Autonomous driving

Our team creates the industry-leading TomTom HD Map for safe and comfortable autonomous driving.

For control to shift from the driver to the vehicle, our map needs to be precise, detailed and fast. And to achieve that, we need to analyze large amounts of incoming data from diverse on-car sensors to help us identify and describe road elements such as lane markings, road borders, barriers, junctions and signage. Machine learning, AI and computer vision techniques help us turn raw data into something scalable and fit for production.

We launched our own self-driving test vehicle to put our tech to the test in real-road conditions. Autonomous driving is a great challenge, and we are solving it bit by bit every day.
What we do
Use one of the largest labeled maps-related data bases in the world
Help achieve and enable the design of safe, self-driving vehicles
Contribute to creating a safer, cleaner, congestion-free world
Make use of both supervised and unsupervised learning algorithms
Apply algorithms using deep networks for classification and segmentation
Have a lot of fun
Technologies we use
Machine learning, AI, computer vision, deep learning (Tensor Flow, PyTorch), automation tools, (Kubernetes, Docker, Swarm), Python, C++, Scala, JavaScript, Type Script, Cloud technologies (GCP, Azure)
Want to join us?
Check out our open positions.
Areas we work 
Other areas we work
Up-to-date, accurate and detailed maps for the hundreds of millions of people using TomTom technology around the world.
Real-time services
Services to help consumers and businesses optimize their journeys, such as TomTom Traffic, On-Street Parking and more.
Software for traffic-dependent routing, route guidance, map visualization, search, positioning and machine learning-based predictions and suggestions.