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19 10 2018 02

In the fourth stage of the PRORETA research project, Continental and TU Darmstadt have developed a machine-learning vehicle system designed to support drivers in urban traffic and have installed it in a prototype.

Data from radar sensors helps drivers to assess the traffic situation when turning left, entering a roundabout or at right-before-left intersections. Machine-learning has played a key role in the three-and-a-half-year research project. Algorithms create an always up-to-date driver profile based on a range of vehicle data, allowing them to adapt the driving maneuver recommendations given by the City Assistant System in line with the driving style.

Acting like a good passenger, an advanced driver assistance system must analyse the driver’s style of driving and, in turn, their subjective sense of safety or risk so that, in complex traffic situations, it can give the driver recommendations that are also met with a high degree of acceptance. The driving profile is created quickly and accurately on the basis of a machine-learning process. For this, a range of data recorded during journeys is evaluated. Acceleration, yaw rates, braking and lateral acceleration in particular give the algorithm an idea of what type of driver is behind the wheel.

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Tags: Mobility

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