Multi-target reflection point model of cyclists for automotive radar

Pushed by the EuroNCAP regulations the number of autonomous emergency braking systems for pedestrians (AEB-P) is rapidly increasing since the year 2016. The same rise is expected for cyclist protection systems driven by new test scenarios of EuroNCAP from 2018 onwards. To get an adequate reaction of the system, target objects have to be classified clearly. Visual sensors provide some benefits in object recognition, but the performance suffers in adverse environmental conditions. In this area radar sensors have proven as very robust against darkness, fog, or rain. For classification unique features are required. To extract relevant features, a motion analysis of cyclists is presented in this work which is subsequently used to derive a reflection point model. Specific properties of radar evaluation have been added to the model too. In combination with an automotive radar sensor model, detection limits of actual sensors can be explored. It is also possible to determine the performance requirements for future sensor generations and to evaluate new signal processing algorithms.