This paper offers an enhanced technique to boost the efficiency of creating bird’s eye view (BEV) maps from 2D dash cam photos. We accomplish this by mapping items from 2D pictures to BEV space using the Detection Transformer network. In particular, we must map the relevant pixels of objects in 2D photographs to the polar coordinates on the BEV map image and ascertain the relative positions of items in those photos. Our approach generates semantic BEV mappings, covering both stationary and moving categories, by using an end-to-end learning transformer model that takes as input a monocular picture with an intrinsic matrix. The big dataset nuScenes, developed by nuTonomy and published in 2019 to aid in research on perception, autonomous driving, and traffic image prediction, is used to assess the approach. According to experimental data, the suggested approach greatly increases accuracy when compared to the state-of-the-art techniques now in use.