# Safe Road-Crossing by Autonomous Wheelchairs: a Novel Dataset and its Experimental Evaluation

arXiv: https://arxiv.org/abs/2403.08984 · HTML: https://mlsanity.com/paper/2403.08984/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 13 Mar 2024.

## Builds on (most cited first, 3 of 3)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys](https://mlsanity.com/paper/2303.17220/) (2023)
- [A Dataset and Model for Crossing Indian Roads](https://mlsanity.com/paper/2211.07916/) (2022)

## Built on by (most cited first, 2 of 2)

- [Experimental Evaluation of Road-Crossing Decisions by Autonomous Wheelchairs against Environmental Factors](https://mlsanity.com/paper/2406.18557/) (2024)
- [OmniPath: A Multi-Modal Agentic Framework for Auditing Wheelchair Accessibility](https://mlsanity.com/paper/2606.24129/) (2026)

## Abstract (arXiv)

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.