Jendrik Joerdening and Anthony Navarro on Self-Racing Cars Using Deep Neural Networks
Published March 16, 2018
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37 min
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    Jendrik Joerdening and Anthony Navarro describe how a team of 18 Udacity students entered a self-racing car event   They had very limited experience of building autonomous control systems for vehicles and had just 6 weeks to do it with only 2 days with the physical car.  They describe the architecture, how they co-ordinated a very diverse team, and how they trained the models. Why listen to this podcast: - Last year a team of 18 Udacity Self-Driving Cars students competed at the 2017 Self Racing Cars event held at Thunderhill Raceway in California. - The students had all taken the first term of a three term program on Udacity which covers computer vision and deep learning techniques. - The team was extremely diverse.  They co-ordinated the work via Slack with a team in 9 timezones and 5 different countries.   - The team developed a neural network using Keras and Tensorflow which steered the car based on the input from just one front-facing camera in order to navigate all turns on the racetrack.  - They received a physical car two days before the start of the event. More on this: Quick scan our curated show notes on InfoQ http://bit.ly/2DykAiJ You can also subscribe to the InfoQ newsletter to receive weekly updates on the hottest topics from professional software development. bit.ly/24x3IVq Subscribe: www.youtube.com/infoq Like InfoQ on Facebook: bit.ly/2jmlyG8 Follow on Twitter: twitter.com/InfoQ Follow on LinkedIn: www.linkedin.com/company/infoq Check the landing page on InfoQ: http://bit.ly/2DykAiJ
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