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Safe Road-Crossing by Autonomous Wheelchairs: A Novel Dataset and Its Evaluation

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Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops (SAFECOMP 2024)

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Abstract

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 lab environment, showing the advantages of using multiple sensors, which can improve decision accuracy and effectively support safety assessment. We made the dataset with these measures 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 by people with reduced mobility.

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Notes

  1. 1.

    https://www.iso.org/standard/72408.html.

  2. 2.

    https://artificialintelligenceact.eu.

  3. 3.

    https://rexasi-pro.spindoxlabs.com/.

  4. 4.

    https://idsia-robotics.github.io.

  5. 5.

    https://www.dji.com/ch/robomaster-ep.

  6. 6.

    https://docs.optitrack.com/v/v2.3.

  7. 7.

    https://github.com/jeguzzi/robomaster_ros.

  8. 8.

    https://github.com/ros2/rosbag2.

  9. 9.

    https://docs.opencv.org/4.x/index.html.

  10. 10.

    https://github.com/ultralytics/yolov5.

  11. 11.

    https://github.com/IDSIA/rexasi-pro/tree/main/DECSoS_Workshop.

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Acknowledgments

This work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract no. 22.00291 (REXASI-PRO project). The project has been selected within the European Union’s Horizon Europe research and innovation programme under grant agreement ID: 101070028 (call HORIZON-CL4-2021-HUMAN-01-01). Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the funding agencies, which cannot be held responsible for them.

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Authors and Affiliations

  1. IDSIA USI-SUPSI, University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland

    Carlo Grigioni, Franca Corradini, Alessandro Antonucci, Jérôme Guzzi & Francesco Flammini

Authors
  1. Carlo Grigioni
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  2. Franca Corradini
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  3. Alessandro Antonucci
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  4. Jérôme Guzzi
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  5. Francesco Flammini
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Corresponding author

Correspondence to Franca Corradini .

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Editors and Affiliations

  1. University of Florence, Florence, Italy

    Andrea Ceccarelli

  2. Fraunhofer IKS, Munich, Germany

    Mario Trapp

  3. University of Florence, Florence, Italy

    Andrea Bondavalli

  4. AIT Austrian Institute of Technology GmbH, Vienna, Austria

    Erwin Schoitsch

  5. Mälardalens University, Vasteras, Sweden

    Barbara Gallina

  6. GTS Deutschland GmbH, Ditzingen, Germany

    Friedemann Bitsch

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Grigioni, C., Corradini, F., Antonucci, A., Guzzi, J., Flammini, F. (2024). Safe Road-Crossing by Autonomous Wheelchairs: A Novel Dataset and Its Evaluation. In: Ceccarelli, A., Trapp, M., Bondavalli, A., Schoitsch, E., Gallina, B., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops. SAFECOMP 2024. Lecture Notes in Computer Science, vol 14989. Springer, Cham. https://doi.org/10.1007/978-3-031-68738-9_4

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  • DOI: https://doi.org/10.1007/978-3-031-68738-9_4

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