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Mobility, node density, and the demand for large volumes of data exchange have aggravated competition for limited resources in the wireless communications environment. This paper proposes a novel MAC scheme called segmentation MAC (SMAC), which can be used in large-scale vehicle-to-everything (V2X) communication networks. SMAC functions to support the dynamical allocation of radio channels. It is compatible with the asynchronous multi-channel MAC sub-layer extension of the IEEE 802.11p standard. A key innovate feature of SMAC is that the segmentation of the network and channel allocations are dynamically adjusted according to the density of vehicles. We also propose a novel efficient forwarding mechanism to ensure inter-segment connectivity. To evaluate the performance of inter-segment connectivity, a rigorous analytical model is proposed to measure the multi-hop dissemination latency. The proposal is evaluated in network simulator NS2 as well as the standard IEEE 1609.4 and two asynchronous multi-channel MAC benchmarks. Both analytical and simulation results demonstrate better effectiveness of the proposed scheme compared with the existing similar schemes in the literature. [ABSTRACT FROM AUTHOR]

current premium vehicle is implemented with a variety of information, entertainment, and communication functions, which are generally referred as an infotainment system. During vehicle development, testing of the infotainment system at an overall level is conventionally carried out manually by an expert who can observe at a customer level. This approach has significant limitations with regard to test coverage and effectiveness due to the complexity of the system functions and human's capability. Hence, it is highly demanded by car manufacturers for an automated infotainment testing system, which replicates a human expert encompassing relevant sensory modalities relating to control (i.e., touch) and observation (i.e., sight and sound) of the system under test. This paper describes the design, development, and evaluation of such a system that consists of simulation of vehicle network, vision-based inspection, automated navigation of features, random cranking waveform generation, sound detection, and test automation. The system developed is able to: stimulate a vehicle system across a wide variety of initialisation conditions, exercise each function, check for system responses, and record failure situations for post-testing analysis. [ABSTRACT FROM AUTHOR]