As an important application form of embodied intelligence, connected and automated vehicles (CAVs) can compensate for the limitations of onboard perception under low-visibility and shortened sensing-range conditions through cooperative perception with external information. However, external cooperative information is still affected by transmission delay and packet loss during communication, making it difficult to directly and reliably support current decision-making. Existing studies have mainly focused on perception enhancement, denoising, or communication compensation separately, lacking a unified modeling of the relationships among the physical environment, network links, and cooperative behaviors. This deficiency makes it difficult for decision-making to maintain a dynamic balance between cooperation and communication under multidimensional disturbances, and may even introduce safety risks due to misleading information. These problems become more severe under adverse weather conditions such as rain and snow, where low visibility, perception degradation, and communication signal fluctuations jointly intensify system uncertainty. Therefore, this paper proposes a network-aware vehicle–infrastructure collaborative autonomous decision-making system for adverse weather conditions such as rain and snow. The proposed system comprehensively considers factors such as link delay, packet loss, and information freshness to impose validity constraints and perform credibility evaluation on external cooperative messages. It further constructs a unified temporal window around the current decision instant to achieve spatiotemporal alignment of asynchronous multimodal information. On this basis, a three-layer coupled model consisting of a spatial layer, a vehicular layer, and an information layer is established to jointly incorporate environmental constraints, vehicle interaction relationships, and information transmission quality into the decision-making process. In this way, the utilization weights of external cooperative information can be dynamically adjusted, thereby improving decision safety, accuracy, and robustness in scenarios involving emergency-event warnings under heavy rain and snow, where visibility is obstructed and braking distance is constrained by slippery road conditions.