Designing a Multimodal, Environmental Interface to Enhance Time-Based Urgency Perception of Takeover Requests and Understanding of Takeover Reasons in Semi-Autonomous Vehicles

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KIM-THESIS-2024.pdf (5.27 MB)
Scenario 1_baseline_1.mp4 (17.53 MB)
Scenario 1_CAA.mp4 (16.75 MB)
Scenario 2_baseline.mp4 (16.94 MB)
Scenario 2_CAA.mp4 (16.7 MB)
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Kim, Seryung
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https://hdl.handle.net/1853/80145
Abstract
The emergence of Semi-Autonomous Vehicles (SAVs), also known as Level 3 Autonomous Vehicles (AVs), creates a need for timely intervention from humans where the drivers need to take over the vehicle control when necessary. However, current SAVs are limited in providing evolving context or urgency level due to their static alerts. The driver may, therefore, misjudge the situation and lead to undesired outcomes: underestimation can result in a delay of action, whereas overestimation can cause panic and trigger erratic or reckless behavior. This paper investigates the design of a multimodal TOR interface to counter this problem by improving human drivers’ perception of time-based urgency and the understanding of the TOR reasons. Two different designs were established for comparison – the baseline design was designed based on current state-of-the-art TOR interfaces (i.e., Autopilot of Tesla, Pilot Assist of Volvo, Super Cruise of Cadillac, etc.), which include static visual cues (i.e., static icons) and auditory cues (i.e., acoustic alerts), whereas the Context-Aware Adaptive (CAA) design has more dynamic visualizations (i.e., animated icons and ambient lighting) and auditory signals (i.e., informative speech and acoustic alerts). Four relevant driving scenarios, later simulated in a Virtual Reality (VR) environment, were identified through a focus group study as the settings for the summative evaluations with 25 participants. Our results show that our CAA design significantly improved people’s time-based urgency perception and the understanding of the TOR alert reasons for some scenarios. This work adds to the literature on Human-Machine Interface (HMI) design for SAVs, specifically on designing a dynamic, multimodal, and environmental interface that improves vehicle-to-driver communication in TOR situations.
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2024-12-16
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