CS Team Wins the ICCV 2019 Learning-to-Drive Challenge

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Students participated in the International Conference on Computer Vision (ICCV) 2019 Learning-to-Drive Challenge as part of the Deep Learning (DL) course taught by adjunct associate professor Iddo Drori. The winning team’s findings were presented at the Autonomous Driving workshop in Seoul, Korea.

The goal of the competition is to develop DL driving models — predicting steering wheel angle and vehicle speed given large scale training data, and using advanced deep learning techniques. Two teams, composed of students from the computer science (CS) department and the Data Science Institute (DSI), won the challenge in all major categories ranking first and second place.

“Winning the top three categories in this international challenge is an excellent achievement,” said adjunct associate professor Iddo Drori. “I am very proud to have mentored the teams to the finish line.”

As part of the unique DL course curriculum, students get to compete in common task framework competitions which enable them to test the waters in the real world while advancing science. This semester Drori and teaching assistants Manik Goyal and Benedikt Dietmar performed feasibility tests on the Learning-to-Drive Challenge and found it in-line with the course goals.

Students used the Drive 360 dataset to to design, develop and train a driving model. Over the course of three weeks, teams worked on and improved their submissions competing with groups from across the world. Students were given cloud resources to develop their models even further. The effort paid off with the CS and DSI students at the top of the competition leaderboard. In order to claim victory they had to quickly write up and submit their findings.

CS graduate students Michael J Diodato and Yu Li won first place, while DSI graduate students Antonia Lovjer and Minsu Yeom won second place.



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