Christen, F., Ewald, C., Eckstein, L., Abel, D. et al., "Traffic Situation Assessment and Intervention Strategy of a Collision Avoidance System based on Galileo Satellite Positioning," SAE Technical Paper 2012-01-0280, 2012, doi:10.4271/2012-01-0280.
Nowadays, collision avoidance systems (CAS) are an intensive research topic since the majority of all traffic accidents are collisions that are caused due to inattention or unadjusted driving behavior of the driver. Up to date prototypic CAS are based on on-board environmental sensors, such as camera or radar systems, that scan the vehicle's surrounding environment in order to assess the situation's hazardousness. The functionality of the used sensors under varying environmental conditions and the limited sensor covering area require an enormous effort to ensure a reliable detection of obstacles, and thus limit the application of the systems.In order to expand the operating field of such systems, a Galileo-based CAS will be developed within the project ‘Galileo above’ (application centre for ground based traffic). This advanced driver assistance system (ADAS) detects surrounding vehicles that are on collision course and reacts autonomously, if the driver does not intervene to avoid the crash. For this purpose the system initiates an emergency stop and/or an emergency steering maneuver.For the development of the CAS the Galileo test centre automotiveGATE in Aldenhoven, Germany will be used. On this test area pseudolites will be installed which provide Galileo-like navigation signals. Thus, the development respectively tuning of Galileo-based vehicle systems will be enabled, so as to have them available on the market when the Galileo satellite system reaches its full operational capability (FOC).The focus of this paper is on the traffic situation assessment and intervention strategy of the CAS. This includes the perception and analysis of the driving situation, the detection of potential collision situation, the definition of an adequate system reaction and the planning of an evasion trajectory. Furthermore, an outlook on the model predictive control for longitudinal and/or lateral control (braking and/or steering maneuver) will be presented.