Advanced vehicle collision avoidance using GNSS, digital maps and V2V communication

The thesis is about an advanced collision avoidance system (CAS) which specifically makes use of GNSS, digital maps and V2V communication additionally to “conventional” sensors such as radar and camera. Current prototypes of CAS – which only use those “conventional” sensors for the detection of surrounding vehicles – are restricted due to the limited detection area of the sensors and their operational capabilities under various environmental conditions. By means of a sensor data fusion, the advantages of all the above mentioned sensors are combined in order to increase the reliability of the CAS. With knowledge of the course of the road ahead and with information of the surrounding vehicles, a predictive system can be developed. The thesis approaches the topic systematically by starting with a review of the state of the art regarding collision avoidance systems. Based on the analysis of the literature, research needs are identified and the research questions of the thesis are derived. In addition, requirements are defined. The aim of the CAS is to detect surrounding vehicles that are on collision course with the ego vehicle and to initiate a collision avoidance action (braking or steering) automatically in case that the driver does not react on time. The collision avoidance system is designed by braking it down into different modules: - Data fusion - Motion prediction - De-escalation and intervention decision - Trajectory following control The development modules take into account the research questions and defined requirements. The algorithms are implemented in a simulation environment and in a test vehicle. Well-defined scenarios are conducted to answer the research questions and to analyze the test results.