Perception of the environment is a fundamental and challenging step in autonomous driving systems. Nowadays, the increased accuracy of deep learning algorithms is taking the lead on traditional computer vision methods for processing sensors data, such as camera images. However, deep neural networks models are characterized by an enormous amount of computations and a large memory occupancy, and their execution is still mainly performed on GP-GPUs. This thesis, developed in partnership with Magneti Marelli, discusses possible hardware solutions and optimization techniques to achieve fast and energy efficient neural networks inference in automotive embedded systems. First of all, a general overview on neural networks is given, with particular focus on Convolutional Neural Networks (CNNs), their layer organization, the Caffe framework, the most popular datasets and state-of-art topologies for autonomous driving tasks, such as image classification, object detection and semantic segmentation.