Test drives showed that the algorithms used in the City Assistant System allow conclusions to be drawn about the driver's current driving style within three to five driving maneuvers. This makes it possible to assign the driver to one or more clusters of driving profiles, allowing the driving recommendations of the City Assistant to be strongly personalized.
Machine-learned algorithms are increasingly finding their way into vehicle systems. It is estimated that by 2015 the number of vehicle system units using artificial intelligence will increase from seven million to 225 million by 2025. Powerful machine-learned algorithms are usually highly complex models whose raw form can only be interpreted by humans to a limited extent or not at all, similar to a black box. This poses special challenges for the safeguarding of assistance systems. For this reason, a hedging strategy was co-developed as part of the algorithm selection for driver assistance systems. In Proreta 4, various methods for reducing the required number of test cases for learned algorithms were developed, which are now being researched further.
"The driver should be able to develop confidence in the City Assistant System and its recommendations," comments Ralph Lauxmann, Head of Systems & Technology in the Chassis & Safety Division at Continental. "Trust is the basis for the acceptance of assistance systems, which in turn are an essential component on the road to accident-free driving.