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Generative AI and Attentive User Interfaces: Five Strategies to Enhance Take-Over Quality in Automated Driving

Patrick Ebel

TL;DR

The paper addresses safeguarding Situation Awareness during Take-Over Requests in Level 3 automated driving by proposing Attentive User Interfaces powered by generative AI to subtly cue drivers. It introduces five strategies that leverage LLMs and diffusion models—Interactive Scenarios, Conversational Primers, Context-Aware/Personalized TORs, Subtle Nudges, and Ambient Scene Generation—and presents a system architecture with a TOR Generator, Conversation Agent, and Scenario Generator that personalizes interventions using sensor data and a Digital Persona. These contributions aim to improve TOR quality and road safety while maintaining driver autonomy, though they acknowledge safety, regulatory, and evaluation challenges inherent to safety-critical, non-deterministic AI systems. The practical impact lies in enabling smoother, safer handovers through non-intrusive, context-aware assistance, provided that risks like hallucination and bias are mitigated and compliance is ensured.

Abstract

As the automotive world moves toward higher levels of driving automation, Level 3 automated driving represents a critical juncture. In Level 3 driving, vehicles can drive alone under limited conditions, but drivers are expected to be ready to take over when the system requests. Assisting the driver to maintain an appropriate level of Situation Awareness (SA) in such contexts becomes a critical task. This position paper explores the potential of Attentive User Interfaces (AUIs) powered by generative Artificial Intelligence (AI) to address this need. Rather than relying on overt notifications, we argue that AUIs based on novel AI technologies such as large language models or diffusion models can be used to improve SA in an unconscious and subtle way without negative effects on drivers overall workload. Accordingly, we propose 5 strategies how generative AI s can be used to improve the quality of takeovers and, ultimately, road safety.

Generative AI and Attentive User Interfaces: Five Strategies to Enhance Take-Over Quality in Automated Driving

TL;DR

The paper addresses safeguarding Situation Awareness during Take-Over Requests in Level 3 automated driving by proposing Attentive User Interfaces powered by generative AI to subtly cue drivers. It introduces five strategies that leverage LLMs and diffusion models—Interactive Scenarios, Conversational Primers, Context-Aware/Personalized TORs, Subtle Nudges, and Ambient Scene Generation—and presents a system architecture with a TOR Generator, Conversation Agent, and Scenario Generator that personalizes interventions using sensor data and a Digital Persona. These contributions aim to improve TOR quality and road safety while maintaining driver autonomy, though they acknowledge safety, regulatory, and evaluation challenges inherent to safety-critical, non-deterministic AI systems. The practical impact lies in enabling smoother, safer handovers through non-intrusive, context-aware assistance, provided that risks like hallucination and bias are mitigated and compliance is ensured.

Abstract

As the automotive world moves toward higher levels of driving automation, Level 3 automated driving represents a critical juncture. In Level 3 driving, vehicles can drive alone under limited conditions, but drivers are expected to be ready to take over when the system requests. Assisting the driver to maintain an appropriate level of Situation Awareness (SA) in such contexts becomes a critical task. This position paper explores the potential of Attentive User Interfaces (AUIs) powered by generative Artificial Intelligence (AI) to address this need. Rather than relying on overt notifications, we argue that AUIs based on novel AI technologies such as large language models or diffusion models can be used to improve SA in an unconscious and subtle way without negative effects on drivers overall workload. Accordingly, we propose 5 strategies how generative AI s can be used to improve the quality of takeovers and, ultimately, road safety.
Paper Structure (12 sections, 2 figures)

This paper contains 12 sections, 2 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Take-Over Requests in Automated Driving
  4. Leveraging Attentive User Interface to Improve Take-Over Requests
  5. How Generative AI can Enhance TOR Quality
  6. Interactive Scenarios
  7. Conversational Primers
  8. Context-Aware and Personalized TORs
  9. Subtle Nudges
  10. Ambient Scene Generation
  11. Proposed System Architecture
  12. Discussion and Conclusion

Figures (2)

  • Figure 1: A hypothetical scenario: A person interacting with their mobile phone while driving in a Level 3 automated car. The current driving situation is under control and there is no reason to trigger a take-over request. However, the intelligent TOR assistant has detected a traffic jam ahead that may require the driver to take over. Knowing that the driver is engaged in a task on the smartphone, the TOR assistant decides to play an AI-generated video of the upcoming traffic situation on the center stack touchscreen. The driver will subconsciously recognize the moving scene on the center stack touchscreen and be more aware of the upcoming traffic scenario. The increased situation awareness will lead to a an increase in take-over quality.
  • Figure 2: System Architecture