Quantifying protocol evaluation for autonomous collision avoidance
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springer_submit.pdf
Description
Final Manuscript
Size
2.53 MB
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Checksum (MD5)
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Author(s)
Woerner, Kyle
Benjamin, Michael
Novitzky, Michael
Leonard, John J
Date Issued
May 2018
Journal
Autonomous Robots
Publisher
Springer-Verlag
Citation
Woerner, Kyle et al. “Quantifying Protocol Evaluation for Autonomous Collision Avoidance.” Autonomous Robots (May 2018): 1-25 © 2018 Springer Science+Business Media, LLC, part of Springer Nature
Version
Author's final manuscript
Abstract
Collision avoidance protocols such as COLREGS are written primarily for human operators resulting in a rule set that is open to some interpretation, difficult to quantify, and challenging to evaluate. Increasing use of autonomous control of vehicles emphasizes the need to more uniformly establish entry and exit criteria for collision avoidance rules, adopt a means to quantitatively evaluate performance, and establish a “road test” for autonomous marine vehicle collision avoidance. This paper presents a means to quantify and subsequently evaluate the otherwise subjective nature of COLREGS thus providing a path toward standardized evaluation and certification of protocol-constrained collision avoidance systems based on admiralty case law and on-water experience. Notional algorithms are presented for evaluation of COLREGS collision avoidance rules to include overtaking, head-on, crossing, give-way, and stand-on rules as well as applicable entry criteria. These rules complement and enable an autonomous collision avoidance road test as a first iteration of algorithm certification prior to vessels operating in human-present environments. Additional COLREGS rules are discussed for future development. Both real-time and post-mission protocol evaluation tools are introduced. While the motivation of these techniques applies to improvement of autonomous marine collision avoidance, the concepts for protocol evaluation and certification extend naturally to human-operated vessels. Evaluation of protocols governing other physical domains may also benefit from adapting these techniques to their cases.
Keywords: COLREGS; Autonomous collision avoidance; Human–robot collaboration; Marine navigation
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Mechanical Engineering
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/s10514-018-9765-y
