LAUSR

Task offloading decision-making plays a crucial role in improving system performance in heterogeneous vehicular networks. This paper provides a systematic review of current research in the field and identifies limitations in existing taxonomy frameworks. It introduces a novel five-category algorithm taxonomy system, further detailing sub-categories for hybrid algorithms. This study examines algorithm performance and computational trade-offs to elucidate the characteristics and suitable application scenarios of different algorithms. It also identifies deficiencies in current research, such as the absence of standardized in performance indicators and insufficient analysis of correlations among key factors. Based on research trends, future directions are proposed, including improving the performance indicators system, establishing key factor correlation models, and optimizing algorithm coordination mechanisms. This survey serves as a foundational reference for future research and paves the way for the practical application of advanced task offloading algorithms in real-world heterogeneous vehicular networks.