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.

翻译：自动驾驶车辆的安全过街是智慧城市中亟待解决的关键问题。本文提出了一种多传感器融合方法，用于支持由自主轮椅和飞行无人机组成的系统在过街决策中的应用。该系统配备了由多样且冗余组件构成的鲁棒感知系统。为此，我们基于单传感器（包括采用机器学习和人工视觉的传感器）评估的可解释物理条件，设计了一种分析性危险函数。作为概念验证，我们在实验室环境中进行了实验评估，结果表明使用多传感器能够提高决策准确性，并有效支持安全评估。我们将数据集公开，供科学界进一步实验研究。本工作是在名为REXASI-PRO的欧洲项目背景下开展的，该项目旨在为行动不便人群的社会导航开发可信赖的人工智能。