<p>Cloud-assisted vehicular computing enables latency-sensitive applications by offloading tasks among vehicles, roadside units (RSUs), and remote clouds, yet it faces stringent challenges due to rapid topology changes, heterogeneous resources, and diverse task requirements. This paper presents a comprehensive study of task offloading and resource scheduling in a vehicle–RSU–cloud environment. We propose a hierarchical computation framework that flexibly supports task execution across local vehicles, neighboring vehicles, RSUs, and cloud resources. To capture realistic operational constraints, we develop joint models incorporating task attributes, security requirements, pricing factors, and heterogeneous computing/communication capabilities. Building on these models, we design an efficient task migration and resource scheduling strategy that improves overall system performance under dynamic network conditions. Extensive simulation-based evaluation and comparative analysis demonstrate that the proposed approach enhances offloading efficiency and robustness, especially in scenarios with multiple scheduling targets, offering a practical basis for scalable vehicular edge–cloud collaboration.</p>