Visible Light Communication and Positioning for Autonomous Vehicles

Automotive research is currently heavily oriented towards autonomy and especially developing reliable vehicular connectivity and localization technologies for autonomous driving. Since existing technologies have failed to satisfy all requirements so far, new complementary technologies are sought. Specifically, radio frequency communications like cellular and ?DSRC? suffer from reliability and security issues at mid-range (<100 m) congested driving scenarios due to heavy interference, and sensor-based localization systems (e.g., GPS, vision) fail to provide the required accuracy and rate due to sensor rate limitations and prohibitively high computational complexity. Recently, visible light communication (VLC) and positioning (VLP) technologies, based on modulated LED head/tail lights and low-cost photodiodes, were conjectured to be promising complementaries that can help solve these problems and enable challenging applications like collision avoidance and platooning. This thesis aims to prove that vehicular VLP can realize this promise. First, we describe the vehicular VLC system model and provide the problem definition for vehicular VLP considering relative positioning of vehicles using VLC signals. Then, we propose a ?VLP-friendly? improvement to the vehicular VLC physical layer: A novel receiver design that enables realizing high-accuracy angle-of-arrival based vehicular VLP methods in low complexity and cost (named ?QRX?). Next, we review state-of-the-art positioning algorithms used in vehicular VLP and propose new algorithms that advance the state-of-the-art, most of which are exclusively enabled by the QRX. Finally, we derive the Cramer-Rao lower bounds on positioning accuracy for all algorithms and also simulate their performances under challenging weather and noise conditions in realistic driving scenarios. The results prove the eligibility of vehicular VLP for use as a suitable complementary technology for collision avoidance and platooning scenarios in future autonomous vehicles.