U2AD: A UAV-Assisted Autonomous Driving Framework for Enhancing Vehicle Risk Perception and Decision-Making Capabilities

• Published in: Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
• Main Authors: Li, Chuangxin, Gao, Yongqiang, Fu, Rao, Chen, Jia
• Format: Conference Proceeding
• Language: English
• Published: IEEE 06.04.2025
• Subjects: Autonomous aerial vehicles; Autonomous driving; Decision making; Delays; Risk perception; Safety; Signal processing; Speech processing; Transportation; Unmanned aerial vehicle; Vehicle dynamics; Vehicle-to-infrastructure; Vehicular ad hoc networks
• ISSN: 2379-190X
• DOI: 10.1109/ICASSP49660.2025.10888200

With the rapid development of intelligent transportation systems, autonomous driving (AD) is gradually becoming the primary mode of transportation for the future. However, safety still remains the critical challenge for the widespread adoption of automated vehicles. The ego vehicle is subject to significant safety risks, primarily due to its limited sensing capabilities and insufficient global situational awareness. To enhance the safety of AD, researchers have proposed two types of frameworks: Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). Although these frameworks can improve vehicle safety, they still face issues such as multi-node data transmission delays and a lack of infrastructure flexibility, which can impair the vehicle's ability to effectively perceive risks in complex environments. In recent years, unmanned aerial vehicle (UAV) has attracted considerable attention from researchers in the AD field, primarily due to its mobility and expansive field of view. The advantages of UAV bring hope for solving the drawbacks of traditional frames. In this context, this paper introduces UAV into the AD environment and proposes an innovative unmanned aerial vehicle assisted autonomous driving (U2AD) framework to address these limitations and enhance vehicles' risk perception and decision-making capabilities. Experimental results show that U2AD, compared to V2V and V2I, increases the average perception range of vehicles by 27.5% and reduces the average collision rate by 14.1%, fully demonstrating its tremendous potential in the field of AD.