The sensing coverage and accuracy of vehicles are vital for autonomous driving. However, the current sensing capability of a single autonomous vehicle is quite limited in the complicated road traffic environment, which leads to many sensing dead zones or frequent misdetection. In this paper, we propose to develop a Vehicular Fog Computing (VFC) architecture to implement cooperative sensing among multiple adjacent vehicles driving in the form of a platoon. Based on our VFC architecture greedy and Support Vector Machine (SVM) algorithms are adopted respectively to enhance the sensing coverage and accuracy in the platoon. Furthermore, the distributed deep learning is processed for trajectory prediction by applying the Light Gated Recurrent Unit (Li-GRU) neural network algorithm. Simulation results based on real-world traffic datasets indicate the sensing coverage and accuracy by the proposed algorithms can be significantly improved with low computational complexity. INDEX TERMS Intelligent vehicles, vehicular fog computing, cooperative sensing, autonomous driving.
The creep and recovery behaviors of lubrication oil based ferrofluids of different particle concentration were systematically investigated to understand the viscoelasticity of ferrofluids. The influence of stress level, magnetic field strength and temperature on creep and recovery behaviors of ferrofliuids was studied experimentally and the microscopic mechanisms behind the rheological phenomenon were discussed. Linear viscoelasticity theory and generalized Burgers models were employed to analyze the experimental results. The experimental results demonstrate that the ferrofluids exhibits unique creep and recovery properties significantly different from other stimuli responsive materials both in the linear and nonlinear viscoelastic region. Furthermore, structures larger than single chains are supposed to be responsible for many experimental results, including the extended relaxation process in recovery phase and the nonlinear increasing trend of creep strain with magnetic field strength and temperature. These findings contribute to a better understanding of the microscopic mechanism of magnetorheology of ferrofluids and also provide guidance for many practical applications.
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