Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

Abstract

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

Intervening, Teaming, Delegating: Creating Engaging Automation Experiences

“…The requirements for such control takeover situations will differ between the respective tasks and contexts, such as when driving automated cars [6,16], managing fleets of automated vehicles in a logistics center [15], or when surveying the quality of an automated manufacturing process [3,18]. The long-term formation of trust in automation and corresponding design patterns for monitoring and interventions are increasingly provided by the HCI research community [8,14], but their applicability across other tasks and contexts is not straightforward.…”

Engaging with Automation: Understanding and Designing for Operation, Appropriation, and Behaviour Change

“…As described above, the irony of this development is that users are no longer engaged with the automated process, and are thus not able to act quickly when the system needs help. This is a major challenge, e.g., with autonomous cars that may need a human to quickly take over in surprising circumstances [4,16]. Similarly, other automated appliances in our everyday surroundings might prepare their users for manual interventions as well.…”

Predicting Takeover Quality in Conditionally Autonomous Vehicles based on Takeover Request Modalities, Driver Physiological State and the Environment

“…Those modality can be used individually or conjointly, and the multimodality of a TOR was shown to indicate urgency and lead to shorter reaction time, compared to a unimodal TOR . Overall, review of the literature showed the potential for advanced UI in conditionally autonomous vehicles (Kim, 2021).…”