Enhancing Internet of Things (IoT) Strategies for Autonomous Electric Vehicles (AEV) with existing Transportation Infrastructure and Road Users

Abstract

Traffic intersections provide an essential mechanism to handle traffic flows in different directions while ironically traffic bottlenecks, gridlocks, and accidents tend to occur in the vicinity of the intersections. Intelligent traffic signal control is essential for intersections not only for preventing accidents but also for efficiently influencing the whole performance of the traffic network where connected autonomous vehicles (CAVs), human-driven vehicles (HDVs), and connected emergency vehicles (CEVs) exist. Travel-time reduction is a dominant target for a CEV to save people’s lives or put out a fire. Additionally, the unwise fuel consumption and delays are undesired for an efficient CAV. Meanwhile, with the recent developments in internet of things (IoT) technologies, and intelligent transportation systems (ITS), there is a great potential for integrating them for improving operational efficiencies of autonomous intersection managers (AIMs), and other road users. However, there are open challenges that require addressing at the architecture and control levels. This thesis aims to leverage the capabilities of AIMs, CAVs, and CEVs for more energy-saving, safe-traffic management, and travel-time reduction. The thesis consists of two main contributions. In the first contribution, an eco-driving framework for CAVs at a signalized intersection in a mixed-traffic environment, where HDVs and CAVs share the same road, is proposed. A two-layer framework is adopted to handle signal and vehicle controls effectively. The first layer is a signal control layer where the AIM receives the traffic network states, trains with the data through machine learning (ML), and outputs a set of optimal green times for each intersection phase. The second layer is a decentralized-vehicle control layer where the CAVs receive the signal phase and timing (SpaT) information from the AIM to compute the optimal speed values. The proposed framework is designed to optimize intersection efficiency and minimize vehicle average delay and fuel consumption. In the second contribution, an intelligent framework for a CEV at multiple traffic intersections is proposed to achieve the minimum travel time and the least undesired traffic impacts on other road users. The framework includes an optimal path-planning mechanism adaptive to sudden traffic delays, a novel traffic signal preemption controller adaptive to the emergency level, and a deep-learning model for giving the way to the CEV. All the aforementioned contributions are conducted through a microscopic traffic simulation environment that simulates real-world dynamics of vehicles and drivers’ behaviors based on decades of field data.

Date of Award	Dec 2022
Original language	American English
Supervisor	AMEENA ALSUMAITI (Supervisor)