An Educational Human Machine Interface Providing Request-to-Intervene Trigger and Reason Explanation for Enhancing the Driver's Comprehension of ADS's System Limitations

2026-02-12

AI Summary

This study addresses the safety challenges associated with Level 3 automated driving systems when they exceed their Operational Design Domain (ODD) and require driver takeover, particularly in complex traffic scenarios where drivers often struggle to understand the reason for the transition request. To mitigate this issue, the authors propose an educational voice-based human–machine interface that provides real-time verbal explanations of the system’s triggering conditions and inherent limitations during takeover requests. This approach uniquely integrates causal explanations into the takeover interaction through an educational design paradigm, aiming to continuously enhance drivers’ understanding of the automated driving system’s capability boundaries. A between-subjects driving simulator experiment demonstrates that the proposed method significantly improves users’ awareness of system limitations, reduces collision rates, and effectively promotes proactive driver intervention in cases of takeover failure.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Application Domains: Mobility, Driving & Flight
• Reasoning under Uncertainty: Causality

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Level 3 automated driving systems (ADS) have attracted significant attention and are being commercialized. A level 3 ADS prompts the driver to take control by issuing a request to intervene (RtI) when its operational design domains (ODD) are exceeded. However, complex traffic situations can cause drivers to perceive multiple potential triggers of RtI simultaneously, causing hesitation or confusion during take-over. Therefore, drivers need to clearly understand the ADS's system limitations to ensure safe take-over. This study proposes a voice-based educational human machine interface~(HMI) for providing RtI trigger cues and reason to help drivers understand ADS's system limitations. The results of a between-group experiment using a driving simulator showed that incorporating effective trigger cues and reason into the RtI was related to improved driver comprehension of the ADS's system limitations. Moreover, most participants, instructed via the proposed method, could proactively take over control of the ADS in cases where RtI fails; meanwhile, their number of collisions was lower compared with the other RtI HMI conditions. Therefore, using the proposed method to continually enhance the driver's understanding of the system limitations of ADS through the proposed method is associated with safer and more effective real-time interactions with ADS.

Problem

• Level 3 automated driving systems
• request to intervene
• system limitations
• driver comprehension
• operational design domain

Innovation

• Educational HMI
• Request-to-Intervene
• System Limitations Explanation
• Level 3 Automated Driving
• Driver Comprehension