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J. P. Fleischer

Publications and source records attributed to J. P. Fleischer.

3 recordsLinked to original sources

Measuring Braking Behavior Using Vehicle Tracking and Camera-to-Satellite Homography Rectification

This paper presents an open-source software application for analyzing traffic camera footage, focusing on vehicle behavior and braking events at signalized urban highways. The core innovation is a robust ground-plane homography estimation that links fixed traffic camera views to satellite orthoimagery. This process rectifies the camera's oblique perspective, ensuring that pixel distances accurately represent real-world distances. This enables the acquisition of features such as vehicle trajectory, speed, deceleration, and braking severity without the need for camera calibration. The pipeline employs the MAGSAC++ estimator to build the homography, converting YOLO11 object detections into a rectified top-down coordinate system. All detection and trajectory data are stored in a ClickHouse database for subsequent analysis. A real-world case study at two signalized intersections in Key West, Florida, showcased the system's capabilities. Across two days of daytime footage, braking activity at the higher-volume intersection peaked around 4 PM at approximately 57.5 events per hour, while the second intersection peaked around 10 AM at roughly 15.5 events per hour. The spatial analysis revealed that most braking events initiated upstream, with mild and moderate braking mostly occurring 30 to 45+ meters away from the stop bar and severe braking distributed throughout, but particularly concentrated in lanes with higher interaction and merging activity. The findings highlight the significant potential of this centralized safety information system to support connected vehicles, facilitating proactive traffic management, crash mitigation, and data-driven roadway design and safety analysis.

cs.SE

Towards Experiment Execution in Support of Community Benchmark Workflows for HPC

A key hurdle is demonstrating compute resource capability with limited benchmarks. We propose workflow templates as a solution, offering adaptable designs for specific scientific applications. Our paper identifies common usage patterns for these templates, drawn from decades of HPC experience, including recent work with the MLCommons Science working group. We found that focusing on simple experiment management tools within the broader computational workflow improves adaptability, especially in education. This concept, which we term benchmark carpentry, is validated by two independent tools: Cloudmesh's Experiment Executor and Hewlett Packard Enterprise's SmartSim. Both frameworks, with significant functional overlap, have been tested across various scientific applications, including conduction cloudmask, earthquake prediction, simulation-AI/ML interactions, and the development of computational fluid dynamics surrogates.

cs.DC

Hybrid Reusable Computational Analytics Workflow Management with Cloudmesh

In this paper, we summarize our effort to create and utilize a simple framework to coordinate computational analytics tasks with the help of a workflow system. Our design is based on a minimalistic approach while at the same time allowing to access computational resources offered through the owner's computer, HPC computing centers, cloud resources, and distributed systems in general. The access to this framework includes a simple GUI for monitoring and managing the workflow, a REST service, a command line interface, as well as a Python interface. The resulting framework was developed for several examples targeting benchmarks of AI applications on hybrid compute resources and as an educational tool for teaching scientists and students sophisticated concepts to execute computations on resources ranging from a single computer to many thousands of computers as part of on-premise and cloud infrastructure. We demonstrate the usefulness of the tool on a number of examples. The code is available as an open-source project in GitHub and is based on an easy-to-enhance tool called cloudmesh.

cs.DC