Master of Science, The Ohio State University, 2019, Mechanical Engineering

Collaboration is an important aspect of many successful natural systems, but it is rare to find in transportation systems. However, recent advances in the standardization of communication technologies, improvements in unmanned aerial systems, and deployments of large autonomous vehicle fleets could be used to collaboratively optimize entire traffic networks and improve the safety of self-driving cars. This thesis considers how unmanned aerial systems could use communication to provide useful information to self-driving cars and transportation systems. A custom unmanned aerial system is designed and built to study dedicated short-range communication (DSRC) technology. The physical layer of DSRC is studied extensively using the unmanned aerial system and concerns are given for antenna design. Then a simulation environment is built to study large scale implementation of communication and unmanned aerial systems in traffic networks. This simulation environment is shown to be useful for a wide array of traffic studies. Finally, considerations are given for future work.

Committee: Levent Guvenc (Advisor); Bilin Aksun-Guvenc (Committee Member) Subjects: Civil Engineering; Computer Engineering; Computer Science; Electrical Engineering; Mechanical Engineering

Doctor of Philosophy, The Ohio State University, 2022, Mechanical Engineering

In recent years, the trend in the automotive industry has been favoring the reduction of fuel consumption in vehicles with the help of new and emerging technologies. This drive stemmed from the developments in communication technologies for Connected and Autonomous Vehicles (CAV), such as Vehicle to Infrastructure (V2I), Vehicle to Vehicle (V2V) and Vehicle to Everything (V2X) communication. Coupled with automated driving capabilities of CAVs, a new and exciting era has started in the world of transportation as each transportation agent is becoming more and more connected. To keep up with the times, research in the academia and the industry has focused on utilizing vehicle connectivity for various purposes, one of the most significant being fuel savings. Motivated by this goal of fuel saving applications of Connected Vehicle (CV) technologies, the main focus and contribution of this dissertation is developing and evaluating a complete Eco-Driving strategy for CAVs. Eco-Driving is a term used to describe the energy efficient use of vehicles. In this dissertation, a complete and comprehensive Eco-Driving strategy for CAVs is studied, where multiple driving modes calculate speed profiles ideal for their own set of constraints simultaneously to save fuel as much as possible while a High Level (HL) controller ensures smooth transitions between the driving modes for Eco-Driving. The first step in making a CAV achieve Eco-Driving is to develop a route-dependent speed profile called Eco-Cruise that is fuel optimal. The methods explored to achieve this optimally fuel economic speed profile are Dynamic Programming (DP) and Pontryagin's Minimum Principle (PMP). Using a generalized Matlab function that minimizes the fuel rate for a vehicle travelling on a certain route with route gradient, acceleration and deceleration limits, speed limits and traffic sign (traffic lights and STOP signs) locations as constraints, a DP based fuel optimal velocity profile is found. The ego CAV (open full item for complete abstract)

Committee: Levent Guvenc (Advisor); Mrinal Kumar (Committee Member); Bilin Aksun-Guvenc (Committee Member) Subjects: Automotive Engineering; Computer Science; Design; Energy; Engineering; Experiments; Mechanical Engineering; Systems Design; Technology; Transportation

Doctor of Philosophy, The Ohio State University, 2022, Electrical and Computer Engineering

Recent developments in connected and autonomous vehicles (CAV) improve traffic safety and fuel efficiency and take away the driving burden partially or completely from the driver. CAVs are improving the traffic safety using their on-board sensors such as camera, lidar, radar and ultrasonic sensors. While these sensors are effective in sensing the objects in their field of view, CAVs can also sense other road users by utilizing communication modems, and learn more about the traffic patterns such as the signal phase and timing (SPaT) information of a traffic light at an intersection. One recent approach to boost capabilities of CAVs is the sharing of perceived target detections with other road users. This practice significantly increases the situational awareness of connected road users. Since the cooperative perception concept is still in early stages, the development of use case scenarios and capabilities of this concept are still active research areas. Therefore, a Cooperative Perception (CP) architecture and CAV functionalities are developed in this research to improve the traffic safety, fuel economy, and ride comfort. Their effectiveness is demonstrated with use case scenarios. The developed CP architecture relies on Joint Probability Data Association (JPDA) multi object tracking algorithm to track detected objects and create CP messages. Then, with the simulations, it is shown that situational awareness of the road users increased significantly, thereby improving their traffic safety. Later, another use case scenario for CP is developed to improve Green Light Optimized Speed Advisory (GLOSA). In this use case, the vehicle not only relies on SPaT and MAP messages but also relies on the shared CP messages by a smart intersection. As a result, two different algorithms are developed to utilize infrastructure CP messages. While the first approach to generate speed advisory was to create a rule-based solution, the second approach utilizes a Deep Deterministic Policy (open full item for complete abstract)

Committee: Levent Guvenc Prof. (Advisor); Benjamin Coifman Prof. (Committee Member); Keith Redmill Prof. (Committee Member); Bilin Aksun Guvenc Prof. (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2020, Electrical and Computer Engineering

With the increasing demands for wireless control, infotainment communication and telematics service, modern cars are equipped with more and more radio frequency (RF) systems such as Global Navigation Satellite System (GNSS), Satellite Digital Audio Radio Service (SDARS), Cellular, Vehicle-to-Everything (V2X), and Wireless Local Area Network (WLAN), for navigation, communication, and entrainment needs. Not only does the number of frequency bands increase, but also the operating frequency goes higher and higher for more bandwidths. However, operating at a higher frequency is accompanied by faster fading issue which can be mitigated via the utilization of diversity reception from multiple antennas with different pattern coverages, polarizations, and spatial locations. However, packing many antennas into a compact low-profile volume as demanded by automobile manufacturers is no easy task because 1) the dimensions of antennas are typically comparable to a quarter to one half of operating wavelengths, and 2) strong coupling and blockage effects occur when other antennas are in proximity (less than a quarter of wavelengths). In addition, the most critical practical requirements of the next generation automobile antennas are low cost, easy installation, low profile, small footprint, covers from 0.7 GHz to 6 GHz, and support up to 4 channels MIMO radios. Unlike the conventional approach of using different antennas for LTE cellular, WLAN, and V2X different systems, Our proposed a compact ultra-wide bandwidth (UWB) universal antenna system reduced the total number of antennas, and thus overall antenna volume, by combing LTE, WLAN, and V2X operations into the same antenna body due to their similarity in pattern coverage and polarization requirements. Note that this is actually very challenging since the antenna needs to work over a very wide bandwidth to support 4G LTE (700 MHz-960 MHz, 1700MHz-2100MHz, 2500MHz-2700MHz, 3400MHz-3700MHz), C-Band of 5G sub6 (3300MHz-5000 (open full item for complete abstract)

Committee: Chi-Chih Chen (Advisor); Nima Ghalichechian (Committee Member); Fernando Teixeira (Committee Member); C.K. Shum (Committee Member) Subjects: Electrical Engineering; Electromagnetics

Doctor of Philosophy, The Ohio State University, 2019, Mechanical Engineering

Connected vehicles promise to increase transportation options and reduce travel times while improving the safety of road users. Convoying/platooning are the common use case of connected vehicles technology and the driveability performance impact of such convoy has never been researched before. The vehicles when following each other in a convoy, using adaptive cruise control (ACC), is augmented by the lead vehicle information (vehicle acceleration) through the vehicle to vehicle communication as a feedforward control is called Cooperative Adaptive Cruise Control (CACC). This dissertation analyses the impact of the desired velocity profile on the driveability characteristics of a convoy of vehicles. In order to assess the driveability performance, a framework consisting of various metrics has been developed. The parameter space robust control methodology has been used to design the controller that improves the convoy's driveability and the performance is compared to the convoy that is being tuned for maintaining the time gap. These simulation results were verified in a real-time setting using a Hardware-in-the-Loop (HIL) setup using a CARSIM high-fidelity car model. With the use of the V2X technology, the fuel economy of the connected vehicle can be improved and it is called Eco-Driving. This dissertation proposes a framework for Eco-driving that is comprised of Eco-Cruise, Greenwave algorithm, and Eco-CACC. The Eco-Cruise is the algorithm which calculates the optimal velocity profile based on the route information such as speed limit, stop sign and traffic sign location and the vehicle powertrain model. A Dynamic programming based algorithm which minimizes the fuel economy is developed. The Eco-Cruise algorithm stops at all the stop signs and traffic light (assuming red light) optimally. Driving scenario has a very big impact on the Eco-cruise algorithm, and a new methodology has been proposed in this dissertation, that formulates a metric based route selection t (open full item for complete abstract)

Committee: Levent Guvenc (Advisor); Vadim Utkin (Committee Member); Bilin Aksun-Guvenc (Committee Member); Abhishek Gupta (Committee Member) Subjects: Automotive Engineering