Dynamic Vision Sensors (DVS), also known as event cameras, are bioinspired imaging sensors that are emerging as a potential valuable tool in the automotive industry. Their extremely low latency and high dynamic range make them well suited to enhance perception in autonomous driving systems. However, despite these advantages, event cameras remain challenging to work with, and their detection performance still lags behind that of conventional RGB sensor based systems. There is a broad consensus that a robust autonomous driving system will consist of multiple sensors including RGB and lidar/radar. According to the authors knowledge, no automotive dataset exist containing event and radar data. This gap limits innovation with DVS cameras in the automotive industry. We created our own measurement setup and captured our own dataset to encourage more researches to work on DVS and RADAR based sensing systems to bridge the gap towards conventional RGB and RADAR based sensing systems.