Technically Speaking

Motional introduces their approach to machine learning and how their Continuous Learning Framework allows them to train their autonomous vehicles faster.

Motional shares how they build a world-class offline perception system to automatically label the data that will train their next-generation vehicles.

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.

Motional is using AI to sift through mountains of vehicle data to find the unique driving scenarios needed to make AV tech smarter.

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

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. 

This article explains how we're using machine learning to speed up the process of mapping public roadways prior to launching commercial passenger service. 

Motional is using Transformer Neural Networks to improve AV perception performance. 

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

Motional introduces PredictNet, a prediction module that gleans insight from past incidents to better predict the behavior of vehicles, people, and other agents in the driving environment, thereby increasing safety. 

By rethinking system architecture and using machine learning to analyze and process previously discarded low-level radar data, Motional is working on dramatically enhancing radar performance to the point that it rivals the point clouds produced by lidar.