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

AI-DO Robotarium Evaluations Underway

We have started evaluating the submissions in our “robotarium”:

Duckiebot onboard camera feed Robotarium watchtower camera feed

To queue your submissions for robotarium evaluation, please follow these instructions:

You need to use the –challenge option to specify 3 challenges: the two simulated ones (testing and validation) and the hardware one:

  • dts challenges submit –challenge aido2-LF-sim-validation,aido2-LF-sim-testing,aido2-LF-real-validation
  • dts challenges submit –challenge aido2-LFV-sim-validation,aido2-LFV-sim-testing,aido2-LFV-real-validation
  • dts challenges submit –challenge aido2-LFV-sim-validation,aido2-LFVI-sim-testing,aido2-LFVI-real-validation

We will evaluate submissions by participants that are in the top part of the leaderboard in the simulated testing challenge.

The robotarium evaluations are limited, and we will do them in a round robin strategy for each user. We aim to evaluate all in the top 10 of the simulated challenge; and then more if there is the possibility.

Participants can have multiple submissions in the “real” challenges. We will evaluate first according to “user priority” or by most recent. The priority is settable through the web interface by using the top right button.

Deadlines

The challenges will close May 21 at 8pm Montreal (EDT) time. Please check the server timestamp for the precise time in your time zone.

Update to Dynamics Model in Duckietown Simulator

We have implemented an improved dynamics model in the simulator. If you are using the simulator to:

  • Train your agent with reinforcement learning
  • Generate data for imitation learning
  • Test and debug your submission

then you may want to retrain/retest with the new dynamics model. This model is much closer to the true Duckiebot and should permit much easier transfer from simulation to the real robot hardware.

AI-DO 2 Validation and Testing Registration

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!

AI-DO technical updates

Here are some technical updates regarding the competition. Thanks for all the bug reports via Github and Slack!

Changes to platform model in simulations

We have changed the purely kinematic model in the simulations with one that is more similar to the real robots obtained by system identification. You can find the model here. Properties:
  • The inputs to the model are the two PWM signals to the wheels, left and right. (not [speed, omega] like last year)
  • The maximum velocity is ~2 m/s. The rise time is about 1 second.
  • There is a simulated delay of 100 ms.
We will slightly perturb the parameters of the model in the future to account for robot-robot variations, but this is not implemented yet. All the submissions have been re-evaluated. You can see the difference between the two models
purely kinematic platform model more realistic platform model
  The new model is much more smooth. Overall we expect that the new model makes the competition easier both in simulation, and obviously, in the transfer.

Infrastructure changes

  • We have update the Duckietown Shell and commands several times to fix a few reported bugs.
  • We have started with provisioning AWS cloud evaluators. There are still sporadic problems. You should know that if your job fails with the host-error code, the system thinks it is a problem of the evaluator and it will try on another evaluator.

Open issues

  • Some timeouts are a bit tight. Currently we allow 20 minutes like for NeurIPS, but this year we have much more realistic simulation and better visualization code that  take more time. If your submission fails after 20 minutes of evaluation, this is the reason.
  • We are still working on the glue code for running the submissions on the real robots. Should be a couple of days away.
  • Some of the changes to the models/protocol above are not in the docs yet.

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