Self-driving systems are commonly categorized into three subsystems: perception, planning, and control. In this thesis, the perception problem is studied in the context of real-time object detection for autonomous vehicles. The problem is studied by implementing a cutting-edge real-time object detection deep neural network called Single Shot MultiBox Detector which is trained and evaluated on both real and virtual driving-scene data. The results show that modern real-time capable object detection networks achieve their fast performance at the expense of detection rate and accuracy. The Single Shot MultiBox Detector network is capable of processing images at over fifty frames per second, but scored a relatively low mean average precision score on a diverse driving- scene dataset provided by Berkeley University. Further development in both hardware and software technologies will presumably result in a better trade-off between run-time and detection rate. However, as the technologies stand today, general real-time object detection networks do not seem to be suitable for high precision tasks, such as visual perception for autonomous vehicles. Additionally, a comparison is made between two versions of the Single Shot MultiBox Detector network, one trained on a virtual driving-scene dataset from Ford Center for Autonomous Vehicles, and one trained on a subset of the earlier used Berkeley dataset. These results show that synthetic driving scene data possibly could be an alternative to real-life data when training object detecting networks