Navigation, Guidance, Control, and Dynamics Commons^™

A Comparative Study Of Model Predictive Control And The Stanley Method For Vehicle Path Tracking Applications, Noah S. Fitzgerald

Master's Theses

This thesis compares a model predictive controller (MPC) and a lateral Stanley controller for vehicle path-tracking applications under simulation-based and perception-driven operating conditions. Both controllers were evaluated in simulation using a nonlinear dynamic bicycle model executing single and double lane change maneuvers. Following simulation-based evaluation, both controllers were implemented on hardware within a perception-driven steering-control pipeline. This pipeline utilized recorded sensor data from the MXcarkit 1/8^th-scale autonomous vehicle platform, incorporating lane instance segmentation and homography-based roadway estimation.

Under idealized simulation conditions, the MPC demonstrated improved trajectory-tracking performance during aggressive maneuvers while requiring greater steering activity and computational effort …

Gem-Can: A Real-World Dataset Of Can-Bus Attack Scenarios On An Autonomous Vehicle For Intrusion-Detection Research, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini, Tienake Phuapaiboon, Milad Khaleghi, Daniel Tobias

Electrical & Computer Engineering Faculty Publications

This paper presents GEM-CAN, a labelled Controller Area Network (CAN) dataset captured from an autonomous GEM e6 platform under both normal operation and controlled cyber-attack conditions.

The dataset contains ∼143 K frames comprising (i) ∼ nominal autonomous operation (∼100k messages), (ii) DoS floods using arbitration ID 0 × 00000000 (∼41 K messages), and (iii) data-tampering injections that reuse legitimate IDs for brake and steering-lock (∼1.3 K messages). Each record includes timestamp, arbitration ID (11/29-bit), DLC, eight payload bytes, and a Normal/Attack label. A companion metadata file enumerates attack windows, PCAN bus-load traces, bitrate, and test conditions. Data were collected with …

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 …

Intelligent Systems For Synergistic Optimization Of Vehicle Dynamics And Energy Consumption In Connected And Automated Vehicles, Aman Poovalappil

Dissertations, Master's Theses and Master's Reports

This dissertation focuses on the development and validation of on-board, real-time estimation algorithms and the investigation of energy-saving strategies for modern light-duty vehicles, including connected and automated vehicles (CAVs). The accurate estimation of key vehicle parameters is critical for enhancing energy efficiency, enabling advanced driver-assistance systems (ADAS), and optimizing vehicle performance and energy efficiency. First, this research introduces a novel real-time algorithm to dynamically learn vehicle mass using readily available sensor data. Based on longitudinal vehicle dynamics, a sensitivity analysis was conducted to identify the conditions under which mass estimation is most robust. The algorithm was extensively validated through on-road …

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 …

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 …

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 …

Local Climate Action Planning As A Tool To Harness The Greenhouse Gas Emissions Mitigation And Equity Potential Of Autonomous Vehicles And On-Demand Mobility, Serena Alexander, Asha Weinstein Agrawal, Benjamin Y. Clark

Mineta Transportation Institute

This report focuses on how cities can use climate action plans (CAPs) to ensure that on-demand mobility and autonomous vehicles (AVs) help reduce, rather than increase, green-house gas (GHG) emissions and inequitable impacts from the transportation system. We employed a three-pronged research strategy involving: (1) an analysis of the current literature on on-demand mobility and AVs; (2) a systematic content analysis of 23 CAPs and general plans developed by municipalities in California; and (3) a comparison of findings from the literature and content analysis of plans to identify opportunities for GHG emissions reduction and mobility equity.

Findings indicate that maximizing …

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 …

Vehicle Dynamics Modeling For Autonomous Drifting And Clothoid Based Waypoint Interpolation, Shubham Vatsa

Dissertations, Master's Theses and Master's Reports

The advent of autonomous vehicles necessitates a redefinition of road safety regulations, considering a controller can possess better driving skills than an average person. The work presented here partly focuses on a vehicle dynamics model development to help imitate and control vehicle drifting maneuvers. As we see, a professional driver drifting through the traffic while keeping the car safe, it can be utilized to avoid accidents at high speeds, if required. Although drifting can produce higher yaw rates than the regular driving regime, these control capabilities have not yet been exploited in the current automotive control systems. Therefore, this report …

Analysis Of Disengagements In Semi-Autonomous Vehicles: Drivers’ Takeover Performance And Operational Implications, Francesca M. Favaro, Sky Eurich, Syeda Rizvi, Shivangi Agarwal, Sumaid Mahmood, Nazanin Nader

Mineta Transportation Institute

This report analyzes the reactions of human drivers placed in simulated Autonomous Technology disengagement scenarios. The study was executed in a human-in-the-loop setting, within a high-fidelity integrated car simulator capable of handling both manual and autonomous driving. A population of 40 individuals was tested, with metrics for control takeover quantification given by: i) response times (considering inputs of steering, throttle, and braking); ii) vehicle drift from the lane centerline after takeover as well as overall (integral) drift over an S-turn curve compared to a baseline obtained in manual driving; and iii) accuracy metrics to quantify human factors associated with the …

Dscr Based Sensor-Pooling Protocol For Connected Vehicles In Future Smart Cities, Mostafa El-Said, Samah Mansour, Vijay Bhuse

Peer-Reviewed Publications

Smart cities are racing to create a more connected Intelligent Transportation Systems (ITS) that rely on collecting data from every possible sensor such as a smart utility meter or a smart parking meter. The use of more sensors resulted in generating a lot of information that maps the smart city environment conditions to more real time data points that needed to be shared and analyzed among smart city nodes. One possibility, to carry and share the collected data, is in autonomous vehicles systems, which use the Dedicated Short Range Communications (DSRC) technology. For example, in a Car-to-Parking-Meter or a Vehicle-to-Vehicle …