Technically Speaking

In Part 1, we introduce our approach to machine learning and how our Continuous Learning Framework allows us to train our autonomous vehicles faster.

In Part 2, we share how we build a world-class offline perception system to automatically label the data that will train our next-generation vehicles.

Part 3 focuses on Motional's expanded open-source planning dataset will allow our industry and researchers to better understand how a driverless vehicle can find its way through a dynamic environment full of obstacles and ever-changing circumstances – like a human driver does every day.

In Part 4, Henggang Cui explains Motional’s approach to prediction, and how we use multimodal prediction models to help reduce the unpredictability of human drivers.

In Part 5, Caglayan Dicle takes a look at how closed-loop testing will strengthen Motional's planning function, making the ride safer and more comfortable for passengers. 

Part 6 explains how Motional is using Transformer Neural Networks to improve AV perception performance. 

Part 7 explores how Motional employs a technique known as fuzz testing, or fuzzing, which stress tests our autonomous vehicle software through the use of randomized, arbitrary, or unexpected inputs.