Udacity and Chinese ridesharing company Didi Chuxing are joint sponsors of a contest with a top cash prize of US $100,000. Part of the appeal of participating is that the top five teams get the chance to run code on the Udacity self-driving car.
Didi Chuxing is a Udacity partner for its Artificial Intelligence Nanodegree and this isn't the first competition they have run jointly. Last year the challenge was to use machine learning to come up with a better ride matching algorithm for Didi
So in this new competition, which is open to teams of self-driving car enthusiasts whether or not they are enrolled on this program, the challenge is to create a redundant, safe, and reliable system for detecting hazards, both other vehicles, pedestrians and general objects.
Explaining the background to this contest, which focuses on a core feature of self-driving cars, the Automated Safety and Awareness Processing Stack (ASAPS) the challenge website states:
One of the most important aspects of operating an autonomous vehicle is understanding the surrounding environment in order to make safe decisions. Udacity and Didi Chuxing are partnering together to provide incentive for students to come up with the best way to detect obstacles using camera and LIDAR data. This challenge will allow for pedestrian, vehicle, and general obstacle detection that is useful to both human drivers and self-driving car systems.
If you haven't come across Kitti before this video will put you in the picture:
The age limit for the contest is 18 or older and it is open to those who are legal residents of the China, United States of America, Canada and Mexico, Brazil, the countries of the European Union, the United Kingdom and India, and any other country where such participation in the Contest is not prohibited by applicable law.
First step, if you are not already a Udacian, is to set up a student account. After that you can start to form, or join a team.
The data for the first round, which asks competitors to identify distance and estimated orientation of multiple stationary and moving obstacles.will be released on March 22nd. Round 1 ends on April 21st, which is also the deadline for user and team registration.
The top 75 teams from round one will be asked to submit runnable code and the top 50 will progress to the next round, from May 1st to May 31st where the challenge is extended to identifying pedestrians.
From that round the top 5 will be invited to attend an in-person event at Udacity headquarters in Mountain View, California where teams will present the ir solutions to a panel of Udacity and DiDi executives and have chance to run their code on Udacity’s self-driving car. Cash prizes of $100K, $3K and $1.5K will be awarded to the top 5 teams.
Lots of people are already working with the Kitti datasets so there is likely to be a lot if interest in this opportunity to run code on the Udacity self-driving car. If a team of Udacity students can get into the finals that would be a great boost for them and for Udacity itself.
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