[论文解读] Open Area Path Finding to Improve Wheelchair Navigation

Anahid Basiri|arXiv (Cornell University)|Nov 7, 2020

Gaze Tracking and Assistive Technology参考文献 50被引用 5

一句话总结

本文提出了一种新型基于图的路径查找算法，适用于公园和草地等开放区域，传统路线规划在这些区域对轮椅用户失效。通过使用机器学习挖掘真实轮椅轨迹，该方法对坡度、路面状况和宽度进行建模，生成以用户为中心的路径，与实际用户路径的相似度达到76.4%，显著提升了无障碍通行性与多模式路线兼容性。

ABSTRACT

Navigation is one of the most widely used applications of the Location Based Services (LBS) which have become part of our digitally informed daily lives. Navigation services, however, have generally been designed for drivers rather than other users such as pedestrians or wheelchair users. For these users the directed networks of streets and roads do not limit their movements, but their movements may have other limitations, including lower speed of movement, and being more dependent on weather and the pavement surface conditions. This paper proposes and implements a novel path finding algorithm for open areas, i.e. areas with no network of pathways such as grasslands and parks where the conventional graph-based algorithms fail to calculate a practically traversable path. The new method provides multimodality, a higher level of performance, efficiency, and user satisfaction in comparison with currently available solutions. The proposed algorithm creates a new graph in the open area, which can consider the obstacles and barriers and calculate the path based on the factors that are important for wheelchair users. Factors, including slope, width, and surface condition of the routes, are recognised by mining the actual trajectories of wheelchairs users using trajectory mining and machine learning techniques. Unlike raster-based techniques, a graph-based open area path finding algorithm allows the routing to be fully compatible with current transportation routing services, and enables a full multimodal routing service. The implementations and tests show at least a 76.4% similarity between the proposed algorithm outputs and actual wheelchair users trajectories.

研究动机与目标

• 解决公园和草地等开放区域轮椅用户缺乏有效导航支持的问题。
• 克服传统基于图的路由系统依赖预定义路径的局限性。
• 通过整合用户特定的移动约束，提升用户满意度和导航效率。
• 通过在开放区域创建兼容的图表示，实现完整的多模式路线集成。
• 开发一种系统，利用真实用户数据动态适应路面质量与坡度等环境因素。

提出的方法

• 使用GPS和传感器数据收集用户在开放区域行驶的真实轮椅轨迹。
• 应用轨迹挖掘技术提取模式，并识别关键路径特征，如坡度、路面状况和宽度。
• 训练机器学习模型，根据其对轮椅可用性的影响对路径特征进行分类和加权。
• 在开放区域内构建新的图结构，编码障碍物、障碍物及用户相关的地形因素。
• 将新图集成到现有路由服务中，实现无缝的多模式导航。
• 在新图上使用Dijkstra算法或类似最短路径算法，计算最优且适合轮椅的路径。

实验结果

研究问题

• RQ1 在缺乏预定义路径的开放区域，路径查找算法如何有效支持轮椅用户？
• RQ2 在非结构化环境中，哪些关键环境和移动因素显著影响轮椅导航？
• RQ3 在多大程度上可利用真实用户轨迹对最优轮椅路径进行建模与预测？
• RQ4 与现有的栅格化或网络化路由方法相比，所提出的基于图的方法在准确性上如何？
• RQ5 集成用户特定的路径特征能否提升路由性能和用户满意度？

主要发现

• 所提出的算法在计算路径与真实轮椅用户轨迹之间实现了至少76.4%的相似度。
• 该方法成功利用真实轨迹数据对轮椅导航的关键因素（如坡度、路面状况和路线宽度）进行建模。
• 基于图的方法实现了与现有交通路由服务的完全兼容，支持多模式导航。
• 在性能、效率和以用户为中心的路径选择方面，该系统优于传统的栅格化方法。
• 轨迹挖掘与机器学习技术实现了对可用性关键路径特征的自动识别，无需人工标注。
• 该方法在实际城市和休闲开放空间中部署方面展现出实际可行性。