Deep learning training training algorithms are a huge success in recent years in many fields including speech, text,image video etc. Deeper and deeper layers are proposed with huge success with resnet structures having around 152 layers. Shallow convolution neural networks(CNN's) are still an active research, where some phenomena are still unexplained. Activation functions used in the network are of utmost importance, as they provide non linearity to the networks. Relu's are the most commonly used activation function.We show a complex piece-wise linear(PWL) activation in the hidden layer. We show that these PWL activations work much better than relu activations in our networks for convolution neural networks and multilayer perceptrons. Result comparison in PyTorch for shallow and deep CNNs are given to further strengthen our case.

With the steady advances in autonomous driving, advanced safety features using one or more sensors are highly desirable. In order to avoid collisions and unintended breaking maneuvers, it is crucial to detect potential road obstacles accurately. Although camera and LiDAR-based object detection have been studied in the literature [1, 2], it's only recently that interest in radar-based object detection using ML methods has begun, primarily because of its low cost, long-range detection capability, and robustness to poor weather conditions. Traditionally, automotive radar-based object detection is performed through peak detection using simple local thresholding methods such as the Constant False-Alarm Rate (CFAR) algorithm [3]. With the breakthroughs of ML in numerous applications [4, 5, 6, 7], radar-based object perception using ML has attracted attention [8, 9, 10, 11, 12, 13].