Articles

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

Motional recently completed a multi-million dollar expansion project that doubled the amount of office and garage space located in the heart of Singapore’s high-tech district, growing the company's R&D and Operations footprint in the country

Motional's CEO explains how the company will use its resources to create positive change across a wide variety of causes impacting road safety in urban areas.

Chapter 4 of Motional's DriverlessEd series details the behind-the-scenes work that takes place before robotaxis can start picking up passengers,  including creating high-detail street maps, building a garage, and completing test drives.

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.

Chapter 2 of Motional's DriverlessEd series explains the difference between advanced driver-assistance systems (ADAS) - such as adaptive cruise control and blind spot detection -  and automated vehicles (AVs). 

Chapter 1 of Motional's DriverlessEd series explains what driverless vehicles are, what a robotaxi is, and the rigorous safety practices that go into developing the technology.

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.