EdTech awards 2021: Duckietown finalist in 3 categories!

Duckietown reaches the finals in the EdTech Awards 2021

The EdTech awards are the largest and most competitive recognition program in all of education technology.

The competition, led by the EdTech digest, recognizes the biggest names in edtech – and those who soon will be, by identifying all over the world the products, services and people that bet promote education through the use of technology, for the benefit of learners.

The 2021 edition has brought a big surprise to Duckietown, as it was nominated as a finalist in 3 different categories:

  • Cool Tool Award: as robotics (for learning, education) solution;
  • Cool Tool Award: as higher education solution;
  • Trendsetter Award: as a product or service setting a trend in education technologies.

Although a final is just a starting point, we are proud of the hard work done by the team in this particularly difficult year of pandemic and lockdowns, and grateful to you all for the incredible support, constructive feedback and contributions!

To the future, and beyond!

(hidden) Want to learn more about us?

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

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)

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)

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)

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

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.

Thank you to our generous sponsors!

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

AI-DO 3 – Urban Event Winners

In case you missed it AI-DO 3 has come and gone. Interested in reliving the competition? Here’s the video.

We had a great time at NeurIPS hosting the Third Edition of the AI Driving Olympics. As usual the sound of Duckies attracted an engaging and supportive crowd.

 

Racing Event

The competition began with the Racing Event, hosted by AWS DeepRacer. They ran their top 10 submissions and selected the winner by who could complete the fastest lap.

Racing Event Winner 
Ayrat Baykov at 8:08 seconds

 

Advanced Perception Event

The winners of the Advanced Perception Event hosted by APTIV and the nuScenes dataset were announced. Luckily a member of the winning team was present to accept the award.

Rank 3
CenterTrack – Open and Vision

Rank 2
VV_Team

Rank 1
StanfordlPRL-TRI

 

Urban Event

The competition culminated with Duckietown’s own Urban Driving Event, where we ran the top submissions for each of the three challenges on our competition tracks.

Winners

 

Lane Following 

JBRRussia1: Konstantin Chaika, Nikita Sazanovich, Kirill Krinkin, Max Kuzmin

Lane Following with Vehicles

phmarm

Lane Following with Vehicles and Intersections

frank_qcd_qk

 

Final Scoreboard

A few pictures from the event

Congratulations to all the winners and thanks for participating in the competition. We look forward to seeing you for AI-DO 4!

Round 3 of the the AI Driving Olympics is underway!

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

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

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

Check out the leaderboard

View your submission in simulation

Step 3: Run your submission on a robot

in a Robotarium

Round 2 of the the AI Driving Olympics is underway!

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!

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