Navigation, Guidance, Control, and Dynamics Commons^™

Similarity May Be Safer: The Effect Of Similarity Between Speech-Based Takeover Request Style And Driver Personality On Self-Driving Takeover Performance, Keer Ma, Jianfeng Wu, Yanxi Lin, Zihan Li, Songyang Guo, Dongfang Jiao, Shihan Yu

Psychology Faculty Publications

In Level 3 automated driving, it is critical that drivers can rapidly and effectively shift from non-driving related tasks (NDRT) back to the driving task. While previous research has examined the modality, timing, and vocal characteristics of takeover requests (TORs), little is known about how the style of speech-based TORs interacts with drivers’ personality traits. This study conducted a driving simulator experiment with 49 participants using a 2 × 2 within-subjects design. Drawing on the dominant-submissive dimension of personality, we examined the similarity of personality tendencies between speech-based TORs and drivers under takeover scenarios of varying urgency (low: road construction; …

Vector Estimation For Continuous Tracking Of Observed Radio Signals (V.E.C.T.O.R.) Lunar Navigation System, Dimitry Melnikov, Evan Bartel, Andrew Burrier, Goran Gjorgievski

Williams Honors College, Honors Research Projects

NASA's Artemis program requires precise navigation capabilities to establish the first sustained presence on the lunar surface. However, as launches bring necessary orbital infrastructure, the Artemis program will face a critical period during which reliable lunar navigation is not possible. To address this challenge, the V.E.C.T.O.R. system tracks assets, such as rovers and astronauts, as User Terminals relative to a pre-existing cell tower, or Base Station. To do so, the system leverages existing Base Station hardware to calculate the location of User Terminals in conjunction with existing communications infrastructure.

Valve Train Design And Material Testing, Luis Luna

Publications and Research

In automotive engineering, CAD software like SolidWorks enables engineers to design and simulate mechanical components efficiently, reducing the need for extensive prototyping. This project examines the rocker arm's performance and durability within a valve train system. Components like pushrods, camshafts, and rocker arms control air intake and exhaust timing. Due to its high-impact motion and exposure to elevated temperatures, the rocker arm endures considerable wear. This study uses SolidWorks and Finite Element Analysis (FEA) to analyze the rocker arm's stress, deformation, and wear potential during camshaft movement. Material testing and motion analysis results provide insights into improving the durability of …

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

All Dissertations

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 …

Faulty Perception Correction Of Autonomous Vehicles In Real-Life Driving Scenarios, Mark Omwansa

Dissertations

Driving is one of the most popular modes of transportation in the world. The United States Department of Transportation’s (USDOT) Federal Highway Administration (FHWA) reported 2.8 trillion vehicle-miles traveled (VMT) in 2020, and the National Highway Traffic Association (NHTSA) recorded 3.2 trillion VMT in 2019. Also recorded in the NHTSA report were 39,096 fatalities and 2.7 million injuries due to traffic accidents, costing the economy an estimated $242 billion. Most of these recorded accidents can be attributed to human error or misjudgment. It is for this reason that governments and the automotive industry are looking at autonomous vehicle (AV) technologies …

Sdf-1Α Mediates Primary Tumor Escape In Glioblastoma Through Activation Of Mesenchymal Transitions, Charles T. Froman-Glover

The Cardinal Edge

Glioblastoma (GBM), a highly aggressive primary brain tumor originating in glial cells, poses a significant challenge due to its rapid growth and invasive nature within healthy brain tissue.

Current treatments involve surgical resection, chemotherapy, and radiation. These treatments alone are not enough to cure this disease, and a better understanding of the mechanics of the tumor's micro-environment is imperative to furthering the field of cancer research. This research focuses on understanding the tumor microenvironment's impact, specifically investigating the role of stromal cell-derived factor 1 (SDF-1) mechanics on GBM aggressiveness. SDF-1 is known to facilitate disease progression by facilitating chemotaxis toward …

Capacitive Sensors With Ultra-Small Capacitance For Smart Measurement And Control Systems, Rustam Baratov, Farrux Jabbor O'G'Li Ko'charov

Chemical Technology, Control and Management

This article discusses methods of low capacitance measurement of capacitive sensors. The results of the study of the amplitude-frequency (AF) and phase-frequency (PF) response of measurement circuits of capacitive sensors for low capacitances measurement, consisting of RC - differentiating elements are presented. The input and output parameters of the differentiating circuit and the complex transfer function are analyzed. The research results show that low capacitances measurement by measuring the rectangular pulses duration is the most effective measurement method.

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

All Dissertations

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. …

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

All Dissertations

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 …

Navigating The Rules: Integrating Td3 And Sensor Fusion For Traffic-Aware Autonomous Vehicle Path Planning, Mahmoud Ayman Mohamed Elsayed

Theses and Dissertations

This work presents a novel algorithm for local path planning for autonomous vehicles (AVs) which prioritizes both safety and adherence to traffic regulations, addressing critical functions for AV navigation, such as navigating complex environments, avoiding obstacles, and ensuring passenger and road users safety. The algorithm integrates the Twin Delayed Deep Deterministic Policy Gradient (TD3) with sensor fusion based on Nvidia Convolutional Neural Network (NCNN). The study utilizes the CARLA simulator, and real-world datasets, including KITTI and WAYMO, to train and evaluate the proposed algorithm. The proposed algorithm leverages the complementary strengths of Imitation Learning (IL) and Deep Reinforcement Learning (DRL) …

Autonomous Apple Harvester Robot, Jack Ryan Cline, Tyus Green, Devon Woolston

Electrical Engineering

As agricultural demands rise and manual labor costs increase, there has become a dire need to automate apple harvesting. However, the precision and speed necessary for cost-efficient apple harvesting pose a significant challenge for robotic automation. To maintain cost-effective production, a harvester must be able to operate fast enough and long enough to compete with human labor. It must also be able to navigate and traverse apple orchards autonomously and pick apples without damaging the fruit or tree. This project presents an apple harvesting robot that uses a Mask R-CNN vision system with an RGB-D camera to detect the location …

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

All Dissertations

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. …

Aerodynamic Design And Analysis Of A Modified 2006 Mazda Miata, William N. Recher

Honors College Theses

Aerodynamic forces developed by automobiles have destabilizing effects at high speed. These forces tend to skew toward a vehicle’s rear which can present safety concerns, especially for rear-wheel-drive automobiles like the Mazda Miata. To address oversteer and high-speed instability, a vehicle’s design can be tailored to bring about aerodynamic balance and improve traction. LiDAR was used to bring the physical automobile into the digital space. Then, a splitter and diffuser were added to reduce the magnitude of the destabilizing forces. Next, the size and shape of the rear-wing required to balance the vehicle was calculated using a combination of parameters …

Exploration Of Characteristic Curve In Fox Float 3 Shock Dampers To Expedite Shock Damp Tuning., Joshua R. Moore

Honors College Theses

The shock absorber is an integral part of a vehicle suspension system and has a strong influence on its performance, especially in the case of motorsports. It is important to study the force versus velocity relationship, commonly known as the characteristic curve of the shock absorber both during compression and rebound. Vendor-supplied characteristics often reflect the behavior of the shock absorber in a particular setting. However, during the installation, the settings inside the shock absorber are adjusted to increase the human comfort level and performance of the vehicle. This may change the characteristic curve of the shock. The available data …

Autonomous Roadside Mower Testing And Evaluation In Digital Twin Environments, Michael Mardikes, Ethan Brown, Timothy Wiegman, Nathan Sprague, Shreyas Supe, J. T. Evans

Graduate Industrial Research Symposium

Modern day roadside mowing operations in Indiana are performed by contractors operating tractors that pull flex-wing mowers. These mowing operations span over 11,000 miles of road across the state and create serious risk for human operators. Autonomous mowers can provide a safer alternative, but a replacement system must demonstrate an acceptable level of trust before large-scale deployment. A high-fidelity digital-twin environment can facilitate the capability to rapidly test and evaluate platforms in their desired application, while enabling zero real-world risk, a wide range of test variables, and repeatability of a test setup. The simulation environment must be at a level …

Investigating The Effects Of Alcohol Consumption On Manual And Automated Driving: A Systematic Review, Miaomiao Dong, Yuni Lee, Jackie Cha, Gaojian Huang

Mineta Transportation Institute

There are direct correlations between drunk driving and car-related injuries, disabilities, and death. Autonomous vehicles (AVs) may provide useful driver support systems to prevent or reduce road accidents. However, AVs are not yet fully automated and require human drivers to take over the vehicle at times. Therefore, understanding how alcohol affects driving performance in both manual and automated driving is important because manual drives may offer insights into the takeover process in AVs. A systematic review of 53 articles from eight databases was conducted. Findings were categorized based on the human information processing model, which can be extended to the …

Enhancing Bridge Resilience And Overheight Vehicle Mitigation Through Innovative Sacrificial Cushion Systems, Aly Mousaad Aly, Marc Hoffmann

Faculty Publications

Transportation departments have made significant strides in addressing the challenges posed by the increasing weights of trucks on bridges. While there is a growing awareness of overheight vehicle collisions with bridges, implementing effective countermeasures remains limited. The susceptibility of bridges to damage from such collisions is on the rise, further exacerbated by unpredictable lateral impact forces. This study employs nonlinear impact analysis to assess the response of an unprotected vehicle-girder model, yielding realistic deformation outcomes comparable to observed impacts on the US-61 bridge. Predictions for a truck traveling at 112.65 km/h indicate deformations of 0.229 m, 0.161 m, and 0.271 …

Development And Evaluation Of Blind Spot Detection Safety System Based On 2d-Lidar Technology As An Optimization For Adas Systems, Timothy J. Sutton

Honors College Theses

Distracted driving poses a significant safety hazard and will only exacerbate as the number of modern-day distractions increases. To mitigate this problem, Advanced Driver Assistance Systems (ADAS) features, such as blind spot detection, have been pivotal for the safer operation of vehicles. Towards the same objective, the goal of this research is to utilize 2D LiDAR sensors to create a blind spot detection system that will detect objects and surfaces that are outside of the driver’s field of view. A comparative analysis was conducted by developing a 2D LiDAR-based system utilizing NVIDIA Jetson Orion Nano and Python alongside an ultrasonic-based …

V2i-Based Adaptive Collision Avoidance For Safety And Traffic Efficiency, Vaishnavi Balambeed

Dissertations, Master's Theses and Master's Reports

To leverage the growing communication and connectivity among modern vehicles, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) systems are increasingly being used to implement active safety applications. However, current research often overlooks the impact of algorithms such as collision avoidance on traffic flow efficiency. This work investigates the adaptation of a collision avoidance algorithm implemented in V2I to incorporate a variable time headway and spacing control strategy. The proposed approach aims to maintain higher average speeds among vehicles, lower individual vehicle’s waiting, and travel times in the vicinity of the infrastructural unit while simultaneously avoiding collisions; thereby enhancing both safety and traffic …

Predictive Modeling Of Healthcare Traffic Using Machine Learning: A Comparative Study, Shadman Mahmood Khan Pathan, Sakan Binte Imran, M. M. Shabab Iqbal, Muhammad Enayetur Rahman, Md Nurul Absar Siddiky, Muhammad Rezaur Rahman, Md. Rafid Hassan, Nondon Lal Dey, Md. Sobuj Hossain

Electrical & Computer Engineering Faculty Publications

Effective healthcare traffic management is critical for ensuring prompt medical services, particularly in emergencies where delays can have life-threatening consequences. This study conducts a comparative analysis of three popular machine learning models—Linear Regression, Decision Trees, and Random Forests—for predicting healthcare-related traffic volumes. Utilizing a comprehensive dataset from a metropolitan interstate traffic system, the models were evaluated based on key performance metrics, including Mean Squared Error (MSE), R² Score, and execution time. The findings demonstrate that the Random Forest model outperforms the others, offering superior predictive accuracy and efficiency. These insights are valuable for optimizing traffic management in healthcare, ultimately contributing …