This thesis introduces a cutting-edge approach to enhance the perception capabilities of autonomous vehicles in real-world driving scenarios. The research aims to recognize and classify various road actors, including vehicles, pedestrians, cyclists, and obstacles, employing advanced computer vision techniques, and leveraging tools like Qt Quick 3D and C++ programming language. The study initiates with an in-depth review of existing methodologies in road actor recognition and classification, outlining their strengths, limitations, and avenues for improvement. Building upon this foundation, the thesis proposes an approach integrating state-of-the-art deep learning algorithms with real-time 3D rendering techniques, using Qt Quick 3D. The key part of this thesis is the creation and implementation of an algorithm capable of extracting environmental information using a stereo camera.