The Role Of Maps In The Age Of Autonomous Vehicles

Michael Harrell is the SVP of Engineering Maps at TomTom.

AI’s growing role in vehicles has fueled claims that maps may soon become obsolete. With advances in sensors, end-to-end (E2E) AI and predictive models, a narrative has emerged in autonomous vehicle (AV) development that labels maps as optional, arguing that vehicles will largely infer the world from what they see in the moment.​​

But seeing the road isn’t the same as knowing the road. Sensors provide a snapshot of the present, but safe automated driving also depends on anticipation, memory and verified context. A vehicle that relies only on real-time perception drives like a tourist, discovering every curve and restriction as it goes. One supported by map intelligence behaves more like a local, anticipating what lies ahead.​

This is the crux of the “mapless” debate: Maps, including high-definition (HD) or lane-model types, back most autonomous use cases today, whether for training, evaluation or real-time inference. Even systems described as “mapless” are often developed or tested using mapped representations of the road; in nearly every case, a map is still part of the system. They’re invisible infrastructure, helping vehicles understand the road beyond immediate perception.

The debate, then, isn’t really whether maps exist in the stack, but how much detail is required at each stage to deliver the necessary performance, safety and scalability.

From Perception To Reliable Performance​

E2E AI processes combined sensor data to make driving decisions and allows vehicles to perceive and decide as a more cohesive system. Instead of separate systems for lane detection, object recognition and path planning, a single model interprets all inputs at once, such as how vehicles interact, how pedestrians behave and how road geometry shapes movement. This provides new capabilities in dense urban settings where modular systems have struggled, such as autonomous overtaking.​