Conventional evaluations prior to electrifying Public Services Vehicles (PSVs) generally provide macro conclusions based on urban layout and fleet magnitude. These inaccurate estimations could further result in recommendations to purchase unsuitable vehicles. Predicting the energy of replacement Electric Vehicles (EVs) using city-designated driving cycle data and geographical information could significantly enhance the accuracy, however, requires vehicle's en-route driving data (cycle) which challenging to obtain before the actually deploy of vehicles. In this paper, taking only dashcam videos as input, through an emergent image processing and recognition technology, textual en-route driving data can be extracted. Thus a vehicle model could simulate the driving of an EV and accurately predict the energy usage of each filmed route. The theories and technology presented in this paper were evaluated using real-life dashcam videos and results demonstrating a negligible error rate while energy requirement from operating an EV on the same route is predicted.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Dashcam Video-Driven, Route Distinctive Energy Consumption Pre-Evaluation for Electrifying Public Services Fleets


    Contributors:


    Publication date :

    2021-05-05


    Size :

    701609 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Classifying Ego-Vehicle Road Maneuvers from Dashcam Video

    Zekany, Stephen A. / Dreslinski, Ronald G. / Wenisch, Thomas F. | IEEE | 2019


    POLICE DASHCAM

    CHEN CHI-HSIU | European Patent Office | 2024

    Free access

    Analysis of Dashcam Video for Determination of Vehicle Speed

    Marquez, Alvaro / Leifer, Jack | SAE Technical Papers | 2020


    Electrifying New York City Ride-Hailing fleets: An examination of the need for public fast charging

    Moniot, Matthew / Borlaug, Brennan / Ge, Yanbo et al. | Elsevier | 2022

    Free access