Abstract
In this paper, we consider the current status and technical issues involved in the use of optical camera communication (OCC)/visible light communication (VLC) technologies in vehicular communication systems. Hybrid spatial phase-shift keying was introduced in IEEE 802.15.7-2018 as the standard hybrid modulation scheme for vehicular OCC/VLC systems. We herein propose a functional communication system architecture for vehicular systems based on this hybrid waveform, and we also present state-of-the-art research work on an artificial intelligence (AI)-based vehicular OCC system. Every AI module within the proposed system architecture is discussed in detail. Finally, our experimental procedures and results are analyzed to evaluate the performance of the proposed system over a complex channel model in a vehicular environment. We effectively employed the popular You Only Look Once version 2 object detection algorithm for real-time region-of-interest tracking in city driving (at a vehicular velocity of around 30 km/h and highway night driving (at a vehicular velocity of > 60 km/h) scenarios. Moreover, our novel neural-network-based decoder and AI-based error correction proved effective in improving the data decoding accuracy, resulting in a best-case reduction of 2.2 and 9.0 dB, respectively, in the signal-to-noise ratio needed to achieve the desired bit error rate of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> in a vehicular OCC/VLC system.
Highlights
Intelligent transportation systems (ITSs) are currently a promising area of research, whose key features include autonomous vehicle management, traffic efficiency, road safety, and inter-vehicle and inter-passenger communication [1]
Our aim in this paper was to provide a comprehensive architecture for a vehicular optical camera communication (OCC) system with AI support
Based on a conventional ROI signaling OCC system architecture with two predefined tasks, different AI techniques were considered in terms of performance enhancement in the system under investigation to address the challenges of vehicular environments
Summary
Intelligent transportation systems (ITSs) are currently a promising area of research, whose key features include autonomous vehicle management, traffic efficiency, road safety, and inter-vehicle and inter-passenger communication [1]. Image sensor is beneficial compared to PD as it can perform spatial separation of light using its existing lens and can communicate with multiple sources In such systems, a high-speed camera is used to receive lighting signals from both vehicle lights and street lighting infrastructures. The phenomenon of blur, either motion blur or blurring due to rainy, foggy, or snowy weather conditions, as what typically occurs in optical vehicular communication systems, was analyzed in [15] In such cases, the VLC or optical camera communication (OCC) receiver should perform region-of-interest (ROI) selection to detect the target transmitter, in order to reduce the amount of incident parasitic light. Simulation work shows that the convolution-based mechanism results in blurring that flattens all LED intensities within an image
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