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首页> 外文期刊>Engineering Applications of Artificial Intelligence >Collision risk prediction for visually impaired people using high level information fusion
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Collision risk prediction for visually impaired people using high level information fusion

机译：使用高级信息融合的视力障碍者碰撞风险预测

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摘要

The technologies developed so far to help visually impaired people (VIP) navigate meet only some of their everyday needs. This project allows the visually impaired to improve the comprehension of their context by generating a risk map following an analysis of the position, distance, size and motion of the objects present in their environment. This comprehension is refined by data fusion steps applied to the High Level Information Fusion (HLIF) to predict possible impacts in the near future. A risk map is made up of probabilities generated after executing a set of inferences. These inferences allow the evaluation of future collision risks in different directions by detecting static objects, detecting free passage and analyzing paths followed by dynamic objects in a 3D plane. Different datasets were modeled and a comparative analysis was performed to check the percentage of correct answers and the accuracy of the inferences made using different classifiers. Thus, in order to demonstrate the advantages of the HLIF implementation in a dedicated VIP navigation system, the proposed architecture was tested against three other navigation systems that use different approaches. The generation of specific results made it possible to validate and compare these navigation systems. For this comparative analysis, different environments were used with the goal of indicating a direction for the VIP to move in with fewer collision risks. In addition to providing a risk map giving possible collisions, this project system provided greater reliability for navigation, especially when obstacles were very close and moving objects were detected and tracked.
机译：迄今为止，开发的可帮助视觉障碍者（VIP）导航的技术仅能满足他们的一些日常需求。通过对存在于环境中的物体的位置，距离，大小和运动进行分析，生成一个风险图，该项目可以使视障人士提高对上下文的理解。通过应用于高级信息融合（HLIF）的数据融合步骤来完善此理解，以预测不久的将来可能产生的影响。风险图由执行一组推断后生成的概率组成。这些推论可通过检测静态物体，检测自由通过并分析3D平面中动态物体所遵循的路径，来评估不同方向上未来的碰撞风险。对不同的数据集进行建模，并进行比较分析，以检查正确答案的百分比以及使用不同分类器得出的推论的准确性。因此，为了展示在专用VIP导航系统中HLIF实施的优势，针对使用不同方法的其他三个导航系统对所提出的体系结构进行了测试。特定结果的生成使得可以验证和比较这些导航系统。为了进行这种比较分析，使用了不同的环境，目的是指示VIP进入方向的可能性较小，并且发生碰撞的风险也较小。除了提供可能发生碰撞的风险图之外，此项目系统还为导航提供了更高的可靠性，尤其是在障碍物非常靠近且检测到并跟踪了移动物体的情况下。

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