Building a Better Self-Driving Car: Hardware, Software, and Knowledge

Abstract

Lyft's mission is to improve people's lives with the world's best transportation. Self driving vehicles have the potential to deliver unprecedented improvements to safety and quality, at a price and convenience that challenges traditional models of vehicle ownership. A combination of hardware, software, and knowledge technologies are needed to build self-driving cars. In this talk, I'll present the core problems in self-driving and how recent advances in computer vision, robotics, and machine learning are powering this revolution. The car is carefully designed with a variety of sensors that complement each other to address a wide variety of driving scenarios. Sensor fusion bring all of these signals together into an interpretable AI engine comprising of perception, prediction, planning, and controls. For example, deep learning models and large scale machine learning have closed the gap between human and machine perception. In contrast, predicting the behavior of other humans and effectively planning and negotiating maneuvers continue to be hard problems. Combining AI technologies with deep knowledge about the real world is key to addressing these.