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! 

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 Get involved in the community by:

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See here and report any broken links. There will be more updates soon.

Didn’t get a chance to compete in the AI Driving Olympics at NeurIPS this past December? Not to worry! The second iteration of the AI-DO will take place at ICRA this May. Get your engines and algorithms up and running by checking out the information on the AI-DO website.

The winners of AIDO-1 at NeurIPS

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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.”

 

 

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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

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We’re just about at the end of the road for the 2018 AI Driving Olympics.

There’s certainly been some action on the leaderboard these last few days and it’s going down to the wire. Don’t miss your chance to see you name up there and win the amazing prizes donated by nuTonomy and Amazon AWS!

Submissions will close at 11:59pm PST on Thursday Dec. 6.

Please join us at NeurIPS for the live competition 3:30-5:00pm EST in room 511!

We are going to roll out an improvement to the LF and LFV challenges competitions. This change fixes the following problems:
  • The robot will always start in the right lane - a legal position.
  • The evaluation and visualization code are going to be richer, with more statistics plotted (example).
  • The evaluation rulebook is slightly changed to address a couple of bugs of how the metrics were computed.
What is going to happen is the following:
  • The moment that we update the evaluation code, all existing submissions are set back to the state of "evaluation".
  • The evaluators will then re-evaluate all of them. This will take 2-3 hours.
During this time the leaderboards are going to be blank, and slowly will re-populate as the evaluators do their job. (To speed up evaluation of your submissions, you can run dts challenges evaluator.)

We increased the server capacity. This should mitigate the slowness issues due to the unexpected number of participants/submissions.

We updated the logic of how we compute the leaderboard; now there can be only one submission per user.

The AI Driving Olympics, presented by the Duckietown Foundation with help from our partners and sponsors is now in full swing. Check out the leaderboard!

We now have templates for ROS, PyTorch, and TensorFlow, as well as an agnostic template.

We also have baseline implementation using the classical pipeline, imitation learning with data from both simulation and real Duckietown logs, and reinforcement learning.

We are excited to announce that we will be hosting a series of interactive tutorials for competitors to get started. These tutorials will be streamed live from our Facebook page.

See here for the full tutorial schedule.

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