May Mobility launches New AV Architecture That Understands and Reasons Through the Physical World

The fifth-generation system integrates deep learning with May Mobility's proven reasoning engine, accelerating the path to scalable driverless operations

ANN ARBOR, Mich., May 20, 2026 /PRNewswire/ -- May Mobility Inc., a leading autonomous vehicle (AV) technology company, today announced the launch of its fifth-generation autonomy system. The autonomous driving system fuses deep learning, a predictive world model and May Mobility's proven reasoning engine into a radically efficient on-vehicle architecture.

May Mobility's technology takes a fundamentally different architectural approach from both modular AV stacks and pure end-to-end models. The integration of deep learning and reasoning allows the system to benefit from training data while understanding how the vehicle's actual context may be different and reacting accordingly. The combination of these approaches enables May Mobility's AVs to generalize to handle novel situations, new geographies, and complex driving conditions without the intensive data and compute requirements that can limit typical autonomy systems.

The latest updates enhance May Mobility's proven autonomy capabilities, which have successfully delivered more than 525,000 commercial rides and more than 1.1 million autonomous miles commercially to date, including driverless deployments in three U.S. states. On public roads, the system delivers noticeable improvements in ride smoothness and more driving confidence when navigating through complex environments.

"Driving by memorization is bad—humans don't need to see a billion miles of road to drive safely. The brain instantly builds a mental model of the world and then reasons through it. Our new system approaches driving the same way, and it dramatically changes how autonomy can safely scale," said Dr. Edwin Olson, CEO of May Mobility.

Next-Gen Advantages
Conventional AV models trained on large volumes of driving data can capably handle situations they've seen before. Yet these systems may be brittle when encountering situations outside their training data. May Mobility can address such edge cases with two fused components running in tandem on-vehicle:

World Model
May Mobility's integrated world model enables it to reason through complex situations outside its training data in real time. It understands the environment around the vehicle through a distillation of physics, rules of the road and driving culture.

Applying the world model repeatedly gives the vehicle hundreds of "what if" simulations to analyze every 200ms, each one representing a possible future. The model predicts how every road user's behavior will affect every other, simulating up to 10 seconds into the future in each simulation. By evaluating the array of probable futures, May Mobility's autonomous technology doesn't just match situations to training data. Instead, it thinks through the scene and identifies the safest path.

Reasoning & Planning Engine
While most AV systems output a single driving strategy with no alternative to validate against, May Mobility's multi-policy reasoning system selects from multiple strategies, all of which compete to control the vehicle based on how well each strategy handles the simulated futures generated by the World Model. In a fraction of a second, it simulates the outcomes of deep learned and proven strategies and rejects any action that fails its safety parameters. Thus, vehicle control is always earned and can be traced back to its source, a key distinction versus end-to-end models.

Smaller models, lower-cost hardware
May Mobility's fifth-generation autonomy system lays the foundation for a cost-efficient system that scales effectively across autonomous ride-hail markets, challenging industry assumptions that massive datasets and custom hardware are required to achieve full autonomy. Conventional AV stacks that "memorize every situation" are expensive because of the need to collect and train on massive datasets, yielding models that can be enormous. May Mobility's models are based on understanding how the world works, rather than memorizing everything they've seen, allowing them to be much smaller. That in turn can result in lower-cost hardware that safely handles the "long tail" of complexity more effectively than may be possible using conventional approaches.

Advancing Autonomous Ride-hail
May Mobility has begun rolling out its technology update to expand the capabilities of its current fleet and enable new driverless deployments in the near future. Ride-hail networks will be among the first to experience the new technology, including May Mobility's upcoming deployment on the Uber platform in Arlington, Texas.

About May Mobility
May Mobility develops autonomous vehicle technology for commercial ride-hail services. Its patented in-situ AI integrates deep learning, a dynamic world model, and a real-time reasoning engine to navigate through new and complex situations on the road. In partnership with Toyota Motor Corporation, NTT, Lyft, Uber, and Grab, May Mobility delivers Autonomy as a Service (AaaS) at commercial scale and has completed more than half a million commercial autonomous rides across deployments in the U.S. and Japan. For more information, visit www.maymobility.com.

SOURCE May Mobility