  An accurate understanding of a self-driving vehicle's surrounding environment
is crucial for its navigation system. To enhance the effectiveness of existing
algorithms and facilitate further research, it is essential to provide
comprehensive data to the routing system. Traditional approaches rely on
installing multiple sensors to simulate the environment, leading to high costs
and complexity. In this paper, we propose an alternative solution by generating
a top-down representation of the scene, enabling the extraction of distances
and directions of other cars relative to the ego vehicle. We introduce a new
synthesized dataset that offers extensive information about the ego vehicle and
its environment in each frame, providing valuable resources for similar
downstream tasks. Additionally, we present an architecture that transforms
perspective view RGB images into bird's-eye-view maps with segmented
surrounding vehicles. This approach offers an efficient and cost-effective
method for capturing crucial environmental information for self-driving cars.
Code and dataset are available at
https://github.com/IPM-HPC/Perspective-BEV-Transformer.
