Social Knowledge To Improve Situation Awareness Of Assistance Systems In City Driving

City traffic is getting more multi-modal, with a variety of actors and mobility options in mixed spaces. This makes decisions on traffic behaviour and control more complex. Beyond traditionally considered aspects (e.g. traffic state or used vehicle), human aspects (e.g. physical state, displacement goal, or companion), gain increasing relevance. They can greatly modify how people move and interact with others. Introducing social knowledge about human behaviour and context can help to better understand and anticipate the environment and its actions. This paper proposes the development of Social-Aware Driver Assistance Systems (SADASs) for that purpose. A SADAS uses traffic social properties that formalize social knowledge using a template organized around diagrams. The diagrams are compliant with a specific modelling language, which is intended to describe social aspects in a given context. They facilitate the integration of this knowledge with system specifications, and its semi-automated verification both in design and run time. A case study on a distributed obstacle detection system for vehicles extended with social knowledge to anticipate people' behaviour illustrates the approach.