The key to success is continuous development

The key to success is continuous development

Therefore, we are always open to participate in research projects – because we truly believe that these not only support our cause but also assist us in creating the safer roads of the future. For this purpose, we teamed up with the Budapest University of Technology and Economics and the Mobility Platform, which is a scientific forum lead by the Hungarian Institute of Transport Sciences. In this research group the science institutes provided the research background, while AImotive supported them with its practical experience.

Scientific base of HD maps

When driving any vehicle, safety should always come first. Not surprisingly safety is also a crucial part of automated driving. It is one of, if not the most important aspect that we keep in mind throughout every step of our development cycle. Positioning – a key aspect of safety – is one of the challenges we are facing. The vehicle must know its precise location to be able to make decisions. Sensors and HD maps must complement each other to achieve a redundant input of the environment, ensuring the highest level of safety. HD maps play an important role in self-driving, still data providers are often unsure what to include in these maps. The study aims to provide an openly available recommendation set for them, based on scientific research and experience from companies developing autonomous vehicles. For this reason, we are proud to have contributed to the team’s work towards the completion of the first part of a multi-phase research project aiming to create a scientific base for HD map technology.

But what is an HD map? An HD (High Definition) map is a road network model that defines the attributes, structure and topology of roads, lanes, road markings, and other traffic related objects. It is a large-scale map with an absolute precision of at least 10 cm at 2σ = 95%. It should consist of a road model layer for standard navigation, a lane model layer for lane level navigation and a localization layer to help positioning. Additionally, it can store static or dynamic data that can assist driving and navigation. This data has high value in various use cases. It complements “traditional” navigation with lane level information, which is especially useful in complex urban situations such as passing through a junction. Also, traffic related objects (e.g. traffic lights, or traffic signs) can support the localization of the vehicle in dense urban areas where the GNSS signals are inaccurate, or in parking garages where there are no satellite signals at all. But HD maps don’t necessarily have to be used for driving: by auto annotating georeferenced images with them, we can speed up the creation of training datasets for our neural networks.

Partnerships are the only solution

It has already become clear that no one will solve the problem of automated driving alone. The only possible way is to work with partners who have outstanding knowledge and experience in their respective fields. AImotive is also currently participating in a research project that seeks to map (pun intended) and develop the components of automated driving. That is the reason why we think that this research and its findings are an important milestone for AImotive and also for the ADAS community as a whole, because they bring the future of automated mobility one step closer.