Understanding the environment is essential for an autonomous driving system. Most learning-based perception models leverage data acquired with Lidar or camera sensors, leaving automotive radars relying on traditional algorithms. However, autonomous systems must be robust and reliable in all-weather and all-lights conditions. Radars have proven to be effective even in adverse weather conditions, whereas other sensors lose accuracy. Previous research in radar target detection investigated the benefits of a learning-based method as compared to traditional ones. Though, those methods have only been tested on single point targets collected in an anechoic chamber. In this work, we extend those studies to locate the targets in complex automotive scenarios.