• Title/Summary/Keyword: vehicular augmented reality

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Development of an IGVM Integrated Navigation System for Vehicular Lane-Level Guidance Services

  • Cho, Seong Yun
    • Journal of Positioning, Navigation, and Timing
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    • v.5 no.3
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    • pp.119-129
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    • 2016
  • This paper presents an integrated navigation system for accurate navigation solution-based safety and convenience services in the vehicular augmented reality (AR)-head up display (HUD) system. For lane-level guidance service, especially, an accurate navigation system is essential. To achieve this, an inertial navigation system (INS)/global positioning system (GPS)/vision/digital map (IGVM) integrated navigation system has been developing. In this paper, the concept of the integrated navigation system is introduced and is implemented based on a multi-model switching filter and vehicle status decided by using the GPS data and inertial measurement unit (IMU) measurements. The performance of the implemented navigation system is verified experimentally.

A Context-aware Task Offloading Scheme in Collaborative Vehicular Edge Computing Systems

  • Jin, Zilong;Zhang, Chengbo;Zhao, Guanzhe;Jin, Yuanfeng;Zhang, Lejun
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • v.15 no.2
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    • pp.383-403
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    • 2021
  • With the development of mobile edge computing (MEC), some late-model application technologies, such as self-driving, augmented reality (AR) and traffic perception, emerge as the times require. Nevertheless, the high-latency and low-reliability of the traditional cloud computing solutions are difficult to meet the requirement of growing smart cars (SCs) with computing-intensive applications. Hence, this paper studies an efficient offloading decision and resource allocation scheme in collaborative vehicular edge computing networks with multiple SCs and multiple MEC servers to reduce latency. To solve this problem with effect, we propose a context-aware offloading strategy based on differential evolution algorithm (DE) by considering vehicle mobility, roadside units (RSUs) coverage, vehicle priority. On this basis, an autoregressive integrated moving average (ARIMA) model is employed to predict idle computing resources according to the base station traffic in different periods. Simulation results demonstrate that the practical performance of the context-aware vehicular task offloading (CAVTO) optimization scheme could reduce the system delay significantly.