Outdoor Visible Light Positioning Using Artificial Neural Networks for Autonomous Vehicle Application

Abstract

This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Vehicular Visible Light Positioning Using Receiver Diversity with Machine Learning

“…The use of traditional localisation methods fails due to collinearity [21] caused by a linear array of transmitters for straight roads. Hence, this paper proposes the use of angular receiver diversity with ML algorithms to overcome these challenges [14], and map the received signal from the transmitter to the vehicle's positional coordinates. Note that this research focuses on positioning in the sensor's frame.…”

“…First, we investigate the optimum number of receivers in the model to demonstrate the need for receiver diversity in VLP. Note that initial optimization of the VLP system structure is achieved using the MLP model in [14]. Thereafter, the NN is re-optimised.…”

“…Hence, for VLP to work universally in all the streets, an algorithm must be developed, which works in the worst-case scenario, where the streetlights are located in a linear array on only one side of the road. In our previous work [14], we proposed the use of receiver diversity and supervised artificial neural network (ANN) to solve this aforementioned issue. We extended the work in [14] by using spatial and angular diversities with different machine learning (ML) approaches such as simple recurrent neural network (sRNN), gated recurrent unit (GRU) and long short term memory (LSTM) [15] to accurately estimate the position irrespective of the relative locations of the streetlights and further explore the effect of different weather conditions.…”

“…In our previous work [14], we proposed the use of receiver diversity and supervised artificial neural network (ANN) to solve this aforementioned issue. We extended the work in [14] by using spatial and angular diversities with different machine learning (ML) approaches such as simple recurrent neural network (sRNN), gated recurrent unit (GRU) and long short term memory (LSTM) [15] to accurately estimate the position irrespective of the relative locations of the streetlights and further explore the effect of different weather conditions. To the best of the authors' knowledge, this paper is the first study of VLP with a linear array of streetlights using receiver diversity for autonomous vehicle and other outdoor applications.…”