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Drivers’ Manoeuvre Prediction for Safe HRI

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Original languageEnglish
Title of host publication2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2018)
Subtitle of host publicationProceedings of a meeting held 1-5 October 2018, Madrid, Spain
Publisher or commissioning bodyInstitute of Electrical and Electronics Engineers (IEEE)
Pages8609-8614
Number of pages6
ISBN (Electronic)9781538680940
ISBN (Print)9781538680933
DOIs
DateAccepted/In press - 29 Jun 2018
DateE-pub ahead of print - 7 Jan 2019
DatePublished (current) - Mar 2019

Publication series

Name
ISSN (Print)2153-0858
ISSN (Electronic)2153-0866

Abstract

Machines with high levels of autonomy such as robots and our growing need to interact with them creates challenges to ensure safe operation. The recent interest to create autonomous vehicles through the integration of control and decision-making systems makes such vehicles robots too. We therefore applied estimation and decision-making mechanisms currently investigated for human-robot interaction to humanvehicle interaction. In other words, we define the vehicle as an autonomous agent with which the human driver interacts, and focus on understanding the human intentions and decisionmaking processes. These are then integrated into the robot’s/vehicle’s own control and decision-making system not only to understand human behaviour while it occurs but to predict the next actions. To obtain knowledge about the human’s intentions, this work relies heavily on the use of motion tracking data (i.e. skeletal tracking, body posture) gathered from drivers whilst driving. We use a data-driven approach to both classify current driving manoeuvres and predict future manoeuvres, by using a fixed prediction window and augmenting a standard set of manoeuvres. Results are validated against drivers of different sizes, seat preferences and levels of driving expertise to evaluate the robustness of the methods; precision and recall metrics higher than 95% for manoeuvre classification and 90% for manoeuvre prediction with time-windows of up to 1.3 seconds are obtained. The idea of prediction adds a highly novel aspect to human-robot/human-vehicle interaction, allowing for decision and control at a later point.

    Structured keywords

  • Brain and Behaviour
  • Cognitive Science
  • Visual Perception

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  • Full-text PDF (accepted author manuscript)

    Rights statement: This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at https://ieeexplore.ieee.org/document/8593957 . Please refer to any applicable terms of use of the publisher.

    Accepted author manuscript, 3 MB, PDF document

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