To drive safely, an automated vehicle needs to know exactly where it is on the road. Is it in the middle lane or the right lane? How far away is the curb? Automated Driving (AD) systems answer questions like these through a process known as localization, which allows a vehicle to determine its precise relationship to its surroundings.
For effective localization, onboard sensors must work together with high-definition (HD) map data. The latter allows vehicles to match what their onboard sensors see with what’s on the map, helping them establish their exact location on the road and plan safe maneuvers.
Until recently, AD systems have only been able to link one sensor (for example, a camera) with one type of data in the map – a mono-sensor localization approach. The drawback here is that if a sensor malfunctions or map data is invalid, it dramatically impacts an automated vehicle’s ability to make safe maneuvers on its own.
Now, thanks to a pioneering collaboration between Valeo and TomTom, we’ve been able to successfully test a multi-sensor approach: harnessing multiple sensors and multiple layers of map data to improve the robustness of localization and the safety of AD.