Semantic state space for high-level maneuver planning in structured traffic scenes

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Abstract
Originating from simple cruise control systems that monitor and control the speed of the vehicle, driver assistance systems have evolved into intelligent systems. Future assistance systems will combine information from different sensors and data sources to build up a model of the current traffic scene. This way they will be able to assist in challenging tasks in complex situations. Towards this goal, we present a semantic scene representation for modeling traffic scenes. Based on a geometric representation a semantic representation is defined using an ontology to model relevant traffic elements and relations. Considering potential relations of the ego vehicle, a semantic state space of the ego vehicle is derived. Transitions are defined that model state changes (maneuvers). The model can be used for example for situation analysis and high level planning for driving hint generation or automated driving. The method is evaluated in different traffic situations and on real sensor data. It is going to be applied to (semi-)automated driving in a real test vehicle.