Online Location Planning for AI-Defined Vehicles: Optimizing Joint Tasks of Order Serving and Spatio-Temporal Heterogeneous Model Fine-Tuning

Abstract--Advances in artificial intelligence (AI) including foundation models (FMs), are increasingly transforming human society, with smart city driving the evolution of urban living. Meanwhile, vehicle crowdsensing (VCS) has emerged as a key enabler, leveraging vehicles' mobility and sensor-equipped capabilities. In particular, ride-hailing vehicles can effectively facilitate flexible data collection and contribute towards urban intelligence, despite resource limitations. Therefore, this work explores a promising scenario, where edge-assisted vehicles perform joint tasks of order serving and the emerging foundation model finetuning using various urban data. However, integrating the VCS AI task with the conventional order serving task is challenging, due to their inconsistent spatio-temporal characteristics: (i) The distributions of ride orders and data point-of-interests (PoIs) may not coincide in geography, both following a priori unknown patterns; (ii) they have distinct forms of temporal effects, i.e., prolonged waiting makes orders become instantly invalid while data with increased staleness gradually reduces its utility for model fine-tuning. To overcome these obstacles, we propose an online framework based on multi-agent reinforcement learning (MARL) with careful augmentation. A new quality-of-service (QoS) metric is designed to characterize and balance the utility of the two joint tasks, under the effects of varying data volumes and staleness. Each RSU, equipped with a server, stores a complete base model, enabling vehicles to perform real-time fine-tuning as they collect data and transfer the I. X. Zhang are with the School of Computer Science and A previous version appears at IWQoS 2024 as a short paper. Due to the large volume, data stored in the government agencies in better city management. Notably, ridehailing RSU server can be discarded in a certain period of time. In vehicles are particularly advantageous for VCS tasks, practice, these data can be descriptive features and feedbacks due to their centralized ride-hailing platform management, (labels) of recommendation or generative AR applications, which reduces the cost of deploying and executing crowdsensing generated by nearby visitors or residents. They can also be tasks, and utilizes the data and computing resources traffic/environment monitoring data with labels generated by from ride-hailing vehicles to maximize the VCS task utilities. The government or any company that collaborates model (FM)-powered AI applications have revolutionized with the ride-hailing vehicle company has multiple types of numerous aspects of human lives, including healthcare, education, VSC tasks to fulfill, each of which needs certain locations industry, etc. FMs, e.g., BERT, GPT-4, ViT, serve of data for fine-tuning UFMs.