Safe Road-Crossing by Autonomous Wheelchairs: a Novel Dataset and its Experimental Evaluation
This addresses safety for people with reduced mobility in smart cities, but it is incremental as it builds on existing sensor fusion methods.
The paper tackles safe road-crossing for autonomous wheelchairs by introducing a multi-sensor fusion approach with a drone, using an explainable danger function, and shows in lab experiments that multiple sensors improve decision accuracy and safety assessment.
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.