用虚拟人员模拟心理行为，提升搜救定位精度。
Predictive Probability Density Mapping for Search and Rescue Using An Agent-Based Approach with Sparse Data
- 构建基于心理特征的虚拟人员模型，自主决策导航真实地形。
- 结合蒙特卡洛与时间移动采样生成概率分布图，预测成功率高。
- 无需特定地点训练，适用于多种地理环境，适合救援团队使用。
在资源有限的搜救行动中，精准预测失踪者可能位置至关重要。本文提出一种创新的基于代理的模型，可模拟不同心理状态的失踪人员行为，使虚拟代理在真实地形中自主决策，无需特定地点训练。通过蒙特卡洛模拟与基于移动时间的采样，生成潜在位置的概率密度图。利用真实搜救数据训练高斯过程模型，实现对初始起点的泛化采样。与历史数据对比分析显示，该方法优于现有技术。本模型具有高度灵活性，可适配多种地理环境，适用于实际搜救任务。
原文摘要 · Abstract (English)
Predicting the location where a lost person could be found is crucial for search and rescue operations with limited resources. To improve the precision and efficiency of these predictions, simulated agents can be created to emulate the behavior of the lost person. Within this study, we introduce an innovative agent-based model designed to replicate diverse psychological profiles of lost persons, allowing these agents to navigate real-world landscapes while making decisions autonomously without the need for location-specific training. The probability distribution map depicting the potential location of the lost person emerges through a combination of Monte Carlo simulations and mobility-time-based sampling. Validation of the model is achieved using real-world Search and Rescue data to train a Gaussian Process model. This allows generalization of the data to sample initial starting points for the agents during validation. Comparative analysis with historical data showcases promising outcomes relative to alternative methods. This work introduces a flexible agent that can be employed in search and rescue operations, offering adaptability across various geographical locations.
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