Master of Science, The Ohio State University, 2024, Electrical and Computer Engineering

This document presents the design, optimization, and testing of the longitudinal controller developed for the Year 2 EcoCAR EV Challenge. It provides a comprehensive exploration of the cascaded PI Longitudinal Control algorithm, with a focus on handling mode switches and nonlinearities using a bumpless transfer technique. The physics model that removes all resistive forces, allowing the cascaded PI controller to operate in a linear environment is discussed in detail. The longitudinal controller underwent rigorous testing using XIL methodologies. In the MIL environment, the controller was validated against functional scenarios for both city and highway driving. Using Particle Swarm Optimization, the controller results showed a 15% enhancement in energy savings and a 76% improvement in drive quality. These optimized gains were further validated in highway merge scenarios, assessing the LC system's safety under real-world conditions, including sensor noise in inclement weather. Finally, the performance of the physics model was checked in the VIL by comparing the feedback model with the physics only model.

Master of Science, The Ohio State University, 2024, Mechanical Engineering

EcoCAR EV Challenge is a four-year competition to redesign, integrate, test and refine an all-wheel drive (AWD) propulsion system in a Cadillac LYRIQ. The objective of this project is to improve the performance of the vehicle while maintaining the efficiency of the system along with added autonomous capabilities. This thesis shines light on the controls implementation of the torque distribution strategy to the two electric machines that constitute the AWD architecture. The controller architecture is defined and developed to meet the requirements set by the competition and the vehicle to interact seamlessly while implementing controller strategies to improve efficiency. Various methods to optimize the efficiency are discussed with simulations of expected system behavior and performance comparisons. Two optimization cost functions and their solutions are compared. Each solution is implemented and evaluated in simulation and vehicle testing for efficiency, performance, drive quality and system safety. The strategy with minimization for the losses of the electric machines is projected to improve the overall energy consumption by 12% with the use of the rear disconnect unit and reduce the rolling losses in highway cruising conditions.

Master of Science, The Ohio State University, 2024, Mechanical Engineering

This thesis presents a methodology for energy-efficient routing and accurate energy consumption modeling for Class 6 vehicles, including ICEVs, HEVs, and BEVs. The study integrates realistic driving conditions, such as stop signs, which significantly impact energy consumption and travel time. The detailed energy consumption model considers vehicle-specific parameters, aerodynamic drag, rolling resistance, and kinetic energy changes due to stops, enhancing the precision of energy usage estimation. This accuracy is crucial for optimizing routes, battery management, fleet operations, and infrastructure planning. The methodology involves data integration, sparse matrix representation, stop sign handling, and the calculation of energy and time metrics. Real-world data on speed profiles, distances, slopes, and stop signs are used to create a detailed energy consumption digraph for Class 6 vehicles. The model accounts for stop signs by setting final speeds to zero at specific nodes and calculating the resulting kinetic energy loss. This framework emphasizes the significant impact of stops on travel time and energy use, providing valuable insights for optimizing vehicle operations and supporting the transition to greener transportation systems.

Master of Science, The Ohio State University, 2024, Mechanical Engineering

In the progression towards SAE Level 4 automation, the functional safety of automated driving systems is deemed essential, especially in the event of faults. The ISO 26262 functional safety standard is utilized to evaluate the risks associated with malfunctions in electrical/electronic (E/E) systems, based on a subjective assessment by safety experts. Yet, this standard primarily relies on qualitative measures and lacks provisions for real-time risk estimation. In this thesis, a risk estimation methodology has been developed to fill this gap, offering a quantitative method suitable for real-time risk analysis. A diagnostic system has been created to supplement the existing onboard diagnostic modules provided by the OEM. This integration creates a dual-layer safety net, ensuring secure operation in autonomous mode and providing a reliable fallback to the human operator when required. The quantitative risk estimation model that calculates the probability of collision, accounts for sensor and actuator faults amid measurement uncertainties. Based on the estimated probability, fault behavior is dynamically classified into distinct risk regions. The system is designed to respond appropriately to the situation by tailoring mitigating actions from minor adjustments to fallback protocols based on the level of risk and the type of fault. The proposed framework is illustrated through scenario-based testing via multiple simulations and closed-course evaluation using the test vehicle. This research has been conducted to contribute towards OSU's team, Buckeye AutoDrive, participating in Year 3 of the SAE AutoDrive Challenge II.

Master of Science, The Ohio State University, 2024, Mechanical Engineering

Cooling indoor spaces is energy intensive but essential to ensure occupant comfort, regardless of environmental conditions. Therefore, increasing the energy efficiency of cooling systems can yield significant energy savings. This thesis presents data-enabled hybrid modeling approaches to optimize cooling systems in both commercial buildings and light-duty vehicles, aiming to enhance energy efficiency through both static and dynamic optimization strategies. First, a static optimization strategy is developed for the operation of individual chillers in a central chiller plant, with the goal of reducing power demand while meeting the cooling load. This is achieved by developing a hybrid model that combines energy-based and data-driven methods to describe the energy demand of the plant under varying cooling loads and environmental conditions. The model is calibrated and validated using operational data from The Ohio State University. The validated model is then integrated into a particle swarm optimization algorithm to determine the optimal load distribution for each chiller under different weather and operational conditions. Simulation results for a year of operation in Central Ohio show that the optimized strategy achieves, on average, a 4% reduction in daily peak power consumption during four mild weather months, with reductions reaching up to 12% in certain instances. Second, a dynamic optimization strategy is presented to improve the energy efficiency of a light-duty vehicle air conditioning system. By employing data-driven Koopman operator theory to characterize the non-linear dynamics of the system, a linear Model Predictive Control problem is formulated within the Koopman subspace. The computational efficiency of this quadratic programming problem is demonstrated by average computation times ranging from 2 to 50 milliseconds, depending on the lengths of the control and prediction horizons. When tested across four different driving routes, th (open full item for complete abstract)

Master of Science, The Ohio State University, 2024, Mechanical Engineering

For the past 30 years engineering students at The Center for Automotive Research have been designing, building and racing electric vehicles on the Bonneville Salt Flats. Over the past 2 years, Buckeye Current, a student electric motorcycle team at Ohio State, has focused its efforts on developing a land speed racing (LSR) motorcycle for the under 150kg weight category for the Federation of International Motorcycles (FIM)n LSR records. This document begins with a high level development of the Buckeye Current RW-5 motorcycle and follows with a focus on the powertrain development. The document concludes with a review of the results to date and the future work remaining on the RW-5. The Buckeye Current motorcycles are named RW, in honor of Ryan Williams a former team member who passed away due to a motorcycle accident. The 5 stands for the motorcycle being in the fifth series of bikes the team has produced.

Master of Sciences (Engineering), Case Western Reserve University, 2024, EMC - Mechanical Engineering

This paper focuses on designing, modeling, and controlling a suspension system for a Baja SAE vehicle with an integrated semi-active suspension damping system. The main goals in order to optimize performance are to maximize vehicle speed through various inputs and minimize stress on critical components. Sensor technology including inertial measurement units (IMU), strain gauges, and hall effects are used to measure vehicle parameters throughout road inputs. This data is then analyzed using machine learning techniques to determine the most influential parameters in maximizing speed and minimizing stress with the fastest reaction time. Once these most influential and implementable parameters are determined to be pitch and roll, they are used to create a simplified on car optimization algorithm to improve vehicle performance, as validated by data collected on the vehicle.

Master of Science (MS), Ohio University, 2024, Civil Engineering (Engineering and Technology)

In the field of transportation engineering, there has been a shift towards implementing Connected Vehicle (CV) Technology as a means of improving transportation systems. This approach is becoming increasingly important due to limited space, high delay on roadways, and significant crashes. The CV technology is expected to be the most effective solution for making transportation systems more functional and safer, as it enhances drivers' decision-making abilities and helps to control traffic flow. To assess the impacts of CV technology, simulations and closed-course testing have been conducted. In addition, some pilot studies have been carried out in urban settings, where the goal is to achieve "zero deaths." However, a comprehensive understanding of the applications of Connected Vehicles in rural settings is necessary, as driver behavior can be unpredictable and location-dependent. This research aims to evaluate the performance of four CV applications in a rural environment: Red Light Violation Warning (RLVW), Pedestrian in Signalized Crosswalk (PEDINXWALK/ PEDPSM), Curve Speed Compliance (CSPDCOMP), and Speed Compliance in work zones (SPDCOMPWZ). There were no work zones in the study area hence analysis on SPDCOMPWZ was not included in this study. Though the research had four CV applications but each driver had only three applications installed in their vehicle. Hence the study obtained 4 different groups for all 3-paired CV applications. The study analyzed the impact of these applications on drivers' behavior and their reactions to evaluate the performance of CV applications. The analysis focused on drivers' speeds since speed happens to be one of the primary traffic parameters that can provide in-depth information on driver's behavior on the road. The driver's speed is analyzed once they receive a warning or trigger prior to a potential violation of these specific traffic rules; 1.Running red-light, 2. Conflict/ potential crash between vehicle and pedestri (open full item for complete abstract)

Master of Science (M.S.), University of Dayton, 2024, Aerospace Engineering

This research introduces an Origami-inspired dynamic spacecraft radiator, capable of adjusting heat rejection in response to orbital variations and extreme temperature fluctuations in lunar environments. The research centers around the square twist origami tessellation, an adaptable geometric structure with significant potential for revolutionizing radiative heat control in space. The investigative involves simulations of square twist origami tessellation panels using vector math and algebra. This study examines both a two-dimensional (2- D), infinitely thin tessellation, and a three-dimensional (3-D), rigidly-foldable tessellation, each characterized by an adjustable closure or actuation angle “φ”. Meticulously analyzed the heat loss characteristics of both the 2D and 3D radiators over a 180-degree range of actuation. Utilizing Monte Carlo Ray Tracing and the concept of “view factors”, the study quantifies radiative heat loss, exploring the interplay of emitted, interrupted, and escaped rays as the geometry adapts to various positions. This method allowed for an in-depth understanding of the changing radiative heat loss behavior as the tessellation actuates from fully closed to fully deployed. The findings reveal a significant divergence between the 2D and 3D square twist origami radiators. With an emissivity of 1, the 3D model demonstrated a slower decrease in the ratio of escaped to emitted rays (Ψ) as the closure/actuation angle increased, while the 2D model exhibited a more linear decline. This divergence underscores the superior radiative heat loss control capabilities of the 2D square twist origami geometry, offering a promising turndown ratio of 4.42, validating the model's efficiency and practicality for radiative heat loss control. Further exploration involved both non-rigidly and rigidly foldable radiator models. The non-rigidly foldable geometry, initially a theoretical concept, is realized through 3D modeling and physica (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2024, Mechanical Engineering

The emergence of electric vehicles (EVs) along with the movement toward “zero-emission” has put the lithium-ion batteries (LIBs) system as a pivotal technology for a sustainable future. While the LIBs offer high energy density, extended cycle life, and fast charging capabilities, the battery system always has been incorporated with safety concerns due to the flammable liquid electrolytes used for traditional LIBs. Solid electrolytes (SEs) offer as a promising solution to replace liquid electrolytes, enhancing safety and performance. However, there are challenges associated with SEs, including low ionic conductivity of SE materials, large interface resistance, chemical instability between electrode and electrolyte, and Li-dendrite growth, necessitate thorough investigation. Especially, the interface between cathode and solid electrolyte materials is a critical focus, requiring compatibility with SEs in the selection of a cathode active materials (CAM) for the development of all-solid-state battery system. This dissertation explores various types of cathode materials for integration into all-solid-state Li-ion battery systems. For example, layered cathode materials with various Ni/Mn/Co compositions (LiNixMnyCo1-x-yO2 (NMC)) were explored, revealing the degradation behavior against the moisture impact, which is a crucial factor for the practical applications. Li-Mn-rich cathode has prominent features of high energy and power density. While it exhibits large irreversible capacities and rapid degradation over cycles, strategies such as core-shell synthesis and fluorine substitution were applied to address the defects. Meanwhile, aluminum substituted spinel cathode LiCo1-xAlxO2 (x = 0 – 0.3) was introduced along with its “zero-strain” property, which is an attractive feature for the all-solid-state battery application. It was confirmed that Al-doping can effectively suppress the evolution of impurity phase and structure decomposition over extended cycles. On the other (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Mechanical Engineering

Decades of successful research and development on automotive silencers for engine breathing systems have brought about significant reductions in engine noise emissions. Much of this research has pursued airborne noise at relatively low frequencies, which typically involves planar wave propagation. However, with the increasing demand for downsized turbocharged engines in passenger cars, high-frequency compressor noise has become a challenge in engine induction systems. Elevated frequencies on the order of 10 kHz promote multi-dimensional wave propagation rendering at times conventional silencer treatments ineffective due to the underlying assumption of one-dimensional wave propagation in their design. Multi-dimensional waves also cause experimental in-duct acoustic measurements to be sensitive to the angular and axial location of pressure transducers, therefore making it more challenging to characterize the acoustics of modern turbocharger compressors. The present study focuses on developing a reflective high-frequency silencer for turbocharger compressors to mitigate tonal noise at the blade-pass frequency (number of main impeller blades times the compressor shaft rotational speed in revolutions per second) within the compressor inlet duct for a wide range of rotational speeds. The approach features a novel “acoustic straightener” that creates exclusive planar wave propagation near the silencing elements, hence improving overall acoustic attenuation. An analytical treatment using acoustic lumped impedance models is combined with three-dimensional (3D) acoustic finite element method (FEM) to conceptualize a silencer configuration comprising quarter-wave resonator (QWR) arrays. The effects of mean flow and nonlinearities on acoustics are captured by 3D computational fluid dynamics (CFD) simulations, which are also utilized to introduce geometry modifications that reduce flow losses. The CFD simulations reveal noise generation due to flow-acoustic coupling: a phenomeno (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Nuclear Engineering

This research can be divided into two aspects: the development of a deductive failure analysis method for Backwards Logic Inference based Propagation for System analysis (BLIPS), and its Probabilistic Augmentation (PA) to enhance the interpretation of results from BLIPS by assessing the effect of variation in likelihood of states of controlled parameters on the system being analyzed. Although inductive fault analysis (forward-looking) has its own significance, a designer would innately prefer to trace failures backward and remedy the causes of those failures, as compared to a more cumbersome activity of identifying the faults individually and sifting through the combinations that lead to the failure of interest. The work presented here is aimed at the development of a backward failure propagation methodology for analyzing the origins of functional failures in a conceptual design of systems including but not limited to Nuclear, Mechanical, Aerospace, Process, Electrical/Electronics, Telecommunication, Automotive, etc. BLIPS allows the designer to achieve a robust early design by identifying the causes that lead to critical system failures before proceeding to the detailed design and testing stages. The insights provided by the analysis at the conceptual design stage also reduce re-design efforts, testing costs, and project completion time. The proposed method is a functional analysis approach that leverages the logical rules used in the Integrated System Failure Analysis (ISFA)— ISFA is a method for functional analysis with two levels of abstractions i.e. functional and component levels. ISFA needs the system configuration (schematic diagram) and the system's functional and behavioral models, to infer all the parameters of the system's state (the parameters include: component's modes, physical variables, and the states of functions) when provided with the initial condition of a system (typically a failure). BLIPS is a (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

Autonomous vehicles (AVs) have the potential to revolutionize transportation safety. However, there is no consensus yet on how to effectively evaluate the safety of self-driving cars. This dissertation addresses the challenge of safety evaluation for AVs by integrating concepts from vehicle and traffic modeling, control theory, optimization, and both naturalistic and simulation-based data-driven methods. An alternative to the exhaustive testing of a system under all environmental and operational configurations are adaptive adversarial approaches, which primarily aim to expose the vehicle to safety-critical situations, also known as 'Falsification'. This dissertation evaluates the effectiveness of these algorithms, and creates a unified approach for generating adversarial testing algorithms and conducting safety analysis. We contribute to the model-based falsification task by ensuring theoretical guarantees under standard assumptions. This involves considering the environment as a gray-box, where its dynamics are partially known, and approximating the unknown model of the autonomous system. Preliminary works used deterministic and expert models, but this dissertation treats them as stochastic systems by incorporating a naturalistic behavior fitting. We make thee contributions to the safety analysis task. First, a systems' safety engineering approach is proposed for hazard analysis that considers the operational requirements from various safety standards. Second, a dynamic probabilistic assessment approach is presented for risk assessment, involving a Backtracking Process Algorithm (BPA), traditionally based on a discretized cell-to-cell probabilistic state transition mapping, for the probabilistic quantification of hazardous events. We propose using a sticky Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) for estimating system transition probabilities, aiming to reduce computational burden and allow meaningful state and transition identification (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Mechanical Engineering

Lithium-ion (Li-ion) batteries adopted in electric vehicles (EVs) require significant increase in energy density (> 750 Wh/L) and reduction of costs to enable widespread commercialization. To address these challenges, R&D efforts have been directed towards (a) finding materials with high energy density (b) improving electrode design and (c) enhancing conductivity of the electrode materials. The former strategy involves implementing Nickel and Manganese based chemistries such as NMC, LNMO. In particular, the LNMO spinel cathode material is a promising material which provides high energy densities of 650 Wh/kg due to increased operating voltage of 4.75 Vvs Li/Li+. However, the increased voltage also accelerates oxidative decomposition reactions in the electrolyte and causes capacity fade in LNMO full cells paired with graphite anode. Using a composite binder can help passivate the carbon and cathode material surfaces against decomposition products from the electrolyte. Further, the composite binder also has the advantage of using water as the solvent making the process environmentally benign and cheaper compared with currently adopted N-Methyl 2-Pyrrolidone (NMP) solvent. The second strategy includes minimizing the use of inactive materials (e.g., current collectors and separators) and increasing the thickness of electrodes (> 250 µm), which in turn offers improved energy density with reduced cost. To achieve this an aqueous composite binder system is utilized which can sustain high thickness of electrodes while creating unique electrode architectures conducive to ionic and electronic conductivity. The third strategy utilizes a conductive polymer additive to create ion and electron conducting interfaces across the cathode material surface thereby providing better cycle and rate performance. The performance improvement in each of these strategies is demonstrated through electrochemical tests and their mechanisms are understood by utilizing several characterization tec (open full item for complete abstract)

Doctor of Philosophy, The Ohio State University, 2023, Electrical and Computer Engineering

This dissertation is centered around the study of developing fair, efficient, repeatable, and provable testing methodologies to analyze, characterize, verify, validate, benchmark, certify, diagnose, and falsify the safety performance of cyber-physical systems (CPS). It is intended for the objective perspectives from third-party entities such as standard organizations, regulatory bodies, industry-specific associations, and customers. The concept of safety testing for CPS is a relatively new area of research. It wasn't until the late 19th century that the need for safety testing emerged, as autonomous machines began to result in fatalities when working alongside human operators in manufacturing settings. However, it took several decades till the 1960s for society to recognize the importance of safety testing and for administrative bodies to appreciate the need for regulations, standards, and policies. The safety testing of CPS is also a highly challenging research topic due to several inherent characteristics, including (i) complexity (featuring nonlinear dynamics and intricate interdependencies between their physical and computational components), (ii) stochasticity (influenced by uncertain inputs, environmental conditions, and stochastic algorithms adopted by the subject CPS), and (iii) unknowability (featuring the black-box nature). This dissertation makes several concrete attempts to tackle the above challenges. The studies are presented in two parts. The first part of this dissertation utilizes the equivalence between the system ``being almost safe” and the system rendering a ``(controlled) almost forward invariant set”. Different from the existing modeled approaches and formal methods admitting the similar concept, our study is featured with a testing-oriented and data-driven approach that provably quantifies, and fails to quantify, the almost safe sets, or even the optimal almost safe set, of the subject as the number of tests tends to infinity. V (open full item for complete abstract)

Master of Science, The Ohio State University, 2023, Mechanical Engineering

The EcoCAR EV Challenge is a four year competition where students from 15 different universities compete to re-engineer a 2023 Cadillac Lyriq focusing on autonomous features, powertrain design, and inclusive design. As part of this competition teams are required to develop an automatic parking feature. In order to develop an automatic parking feature, The Ohio State University and Wilberforce University team is integrating four LiDAR sensors onto the vehicle. Two of these sensors will be mounted onto the sideview mirrors, one will be mounted to the front grill, and the other will be mounted onto the rear bumper under the tow hitch. Since the sideview mirrors are attached to the front doors this creates a potential problem with changes in the door close position having effects on the LiDAR sensor positioning leading to uncertainty in LiDAR detections. This research focuses on the effects of the door tolerancing in creating uncertainty in LiDAR sensing position and on developing design loads for simulating sideview mirror movement from various parking conditions. Variations in the static door closed position and variations in the door closed position due to the effect of cabin pressurization while the vehicle is in motion were experimentally measured and evaluated using an analysis of variance (ANOVA). Then the results from the variation study were used to calculate the uncertainty in the LiDAR detections at various distances to evaluate if the variation in the door closed position will have an effect on the team's strategy for the automated parking feature. A vibration analysis was done using experimental data from an accelerometer of the vibrations in the LiDAR mounting point when driving in potential parking conditions such as gravel roads, potholes, speed bumps, and brick roads. From this research design loads for conducting a finite element analysis (FEA) of the side LiDAR mounts were found.

Master of Science in Engineering, University of Akron, 2023, Mechanical Engineering

With advancements in modern battery technology, electric vehicles (EVs) have become increasingly more prevalent on the road. While the technology is still evolving, it has become clear that EVs have numerous benefits, and some of which are performance oriented. One of these benefits is the ability to package electric motors that are directly connected to individual wheels or axles. With this, combined with the considerably reduced feedback loop of electric motors with respect to a combustion engine, it has become easier to implement advanced motor control systems, such as traction control (TC) and torque vectoring (TV). In this thesis, a MATLAB program to generate Milliken Moment Diagrams (MMDs) will be created using tire models and vehicle parameters, in which the cornering response of a given vehicle will be calculated given a set of input conditions. Various motor configurations will be simulated in these MMDs to compare the differences in vehicle behavior to demonstrate the potential benefits of torque vectoring. Several input conditions are also varied to explore the robustness of the models, in which a TV control “map” is created and applied to show how the model can be utilized as a tool to improve vehicle performance in coordination with testing.

Master of Science, The Ohio State University, 2023, Electrical and Computer Engineering

Over the past decade, there has been a growing movement towards reducing the carbon footprint which involves striving for net-zero emissions and developing an infrastructure that can sustain it. Among the end-use sectors, transportation accounts for nearly a third of overall greenhouse gas (GHG) emissions, with commercial vehicles as a huge contributor, making it imperative for this industry to adapt to emerging technologies to accommodate the expectations of a green and sustainable mobility vision. Battery electric and fuel cell vehicle technologies are suitable candidates to replace the existing conventional fossil fuel powered vehicle architectures but a complete transformation is nigh realizable due to various impediments. A soft impact is vital to ease the transformation process to foster growth and acceptance among industry partners as well as prepare for any unseen hurdles along the way. The work presented in this thesis focuses on designing and operating commercial vehicle fleets, introducing a novel fleet management system (FMS) framework capable of providing energy efficient mobility solutions. The FMS uses a recommender system comprised of a Design Space Filter (DSF) module to provide a feasible set of powertrains from the vehicle configuration database and uses machine learning algorithms to estimate the energy consumption for a given drive cycle, ranking them on the basis of their freight energy efficiency metric and ultimately aiding in the fleet composition design process. Due to the usage of highly confidential data pertaining to vehicle behavior, operators and OEMs are not keen on sharing this data unless there are agreements and secure data-sharing procedures established. Aware of this data bottleneck, the FMS leverages federated learning technique to estimate vehicular performance attributes and provides inferences which can be utilized for analyzing fleet behavior and enhancing fleet operations. This is extended to learn the mobility dynamics of (open full item for complete abstract)

Master of Science, The Ohio State University, 2023, Mechanical Engineering

As a maturing technology, self-driving vehicles have the potential to revolutionize mobility by improving the safety, accessibility, efficiency, and convenience of automotive transportation. Critical to ensuring the safety of self-driving vehicles are tasks such as motion planning in a dynamic environment shared with other vehicles and pedestrians. Motion planning of autonomous vehicles on highways, specifically focusing on lane changing scenarios is an active area of research. Addressing uncertainties such as road friction coefficient and vehicle mass during the lane change scenarios is critical since they directly affect the ability of the vehicle to maintain traction with the road surface. During a lane change maneuver, the vehicle may experience lateral forces that can cause it to slip or lose traction, especially if the road surface is wet or icy. In such conditions, the vehicle's handling and stability can be compromised, leading to a higher risk of accidents. This thesis provides a framework to model the behavior of autonomous vehicles using artificial potential fields by considering the effect of friction coefficient and vehicle mass to ensure safe and efficient lane changes. Simulation results demonstrate that the proposed approach is effective in handling uncertain environmental conditions and can successfully navigate the autonomous vehicle through lane changing scenarios. The approach provides a promising direction for future research in the development of autonomous vehicle motion planning and control systems.

Master of Science, The Ohio State University, 2023, Electrical and Computer Engineering

This thesis deals with the problem of local horizon motion planning for an autonomous vehicle (AV) at an urban unsignalised intersection scenario. The chances of crashes are high due to uncertainties in the behavior of other vehicles. It is assumed that the ego vehicle moves within a lane using only the ego information (global x, y, z position), global heading direction ($\theta$), and a reference velocity(v). Environment information is obtained from LIDARs and cameras mounted on the front end covering $180^o$ field of view (FOV). Some previous knowledge of the road (from HD map) is also used. The roads and the vehicles are modeled using an Artificial Potential Field (APF) approach. This field generates an online reference trajectory and desired orientation for the vehicle in a local horizon, ensuring that the vehicle stays in the lane. The vehicle also waits for its turn at the intersection and for the intersection to be clear, following the road traffic rules. It takes into consideration the risk associated with each vehicle/ pedestrian/ static obstacle. A gradient descent optimization approach is used to identify a collision-free trajectory. The corresponding desired speed and lateral motion are computed. An additional set of feasibility checks based on the vehicle constraints is added to smoothen the inputs to the controller. A Proportional-integral-based longitudinal controller is used for tracking speed. Lateral Stanley Controller is used for the steering control. A two-way stop sign scenario is used as a testing scenario to evaluate the performance of the proposed approach.