State machine based method for consolidating vehicle data

Abstract

The increasing number of information and assistance systems built into modern vehicles raises the demand for appropriate preparation of their output. On one side, crucial information has to be emphasized and prioritized, as well as relevant changes in the driving situation and surrounding environment have to be recognized and transmitted. On the other side, marginal alterations should be suitably filtered, while duplications of messages should be avoided completely. These issues hold in particular when assistance systems overlap each other in terms of their situation coverage. In this work it is described how such a consolidation of information can be meaningfully supported. The method is integrated in a system that collects messages from various data acquisition units and prepares them to be forwarded. Thus, subsequent actions can be taken on a consolidated and tailored set of messages. Situation assessment modules that rely on immediate estimation of situations are primary recipients of the messages. To meet their major demand—rapid decision taking—the method generates events by applying the concept of state machines. The state machines form the anchor to merge and fuse input, track changes, and generate output messages on higher levels. Besides this feature of consolidating vehicle data, the state machines also facilitate the transformation of continuous data to event messages for the rapid decision taking. Eventually, comprehensive driver support is facilitated, also enabling unprecedented features to improve road safety by decreasing the cognitive workload of drivers.