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Technically Speaking

Welcome to Motional’s Technically Speaking series, where we take a deep dive into how our top team of engineers and scientists are making driverless vehicles a safe, reliable, and accessible reality. 

Part 1:

Learning With Every Mile Driven

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

Read Part 1
Learning with every mile driven

Part 2:

Auto-Labeling With Offline Perception

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.

Read Part 2
Offline Perception

Part 3:

Motional's nuPlan Dataset Will Advance AV Planning Research

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.

Read Part 3
nuPlan

Part 4:

Predicting the Future in Real Time for Safer Autonomous Driving

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.

Read Part 4
Perception

Part 5:

Closing the Loop: Traveling Back in Time to Help AVs Plan Better

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. 

Read Part 5
Closed Loop

Part 6:

Improving AV Perception Through Transformative Machine Learning

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

Read Part 6
Mapping

Part 7:

How Continuous Fuzzing Secures Software While Increasing Developer Productivity

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. 

Read Part 7
Fuzz Test