The topic of this masterwork is ''Detection of vehicles in an autonomous vehicle environment for the purpose of warning of potential collision''. One of the important things in an autonomous drive is the perception of the vehicle's environment. In this paper is developing a system that detects vehicles in an autonomous vehicle environment and carries out an assessment of the distance of detected vehicles. For vehicle detection, a Yolo v2 algorithm is used, which is trained on a newly created image set and carries out detection of four classes of vehicle: car, van, truck and bus. The system is implemented in ROS using nodes. Two nodes were created to estimate the distance. One node is used to estimate the distance in the Carla simulator and is obtained experimentally, while the second node is used to estimate the distance in the real world and is based on distance estimation using the camera parameters and the reference width of the vehicle in the real world. The algorithm testing on the desktop computer was done using images from the simulator and the real world. It is noted that the algorithm runs at an average of 40 frames per second on the NVIDIA GTX1060 graphics card, which means that the algorithm works in real time. The developed algorithm allows detection of objects, even when only part of the object is inside the image or when more than one object overlaps. Vehicle detection from all positions around the vehicle is possible. It was noticed that the algorithm does not detect objects at a distance more than 50 meters, and that it detects objects at night and rain less often. It has also been noted that by increasing the distance, the precision of the estimated distance is reduced and certain adjustments have to be made to make the system better detection and precision for the distance estimation. The developed algorithm is easily customizable and easy to upgrade to detect other objects in traffic.