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BAZ, ABDULLAHAUTONOMOUS VEHICLE DECISION MAKING AT INTERSECTION USING GAME THEORY
Doctor of Philosophy, University of Akron, 2018, Civil Engineering
One of the most critical subjects in Intelligent Transportation System (ITS) nowadays is the autonomous vehicle (AV). It is rapidly improving, and it will have a substantial positive effect on traffic safety and efficiency. Most of auto manufacturer companies and tech industries are spending a lot of money on research for developing autonomous vehicles. AV would have an excellent contribution to managing and controlling intersections. This study introduces a decision-making algorithm for autonomous vehicles at an intersection to optimize the intersection capacity and minimize delay time by using Game Theory mathematical models. This model using vehicle-to-infrastructure (V2I) communication features that will be available in AV so that vehicles are able to communicate with roadside unit (RSU) and with each other to determine which one goes first, depending on different factors such as their speeds and locations, and vehicle size, taking in consideration the safety of the vehicles so we can have collision free intersection. Two different mathematical models were developed; one with %100 autonomous vehicles and the other one is when we have mix traffic, autonomous vehicles, and ordinary vehicles. A simulation model was developed using a standard microscopic simulation platform VISSIM to implement this algorithm. A comparison of the proposed method and two other ordinary intersection control method; traffic lights, and roundabout was made to calculate the total delay of the intersection for each intersection management method. The simulation ran on three different traffic volume, High, moderate, and low volume. Moreover, three different speeds for each traffic volume. The results shows that the proposed system reduces the total delay by more than 65 percent compared with the roundabout, and about 85 percent comparing with a signalized intersection. Another simulation was done for the second scenario, mixed traffic, also a comparison between the proposed methods; roundabout, and the signalized intersection was made for the same cases of various speeds and volume. For model two, results show 30% reduction in delay compared to the roundabout and 89% compared to signalized intersections.

Committee:

Ping Yi, Prof. (Advisor); Yilmaz Sozer, Prof. (Committee Member); Qindan Huang, Dr. (Committee Member); Zhe Luo, Dr. (Committee Member); Jun Ye (Committee Member)

Subjects:

Civil Engineering; Transportation Planning

Keywords:

Autonomous Vehicles, Intersection Management, Mixed Traffic, AV intersection control, Autonomous vehicles and ordinary vehicles

Regatti, Jayanth ReddyDynamic Routing for Fuel Optimization in Autonomous Vehicles
Master of Science, The Ohio State University, 2018, Electrical and Computer Engineering
We consider the problem of minimizing fuel consumption in autonomous vehicles and pose it from the point of view of developing algorithms to route the vehicle along a fuel efficient path in the presence of complete information. We first adopt a dynamic model of traffic flow and using this model, develop the optimization problem as a stochastic shortest path problem in an infinite horizon general state MDP. The special structure of the problem enables us to prove the existence of optimal and epsilon optimal policies. In addition to this, we developed a software simulator CATS, that can aid research in autonomous transportation and cyber-security. As a microscopic traffic simulator, CATS offers several advantages in testing algorithms and logging fine grained details in a simulation environment that mimics a real traffic scenario. We currently support features like fully connected V2V and V2X, traffic light switching, varying driver behaviors, etc. In addition to offering what a traditional traffic simulator offers, CATS can also be customized in many ways, such as altering the driving algorithm of the vehicles. This software can be used as a Monte Carlo simulator to train a vehicle to learn driving policies, as well as to train a traffic controller to learn optimal switching policies. CATS can also aid game theoretic research in cybersecurity due its V2V, V2X modules. CATS can also be integrated with modern machine learning frameworks thereby making deployment of machine learning algorithms more accessible to transportation and connected vehicle researchers. CATS API, developed in python, provides an easy to use interface to researchers to get started with minimal programming knowledge. This software is currently under development and can also be envisioned to be an educational tool to introduce autonomous vehicles in classrooms.

Committee:

Abhishek Gupta (Advisor); Levent Guvenc (Committee Member)

Subjects:

Electrical Engineering; Operations Research; Transportation Planning

Keywords:

Dynamic routing; transportation; autonomous vehicles; simulation; simulator; reinforcement learning; markov decision processes; markov decision process; mdp; borel state mdp; traffic flow; dynamic programming; approximate dynamic programming

Pavlic, Theodore PaulDesign and Analysis of Optimal Task-Processing Agents
Doctor of Philosophy, The Ohio State University, 2010, Electrical and Computer Engineering

This dissertation is given in two parts followed by concluding remarks. The first three chapters describe the generalization of optimal foraging theory for the design of solitary task-processing agents. The following two chapters address the coordinated action of distributed independent agents to achieve a desirable global result. The short concluding part summarizes contributions and future research directions.

Optimal foraging theory (OFT) uses ecological models of energy intake to predict behaviors favored by natural selection. Using models of the long-term rate of energetic gain of a solitary forager encountering a variety of food opportunities at a regular rate, it predicts characteristics of optimal solutions that should be expressed in nature. Several engineered agents can be modeled similarly. For example, an autonomous air vehicle (AAV) that flies over a region encounters targets randomly just as an animal will encounter food as it travels. OFT describes the preferences that the animal is likely to have due to natural selection. Thus, OFT applied to mobile vehicles describes the preferences of successful vehicle designs.

Although OFT has had success in existing engineering applications, rate maximization is not a good fit for many applications that are otherwise analogous to foraging. Thus, in the first part of this dissertation, the classical OFT methods are rediscovered for generic optimization objectives. It is shown that algorithms that are computationally equivalent to those inspired by classical OFT can perform better in realistic scenarios because they are based on more feasible optimization objectives. It is then shown how the design of foraging-like algorithms provides new insight into behaviors observed and expected in animals. The generalization of the classical methods extracts fundamental properties that may have been overlooked in the biological case. Consequently, observed behaviors that have been previously been called irrational are shown to follow from the extension of the classical methods.

The second part of the dissertation describes individual agent behaviors that collectively result in the achievement of a global optimum when the distributed agents operate in parallel. In the first chapter, collections of agents that are each similar to the agents from the early chapters are considered. These agents have overlapping capabilities, and so one agent can share the task processing burden of another. For example, an AAV patrolling one area can request the help of other vehicles patrolling other areas that have a sparser distribution of targets. We present a method of volunteering to answer the request of neighboring agents such that sensitivity to the relative loading across the network emerges. In particular, agents that are relatively more loaded answer fewer task-processing requests and receive more answers to their own requests. The second chapter describes a distributed numerical optimization method for optimization under inseparable constraints. Inseparable constraints typically require some direct coordination between distributed solver agents. However, we show how certain implementations allow for stigmergy, and so far less coordination is needed among the agents. For example, intelligent lighting, which maintains illumination constraints while minimizing power usage, is one application where the distributed algorithm can be applied directly.

Committee:

Kevin M. Passino (Advisor); Andrea Serrani (Committee Member); Atilla Eryilmaz (Committee Member)

Subjects:

Animals; Biology; Computer Science; Ecology; Economics; Electrical Engineering; Engineering; Mathematics; Robots; Systems Design; Technology

Keywords:

optimization; foraging theory; agent-based modeling; autonomous vehicles; game theory

Schepelmann, AlexanderIdentification & Segmentation of Lawn Grass Based on Color & Visual Texture Classifiers
Master of Sciences (Engineering), Case Western Reserve University, 2010, EMC - Mechanical Engineering
CWRU Cutter is an autonomous lawnmower which can reflexively avoid obstacles. While LIDAR was previously used by the robot to determine obstacle locations, the sensor’s price makes its inclusion in commercial versions prohibitively expensive. Cameras can provide similar information at drastically reduced cost, but useful information must first be extracted from incoming images. This can be computationally expensive. Additionally, vision-based methods can be highly sensitive to changing lighting conditions. This thesis presents a method to identify grass based on color and visual texture classifiers for use in an outdoor environment. Neighborhood-based color measurements are calculated using the HSL color model and texture measurements are based on edge-detection and quantified via computationally inexpensive first and second order statistics. Individual measurements are then combined to create a binary representation of mowable terrain in an image. Performance is quantified by measuring recognition performance on a set of sample neighborhoods that contains common backyard obstacles.

Committee:

Roger D. Quinn, PhD (Committee Chair); Francis Merat, PhD (Committee Member); Michael S. Branicky, ScD (Committee Member)

Subjects:

Computer Science; Engineering; Mechanical Engineering; Robots

Keywords:

Computer vision; texture; grass identification; autonomous vehicles; autonomous lawnmower; robotics

Blau, Michael ArmstrongDriverless Vehicles’ Potential Influence on Cyclist and Pedestrian Facility Preferences
Master of City and Regional Planning, The Ohio State University, 2015, City and Regional Planning
Research in the field of autonomous vehicle technology focuses on the enhanced safety and convenience it will likely convey to vehicle occupants. This thesis seeks to establish a new and equally important line of inquiry that addresses the same implications for cyclists and pedestrians. It is well-established that motorized traffic volume and speed have a strong influence on non-motorized agents’ behavior and facility preference but whether this will continue to be the case in a driverless environment remains unknown. A stated-preference survey was crafted asking respondents to select their preferred facility in various scenarios with and without the presence of driverless vehicles and on street types of varying motorized traffic volumes and speeds. An ordered logit model was estimated to illustrate that street type had a very strong influence on cyclists’ preferences for more separated facilities as traffic volume and speed increased. The presence of driverless vehicles significantly amplified this trend. Preferences for bike intersection features, pedestrian facilities, and pedestrian crossing behavior are also examined. Infrastructure and policy recommendations are presented as well as suggestions for future research in this nascent field of study.

Committee:

Gulsah Akar (Advisor); Jack Nasar (Committee Member); Jason Sudy (Committee Member)

Subjects:

Area Planning and Development; Civil Engineering; Psychobiology

Keywords:

Driverless vehicles; autonomous vehicles; pedestrian; active transportation; bike; cyclist; behavior determinants; facility preference

Gadepally, Vijay NarasimhaEstimation of Driver Behavior for Autonomous Vehicle Applications
Doctor of Philosophy, The Ohio State University, 2013, Electrical and Computer Engineering
Cyber-physical systems (CPS) refer to the co-joining of environmental and computational elements of a system. One CPS application area is in autonomous vehicles. Autonomous (or self-driving) vehicles are likely to be an upcoming revolution in personal and commercial transportation. While there are many outstanding public policy questions, this technology promises to improve our quality of life by providing transportation that is safe and efficient. A likely technology adoption path includes a period in which human driven and autonomous vehicles will need to coexist. In such an environment, referred to as a Mixed Urban Environment, autonomous vehicles may only be able to obtain information from human driven vehicles through on board sensors or vehicle-to-vehicle communication. From this information, an autonomous vehicle will need to determine the likely behavior of the human driven vehicle, a task which is referred to as driver behavior estimation. This task requires a qualitative-quantitative architecture capable of explaining the driver/vehicle coupling being observed. A vehicle's ability to determine other vehicle's likely behavior also has applications to driver safety and collision avoidance systems. In essence, a vehicle must be able to estimate the behavior of another vehicle, and determine its course of action. This thesis proposes an architecture for driver behavior estimation through the unified development of two theoretical concepts, namely: Graphical models, and Hybrid State Systems. Hybrid State Systems (HSS) provide the qualitative relationship between driver/vehicle couplings through a two layer model. Pattern recognition techniques in conjunction with Hidden Markov Models (HMMs), a type of graphical model, provide the quantitative relation between HSS layers. The estimation of current driver state is based on easy-to-measure continuous observations. The proposed system uses machine-learning concepts and requires extensive data collection, which is discussed. This thesis further provides an extension of the proposed system that includes external factors such as roadway type conditions in the decision making process. Results are provided for driver behavior estimation and system extension. A discussion of some of the public policy questions behind autonomous vehicles is also provided.

Committee:

Ashok Krishnamurthy, Dr. (Advisor); Umit Ozguner, Dr. (Committee Member); Giorgio Rizzoni, Dr. (Committee Member)

Subjects:

Computer Engineering; Electrical Engineering

Keywords:

Autonomous Vehicles; Self Driving Cars; Policy; Driver Behavior; Hidden Markov Models;

Burgei, DavidAutonomous Edge Cities: Revitalizing Suburban Commercial Centers with Autonomous Vehicle Technology and New (sub)Urbanist Principles
MARCH, University of Cincinnati, 2017, Design, Architecture, Art and Planning: Architecture
Edge cities, suburban commercial districts on the outskirts of larger metropolitan areas, have always been centered on the convenience of accessibility. Due to the personal automobile often being the only means of transit in these suburban zones, edge cites today are dominated by wide-multilane streets, and expansive parking. This convenience for the driver comes at the expanse of pedestrian traffic, public space, and urban connection. The rapidly emerging technology of driverless vehicles will prove to change the focus of edge cities. Driverless vehicles will be safer, and travel more efficiently than cars driven today. Without the need for convenient parking, and clear delineation of vehicle and pedestrian zones, edge cities can become richer, more pedestrian friendly environments, while retaining and improving upon current benefits of easy accessibility. This thesis explores the recent advancements of autonomous vehicles, and the opportunities they create for people and urban design. These opportunities are integrated with principals of New Urbanism to develop a revitalization of Tri-County, an edge city of Cincinnati, OH.

Committee:

Udo Greinacher, M.Arch. (Committee Chair); Aarati Kanekar, Ph.D. (Committee Member)

Subjects:

Architecture

Keywords:

Autonomous Vehicles;Self-Driving;New Urbanism

Thornton, Douglas AnthonyHigh Fidelity Localization and Map Building from an Instrumented Probe Vehicle
Doctor of Philosophy, The Ohio State University, 2017, Electrical and Computer Engineering
The lack of high fidelity data sources measuring roadway infrastructure has long handicapped the modeling of vehicular interaction and traffic flow. To date embedded loop detectors and other point detectors provide the data source for these models, aggregating data over significant time intervals, often minutes, performing point measurements at spacing much larger than the characteristics being measured. The result of this inadequacy is a scientific disagreement on even the most basic relationships in traffic flow theory, such as the fundamental relationship between flow, occupancy and density. Beginning in 2005, the Ohio State University began collecting high fidelity traffic flow data from an instrumented probe vehicle. The data mitigates a number of problems of both traditional data sources such as loops, and experimental data sources such as NGSIM, ultimately providing utility to solve or refine a variety of open traffic flow theory problems. As a precursor to applying the instrumented probe vehicle data, raw sensor information must be aggregated and processed using a variety of techniques found in control, transportation, and robotics literature. Data were collected in hundreds of runs over six years under a variety of changes to the environment and sensor suite itself, requiring the data processing to be automated and robust. This research resolves a number of issues with the instrumented probe vehicle data extraction by: 1) providing a method to validate a global localization estimates, 2) designing and implementing a new, observational, globally referenced mapping framework and applying that framework to Bayesian occupancy and evidential grid representations, and 3) developing a suite of applications supporting the processing of the data, including LiDAR mounting calibration, localization refinement, map structure change identification, road boundary detection, and lane finding. The novel use of a perception sensor, specifically a vertically scanning LiDAR, solves the issue of verifying a large, historic dataset’s global positioning system derived global localization. This validation supports trust in instrumented probe vehicle by verifying the localization achieves lane level accuracy, as well as in future automated vehicle applications. To aid in the storage and retrieval of observational data of large, city-scale regions, this research creates the Map Oriented Grid, which supports the efficient global referencing of observational data stored in a grid structure. These grid structures support many opportunistic mapping applications including the identification of salient structures nearby a road, and the areas on a roadway where movement if regularly observed. This framework could be applied in crowd sourcing maps in a connected vehicle environment. Finally, a chief goal of the instrumented probe vehicle is to accurately and precisely track the nearby ambient vehicles. The Map Oriented Grid supports such needs by developing applications on top of this framework that were necessary to both simplifying future vehicle trackers based upon this work, and provide the highest quality, calibrated location and sensor data to such a vehicle tracker. In providing the above capabilities, this work assists in the extraction of value from the instrumented probe vehicle data, and correspondingly advances the state of the art in traffic flow theory

Committee:

Benjamin Coifman (Advisor); Levent Guvenc (Committee Member); Ümit Özgüner (Committee Member)

Subjects:

Electrical Engineering

Keywords:

Probe vehicles;traffic flow theory;autonomous vehicles; automated vehicles;LIDAR; road mapping; global localization; lane finding;Occupancy Grids; Evidential Grids;

Pavlic, Theodore P.Optimal Foraging Theory Revisited
Master of Science, The Ohio State University, 2007, Electrical Engineering
Optimal foraging theory explains adaptation via natural selection through quantitative models. Behaviors that are most likely to be favored by natural selection can be predicted by maximizing functions representing Darwinian fitness. Optimization has natural applications in engineering, and so this approach can also be used to design behaviors of engineered agents. In this thesis, we generalize ideas from optimal foraging theory to allow for its easy application to engineering design. By extending standard models and suggesting new value functions of interest, we enhance the analytical efficacy of optimal foraging theory and suggest possible optimality reasons for previously unexplained behaviors observed in nature. Finally, we develop a procedure for maximizing a class of optimization functions relevant to our general model. As designing strategies to maximize returns in a stochastic environment is effectively an optimal portfolio problem, our methods are influenced by results from modern and post-modern portfolio theory. We suggest that optimal foraging theory could benefit by injecting updated concepts from these economic areas.

Committee:

Kevin Passino (Advisor)

Keywords:

robotics; automation; autonomous vehicles; behavior; behavioral ecology; intelligent control; portfolio theory; modern portfolio theory; MPT; post-modern portfolio theory; PMPT; optimal foraging theory; OFT; optimal diet selection; predator; prey

Mostafa, Ahmad APacket Delivery Delay and Throughput Optimization for Vehicular Networks
PhD, University of Cincinnati, 2013, Engineering and Applied Science: Computer Science and Engineering
Vehicular networking is a new emerging wireless technology that supports the communication amongst vehicles and enables vehicles to connect with the Internet. This networking technology provides vehicles with endless possibility of applications, including safety, convenience, and entertainment applications. Examples for these applications are safety messaging, real-time traffic, route updates, and general purpose Internet access. The goal of vehicular networks is to provide an efficient, safe, and convenient environment for the vehicles. In vehicular networking technology, vehicles connect either through other vehicles in an ad-hoc multi-hop fashion or through road side units (infrastructure) which connects them to the Internet. Each approach has its own advantages and disadvantages. However, one of the main objectives of vehicular networking is to achieve a minimal delay for message delivery, and encourage a continuous connectivity for vehicles. This dissertation introduces a novel hybrid communication paradigm for achieving seamless connectivity in Vehicular Ad-hoc NETworks (VANET), wherein the connectivity is often affected by changes in the dynamic topology, vehicles' speed, as well as traffic density. Our proposed technique ---named QoS-oriented Hybrid Vehicular Communications Protocol (QoSHVCP)--- exploits both existing network infrastructure through a Vehicle-to-Infrastructure (V2I) protocol, as well as a traditional Vehicle-to-Vehicle (V2V), that satisfies Quality-of-Service requirements. We analyze time delay as a performance metric, and determine delay propagation rates when vehicles are transmitting high priority messages via QoSHVCP. Focusing on V2V communication, we propose a novel reliable and low-collision packet-forwarding scheme, based on a probabilistic rebroadcasting. Our proposed scheme, called Collision-Aware REliable FORwarding (CAREFOR), works in a distributed fashion where each vehicle receiving a packet, rebroadcasts it based on a predefined probability. The success of rebroadcast is determined based on allowing the message to travel the furthest possible distance with the least amount of packet rebroadcast collision. Moreover, we present a QoS-Aware node Selection Algorithm (QASA) for VANET routing protocols. Our algorithm is focused on selecting the vehicle to communicate with, and is achieved by exploiting the bridging approach for message forwarding i.e., vehicles on the east (west) select from west (east). The QoS metrics that are being optimized are the throughput in the network, as well as end-to-end delay for packets. Finally, we exploit the use of autonomous vehicles in order to optimize the end-to-end packet delivery delay. Our protocol introduces a dynamic metric that depends on the vehicular density on the highway in order to control the inter-vehicle distance. Our results show a great promise for their future use in vehicular technology.

Committee:

Dharma Agrawal, D.Sc. (Committee Chair); Raj Bhatnagar, Ph.D. (Committee Member); Yizong Cheng, Ph.D. (Committee Member); John Franco, Ph.D. (Committee Member); Chia Han, Ph.D. (Committee Member); Yiming Hu, Ph.D. (Committee Member)

Subjects:

Computer Science

Keywords:

Vehicular Networks;Ad Hoc Networks;QoS;Autonomous Vehicles;

Kurt, ArdaHybrid-State System Modelling for Control, Estimation and Prediction in Vehicular Autonomy
Doctor of Philosophy, The Ohio State University, 2012, Electrical and Computer Engineering
This thesis studies the Hybrid-State System models and their properties for different pieces of the urban autonomy problem. For autonomous vehicles that operate in real-life, mixed-mode traffic, a number of parallels between the human-driven system and the autonomous counterpart can be identified and captured in the hybrid-state system setting. For the control subproblem of the urban autonomy, this thesis proposes a system architecture, related design approaches for autonomous mobile systems and guidelines for self-sufficient operation. Development of a tiered layout for a hybrid-state control in a series of stages as well as the integration of such a controller in the overall autonomy structure are proposed and demonstrated as part of multiple examples, including The Ohio State University participation in Defense Advanced Research Projects Agency Urban Challenge 2007. The hierarchical layout and the iterative design methodology enable design flexibility through compartmentalization of the overall system and helps prepare for various contingencies, as illustrated on specific development cycles. The sensing and perception part of the autonomy implementation relies on a probabilistic hybrid-state system modelling method that is developed for driver-behavior analysis and prediction. The model fits into and captures the central modules of the existing Human Driver Model. The stochastic models, based on the observable actions of the driver/vehicle interaction, are useful in representing the behavior of human-driven vehicles in certain urban decision-making scenarios. The Driver Intention Estimator presented utilizes the developed stochastic models to detect and predict high-level, abstract decisions of observed drivers through traffic scenarios and it can be expanded to form scenario safety estimation tools as demonstrated. As for the analysis of the developed estimators and as a useful tool for hybrid-state systems in general, this study develops an encoding scheme for discrete-state systems as part of a hybrid-state hierarchy. The codes are command-based, in the sense that the interactions of the discrete states with the continuous states are exploited to attach significance to what each discrete state does to the continuous subsystem. The resultant codeset is independent of how the discrete-state transitions are designed and conventional binary tools such as truth tables and K-maps are easily applicable in the binary representation of the codes. Code-based representation of every possible combination of commands/behaviors governed by the discrete subsystem is useful in a number of design scenarios, an example of which is the generation of a consistent norm for discrete states. Such a norm is demonstrated to be useful in hybrid-state estimation.

Committee:

Umit Ozguner, PhD (Advisor); Ashok Krishnamurthy, PhD (Committee Member); Keith A. Redmill, PhD (Committee Member)

Subjects:

Electrical Engineering

Keywords:

hybrid-state systems; autonomous vehicles, intelligent transportation systems, cyber-physical systems

Schmidt, Kelsey LAutonomous Vehicles: changing the surface landscape of communities through increased green infrastructure adoption and implementation to help US cities combat stormwater runoff
MCP, University of Cincinnati, 2018, Design, Architecture, Art and Planning: Community Planning
Today many communities are trying to find different solutions for mitigating the negative impacts of growth, impervious surfaces, and stormwater runoff on the environment. Sustainable stormwater management is a challenge for cities but there is also opportunity. The purpose of this research was to explore an environmentally positive scenario to how Autonomous Vehicles will impact communities. The research attempted to gain insight about Autonomous Vehicles and their impact on the built environment, trees, and stormwater. For this report three methods of research were used: background experience, four case studies, and a site selected scenario case study. With the idea that Autonomous Vehicle adoption is going to occur in the next 10-30 years this is going to change not only the way we travel but also create changes to the built environment. Autonomous Vehicles can have positive implications to communities by allowing new ways to incorporate trees as green infrastructure and to reduce impervious surface leading to stormwater problems. Autonomous Vehicle technology has the potential to create available spaces in our communities. The built environment changes would most affect street design width and surface parking lots. The study revealed new areas of analysis to be researched in terms of stormwater and Autonomous Vehicles. Green infrastructure implementation, particularly tree planting, can be used to mitigate stormwater runoff in cities due to changes to the built environment resulting from the adoption of Autonomous Vehicles.

Committee:

David Edelman, Ph.D. (Committee Chair); Leah Hollstein, Ph.D. (Committee Member); Travis Miller, MCP (Committee Member)

Subjects:

Urban Planning

Keywords:

Impervious Surface;Stormwater;Autonomous Vehicles;Green Infrastructure;Community Planning;Built Environment

Mathur, KovidConversion of a Hybrid Electric Vehicle to Drive by Wire Status
MS, University of Cincinnati, 2010, Engineering and Applied Science: Mechanical Engineering

With advancements in the automotive driving and safety technology the new age is looking to redefine transportation as we know it. The thrust area of study at the Center for Robotics Research at University of Cincinnati is building technology for autonomous vehicles and it has made considerable advancements over the years to building the future car.

In summer 2006, Defense Advanced Research Projects Agency announced the third Grand Challenge which would feature ground vehicles executing “simulating military supply missions safely and effectively in a mock urban area”. The center entered the competition with a team of students and researchers to achieve this goal with generous contributions from Tank Automotive Research Development and Engineering Center (TARDEC), Applied Research Associates Inc (ARA) and the University of Cincinnati amongst others.

An all terrain hybrid electric vehicle built by Cal Motors as an economical non tactical base transport vehicle was used as the competition entry, which was donated as per a Co-operative Research and Development Agreement (CRADA). This thesis presents the drive by wire solution along with the custom changes which were made on the vehicle in order to put the till now theories into practice.

The design solution introduced control of linear actuators by more responsive and energy efficient servo motors which were driven by a Galil™ motion controller. Each axis of the controller was responsible for the control of electronic braking, steering and speed control systems respectively. The process involved some metal fabrication to incorporate the positioning of components for improved space usage and definite mounting, after which the systems were calibrated for optimum functioning. A series-hybrid approach was introduced for the ATV to provide longer hours of operation. Before the vehicle was allowed to ply on city streets it was brought to Ohio state law standards and in addition underwent a thousand mile durability test.

The result of the research and development was a robust and effective system which could control by wire the all terrain vehicle (ATV) and met all primary goals of the project.

Committee:

Manish Kumar, PhD (Committee Chair); Ernest Hall, PhD (Committee Member); Janet Dong, PhD (Committee Member)

Subjects:

Mechanical Engineering

Keywords:

Autonomous vehicles;Drive by wire