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 is now conducting highway speed testing with our all-electric IONIQ 5 robotaxis. 

Yi Wang, Senior Principal Engineer at Motional, does a deep dive into our approach for scaling our ML pipelines, and demonstrates three example design cases.

Motional's President and Chief Executive Officer (CEO) Karl Iagnemma will transition to the role of Senior Strategy Advisor. Motional’s Chief Technology Officer (CTO) Laura Major will take on an expanded leadership role as interim CEO and CTO.

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. 

Motional is part of Pittsburgh’s growing high tech ecosystem, which is providing opportunities for talented innovators to plant their roots in the region, instead of moving away. 

Arthur Safira, a Senior Engineering Director at Motional, talks about his background, his growth at Motional, and his mission to make a positive, large-scale societal impact with technology.

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.

When faced with an unusual driving condition, Motional's robotaxis can rely on trained Remote Vehicle Assistance agents to provide guidance on a safe path forward. 

Motional's VP of Commercialization, Akshay Jaising, talks about what the company has learned as it grows its food delivery business and starts delivering Shake Shack orders made through Uber Eats  

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.

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.

Teams from around the world competed in Motional's 2023 nuPlan Challenge, which helped research into advanced autonomous driving planners.