Autonomous vehicles

Every passenger vehicle must rely on a safe and optimal trajectory to eliminate traffic incidents and congestion as well as to reduce environmental impact, and travel time. Autonomous intersection management systems (AIMS) enable large scale optimization of vehicular trajectories with connected and autonomous vehicles (CAVs). The first contribution of this dissertation is the fastest trajectory planner (FTP) method which is geared for computing the fastest waypoint trajectories via performing graph search over a discretized space-time (ST) graph (Gt), thereby constructing collision-free space-time trajectories with variable vehicular speeds adhering to traffic rules and dynamical constraints of vehicles. The benefits of navigating a connected and autonomous vehicle (CAV) truly capture effective collaboration between every CAV during the trajectory planning step. This requires addressing trajectory planning activity along with vehicular networking in the design phase. For complementing the proposed FTP method in decentralized scenarios, the second contribution of this dissertation is an application layer V2V solution using a coordinator-based distributed trajectory planning method which elects a single leader CAV among all the collaborating CAVs without requiring a centralized infrastructure. The leader vehicular agent calculates and assigns a trajectory for each node CAV over the vehicular network for the collision-free management of an unsignalized road intersection. The proposed FTP method is tested in a simulated road intersection scenario for carrying out trials on scheduling efficiency and algorithm runtime. The resulting trajectories allow high levels of intersection sharing, high evacuation rate, with a low algorithm single-threaded runtime figures even with large scenarios of up to 1200 vehicles, surpassing comparable systems.

The goal of this thesis is to simulate, design and build an automated device that allows unmanned vessels to anchor themselves in specified locations while being United States Coast Guard Navigation Rules compliant. This is a part of a larger project funded by the U.S. Department of Energy for Florida Atlantic University to build an unmanned platform with an Undershot Water Wheel on it. By simulating the environment of the South Florida Intercoastal Water Ways, forces acting on the line, anchor and the vessel are analyzed. These forces are used as the guide for the design and build of a line locking mechanism that takes the tension off the winch and a sensor package to monitor the environment the platform is in as well as control of the system. Based off experimental testing, the system was successful in handling all emulated environments with loads exceeding 150lbs of tension.

Most of recent studies indicate that people are negatively predisposed toward utilizing
autonomous systems. These findings highlight the necessity of conducting research
to better understand the evolution of trust between humans and growing autonomous technologies
such as self-driving cars (SDC). This research therefore presents a new approach
for real-time trust measurement between passengers and SDCs. We utilized a new structured
data collection approach along with a virtual reality (VR) SDC simulator to understand
how various autonomous driving scenarios can increase or decrease human trust and
how trust can be re-built in the case of incidental failures. To verify our methodology, we
designed and conducted an empirical experiment on 50 human subjects. The results of this
experiment indicated that most subjects could rebuild trust during a reasonable timeframe
after the system demonstrated faulty behavior. Furthermore, we discovered that the cultural
background and past trust-related experiences of the subjects affect how they lose or regain
their trust in SDCs. Our analysis showed that this model is highly effective for collecting
real-time data from human subjects and lays the foundation for more-involved future
research in the domain of human trust and autonomous driving.