Safety-Oriented Personalized Service Strategy in Air-Ground Integrated Mobility

Artikel i vetenskaplig tidskrift, 2024

Air-ground Integrated Mobility (AIM) can effectively alleviate the current urban traffic pressure by expanding transportation resources in the near-ground field. However, the following problems in AIM need to be addressed urgently: 1) The high mobility of Personal Aerial Vehicles (PAVs) in low-altitude airspace leads to a sharp increase in risk factors; 2) Due to the limited communication distance, antenna direction angle, and frequent handover caused by high-speed movement, the communication quality in the air is unreliable; 3) AIM incorporates vehicles on the ground and PAVs in the air leading to the high variability of user requirements. Confronted with the personalized resource requirements of high-speed mobile PAVs in airspace with unreliable communication quality, traditional resource allocation strategies struggle to guarantee service quality. Therefore, we propose a safety-oriented personalized resource allocation strategy in AIM, which jointly considers the user requirements and resource distribution. Specifically, we first build a three-dimensional (3D) safety distance model by analyzing the motion process of PAVs with the help of a kinematics model. Then, according to the location, speed, and environmental information of the PAVs, the communication and computing resources required by each PAV under the premise of maintaining the optimal safety distance are obtained through the transmission model. Furthermore, the 3D safety distance and resources are jointly optimized, and an on-demand resource allocation algorithm enabled by Deep Reinforcement Learning (DRL) is constructed to provide the resource allocation strategy based on the personalized requirements of the users.