Articles

Motional’s all-electric IONIQ 5 robotaxis are trained to ignore all the lights and sights that make Las Vegas a memorable experience. and instead focus solely on safely navigating through the complex driving environment. 

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.

The report takes a deep dive into the public perception and understanding of autonomous vehicle (AV) technology, including the headwinds AV companies face from the public, generational perceptions, and factors driving adoption. 

As AV technology advances, and the global supply chain responds to industry demand, radars could emerge as the central sensors for robotaxis, says Motional's Chief Technology Officer.

When developing autonomous vehicles, nothing is more important than safety. The safety of those driving, walking, or biking near our robotaxis is just as important as the safety of the person inside the vehicle. Learn how our vehicles respond to their environment in Chapter 7 of #DriverlessEd.

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. 

By testing our AV technology in multiple, unique cities we can ensure Motional's driverless vehicles can adapt quickly to new driving environments.

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’s research has unlocked an approach to streaming object detection that reduces latency while increasing accuracy, giving AVs even better data needed to make safe decisions.