NeTS: Small: Infrastructure-free Robust Relative Localization of Vehicles on the Road

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The automotive industry is undergoing a giant transformation as vehicles are gearing up to take control of driving away from humans. This will lead to reduced chance of accidents, stress-free driving, increased passenger comfort, increased fuel-efficiency and reduced travel time. One of the most critical pieces of information for enabling this vision of autonomous vehicles is accurate determination of locations of nearby vehicles. The overarching goal of this project is to develop robust approaches that are practically implementable for fine-grained relative localization of nearby vehicles with no support from the infrastructure. In particular, on-board sensing capabilities and RF (radio-frequency) communication between vehicles will be leveraged. The key distinguishing features of the project are as follows: 1) a comprehensive approach to the problem of fine-grained vehicular localization while considering legacy vehicles which is the most critical hurdle in rolling out autonomous vehicles; 2) innovative physical layer techniques to achieve high accuracy relative localization; and, 3) techniques to use noisy data collected by various sensors and still robustly derive the locations of the vehicles. The project is potentially transformative as it addresses the key question of accurate relative localization of vehicles while considering practical challenges, which is crucial for the autonomous driving technology. The broader impacts include enhancing undergraduate and graduate curriculum. In addition, existing collaboration with the automotive industry will be effectively leveraged for obtaining guidance on the objectives and understanding the feasibility of our solutions throughout the project.The specific inter-related research thrusts are as follows. 1) Frequency-Pair based Analysis for Single Antenna Vehicles: The key innovation is an approach that can robustly tackle the distance estimation error and the vagaries of a real driving environment using analysis of frequency-pairs. 2) Exploiting Spatial Diversity in Multi-Antenna Vehicles: The key innovation of this thrust is that it uses the spatial diversity offered by multiple antennas to increase robustness. 3) Collaborative Vehicle-Map Construction in Presence of Legacy Vehicles: The key innovation is to robustly fuse noisy information obtained through cameras mounted on the participating vehicles.