Navigation, Guidance, Control, and Dynamics Commons^™

Enhancing Autonomous Vehicle Resilience Through Engineering Requirements And Snow-Adaptive Lane Detection, Alexandra Marie Masterson

Masters Theses

This thesis investigates two distinct but interrelated challenges in the development of resilient autonomous vehicle (AV) systems: the formalization of engineering requirements for AV perception subsystems and the enhancement of visual lane detection under snow-covered road conditions. In the first study, field experiments were conducted using a campus-deployed autonomous research vehicle to evaluate the impacts of perception related failures including GPS outages, HD map inconsistencies, and weather interference—on vehicle operation. These findings were used to develop a set of qualitative engineering requirements that promote AV resilience through proactive design. In the second study, a custom snow-focused lane detection dataset was …

Analyzing The Resilience Of Infrastructure-Based Vs. Camera-Based Lane Detection In Autonomous Vehicles, Pritesh Yashaswi Patil

Masters Theses

Traditional autonomous vehicle perception subsystems that use on-board sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high-quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions. This need is addressed by evaluating …

Techniques For Enabling Advanced Adas Features: Operation In Inclement Weather And Use Of Infrastructure Information, Parth Kadav

Dissertations

Modern vehicles have undergone a transformation with the widespread integration of Advanced Driver Assistance Systems (ADAS) technology becoming the new standard and are set to be mandated by the by the National Highway Traffic Safety Administration (NHTSA) for all passenger vehicles and light trucks. ADAS features have proven to prevent or mitigate crashes by either alerting or assisting the driver. ADAS typically utilizes a forward-facing camera, which comes standard in modern vehicles to provide limited automation features such as Lane Keeping Assist (LKA), and Lane Centering Assist (LCA) to improve driver safety. These systems rely on the assumption that vehicle …

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 …

Av Operation And Energy Efficiency Improved Through The Evaluation And Demonstration Of Av Sensor Technology, Nicholas Brown

Dissertations

The majority of states have passed legislation or have signed an executive order enacting safe testing, development, and deployment of level 4 and level 5 autonomous vehicles (AVs) in accordance with SAE standard J3016, which has led to an increase in the frequency of AV testing. The major driving force behind the push for AVs on public roads appears to be the increases the number of AVs on the road to decrease the chances of fatalities from distracted drivers. There are reports of disengagement from companies, which are required to report them to operate within California. The continuance of disengagements …

Instance Segmentation-Based Depth Completion Using Sensor Fusion And Adaptive Clustering For Autonomous Vehicle Perception, Mohammad Z. El-Yabroudi

Dissertations

Depth sensing is critical for safe and accurate maneuvering in robotics and self-driving car (SDC) applications. Most recent LiDAR sensors, such as Ouster and Velodyne, offer 360 degrees of scanning at the rate of ten frames per second, making them very appropriate for autonomous driving applications. However, LiDAR point cloud data show many shortcomings, especially its data sparsity and unassigned nature, making it very challenging to utilize in applications such as perception, 3D object detection, 3D scene reconstruction, and simultaneous localization and mapping.

In this study, a novel framework using instance image segmentation and the raw LiDAR data for the …

Optimized System For On-Route Charging Of Battery Electric Buses And High-Fidelity Modelling And Simulation Of In-Motion Wireless Power Transfer, Yogesh Bappasaheb Jagdale

Masters Theses

Electrifying cars, buses and trucks is an attractive means to reduce energy use and emissions, because it involves minimal restructuring of the transportation network. Transit buses drive fixed routes, minimizing driver range anxiety by properly sizing energy storage system but the major challenge to fully electrifying transit buses, is the amount of energy they consume in a day of driving. To enable a full day of operation, batteries need to be large, which is expensive and heavy. This work utilizes real-world transit bus data fed to a battery electric drive-train model to co-optimize charger locations, charger power levels, and vehicle …

Vehicle Performance Analysis Of An Autonomous Electric Shuttle Modified For Wheelchair Accessibility, Johan Fanas Rojas

Masters Theses

Autonomous vehicles (AV) have the potential to vastly improve independent, safe, and cost-effective mobility options for individuals with disabilities. However, accessibility considerations are often overlooked in the early stages of design, resulting in AVs that are inaccessible to people with disabilities. The needs of wheeled mobility device users can cause significant vehicle design changes due to requirements for stepless ingress/egress and increased space for onboard circulation and securement. Vehicles serving people with disabilities typically require costly aftermarket modifications for accessibility, which may have unforeseen impacts on vehicle performance and safety, particularly in the case of automated vehicles. In this research, …

Vehicle Velocity Prediction Using Artificial Neural Networks And Effect Of Real-World Signals On Prediction Window, Tushar Dnyaneshwar Gaikwad

Masters Theses

Prediction of vehicle velocity is essential since it can realize improvements in the fuel economy/energy efficiency, drivability, and safety. Many publications address velocity prediction problems, yet there is a need for the understanding effect of different signals for the prediction. There are numerous new sensor and signal technologies like vehicle-to-vehicle and vehicle-to-infrastructure communication that can be used to obtain comprehensive datasets. Several references considered deterministic and stochastic approaches that use the datasets as input to determine future operation predictions. These approaches include different traffic models and artificial neural networks such as Markov chain, nonlinear autoregressive model, Gaussian function, and recurrent …

Comparison Of Optimal Energy Management Strategies Using Dynamic Programming, Model Predictive Control, And Constant Velocity Prediction, Amol Arvind Patil

Masters Theses

Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust which allows fuel economy (FE) improvements in hybrid electric vehicles through optimal energy management strategies (EMS). A real-world highway drive cycle (DC) and a controls-oriented 2017 Toyota Prius Prime model are used to study potential FE improvements. We proposed three important metrics for comparison: (1) perfect full drive cycle prediction using dynamic programming, (2) 10-second prediction horizon model predictive control (MPC), and (3) 10-second constant velocity prediction. These different velocity predictions are put into an optimal EMS derivation algorithm to derive optimal engine …