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

TL;DR

The paper tackles the problem of driver understanding of Level 3 ADS limitations and RtI triggers, which can become ambiguous when multiple cues are present. It introduces a voice-based educational HMI that provides RtI trigger cues and post-trigger reason explanations to cultivate an accurate mental model of ADS limitations. In a driving-simulator study with three HMI conditions, the trigger-cue-and-reason design improved post-experiment comprehension and reduced collisions, while also promoting earlier proactive take-overs. The findings support the practical value of continuous, explainable RtI education for safer real-time ADS interactions and informed driver engagement during take-over scenarios.

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.

Figures (13)

• Figure 1: The ADS issues an RtI due to the sharp curve ahead rather than the thin fog. In such a complex traffic scenario, the driver may encounter challenges in identifying the cause of the RtI trigger.
• Figure 2: Proposed HMI that provides the driver information about the trigger cues and reasons for the RtI.
• Figure 3: Driving simulator used in this experiment with a HUD displaying ADS status and a speaker for RtI HMI voice cues.
• Figure 4: The three RtI HMIs used in the experiment for two RtI triggers (thick fog and sharp curve) in take-over (TO) progress. All voice cues were presented in Japanese. The voice durations shown in the figures correspond to the Japanese utterances.
• Figure 5: The various traffic scenarios used in the experiment, where the red car is the ego AV with a speed of 80 km/h. The scenarios (a), (b), and (c) are used in the learning phase I; (d) and (e) are used in the learning phase II; and (f) and (g) are used in the learning phase III; (h) is used in the test phase.