Real-Time Autonomous Driving: A Unified Single-Stage Multi-Task Network Architecture

The need for real-time autonomous driving reveals significant gaps in terms of accuracy rates and processing efficiency. Current systems struggle to maintain high accuracy across all scenarios while ensuring efficient real-time processing. Addressing these gaps, the proposed work presents a novel contribution aimed at advancing the capabilities of autonomous driving systems. The model introduces a single network architecture capable of simultaneously handling multiple crucial tasks including dynamic and static segmentation, depth estimation, and 3D object detection. The proposed architecture is specifically designed to be lightweight, ensuring efficient deployment for real-time autonomous driving applications. Moreover, the proposed single-stage architecture is end-to-end trainable, offering a streamlined approach to model training. Through extensive experimentation, the results demonstrate superior performance over existing architecture, achieving remarkable accuracy rates. Overall, the proposed work represents a significant step forward in the development of efficient and effective solutions for real-time autonomous driving.