Personalized driving assistance system for vehicle collision avoidance has recently received a considerable amount of attention. Consensus has been reached that both the overall driver-vehicle control performance and the driver acceptance can be increased by embedding individual driver preferences and characteristics into the assistance system design. However, the majority of the existing personalized controllers has not yet taken the available computational resource into account. Indeed, as stricter requirements on emissions, safety, and vehicle connectivity drastically complicate the automotive electronics, it becomes common to aggregate several functions inside one single computing unit. Function consolidation simplifies electronic architecture and saves costs. However, it aggravates the competition for computational resources among different applications. Therefore, this paper proposes a novel perspective for personalized driving assistance system design through computational resource re-allocation. For a driver inherently adept at longitudinal (or lateral) control and less capable of lateral (or longitudinal) control, a stronger support from the collision avoidance system and the underlying computational resource can be allocated towards steering (or braking) assistance by this design. Carsim-Simulink conjoint simulations demonstrate that the overall driver-vehicle control performance can be substantially improved with the same computational resource consumption.


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    Titel :

    Personalized Ground Vehicle Collision Avoidance System: From a Computational Resource Re-allocation Perspective


    Beteiligte:
    Wang, Zejiang (Autor:in) / Wang, Junmin (Autor:in)


    Erscheinungsdatum :

    19.10.2020


    Format / Umfang :

    1129898 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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