BoundaryPredictor: Imitating Cost-Minimized Trajectory Sampling for Autonomous Drone Racing; Policy Recommendations for Mitigating the Risks of Autonomous Vehicles
Author:
Parthaje, Shreepa, School of Engineering and Applied Science, University of Virginia
Parthaje, Shreepa, School of Engineering and Applied Science, University of Virginia
Advisors:
Francisco, Pedro Augusto, EN-Engineering and Society, University of Virginia
Vrugtman, Rosanne, EN-Comp Science Dept, University of Virginia
Francisco, Pedro Augusto, EN-Engineering and Society, University of Virginia
Vrugtman, Rosanne, EN-Comp Science Dept, University of Virginia
Abstract:
The technical aspect to this research focuses on the benefits of autonomous navigation through safety and assurance, while the STS aspect to the research focuses on policy recommendations to minimize any risk. The technical aspect uses a specific loss function when mimicking an expert dataset to push error towards safety. The STS aspect uses a past, present, future approach to both predict the impact of AV technology on society and how to preemptively address it.
Degree:
BS (Bachelor of Science)
BS (Bachelor of Science)
Keywords:
Autonomous Navigation, Self-driving policy
Autonomous Navigation, Self-driving policy
Notes:
School of Engineering and Applied Science
Bachelor of Science in Computer Science
Technical Advisor: Rosanne Vrugtman
STS Advisor: Pedro Augusto Francisco
Technical Team Members: Shreepa Parthaje
Language:
English
English
Rights:
All rights reserved (no additional license for public reuse)
All rights reserved (no additional license for public reuse)
Issued Date:
2025/05/09
2025/05/09
