Automotive Engineering Commons^™

Enabling Energy Efficiency In Connected And Automated Vehicles Through Predictive Control Techniques, Farhang Motallebiaraghi

Dissertations

The transportation sector is a significant contributor to global energy consumption and emissions, necessitating the development of sustainable transportation systems. In this regard, connected and automated vehicles (CAVs) have emerged as a potential solution to transform the transportation industry. By harnessing advanced mapping and location technologies, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication, CAVs offer the promise of improving efficiency, reducing traffic congestion, and enhancing safety and comfort. However, the adoption of CAVs also brings about various challenges, including energy efficiency concerns that need to be addressed to fully realize their potential benefits. This dissertation investigates energy-efficient control techniques for transportation …

Sensor Updates For Bigheaded Carp-Tracking Autonomous Boat, Jordan Kaufmann

Honors College Theses

Bigheaded carp are an invasive species that overpopulate and compete with the native species of Kentucky Lake as well as many other North American aquatic ecosystems. The movement patterns of Bigheaded carp are being studied nationwide by the United States Geological Survey and multiple universities. These studies ultimately seek to control their spread and reduce or reverse the ecosystem destabilization caused by this invasive species. Such studies are currently conducted manually on Kentucky Lake by graduate students affiliated with the Murray State University (MSU) Biology Department and Hancock Biological Station, and these manual studies are an arduous and time-consuming effort. …

Risk Assessments And Modeling Of Driver By Using Risk Potential Theory, Riku Kikuta

Theses and Dissertations

Recently, various self-driving and driving assistance systems such as Advanced Driver Assistance System (ADAS) have been developed with the intent to reduce the number of motor vehicle accidents. While self-driving systems have been proven to reduce traffic accidents, the systems sometimes make other drivers confused because of their mechanical behavior. To avoid confusion and possible error, it is necessary to construct self-driving systems that exhibit human-like behaviors. Risk Potential theory has been used to construct models that successfully represent driver behavior, especially expert behavior. This project uses Risk Potential theory to construct and evaluate a collision avoidance driver model which …

A Modeling Framework To Develop Materials With Improved Noise And Vibration Performance For Electric Vehicles, Javad Baqersad, Seyed Jamaleddin Mostafavi Yazdi, Seongchan Pack, Foroogh Rouhollahi

Mechanical Engineering Publications

The automotive and aerospace industries increasingly use lightweight materials to improve performance while reducing fuel consumption. Lightweight materials are frequently used in electric vehicles (EVs). However, using these materials can increase airborne and structure-borne noise. Furthermore, EV noise occurs at high frequencies, and conventional materials have small damping. Thus, there is an increasing need for procedures that help design new materials and coatings to reduce the transferred and radiated noise at desired frequencies. This study pioneered new techniques for microstructure modeling of coated and uncoated materials with improved noise, vibration, and harshness (NVH) performance. This work uses the microstructure of …

Accelerating The Derivation Of Optimal Powertrain Control Strategies Using Reinforcement Learning And Virtual Prototypes, Daniel Egan

All Dissertations

The push for improvements in fuel economy while reducing tailpipe emissions has resulted in significant increases in automotive powertrain complexity, subsequently increasing the resources, both time and money, needed to develop them. Powertrain performance is heavily influenced by the quality of their controller/calibration with modern powertrains reaching levels of complexity where using traditional design of experiment-based methodologies to develop them can take years. Recently, reinforcement learning (RL), a machine learning technique, has emerged as a method to rapidly create optimal controllers for systems of unlimited complexity directly which creates an opportunity to use RL to reduce the overall time and …

Deep Reinforcement Learning And Game Theoretic Monte Carlo Decision Process For Safe And Efficient Lane Change Maneuver And Speed Management, Shahab Karimi

All Dissertations

Predicting the states of the surrounding traffic is one of the major problems in automated driving. Maneuvers such as lane change, merge, and exit management could pose challenges in the absence of intervehicular communication and can benefit from driver behavior prediction. Predicting the motion of surrounding vehicles and trajectory planning need to be computationally efficient for real-time implementation. This dissertation presents a decision process model for real-time automated lane change and speed management in highway and urban traffic. In lane change and merge maneuvers, it is important to know how neighboring vehicles will act in the imminent future. Human driver …

Vanet Applications Under Loss Scenarios & Evolving Wireless Technology, Adil Alsuhaim

All Dissertations

In this work we study the impact of wireless network impairment on the performance of VANET applications such as Cooperative Adaptive Cruise Control (CACC), and other VANET applications that periodically broadcast messages. We also study the future of VANET application in light of the evolution of radio access technologies (RAT) that are used to exchange messages. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assume a fixed time headway value, and do not achieve string stability. In this work, we study the string stability of a …

Decision Support Issues In Automated Driving Systems, William N. Caballero, David Ríos Insua, David Banks

Faculty Publications

Machine learning and computational processing have advanced such that automated driving systems (ADSs) are no longer a distant reality. Many automobile manufacturers have developed prototypes; however, there exist numerous decision support issues requiring resolution to ensure mass ADS adoption. In the coming decades, it is likely that production ADSs will only be partially autonomous. Such ADSs operate within predetermined conditions and require driver intervention when they are violated. Since forecasts of their 20-year market penetration are relatively low, ADSs will likely operate in heterogeneous traffic characterized by vehicles of varying autonomy levels. Under these conditions, effective decision support must consider …

Improving Quality Of Life Using Ict, Iot And Ai (Honet), Charles Koduru

Symposium of Student Scholars

Autonomous robots can be assigned with various tasks such as moving payload, analyzing terrain, and capturing data in an environment. For an Autonomous Mobile Robot (AMR) to execute such tasks the robot (Hussarion ROSbot) will require efficient algorithms and techniques to reference its current location. The robot is relative to surrounding obstacles in its predetermined path. The conducted research study explains the coordinated method used to successfully allow a robot to identify its position in the environment (Gazebo Simulation) and avoid obstructions with increasing velocity - contingent on nearby surroundings. The results show multiple robots individually tasked with distinct roles, …

Development Of Detection And Tracking Systems For Autonomous Vehicles Using Machine Learning, Tyler Ward

Morehead State Theses and Dissertations

A thesis presented to the faculty of the Elmer R. Smith College of Business and Technology at Morehead State University in partial fulfillment of the requirements for the degree of Master of Science by Tyler Ward April 25, 2023.

Optimization Of A Simultaneous Localization And Mapping (Slam) System For An Autonomous Vehicle Using A 2-Dimensional Light Detection And Ranging Sensor (Lidar) By Sensor Fusion, Shaen Mehrzed

Honors College Theses

Fully autonomous vehicles must accurately estimate the extent of their environment as well as their relative location in their environment. A popular approach to organizing such information is creating a map of a given physical environment and defining a point in this map representing the vehicle’s location. Simultaneous Mapping and Localization (SLAM) is a computing algorithm that takes inputs from a Light Detection and Ranging (LiDAR) sensor to construct a map of the vehicle’s physical environment and determine its respective location in this map based on feature recognition simultaneously. Two fundamental requirements allow an accurate SLAM method: one being accurate …

Enhancing Pedestrian-Autonomous Vehicle Safety In Low Visibility Scenarios: A Comprehensive Simulation Method, Zizheng Yan, Yang Liu, Hong Yang

Modeling, Simulation and Visualization Student Capstone Conference

Self-driving cars raise safety concerns, particularly regarding pedestrian interactions. Current research lacks a systematic understanding of these interactions in diverse scenarios. Autonomous Vehicle (AV) performance can vary due to perception accuracy, algorithm reliability, and environmental dynamics. This study examines AV-pedestrian safety issues, focusing on low visibility conditions, using a co-simulation framework combining virtual reality and an autonomous driving simulator. 40 experiments were conducted, extracting surrogate safety measures (SSMs) from AV and pedestrian trajectories. The results indicate that low visibility can impair AV performance, increasing conflict risks for pedestrians. AV algorithms may require further enhancements and validations for consistent safety performance …

Improving Future Vehicle Fuel Economy And Operational Design Domain Through Novel Data Pipelines, Kyle James Carow

Masters Theses

Modern automobiles have greatly advanced in recent years, with technological developments that enhance performance, safety, and comfort. However, there is still much room for improvement. Today’s vehicles are heavily reliant on the combustion of fossil fuels, proven to be harmful for the environment on both a local and global scale. In addition, the safety benefits of autonomous vehicles and advanced driver assistance systems are not yet fully realized due to the limited operational design domain of these technologies. In this research, these needs are addressed through the development of two novel data pipelines. In the first study, a novel methodology …

Measuring Radiation Protection: Partners From Across The Nuclear Enterprise Evaluate The Radiation Protection Of Us Army Vehicles, Andrew W. Decker, Robert Prins

Faculty Publications

Recent mounting nuclear threats and postures from adversary nation-states, such as Russia, China, North Korea, and Iran, represent a clear danger to the interests and security of the United States of America and its Allies. To meet these threats, the 2022 Nuclear Posture Review requires the Department of Defense (DoD) to design, develop, and manage a combat-credible U.S. military which, among other prioritizations, is survivable. A survivable force can generate combat power despite adversary attacks. As such, the US Army must prepare today to set the conditions for successful conventional warfare on the nuclear battlefields of tomorrow. Our Army cannot …

Control And Optimization Of Multi-Agent Systems With Applications In Connected And Autonomous Electric Vehicles, Shaopan Guo

LSU Doctoral Dissertations

A multi-agent system (MAS) is a system in which multiple autonomous agents interact with each other to achieve a common goal. Nevertheless, current designs of MAS controllers typically rely on particular requirements, such as time-triggered communication, linear dynamics, and access to global information. These limitations restrict the applicability of MASs. This thesis aims to eliminate these constraints and optimize the energy consumption of a specific MAS, namely connected autonomous electric vehicles (CAEVs).

This thesis first addresses the consensus problem of linear MASs with intermittent communication. An adaptive distributed control algorithm that integrates edge-based event-triggered (ET) communication mechanisms is presented in …

Jack And Alex Rc-Baja, Alex Vornbrock

All Undergraduate Projects

Teams of two mechanical engineering students were tasked with the construction of an RC Baja car qualified by the rules and regulations of the Baja competition as well as specifications set by the individual team member for their portion of the vehicle. This proposal focuses on the Chassis and Drivetrain of the vehicle while team member Jack Stutz oversaw the steering and suspension. Teams will compete against each other in various modes to determine the best vehicles of the competitors. The chassis was made of standard aluminum, the remaining parts made by the students were made with PLA; both materials …

Rc Baja Suspension And Steering, Garrett Bailey

All Undergraduate Projects

The RC Baja team set out to engineer and construct a working remote-control device that can compete in the ASME Baja Competition. The students have a focus on either suspension and steering or drivetrain and chassis. This report is centered on the suspension and steering. First, multiple analyses were completed using engineering methods such as mechanical design, statics, strengths of materials, and dynamics for parts of the device. The calculations found in the analysis are then used to create designs for the parts so that they will meet the requirements and be successful in the competition. After the parts are …

Efficient Maritime Object Detection And Validation For Enhancing Safety Of Uncrewed Marine Systems, Ahmed Saglam, Yiannis Papelis

VMASC Publications

Safe operation of uncrewed maritime systems is a major concern in the presence of other vehicles or obstacles. Typically, perception algorithms utilize sensor data to identify obstacles that must be avoided, and AI algorithms are used to interpret raw sensor data for use in navigation and object avoidance algorithms. However, perception algorithms are typically computationally expensive. In this paper, we present an efficient method for detecting obstacles using raw lidar data in the form of range or Point Cloud, employing computationally efficient techniques that do not depend on trained models or AI matching. The approach
converts the sensor readings into …

Study Of Eco-Driving And Charging Planning In Connected And Automated Vehicles Environment, Pradeep Bhat

Dissertations, Master's Theses and Master's Reports

In this dissertation, the development of eco-driving and charging planning algorithms in the connected and automated vehicle environment (CAV) are presented. CAV technologies provide opportunities for potential energy savings and efficiency improvement of transportation networks, which are explored through multiple research tasks in this study.

The objective of the first study presented in Chapter 2 is to reduce vehicle dynamic losses and required tractive force while completing trip distance within a given travel time. Sequential Quadratic Programming method is employed for this nonlinearly constrained optimization problem. The validation result illustrates the benefits of optimal velocity trajectories. The objective of the …

Design And Manufacture Of An Aerodynamic Body That Meets Scaling Law Requirements And Survives Severe G-Load Environment For Free-Flight Testing, Anthony R. Gonzalez

Graduate Theses, Dissertations, and Problem Reports (ETD)

Design and Manufacture of an Aerodynamic Body that Meets Scaling Law Requirements and Survives Severe G-load Environment for Free-Flight Testing

Anthony Gonzalez

Developing scale models to test functionality and ascertain aerodynamic properties is critical before manufacturing full-size products. Through dimensional analysis and similitude, engineers create scale models to test any parameter of interest. This research aims to design a scale model of an aerodynamic body for repeated free-flight testing. The Froude number is the dominant scale model law used to determine the geometric, mass, and inertia scaling laws. A critical requirement for this research is that the model should be …

Improving Safety Service Patrol Performance, Mecit Cetin, Hong Yang, Kun Xie, Sherif Ishak, Guocong Zhai, Junqing Wang, Giridhar Kattepogu

Civil & Environmental Engineering Faculty Publications

Safety Service Patrols (SSPs) provide motorists with assistance free of charge on most freeways and some key primary roads in Virginia. This research project is focused on developing a tool to help the Virginia Department of Transportation (VDOT) optimize SSP routes and schedules (hereafter called SSP-OPT). The computational tool, SSP-OPT, takes readily available data (e.g., corridor and segment lengths, turnaround points, average annual daily traffic) and outputs potential SSP configurations that meet the desired criteria and produce the best possible performance metrics for a given corridor. At a high level, the main components of the developed tool include capabilities to: …

Developing A Web-Based System For Remote Collection And Analysis Of Vehicle Electrical Systems Over Canbus Using Carloop, Joshua N. Valle, Alex Columna-Fuentes

Capstone Showcase

Our program collects vehicle data using an OBD-II device called Carloop that is plugged into the vehicle's diagnostic port. The device executes our code which then communicates with the vehicle's onboard computer to collect data such as engine RPM, vehicle speed, fuel level, and other diagnostic information. This data is then sent over WiFi to Particle’s Cloud, which is a platform for managing IoT devices.

Integrations set up on Particle take care of sending data to our InfluxDB Database, which is hosted on our own cloud-based machine. InfluxDB is a high-performance time-series database that is optimized for storing and querying …

Human Tracking Function For Robotic Dog, Andrew Sharkey

Williams Honors College, Honors Research Projects

With the increase the increase in automation and humans and robots working side by side, there is a need for a more organic way of controlling robots. The goal of this project is to create a control system for Boston dynamics robotic dog Spot that implements human tracking image software to follow humans using computer vision as well as using hand tracking image software to allow for control input through hand gestures.

Neuroevolution And Machine Learning Research Applied To Connected Automated Vehicle And Powertrain Control, Frédéric F. Jacquelin

Dissertations, Master's Theses and Master's Reports

This dissertation focuses on advancing Predictive Energy Management (PrEM) functions applied to modern connected and automated vehicles (CAV) cohorts. PrEM aims to utilize connectivity and ADAS functions to adaptively minimize vehicle energy consumption in a wide array of operations, extending the original control designed around a reduced set of test cycle procedures to adapt to real-world stochastic operating conditions. This research document is built upon three journal publications covering two PrEM schemes; the global cohort and local vehicle optimization paths. Both optimal control solutions are generated using various Neuroevolution centric processes.

Chapter 1 discusses the methods and reasoning behind the …

Estimating Battery Size Requirements For Tractor Electrification Of Row-Crop Planting Operations, Cheetown Liew, Andrew Donesky, Mark Freyhof, Ian Tempelmeyer, Santosh Kumar Pitla

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Power sources such as batteries, used for both on-road and off-road vehicles, are advancing at a rapid pace. Electric batteries are becoming more power dense, thus allowing them to be used as a power source to replace previous diesel or gasoline-powered systems. Efforts are underway to transition off-road agricultural vehicles from Internal Combustion Engine (ICE) vehicles to electric vehicles (EVs); however, the energy requirements of typical agricultural field operations need to be fully understood before such a transition can occur. Theoretical prediction equations available in the American Society of Agricultural and Biological Engineers (ASABE) standards or the use of engine …

Cybersecurity And Digital Privacy Aspects Of V2x In The Ev Charging Structure, Umit Cali, Murat Kuzlu, Onur Elma, Osman Gazi Gucluturk, Ahmet Kilic, Ferhat Ozgur Catak

Engineering Technology Faculty Publications

With the advancement of green energy technology and rising public and political acceptance, electric vehicles (EVs) have grown in popularity. Electric motors, batteries, and charging systems are considered major components of EVs. The electric power infrastructure has been designed to accommodate the needs of EVs, with an emphasis on bidirectional power flow to facilitate power exchange. Furthermore, the communication infrastructure has been enhanced to enable cars to communicate and exchange information with one another, also known as Vehicle-to-Everything (V2X) technology. V2X is positioned to become a bigger and smarter system in the future of transportation, thanks to upcoming digital technologies …

Enhancing Traffic Safety In Unpredicted Environments With Integration Of Adas Features With Sensor Fusion In Intelligent Electric Vehicle Platform With Implementation Of Environmental Mapping Technology, David S. Obando Ortegon

College of Graduate Studies: Theses & Dissertations

A major objective on society is to reduce the number of accidents and fatalities on the road for drivers, and pedestrians. Therefore, the automotive engineering field is working on this problem through the development and integration of safety technologies such as advanced driving assistance systems. For this reason, this work was intended to develop and evaluate the performance of different ADAS features and IV technologies under unexpected scenarios. This by the development of safety algorithms applied to the intelligent electric vehicle designed and built in this work, through the use of ADAS sensors based on sensor fusion. Evaluation of AEB, …

Combustion Characteristics Of Methane, Ethane, Propane, And Butane Blends Under Conditions Relevant Of A Dual-Fuel Diesel And Natural Gas Engine, Christopher Joseph Ulishney

Graduate Theses, Dissertations, and Problem Reports (ETD)

As natural gas production infrastructure is already in place in most of the world and will continue expanding for the foreseeable future, natural gas is an alternative to traditional liquid petroleum fuels in heavy-duty engines. Dedicated natural gas or dual-fuel diesel-natural gas heavy-duty engines are alternatives to diesel-only power generation equipment. One challenge is the large variation in the natural gas composition available for such applications, which is known to significantly affect engine’s combustion characteristics and the emissions composition. As the literature on dual-fuel combustion under low load engine operating conditions that use more realistic natural gas mixtures (i.e., mixtures …

Design And Validation Of An Aerodynamic System For A Formula Sae® Vehicle Using Vehicle Dynamic Simulation And Experimentation, Evan S. Cole

Graduate Theses, Dissertations, and Problem Reports (ETD)

This thesis presents the design, simulation, and experimental validation for the aerodynamic system for a Formula SAE® vehicle. The Society of Automotive Engineers ® (SAE) hosts a collegiate competition annually called Formula SAE® where students design, fabricate, and test a small “formula style” vehicle. The vehicle performs in relatively slow maneuvers, around different courses and tests to evaluate the vehicle’s overall performance. Over the years, collegiate teams have put significant research into the design and manufacturing of the aerodynamics of these vehicles. In order to justify this undertaking for future teams at West Virginia University’s Formula SAE® program, a comprehensive …

View Synthesis With Scene Recognition For Cross-View Image Localization, Uddom Lee, Peng Jiang, Hongyi Wu, Chunsheng Xin

Electrical & Computer Engineering Faculty Publications

Image-based localization has been widely used for autonomous vehicles, robotics, augmented reality, etc., and this is carried out by matching a query image taken from a cell phone or vehicle dashcam to a large scale of geo-tagged reference images, such as satellite/aerial images or Google Street Views. However, the problem remains challenging due to the inconsistency between the query images and the large-scale reference datasets regarding various light and weather conditions. To tackle this issue, this work proposes a novel view synthesis framework equipped with deep generative models, which can merge the unique features from the outdated reference dataset with …