Browsing by Author "Marina Martinez, Clara"

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    Driving style recognition for intelligent vehicle control and advanced driver assistance: a survey
    (IEEE, 2017-07-04) Marina Martinez, Clara; Heucke, Mira; Wang, Fei-Yue; Gao, Bo; Cao, Dongpu
    Driver driving style plays an important role in vehicle energy management as well as driving safety. Furthermore, it is key for advance driver assistance systems development, toward increasing levels of vehicle automation. This fact has motivated numerous research and development efforts on driving style identification and classification. This paper provides a survey on driving style characterization and recognition revising a variety of algorithms, with particular emphasis on machine learning approaches based on current and future trends. Applications of driving style recognition to intelligent vehicle controls are also briefly discussed, including experts' predictions of the future development.
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    Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective
    (IEEE, 2017-06-16) Marina Martinez, Clara; Hu, Xiaosong; Cao, Dongpu; Velenis, Efstathios; Gao, Bo; Wellers, Matthias
    Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet.