Abstract
The capability of Connected Vehicles (CVs) connecting to a nearby vehicle, surrounding infrastructure and cyberspace presents a high risk in the aspect of safety and security of the CV and others. Data volume generated from the sensors and infrastructure in CVs environment are enormous. Thus, CVs implementations require a real-time big data processing and analytics to detect any anomaly in the CVs’s environment which are physical layer, network layer and application layer. CVs are exposed to various vulnerabilities associated with exploitations or malfunctions of the components in each layer that could result in various safety and security event such as congestion and collision. The safety and security risks added an extra layer of required protection for the CVs implementation that need to be studied and refined. To address this gap, this research aims to determine the basic components of safety and security for CVs implementation and propose a conceptual model for safety and security in CVs by applying the machine learning and deep learning techniques. The proposed model is highly correlated to safety and security and could be applied in congestion and collision prediction.
Highlights
Connected Vehicles (CVs) is becoming more relevant in recent years after the realization of Industrial Revolution 4.0 (IR4.0), especially for the implementations in the smart cities and the Intelligent Transportation System (ITS) [1], [2]
The basic characteristic for CVs is its ability to connect to other vehicles, to surrounding infrastructure and the internet through sensors such as Laser Detection and Ranging (LiDAR), Radio Detection and Ranging (Radar), Global Positioning System (GPS), Dedicated Short Range Communication (DSRC), Radio Frequency Identification (RFID), Advance Driver Assistance System (ADAS) and sensors that are embedded in the vehicles itself [5], [6]
This paper proposes a conceptual model for safety and security in CVs by applying the machine learning and deep learning techniques
Summary
Connected Vehicles (CVs) is becoming more relevant in recent years after the realization of Industrial Revolution 4.0 (IR4.0), especially for the implementations in the smart cities and the Intelligent Transportation System (ITS) [1], [2]. Various studies [4], [6], [8] has discussed regarding the emergence and implementation of connected and autonomous vehicles, to improve the driving experience and reduce the risk of a crash, improve traffic control and provide real-time interactive communications between other vehicles as well as roadside infrastructure in a network. This proves beneficial, as the future implementation of smart cities requires Intelligent Transportation Systems (ITS) to promote smart mobility in a city. The proposed model is highly correlated to safety and security and could be applied in congestion and collision prediction
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