洞見 - Human-Computer Interaction - # Situational Awareness in Human-Autonomous Vehicle Interaction

Designing Situational Awareness for Effective Human-Autonomous Vehicle Collaboration

Q: How can the framework be extended to account for the dynamic nature of human-AV interactions, where the communicative context may change over time?

In order to address the dynamic nature of human-AV interactions, where the communicative context may change over time, the framework can be extended in several ways: Real-time Adaptation: The framework can incorporate mechanisms for real-time adaptation of communication strategies based on changing contextual factors. This could involve sensors within the AV monitoring the environment and the passengers, adjusting communication modalities accordingly. Machine Learning Integration: By integrating machine learning algorithms, the AV can learn from past interactions and dynamically adjust its communication strategies to suit the evolving communicative context. This adaptive learning can help the AV respond effectively to changing situations. Contextual Awareness: The framework can include a more robust understanding of contextual factors that influence communication needs, such as weather conditions, traffic patterns, and passenger states. By considering a broader range of contextual variables, the AV can tailor its communications more effectively. Feedback Mechanisms: Implementing feedback mechanisms where users can provide input on the effectiveness of the AV's communication can help in refining and improving the communication strategies over time. This feedback loop can ensure that the AV continuously adapts to meet the evolving communicative context.

Q: How might the framework apply to other human-AI collaboration domains beyond autonomous driving, such as healthcare or military applications?

The framework for developing situational awareness within human-AV systems can be applied to other human-AI collaboration domains such as healthcare or military applications in the following ways: Healthcare Applications: In healthcare settings, where AI systems are increasingly being used to assist medical professionals, the framework can help in designing effective communication strategies between healthcare providers and AI systems. By considering the action goals, subject-specific traits, and contextual factors, the framework can ensure that AI systems provide relevant and timely information to support medical decision-making. Military Applications: In military applications, where AI technologies are used for decision support and autonomous operations, the framework can guide the design of communication strategies between military personnel and AI systems. By understanding the shared situation, individual situational awareness, and distributed situational awareness, the framework can enhance coordination and decision-making in complex military environments. Emergency Response: The framework can also be applied to emergency response scenarios, where AI systems play a crucial role in coordinating rescue operations. By considering the dynamic nature of emergency situations and the need for rapid decision-making, the framework can help in designing communication strategies that support effective collaboration between human responders and AI systems. Overall, the framework's principles of understanding joint action goals, individual and shared situational awareness, and tailored communication can be adapted to various human-AI collaboration domains to enhance decision-making and coordination in complex environments.

核心概念

Achieving joint action goals like safe transportation and learning in human-autonomous vehicle (AV) systems requires developing sufficient situational awareness across the human, AV, and the overall system.

摘要

This paper presents a framework for understanding how situational awareness can be developed through joint actions between humans and autonomous vehicles (AVs). The key components of the framework are:

Action Goals and Subgoals: Successful human-AV collaboration requires achieving joint action goals like safe transportation or learning, which have specific success criteria or subgoals that need to be met.
AV Traits: Attributes and affordances of the AV, such as its driving abilities, decision-making processes, and communication capabilities, impact how the AV can act and be acted upon.
Subject-Specific Traits and States: Characteristics of the human user, including their disposition, prior experiences, and current cognitive state, influence their informational needs and behaviors.
Driving Context: Both the external driving environment (e.g., weather, traffic) and the internal cabin context (e.g., passenger dynamics) impact the success criteria for action goals.

The framework posits that to achieve joint action goals, the human, AV, and the overall human-AV system must develop sufficient individual, shared, and distributed situational awareness. This is enabled through strategic communicative actions, where the AV tailors its explanations and feedback based on the current communicative context. Designing effective human-AV communications requires understanding how these four key factors interact to determine the informational needs necessary for goal success.

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統計資料

"Advances in Autonomous Vehicle (AV) technology promise huge individual and societal benefits, from increased driving safety to reduced environmental impact."
"A well documented example is that people often report a lack of trust in AV decision-making, particularly when the AV's decision procedures are opaque and not human-understandable."
"Lacking situational awareness impedes a person from being able to develop trust, a reliance strategy, or expertise from learning, limiting the usefulness of the technology."

引述

"Achieving necessary situational awareness in human-AV partnership requires bi-directional communication."
"To our knowledge, no prior models or frameworks have examined human-AV communication from the lens of these cognitive and behavioral phenomena."
"The systems view of human-AV joint action and situational awareness presented here can be used to inform a future research agenda for human-AV interaction research and design."

從以下內容提煉的關鍵洞見

Developing Situational Awareness for Joint Action with Autonomous Vehicles

by Robert Kaufm... 於 arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.11800.pdf

Developing Situational Awareness for Joint Action with Autonomous Vehicles

深入探究

How can the framework be extended to account for the dynamic nature of human-AV interactions, where the communicative context may change over time?

In order to address the dynamic nature of human-AV interactions, where the communicative context may change over time, the framework can be extended in several ways:

Real-time Adaptation: The framework can incorporate mechanisms for real-time adaptation of communication strategies based on changing contextual factors. This could involve sensors within the AV monitoring the environment and the passengers, adjusting communication modalities accordingly.

Machine Learning Integration: By integrating machine learning algorithms, the AV can learn from past interactions and dynamically adjust its communication strategies to suit the evolving communicative context. This adaptive learning can help the AV respond effectively to changing situations.

Contextual Awareness: The framework can include a more robust understanding of contextual factors that influence communication needs, such as weather conditions, traffic patterns, and passenger states. By considering a broader range of contextual variables, the AV can tailor its communications more effectively.

Feedback Mechanisms: Implementing feedback mechanisms where users can provide input on the effectiveness of the AV's communication can help in refining and improving the communication strategies over time. This feedback loop can ensure that the AV continuously adapts to meet the evolving communicative context.

How might the framework apply to other human-AI collaboration domains beyond autonomous driving, such as healthcare or military applications?

The framework for developing situational awareness within human-AV systems can be applied to other human-AI collaboration domains such as healthcare or military applications in the following ways:

Healthcare Applications: In healthcare settings, where AI systems are increasingly being used to assist medical professionals, the framework can help in designing effective communication strategies between healthcare providers and AI systems. By considering the action goals, subject-specific traits, and contextual factors, the framework can ensure that AI systems provide relevant and timely information to support medical decision-making.

Military Applications: In military applications, where AI technologies are used for decision support and autonomous operations, the framework can guide the design of communication strategies between military personnel and AI systems. By understanding the shared situation, individual situational awareness, and distributed situational awareness, the framework can enhance coordination and decision-making in complex military environments.

Emergency Response: The framework can also be applied to emergency response scenarios, where AI systems play a crucial role in coordinating rescue operations. By considering the dynamic nature of emergency situations and the need for rapid decision-making, the framework can help in designing communication strategies that support effective collaboration between human responders and AI systems.

Overall, the framework's principles of understanding joint action goals, individual and shared situational awareness, and tailored communication can be adapted to various human-AI collaboration domains to enhance decision-making and coordination in complex environments.