Keynote: Inside a Self-Driving Uber

Location: Cyril Magnin Ballroom

Duration: 9:00am - 10:10am

Day of week: Tuesday

Abstract

Over the course of three years, Uber’s self-driving vehicles have driven over 2 million miles and have completed over 50,000 passenger trips in Pittsburgh and Phoenix. Many of you might be curious as to how we built a fleet of self-driving vehicles capable of driving autonomously in varying terrains and conditions. In this talk, Matt will break down the software components that come together to make a self-driving Uber drive itself. You’ll also learn about how we thoroughly test new software before it is deployed to the fleet.

Speaker: Matt Ranney

Sr. Staff Engineer @UberATG

Matt works on Uber ATG's simulation systems to exercise autonomy software before it gets onto the road. Before that, he worked on distributed systems, architecture, and performance at Uber.

Find Matt Ranney at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.