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In the cloud computing era, the analyses of various types of big data gathered from the Internet of Vehicles, the Internet of Things, and smart sensors/devices achieve convenient services. Some famous applications include vehicle traffic loading on roads (from Google MAP), video sharing for fans group (from Facebook or Youtube), and so on. However, to achieve real-time active safe driving (RT-ASD) under unstable driving in high-threat areas on roads becomes an open issue in cloud computing-based intelligent transportation system. This paper proposes a predictive backward shockwave analysis approach (PSA) to achieve the RT-ASD based on the analyses of macroscopic traffic shockwave (PSAMA) and microscopic car-following (PSAₘi). The PSA contributes in several aspects to active safe driving: 1) predicting and analyzing high threat of backward shockwaves from the gathered big data of the driving state information vehicles; 2) informing the analyzed threat messages to the vehicles in high-threat areas via the 3-Tier hierarchical cloud computing mechanism; 3) reducing the driving threat certainly; and 4) PSAMA and PSAₘi can be applied for achieving active safe driving in autonomous self-driving vehicles and human-driving vehicles. The numerical results show that the PSA outperforms in approaches to relative error rate prediction, the accuracy of the backward shockwave determination, average vehicle velocity, average travel time, number of goodput vehicles, time-to-collision, and distance-to-collision.
Chang et al. (Tue,) studied this question.