Open AccessReal-Time Autonomous Driving: A Unified Single-Stage Multi-Task Network Architecture
Author(s) -
S. Raghavendra,
Rajalekshmi Kishore,
S. K. Abhilash,
Venu Madhav Nookala,
Manish Varun Yadav
Publication year - 2025
Publication title -
ieee access
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 0.587
H-Index - 127
eISSN - 2169-3536
DOI - 10.1109/access.2025.3572107
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.