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Navigation, Guidance, Control, and Dynamics Commons™
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- ADAS (1)
- Adaptive Cruise Control (1)
- Automotive (1)
- Autonomous Vehicles (1)
- Autonomous vehicle (1)
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- Computer simulation (1)
- Driver model (1)
- EV architecture (1)
- Energy Consumption (1)
- Hardware in the loop (1)
- Intersection Navigation (1)
- Machine Learning (1)
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- Object Detection (1)
- Power controller (1)
- Risk assessment (1)
- Sensor Fusion (1)
- Simulation Modeling (1)
- Software in the loop (1)
- Torque Controller (1)
- Traffic Lights Detection (1)
- V2I Communications (1)
- Vehicle-to-Everything (1)
Articles 1 - 4 of 4
Full-Text Articles in Navigation, Guidance, Control, and Dynamics
Adaptive Traction, Power And Torque Control Strategies And Optimization In An All-Electric Powertrain, Aymane Hidara
Adaptive Traction, Power And Torque Control Strategies And Optimization In An All-Electric Powertrain, Aymane Hidara
Theses and Dissertations
Electric and hybrid-electric vehicles lean heavily on intricate control algorithms to provide smooth, reliable, and secure operations under any driving conditions. Three distinct supervisory control strategies have been developed, each aiming to improve reliability and vehicle performance of a dual-motor electric vehicle equipped with an all-wheel-drive, fully electric powertrain. These algorithms are adept at dynamically modulating and constraining the torque provided to the wheels, leveraging two autonomous permanent magnet electric drive units. This study utilizes a vehicle model jointly provided by MathWorks and General Motors in partnership with industry sponsors. The these strategies were implemented in the model and enhanced …
Traffic Light Detection And V2i Communications Of An Autonomous Vehicle With The Traffic Light For An Effective Intersection Navigation Using Mavs Simulation, Mahfuzur Rahman
Theses and Dissertations
Intersection Navigation plays a significant role in autonomous vehicle operation. This paper focuses on enhancing autonomous vehicle intersection navigation through advanced computer vision and Vehicle-to-Infrastructure (V2I) communication systems. The research unfolds in two phases. In the first phase, an approach utilizing YOLOv8s is proposed for precise traffic light detection and recognition, trained on the Small-Scale Traffic Light Dataset (S2TLD). The second phase establishes seamless connectivity between autonomous vehicles and traffic lights in a simulated Mississippi State University Autonomous Vehicle Simulation (MAVS) environment resembling a small city with multiple intersections. This V2I system enables the transmission of Signal Phase and Timing …
Risk Assessments And Modeling Of Driver By Using Risk Potential Theory, Riku Kikuta
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 …
Automotive Sensor Fusion Systems For Traffic Aware Adaptive Cruise Control, Jonah T. Gandy
Automotive Sensor Fusion Systems For Traffic Aware Adaptive Cruise Control, Jonah T. Gandy
Theses and Dissertations
The autonomous driving (AD) industry is advancing at a rapid pace. New sensing technology for tracking vehicles, controlling vehicle behavior, and communicating with infrastructure are being added to commercial vehicles. These new automotive technologies reduce on road fatalities, improve ride quality, and improve vehicle fuel economy. This research explores two types of automotive sensor fusion systems: a novel radar/camera sensor fusion system using a long shortterm memory (LSTM) neural network (NN) to perform data fusion improving tracking capabilities in a simulated environment and a traditional radar/camera sensor fusion system that is deployed in Mississippi State’s entry in the EcoCAR Mobility …