casa 18: e2

Research Article

Spatiotemporal lightmorphic computing for Carpathian roads

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  • @ARTICLE{10.4108/eai.27-4-2021.169422,
        author={Dumitru Damian},
        title={Spatiotemporal lightmorphic computing for Carpathian roads},
        journal={EAI Endorsed Transactions on Context-aware Systems and Applications: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={CASA},
        year={2021},
        month={4},
        keywords={Carpathians, BH1750, framework, patterns, lightmorphic, Arduino, Spatiotemporal},
        doi={10.4108/eai.27-4-2021.169422}
    }
    
  • Dumitru Damian
    Year: 2021
    Spatiotemporal lightmorphic computing for Carpathian roads
    CASA
    EAI
    DOI: 10.4108/eai.27-4-2021.169422
Dumitru Damian1,*
  • 1: Information and Communication Engineering, Research and development consultant, Timis,oara, 300003, Romania
*Contact email: dumitrudamian@yahoo.com

Abstract

Energy consumption optimization by predicting vehicle behaviour in a dynamic environment represents an active research topic for the automotive industry. As vehicles are increasingly being equipped with driving assistance systems that function under dynamic driving conditions, a trajectory specific energy saving strategy must consider the trajectory particularities and predict in real time the opportunities for energy savings. Researching and understanding the interactions between complex light intensity shapes and the trajectory spatiotemporal specificity is the main objective of the presented spatiotemporal lightmorphic computing framework for the Romanian Carpathian A1 and DN7 road network. Alternating start and stop locations are included, between the following major cities: Bucures,ti, Timis,oara, Deva, Sibiu, Pites,ti. Each trajectory segment measurement is composed from various slices defined as segmentation lengths (SL) that characterize the light signatures and trajectory profile. The light intensity variations are contained in the light distribution tensor Γt. When analyzing the measured values, similarities between measurements are captured in a trajectory specific data-set Φ. This spatiotemporal light distribution symmetry is used to predict the trajectory unique virtual light shape evolution. Observing the light intensity variations offers a unique perspective on the mentioned route. Having aframework to characterize the light signature structural patterns for specific road trajectories, helps tosolve several real-world problems like: achieving optimal energy balance for specific trajectories or accurate estimation of light intensity phenomena that can impact the interaction between vehicle and traveling environment.