In the fourth round of the Proreta research project, the partners Continental and TU Darmstadt developed a machine-learning vehicle system that supports drivers in inner-city traffic situations and implemented a prototype. Data from radar sensors help to assess the traffic situation when turning left, entering a roundabout or passing right-over-left junctions. Machine Learning mechanisms were used in the project to generate algorithms that use various vehicle data to generate a current driving type profile of the person behind the steering wheel. On this basis, the City Assistant System's recommendations for driving manoeuvres are adapted to the driver's driving style.
The task of the Proreta 4 project was to use adaptive systems to implement solutions that so far could not been tackled due to a lack of adaptability. "In order for an assistance system to make a recommendation to the driver in a complex traffic situation that is accepted with confidence, the system must analyze the driver's driving style and thus his subjective perception of safety or risk," explains Hermann Winner, Head of the Vehicle Technology Department at Darmstadt Technical University. The scientist assumes that such a driving profile can be created comparatively safely and quickly on the basis of a machine learning process. For this purpose, the system evaluates data that is captured while the vehicle is in motion. Among other things, acceleration, yaw rates, braking processes and lateral acceleration provide the algorithm with information about the type of driver involved.