The Effects of Transparency and Reliability of In-Vehicle Intelligent Agents on Driver Perception, Takeover Performance, Workload and Situation Awareness in Conditionally Automated Vehicles

Abstract

In the context of automated vehicles, transparency of in-vehicle intelligent agents (IVIAs) is an important contributor to driver perception, situation awareness (SA), and driving performance. However, the effects of agent transparency on driver performance when the agent is unreliable have not been fully examined yet. This paper examined how transparency and reliability of the IVIAs affect drivers’ perception of the agent, takeover performance, workload and SA. A 2 × 2 mixed factorial design was used in this study, with transparency (Push: proactive vs. Pull: on-demand) as a within-subjects variable and reliability (high vs. low) as a between-subjects variable. In a driving simulator, 27 young drivers drove with two types of in-vehicle agents during the conditionally automated driving. Results suggest that transparency influenced participants’ perception on the agent and perceived workload. High reliability agent was associated with higher situation awareness and less effort, compared to low reliability agent. There was an interaction effect between transparency and reliability on takeover performance. These findings could have important implications for the continued design and development of IVIAs of the automated vehicle system.

Reliable and transparent in-vehicle agents lead to higher behavioral trust in conditionally automated driving systems

“…For example, in an online study, Lanzer et al (2021) showed that unreliable automation led to higher willingness to takeover. On the other hand, Zang and Jeon (2022) showed that a combination of high reliability and high transparency level led to higher maximum lateral acceleration in takeover, and a combination of low reliability and low transparency level also led to the similar outcome. Therefore, more research is still required on the effects of system reliability in automated vehicles.…”

“…Both objective and subjective measures were used to evaluate the effects of reliability and transparency on drivers’ performance and perception. Objective measures mainly included takeover request (TOR) compliance ( Zang and Jeon, 2022 ) and takeover performance. The number of times participants complied with the agents’ TOR was collected as an indicator for their decision-making.…”

Trust Development and Explainability: A Longitudinal Study with a Personalized Assistive System

“…For an AI-based system to be considered reliable, behavioral consistency over time is required, as reliability is a property that can be attributed to a system only in relation to its past performance [47][48][49]. In turn, when an AI-based system proves reliable, people grow confident in its capacity; trust and familiarity stabilize, and performance improves [17,[50][51][52].…”

“…This is relevant with regard to the increasing popularity of models such as neural networks (black box models) and is in light of their intrinsic opaqueness and inscrutability [13,15]. It is all the more relevant in interaction contexts that entail potential risks for the users, such as with automated vehicles [16,17]. In this regard, researchers argue that making the causal chains behind models' decisions interpretable is likely to help people understand the rationales behind those decisions and, importantly, calibrate their expectations and trust [18][19][20].…”

Exploring the role of robotics in Alzheimer’s disease care: innovative methods and applications

“…Additionally, Milo R25 and Maggie may also serve as semi humanoid robots for individuals with AD. An eminent characteristic of this automaton lies in its ability to dispense instructional modules engagingly and compassionately (Salemi et al , 2005; Zang and Jeon, 2022).…”