Smart Car is an automobile with an advanced driver assistance system that can provide a more enjoyable driving experience as well as active safety features. The aim of these systems is to ensure road safety and reduce the risk of road accidents. For these purposes, a driver assistance system must be context-sensitive by monitoring the car and its environment in real time besides sensing, analyzing, predicting and reacting according to the following contextual situation: the vehicle state, the driver state, and the physical environment surrounding them. This paper presents a state of the art of the research published in the last three years on context-based driver assistance systems. Simply put, the context-aware plays a critical role and the inclusion of contextual factors influence the intelligence level of the Smart Car. The proposed comparison which falls within the scope of the research addresses the context of the system (driver, vehicle, physical environment), the algorithms used (machine learning algorithm ML, deep-learning algorithm DL, HMM etc.), the technology used (internet of thing sensor, Smartphone sensor, Radar, Lidar…), and the performance provided. The first analysis indicates that machine learning algorithms are widely used in the suggested solutions in general and in the support vector machine (SVM) and the artificial neural network (ANN) in particular. Moreover, the given analysis shows that the monitoring of a complex driving context becomes possible with the emergence of IoT technology.


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    Title :

    Providing Context Awareness in the Smart Car Environment: State of the Art


    Additional title:

    Lecture Notes in Intelligent Transportation and Infrastructure



    Conference:

    The Proceedings of the Third International Conference on Smart City Applications ; 2019 ; Casablanca, Morocco October 02, 2019 - October 04, 2019



    Publication date :

    2020-02-01


    Size :

    14 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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