Autonomous vehicle has come to reach on the road; however accurate road perception in real-time is one of the crucial factors towards its success. The greatest challenge in this direction includes occlusion, truncation, lighting conditions, and complex backgrounds. In order to improve the accuracy and detection speed of vehicle detection, a dynamic scaling network is proposed that assists in constructing a balanced shape neural network to achieve optimum accuracy with minimal hardware. The net architecture is influenced by YOLOv5 and is composed of Cross-Stage Partial Network (CSPNet) as its backbone. In order to go even further, we have proposed an auto-anchor generating method that makes the network suitable for any datasets. Our neural network is fine-tuned by using activation, loss, and optimization functions so as to get the optimum results. Our experimental results demonstrate that the proposed net provides comparable performance of YOLOv4 and Faster R-CNN based on KITTI dataset as the benchmark.