Automotive Engineering Commons^™

Safe Control Design For Quadruped Locomotion In Unstructured Environments Using Linear Transfer Operators, Sriram Sundar Krishnamoorthy Shankara Narayanan

All Dissertations

Deploying quadruped robots in unstructured, obstacle-rich environments requires control and planning methods that remain safe and reliable despite complex terrain geometry, limited sensing, and inevitable modeling errors. This thesis develops operator-theoretic tools for safe control design of robotic systems using linear transfer operators, with a focus on quadruped locomotion in unstructured environments. The central goal is to develop a unified operator-theoretic framework for safe control design based on the Perron–Frobenius (P–F) and Koopman operators. In particular, the thesis leverages \emph{density functions} to develop safe navigation frameworks in the dual space of densities. In the operator-theoretic perspective, the P–F operator governs …

Development Of A Mixing-Controlled Combustion Model For 0d Engine Modeling: Using High-Order Models To Guide The Formulation Of Reduced-Order Models, James Gohn

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Vehicles are becoming increasingly complex to comply with the increasingly stringent regulations placed on all market sectors while still maintaining performance requirements. This increased complexity leads to increases in the cost and time it takes to develop and produce these next generation vehicles. To meet demand therefore, it becomes necessary to evaluate numerous design iterations using computer models. There are multiple levels of models that may be necessary for different purposes or use cases. All levels of modeling for virtual prototyping, however, require a level of validation and predictive ability to be useful. To this end, the following thesis presents …

Multi-Modal Data-Efficient Learning For 3d Machine Vision, Zhimin Chen

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The rapid progress of 3D computer vision has enabled a wide range of applications in autonomous driving, robotics, and augmented reality. Despite this growth, training robust 3D perception models remains challenging due to limited labeled data, the complexity of integrating multiple modalities, and the inherently imbalanced and long-tailed nature of 3D datasets. This dissertation addresses these challenges by proposing data-efficient, multi-modal learning frameworks that improve the accuracy, generalization, and scalability of 3D scene understanding.

In the semi-supervised setting, this work presents novel approaches that combine limited annotations with large amounts of unlabeled data to enhance 3D object classification and retrieval. …

Hybrid Learning For Rough Terrain Navigation Of Actively Articulated Wheeled Vehicles, Dhruv Mehta

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Conventional wheeled ground vehicles have been used for rough terrain navigation in the recent years. They consist of a chassis connected to wheels through passive, semi-active, or active suspension systems. However, their fixed configurations limit mobility and maneuverability, constraining their ability to autonomously navigate diverse and rough terrains. Autonomous Ground Vehicles (AGVs) face significant challenges in this regard, including varying terrain roughness, soil hardness, and obstacle crossing.

To address these limitations, Actively Articulated Wheeled Vehicle (AAWV) architectures have recently emerged, offering real-time geometric adaptability. AAWVs have chassis and wheels connected via articulated serial or parallel linkages. However, increased articulation introduces …

Data-Driven Discovery Of Finite-Dimensional Koopman Operator For Modeling And Control Of Uncrewed Ground Vehicles, Ajinkya Joglekar

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This dissertation advances data-driven modeling and adaptive control techniques for Uncrewed Ground Vehicles (UGVs), with a focus on autonomy in mission-critical and safety sensitive environments. UGVs are deployed across a wide spectrum of domains, from structured manufacturing shop floors to unstructured off-road terrains, including planetary exploration, precision agriculture, and disaster response. These platforms, operating in dull, dirty, and dangerous conditions, demand autonomy that is both adaptable and robust. While traditional model-based control methods offer interpretability and robustness, they struggle with unmodeled dynamics, parameter variations, and integration of high-dimensional sensing. Conversely, modern machine learning approaches can directly exploit sensory data but …

Resilient Control Framework For Ev Motor Drive System Subject To Cyber-Physical Security, Ali Arsalan

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The electric drive system (EDS) in electric vehicles (EVs) is one of the key safety-critical components. As IoT-enabled communication infrastructure for modern cyber-physical automotive systems continues to evolve, the importance of securing EDS against cyber threats along with physical faults, has become increasingly prominent. Among physical faults, power switches are particularly vulnerable and exhibit the highest susceptibility to open-circuit faults (OCFs). A compromised EDS, whether due to cyber threats or physical issues, can lead to excessive mechanical vibrations, increased thermal stress, fluctuations in electromagnetic torque, and elevated total harmonic distortion. These factors can substantially undermine traction control stability and jeopardize …

Robustness Investigation, Detection, And Defense Of Deep Learning Models Against False Data Injection, Amirhossein Nazeri

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This dissertation addresses the critical challenge of adversarial robustness in deep learning systems, focusing on two fundamental domains: time-series prediction and object detection. As these AI systems become increasingly deployed in safety-critical applications from power grid management to autonomous vehicles their vulnerability to adversarial attacks poses significant risks to infrastructure and human safety.

The first contribution introduces a novel stealthy black-box False Data Injection (FDI) attack specifically designed for quasi-periodic time-series data. Unlike existing attacks that produce easily detectable anomalies, our method generates adversarial perturbations that preserve the underlying periodicity and statistical properties of the data, effectively bypassing traditional anomaly …

Secure Control And Trust Evaluation Framework For Autonomous Transportation Systems, Grace Muriithi

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This dissertation advances the cybersecurity of hybrid tracked vehicles (HTVs) and ship power systems (SPSs) by developing innovative cyber-attack models and corresponding defence frameworks. First, we formulate stealthy false-data-injection attacks (FDIAs) on HTV energy-management systems as a partially observable Markov decision process (POMDP) solved via deep reinforcement learning. A novel sniffing-based reward function guides the attacker to covertly degrade battery capacity and energy efficiency, which we evaluate using custom stealth–impact metrics and a sliding-window anomaly detector (Isolation Forest with Dynamic Time Warping). Additionally, we model sophisticated control-layer attacks in HTVs, including reinforcement-learning-optimised replay attacks and denial-of-service (DoS) attacks targeting generator-speed …

Advancing Life Cycle Assessment For Environmental Sustainability Of Carbon Fiber-Reinforced Polymer Composites (Cfrps)), Hao Chen

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Carbon fiber-reinforced polymer composites (CFRPs) have emerged as promising materials, particularly for lightweight applications, with the potential to reduce environmental impacts across multiple sectors, including automotive, aerospace, and renewable energy. However, fully realizing their sustainability potential requires a more comprehensive and context-specific understanding of their environmental performance throughout the entire life cycle—from raw material production to end-of-life management.

This dissertation advances life cycle assessment (LCA) practices for CFRPs by addressing key challenges across multiple phases of the CFRP life cycle. First, I conducted a critical review and meta-analysis of carbon fiber manufacturing, revealing substantial variability in reported data on energy …

Understanding And Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, And Implications, Rakesh Gangadharaiah

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This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, …

Understanding And Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, And Implications, Rakesh Gangadharaiah

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Ridesharing allows people to share a vehicle with others traveling in the same direction, which can reduce costs and traffic congestion. Pooled rideshare (PR) services, such as UberX Share and Lyft Shared, offer an economical and environmentally friendly alternative by matching passengers traveling similar routes. However, despite these benefits, PR adoption remains low due to concerns about safety, privacy, and convenience. This research explores the factors influencing PR adoption and provides recommendations to improve user acceptance. A nationwide survey of 5,385 participants across the U.S. was conducted to understand why people choose or avoid PR. The study identified five key …

User Acceptance Of Shared Autonomous Vehicles, Haotian Su

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A dissertation is proposed to explore user acceptance of shared autonomous vehicles (SAVs). SAVs are facing limited user acceptance. To systematically tackle the user acceptance barriers of SAVs, the main problem can be disintegrated into two sub-problems of user acceptance of autonomous vehicles (AVs) and ridesharing. The comfort of the ride experience in AVs is a determinant of user acceptance. Understanding the influential factors and developing methodologies to quantify human comfort in AVs are essential to facilitating future research to improve human comfort in AVs. The current pooled rideshare (PR) service closely resembles the anticipated future of SAVs. Understanding why …

User Acceptance Of Shared Autonomous Vehicles, Haotian Su

All Dissertations

A dissertation is proposed to explore user acceptance of shared autonomous vehicles (SAVs). SAVs are facing limited user acceptance. To systematically tackle the user acceptance barriers of SAVs, the main problem can be disintegrated into two sub-problems of user acceptance of autonomous vehicles (AVs) and ridesharing. The comfort of the ride experience in AVs is a determinant of user acceptance. Understanding the influential factors and developing methodologies to quantify human comfort in AVs are essential to facilitating future research to improve human comfort in AVs. The current pooled rideshare (PR) service closely resembles the anticipated future of SAVs. Understanding why …

Model Reference Adaptive Control For Mobile Manipulators And Beyond, Srivatsan Srinivasan

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In recent years, robotics has expanded into various sectors, including manufacturing, transportation, and household services, making the integration of autonomy a critical area of research. This shift aims to ensure safety and enhance the utility of autonomous systems. Traditionally, robotic applications focused separately on mobility, like automated guided vehicles, and manipulation, such as serial-chain arms in manufacturing. Today, however, we see a merging of these capabilities in the growing field of mobile manipulator robots that combine movement with purposeful interactive functionalities.

A typical mobile manipulator is a robotic arm mounted on a wheeled base. This thesis focuses on advancing control …

Low Carbon, High Cooling Potential Alcohol Fuels In A High Compression Ratio Spark Ignition Engine, John Gandolfo

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Even though most of the effort towards implementing low-carbon alternative fuels has been directed towards heavy-duty vehicles that are difficult to electrify, the high-autoignition resistance of these fuels make them challenging to combust in compression ignition engines. However, several fuel candidates, such as ethanol and methanol, are ideal fuels for spark ignition engines. With the demand for electric vehicles slowing down and increasing recognition of the merits of hybrid powertrains, there is a need to maximize the performance of these fuels for spark ignition engines, which are likely to remain the combustion strategy of choice for hybrids due to their …

Vision-Based Autonomy Stacks For Farm Tractors And Intelligent Spraying Systems In Orchards, Shengli Xu

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Autonomous tractors equipped with intelligent sprayers have become a pivotal aspect of smart farming (SF), marking a transformative shift in traditional agricultural practices and holding the potential to revolutionize the farming industry. With 2,453,620 fruit-bearing acres in the United States as of 2022, there is a pressing need for the implementation of autonomous systems for farm tractors and intelligent spraying systems in orchards. These advancements can significantly reduce labor costs, address labor shortages, and minimize spray loss. Furthermore, to enhance profitability and productivity, it is essential to develop low-cost yet effective vision-based autonomy systems that can operate efficiently across various …

Hybrid Physics-Infused Machine Learning Framework For Fault Diagnostics And Prognostics In Cyber-Physical System Of Diesel Engine, Shubhendu Kumar Singh

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Fault diagnosis is required to ensure the safe operation of various equipment and enables real-time monitoring of associated components. As a result, the demand for new cognitive fault diagnosis algorithms is the need of the hour. Existing deep learning algorithms can detect, classify, and isolate faults. Still, most depend solely on data availability and do not incorporate the system's underlying physics into their prediction. Therefore, the results generated by these fault-detecting algorithms sometimes need to make more sense and deliver when tested in actual operating conditions.

Similar to diagnosis, the fault prognosis of diesel engines is paramount in numerous industries. …

Monitoring Vehicle Seat Occupancy Status And Tracking Human Passenger Activities Inside Vehicle Passenger Cabins, Rahul Prasanna Kumar

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An autonomous vehicle (AV) is a promising engineering innovation that can operate without a human driver. Nonetheless, to ensure passenger safety and comfort in the absence of human drivers, the vehicle must continuously monitor and predict how the cabin’s state will evolve in the immediate future. The two major factors influencing the cabin’s state are the occupancy statuses of vehicle seats and the activities of human passengers occupying them. Therefore, this dissertation presents a system employing a capacitance-sensing mat and a machine learning unit to monitor vehicle seat occupancy status and track passenger activities non-intrusively. While the mat integrates with …

A Reinforcement Learning Framework For Powertrain Control Including Shared Learning Among A Fleet Of Vehicles, Lindsey Kerbel

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The transportation sector provides a significant opportunity to reduce global emissions, both through technological advancements and vehicular control strategies. Model-based control systems are popular methods for increasing the operating efficiency of vehicles. However, these systems often rely on models that require costly calibrations that still fail to capture the complexity of modern powertrain systems and the variations found in real-world driving. The recent availability of operational data through connected vehicle technology and/or edge devices has led to the emergence of data-driven control strategies that can learn optimal control policies through the interactions of the vehicle’s control system with the environment. …

Reinforcement Learning-Based Energy Management For Electric Vehicle Application, Yiming Ye

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The increasing concerns about transportation pollution and fossil fuel depletion motivate many studies on vehicle electrification and advanced energy-saving propulsion systems. When Comparing with traditional internal combustion engine vehicles, electrified vehicles, such as battery and supercapacitor electric vehicles, are equipped with more than one power source in the hybrid propulsion system, which can save more energy through efficient power combinations. Lithium-ion batteries are the preferred choice for energy storage in electric vehicles due to their superior energy density and cost-effectiveness. Nevertheless, matching the required power input and output leads to in an unwanted growth in the size of the battery, …

Engineering Multifunctional Silicon Nanostructures From Biorenewable Cellulose Nanocrystals, Nancy Chen

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The imperative search for alternative materials to address the pressing demand for advance energy storage is underscored by the escalating environmental predicaments. Lithium-ion batteries (LIBs) with graphite anodes have become the benchmark in energy storage; however, they are approaching a saturation point in terms of energy density. Silicon emerges as a promising contender to supplant graphite, owing to its profuse availability, cost-effectiveness, and impressive specific capacity of 4200 mAh g^-1. By integrating silicon anodes, LIBs stand to undergo a radical transformation, markedly diminishing in weight and size, thus heralding a novel wave of compact, lightweight energy storage systems. …

Multi-Scale Modeling Of Selective Laser Sintering: From Manufacturing Process And Microstructure To Mechanical Performance In Semi-Crystalline Thermoplastics, Cameron Zadeh

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Selective laser sintering is an additive manufacturing process that opens many design possibilities but is limited in its reliability and reproducibility. Numerical simulations validated by experimental data yield insights into the process and resulting part properties, allowing users to make more informed decisions. In this dissertation, a model for the process and microstructure is developed and validated, followed by a coupling to mechanical models to predict part performance. Further developments include a new addition of a reaction kinetics model to the process model to describe the interplay between thermal degradation and melt pool properties, and an exploration of the parameter …

Real-Time Degradation Abatement Framework For Energy Storage System In Automotive Application Using Data-Driven Approaches, Laxman Timilsina

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The increasing popularity of electric vehicles (EVs) is driven by their compatibility with sustainable energy goals. However, the decline in the performance of energy storage systems, such as batteries, due to their degradation puts EVs and hybrid electric vehicles (HEVs) at a disadvantage compared to traditional internal combustion engine (ICE) vehicles. The batteries used in these vehicles have limited life. The degradation of the battery is accelerated by the operating conditions of the vehicle, which further reduces its life and increases the reliability and economic concerns for the vehicle’s operation. The aging mechanism inside a battery cannot be eliminated but …

Cfrp Delamination Density Propagation Analysis By Magnetostriction Theory, Brandon Eugene Williams

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While Carbon Fiber Reinforced Polymers (CFRPs) have exceptional mechanical properties concerning their overall weight, their failure profile in demanding high-stress environments raises reliability concerns in structural applications. Two crucial limiting factors in CFRP reliability are low-strain material degradation and low fracture toughness. Due to CFRP’s low strain degradation characteristics, a wide variety of interlaminar damage can be sustained without any appreciable change to the physical structure itself. This damage suffered by the energy transfer from high- stress levels appears in the form of microporosity, crazes, microcracks, and delamination in the matrix material before any severe laminate damage is observed. This …

Controlled Manipulation And Transport By Microswimmers In Stokes Flows, Jake Buzhardt

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Remotely actuated microscale swimming robots have the potential to revolutionize many aspects of biomedicine. However, for the longterm goals of this field of research to be achievable, it is necessary to develop modelling, simulation, and control strategies which effectively and efficiently account for not only the motion of individual swimmers, but also the complex interactions of such swimmers with their environment including other nearby swimmers, boundaries, other cargo and passive particles, and the fluid medium itself. The aim of this thesis is to study these problems in simulation from the perspective of controls and dynamical systems, with a particular focus …

Energy-Aware Coordination Of Automated Vehicles, Nathan Goulet

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Energy is an inherently limited resource for two reasons: the sources utilized to obtain energy are limited; and the methods used most often to convert energy also generate pollutants that the planet has a limited capacity to absorb or compensate for without adverse effects. Efficiently utilizing energy is, therefore, an important topic. As around 16% of the energy consumed in the United States is by light vehicles, a significant impact can be made if vehicle energy consumption is reduced. The advent of connected and automated vehicles offers unprecedented opportunities to optimize their behavior with respect to energy consumption through trajectory …

Improved Vehicle-Bridge Interaction Modeling And Automation Of Bridge System Identification Techniques, Omar Abuodeh

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The Federal Highway Administration (FHWA) recognizes the necessity for cost-effective and practical system identification (SI) techniques within structural health monitoring (SHM) frameworks for asset management applications. Indirect health monitoring (IHM), a promising SHM approach, utilizes accelerometer-equipped vehicles to measure bridge modal properties (e.g., natural frequencies, damping ratios, mode shapes) through bridge vibration data to assess the bridge's condition. However, engineers and researchers often encounter noise from road roughness, environmental factors, and vehicular components in collected vehicle signals. This noise contaminates the vehicle signal with spurious modes corresponding to stochastic frequencies, impacting damage monitoring assessments. Thus, an efficient and reliable SI …

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

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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

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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

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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 …