Safe road-crossing by self-driving vehicles is a crucial problem to address
in smart-cities. In this paper, we introduce a multi-sensor fusion approach to
support road-crossing decisions in a system composed by an autonomous
wheelchair and a flying drone featuring a robust sensory system made of diverse
and redundant components. To that aim, we designed an analytical danger
function based on explainable physical conditions evaluated by single sensors,
including those using machine learning and artificial vision. As a
proof-of-concept, we provide an experimental evaluation in a laboratory
environment, showing the advantages of using multiple sensors, which can
improve decision accuracy and effectively support safety assessment. We made
the dataset available to the scientific community for further experimentation.
The work has been developed in the context of an European project named
REXASI-PRO, which aims to develop trustworthy artificial intelligence for
social navigation of people with reduced mobility.

自动驾驶车辆的安全过马路是智能城市中需要解决的重要问题。本文介绍了一种多传感器融合方法，用于支持由自主轮椅和具备多样化和冗余组件的飞行无人机组成的系统中的过马路决策。为此，我们设计了一个基于可解释的物理条件的分析危险函数，通过单个传感器评估这些条件，其中包括使用机器学习和视觉技术的传感器。作为概念证明，我们在实验室环境中进行了实验评估，展示了使用多个传感器的优势，这可以提高决策准确性并有效支持安全评估。我们将数据集提供给了科学界，以进行进一步的实验。这项工作是在一项名为 REXASI-PRO 的欧洲项目的框架下开展的，该项目旨在为行动不便的人们的社交导航开发可信赖的人工智能。