5×5 filters), followed by two fully connected layers (with 768 and 48 neurons) that took as an input four last frames downsampled to 120 by 160 pixels and filtered for white and yellow color. We trained it with Deep Deterministic Policy Gradient algorithm (Lillicrap et al. 2015). The training was done in three stages: first, on a full track, then on the most problematic regions, and then on a full track again.”

5th Place

Team SAIC Moscow

(Anton Mashikhin)


Check out the submission.

The approach: Our solution is based on reinforcement learning algorithm. We used a Twin delayed DDPG and ape-x like distributed scheme. One of the key insights was to add PID controller as an additional  explorative policy. It has significantly improved learning speed and quality

A few photos from the day