Seeing through the eyes of a self-driving car: visualizing autonomous vehicle data on the web
The ATG (Advanced Technologies Group) at Uber is shaping the future of driverless transportation. Over the last two years, the ATG Visualization team built a web visualization platform that enables engineers and operators across ATG to quickly inspect, debug, and explore information collected from offline and online testing. In this talk, we dive into the challenges of combining complex and diverse datasets into a reusable and performant web application, and how Uber’s open-source visualization tech stack brings it to life.
- Data visualization at Uber: the many visualization tools, and why they are crucial to the business
- Introduction to ATG
- Overview of the autonomous vehicle data: what is in there, and why it’s hard to visualize
- Designing a visual language for the decision making process of a self-driving car
- Why invest in the web?
- Uber’s open-source visualization frameworks power beautiful, performant data applications in the web
- Video of the AV web platform
- Use case study: using the AV web platform to triage issues
- Use case study: using the AV web platform to debug software
Xiaoji Chen is a senior software engineer at Uber’s Visualization team. Prior to Uber, she was a designer for Microsoft’s Xbox One and Visual Studio, and published several works during her tenure at MIT’s Senseable City Lab. Her projects look into innovative ways to visually present large amount of data and to reveal patterns in transportation, communication, environment and health; Using data visualization to raise awareness on urban growth issues and influence population behavior; Building visualization tools that democratize open data access and promote informed decision making.