Situation Awareness-Based Safety Assessment Method for Human–Autonomy Interaction Process Considering Anchoring and Omission Biases

Situation Awareness-Based Safety Assessment Method for Human–Autonomy Interaction Process Considering Anchoring and Omission Biases

Autonomy is being increasingly used in domains like maritime, aviation, medical, and civil domains. Nevertheless, at the current autonomy level, human takeover in the human–autonomy interaction process (HAIP) is still critical for safety. Whether humans take over relies on situation awareness (SA) a...

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Bibliographic Details
Main Authors: Shengkui Zeng, Qidong You, Jianbin Guo, Haiyang Che
Format: Article
Language:English
Published: MDPI AG 2025-01-01
Series:Journal of Marine Science and Engineering
Subjects:
human–autonomy interaction
safety assessment
situation awareness
takeover decision
anchoring bias
omission bias
Online Access:https://www.mdpi.com/2077-1312/13/1/158
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Summary:Autonomy is being increasingly used in domains like maritime, aviation, medical, and civil domains. Nevertheless, at the current autonomy level, human takeover in the human–autonomy interaction process (HAIP) is still critical for safety. Whether humans take over relies on situation awareness (SA) about the correctness of autonomy decisions, which is distorted by human anchoring and omission bias. Specifically, (i) anchoring bias (tendency to confirm prior opinion) causes the imperception of key information and miscomprehending correctness of autonomy decisions; (ii) omission bias (inaction tendency) causes the overestimation of predicted loss caused by takeover. This paper proposes a novel HAIP safety assessment method considering effects of the above biases. First, an SA-based takeover decision model (SAB-TDM) is proposed. In SAB-TDM, SA perception and comprehension affected by anchoring bias are quantified with the Adaptive Control of Thought-Rational (ACT-R) theory and Anchoring Adjustment Model (AAM); behavioral utility prediction affected by omission bias is quantified with Prospect Theory. Second, guided by SAB-TDM, a dynamic Bayesian network is used to assess HAIP safety. A case study on autonomous ship collision avoidance verifies effectiveness of the method. Results show that the above biases mutually contribute to seriously threaten HAIP safety.
ISSN:2077-1312

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