Deep Learning for Optical Vehicular Communication

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
^-4
in a vehicular OCC/VLC system.