Performance Assessment of an Integrated Radar Architecture for Multi-Types Frontal Object Detection for Autonomous Vehicle

A hazard-free autonomous vehicle (AV) navigation requires the presence of a dependable perception module. For the frontal object and environment detection, the perception module needs to be able to provide a comprehensive detection region to allow blind spot monitoring. In this work, an integrated perception architecture for frontal object detection of an AV is introduced. The architecture is an integration of central, left and right sides radar for the frontal area. The radar placement of the design is expected to reduce the frontal blind spot region of the vehicle. The architecture's performance is analyzed by assessing each of the radar's performance. This includes their respective ability to measure multi-type of obstacles (pedestrian, black car and motorcycle) as well as evaluating the max range detection of the radars. The results show that each of the radar managed to detect multi-type obstacles as well as possessing a maximum detection range, which will allow for a reduced blind spot area and aids the hazard mitigation actions. The findings of this preliminary study are important to develop a more comprehensive perception module of an autonomous vehicle.