Dubey, AnandAnandDubeyFuchs, Jonas BenjaminJonas BenjaminFuchsReissland, TorstenTorstenReisslandWeigel, RobertRobertWeigelLurz, FabianFabianLurz2020-06-242020-06-242020IEEE Topical Conference on Wireless Sensors and Sensor Networks, WiSNeT: 9037574 (2020)http://hdl.handle.net/11420/6415Unlike optical imaging, it's difficult to extract descriptive features from radar data for problems like classification of different targets. This paper takes the advantage of different neural network based architectures such as convolutional neural networks and long-short term memory to propose an end-to-end framework for classification of vulnerable road users. To make the network's prediction more reliable for automotive applications, a new concept of network uncertainty is introduced to the defined architectures. The signal processing tool chain described in this paper achieves higher accuracy than state-of-the-art algorithms while maintaining latency requirement for automotive applications.enAutomotive RadarAutonomous DrivingDeep Neural NetworksMico-DopplerVRU ClassificationConference Paper10.1109/WiSNeT46826.2020.9037574Other