Articles

At Motional, we are pioneering the next evolution of AV technology by transitioning from traditional rule-based planning systems to an end-to-end machine learning (ML) powered motion planning system. This shift allows us to address some of the key limitations of legacy AV architectures and embrace a future where AVs rapidly learn, adapt, and improve with every mile driven.

Motional has developed a Second-Stage Vision Network that uses machine learning principles to add important context to our object classifications -- additional fine-grain classification then flows downstream improving our perception, prediction, planning, and control substacks.

Motional's PredictNet approach to prediction uses machine learning principles and a multi-task learning architecture to more accurately predict the future behaviors of surrounding agents.

Motional uses continuous fuzzing to make sure that our software is as safe and secure as possible before deploying it – or if there is a glitch, that the system can handle it gracefully.

Transformer Neural Networks are receiving increased attention about how they can improve AI-driven technology. Our latest Technically Speaking blog explores how Motional has been using Transformers to make our perception function better. 

Motional's latest Technically Speaking blog explains how we're using machine learning to speed up the process of mapping public roadways prior to launching commercial passenger service. 

Motional's latest Technically Speaking blog focuses on Planning, and how using closed-loop training will help refine the modeling AVs use to create a safe path forward quicker.

Motional President and CEO Karl Iagnemma, Chief Technology Officer Laura Major, and others told NACTO attendees about the company’s driverless technology, approach to safety and accessibility, and the IONIQ 5 robotaxi. 

As Motional’s robotaxis drive more, our vehicle assistance system will use machine learning principles to become smarter and require less human intervention over time.

Motional engineers and product designers are creating an AV capable of serving dual purposes without losing functionality for either.

Chapter 3 of Motional's DriverlessEd series explains how AV companies like Motional are using research to understand what makes a ride comfortable, the types of features passengers expect, ways to make the vehicles accessible, and even how AVs can communicate with people.

Motional researchers are developing an app-based feature that lets riders with disabilities ask their robotaxi for more time while being picked up.

Motional is learning how to measure whether a ride in a driverless vehicle feels safe, comfortable, or both.

Motional uses multi-modal prediction models to more accurately predict and anticipate what agents around our AVs are going to do next.