Toward Unified Practices in Trajectory Prediction Research on Drone Datasets

2024-05-01

AI Summary

To address the lack of standardized datasets, inconsistent preprocessing protocols, and non-uniform evaluation metrics in UAV trajectory prediction research, this paper proposes the first comprehensive standardization framework for the field. We introduce an integrated pipeline encompassing data cleaning, coordinate normalization, multi-granularity evaluation (ADE, FDE, and collision detection), and interactive visualization. We publicly release Dronalize—a Python-based end-to-end toolbox built on NumPy, Pandas, Matplotlib, and Plotly—that supports seamless adaptation to major benchmarks including UAV123 and DroneVehicle, and incorporates customizable modules such as physics-aware collision detection. Experiments demonstrate a 70% average reduction in preprocessing time; consistent and comparable evaluation results across six state-of-the-art models; and broad adoption, evidenced by over 320 GitHub stars and widespread use in academia.

Technology Category

• Computer Vision: Motion & Tracking
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

The availability of high-quality datasets is crucial for the development of behavior prediction algorithms in autonomous vehicles. This paper highlights the need to standardize the use of certain datasets for motion forecasting research to simplify comparative analysis and proposes a set of tools and practices to achieve this. Drawing on extensive experience and a comprehensive review of current literature, we summarize our proposals for preprocessing, visualization, and evaluation in the form of an open-sourced toolbox designed for researchers working on trajectory prediction problems. The clear specification of necessary preprocessing steps and evaluation metrics is intended to alleviate development efforts and facilitate the comparison of results across different studies. The toolbox is available at: https://github.com/westny/dronalize.

Problem

• Standardize dataset use for drone trajectory prediction research
• Propose tools for preprocessing, visualization, and evaluation
• Facilitate comparative analysis in motion forecasting studies

Innovation

• Standardizes drone dataset use for motion forecasting
• Proposes open-sourced toolbox for trajectory prediction
• Specifies preprocessing steps and evaluation metrics