Automotive Engineering Commons^™

Improving Autonomous Vehicles Operational Performance Using Resilience Engineering, Johan Fanas Rojas

Dissertations

Autonomous vehicles are expected to revolutionize the transportation industry by providing a safer and more efficient means of transportation. However, as autonomous vehicles are deployed on public roads, they are exposed to significant risks, both in terms of safety and system performance. Recent studies have highlighted a range of errors and accidents associated with autonomous vehicles, underscoring the need for a systematic approach to improve their operational resilience. Resilience engineering, a discipline focused on designing and analyzing complex systems to better cope with unexpected events and disruptions, offers a promising framework for addressing these challenges. Despite the potential benefits of …

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 …

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 …

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 …