Quantitative Analysis of Dangerous Driving Behavior through Drone Footage using Mathematical Modeling - Katragadda, Asrith; Lu, Duo (Journal of Student Research, 2023)
This research paper presents a method for analyzing driving using aerial drone images of traffic scenes. By applying principles of motion and calculus we introduce an approach that provides insights into driving dynamics without relying on complex vehicle markings or annotations. This method offers a perspective on road behaviors, allowing for an understanding of potential risks and ultimately enhancing road safety. In our experiment we examined a series of drone images capturing traffic scenarios. We provided a procedure and demonstration of applications of physics and calculus to reconstruct traffic scenes. We constructed a trajectory mechanism that uses all of said concepts to form a concrete plan to reconstruct scenes, using real drone footage with bird’s eye view. The combination of concepts with aerial image analysis showcases the potential for objective and data driven assessments of reckless driving behaviors. Overall this paper serves as a starting point for advancements, in utilizing models to analyze traffic patterns. It contributes to promoting road practices and improving transportation management through evidence based approaches.