MAGMA: Mobility Analytics Generated from Metrics on ADAS

Modern Advanced Driver Assistance Systems (ADAS) have complex logic for determining when and where feature use is appropriate, generally based on geolocation and the vehicle’s sensor suite. This variability can lead to a problem of how to meaningfully measure customer experience from ADAS feature usage data. To provide a broad understanding of customer experience and where the feature should have been active but was not, the data must be viewed relative to the feature availability map. The feature activation and availability experienced by a driver is dependent on numerous design decisions (such as the map and map previewing logic), which may affect the process of understanding the raw data. Therefore, it is critical to compare customer ADAS feature usage data to what the vehicle could have previewed in the best-case customer experience scenario by simulating the feature availability based just on the feature design logic, the vehicle’s location, and the map. This enhanced understanding of customer experience allows for the discovery of corner cases and enables improved feature design. In short, customer ADAS feature usage data can be better understood in the appropriate context, where offline simulations of the designed feature logic provide an appropriate normalization factor.