This thesis addresses two main limitations of the default YOLOP model for autonomous driving: - The primary one being the fact that there is no direct detection of lane center, and the secondary being- no depth or distance estimation in the object detection head. While the baseline YOLOP architecture can perform lane line detection, drivable area segmentation, and object detection at 40 FPS in an efficient manner, post- detection output steps—such as finding regions of interest, OpenCV based transformations, and curve fitting for determining the lane center from the detected lane lines—introduce significant latency (0.04 seconds), reducing the effective frame rate to 15 FPS.