Invited Talk: End to End Learning for Self-Driving Cars (Larry Jackel, NVIDIA)

Abstract:

End-to-End Learning has been demonstrated for controlling steering on a drive-by-wire car. The key software component in this system is a Convolutional Neural Network (CNN) that takes as input the stream from a video camera mounted behind the vehicle windshield and then, as output, provides steering commands to the vehicle. The CNN runs on an NVIDIA Drive PX board. The system has successfully driven on divided highways, narrow two lane roads, and roads without lane markings. The CNN was trained using data gathered by capturing on-board video from vehicles driven by humans while simultaneously recording those vehicles steering commands.

Bio:

Larry Jackel is President of North-C Technologies, where he does professional consulting. From 2003-2007 he was a DARPA Program Manager in the IPTO and TTO offices. He conceived and managed programs in Universal Network-Based Document Storage and in Autonomous Ground Robot navigation and Locomotion. For most of his scientific career Jackel was a manager and researcher in Bell Labs and then AT&T Labs. He has created and managed research groups in Microscience and Microfabrication, in Machine Learning and Pattern Recognition, and in Carrier-Scale Telecom Services. Jackel holds a PhD in Experimental Physics from Cornell University with a thesis in superconducting electronics. He is a Fellow of the American Physical Society and the IEEE.