A systematic review of functions and design features of in-vehicle agents

Lee, Seul Chan; Jeon, Myounghoon

doi:10.1016/j.ijhcs.2022.102864

Cited by 20 publications

(4 citation statements)

References 50 publications

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“…According to Chen and colleagues [8], transparency enables a user's comprehension of the intention, performance, future actions, and reasoning of automated processes. Such in-depth SAT information can be used to help users determine not only whether an agent made an error, but also why it made an error, which is a crucial consideration to develop appropriate trust and dependence [4,12]. For example, according to Koo and colleagues [3], participants drove more safely when the agent provided explanations for both "how" and "why" the imminent actions were chosen by the automated vehicle.…”

Section: Transparency Design Of Interactionmentioning

confidence: 99%

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

Zang

Jeon

2022

MTI

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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.

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Section: Transparency Design Of Interactionmentioning

confidence: 99%

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

Zang

Jeon

2022

MTI

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“…An intelligent agent is a computing system that provides intelligent support for user tasks (Lee & Jeon, 2022). With the development of artificial intelligence technology, various intelligent agents have been proposed and deployed.…”

Section: Introductionmentioning

confidence: 99%

The effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload: Evidence from subjective reporting, ECG, and fNIRS

Ren,

Guo,

et al. 2024

Hum Ftrs & Erg Mfg Svc

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To improve the interaction between drivers and the in‐vehicle information system (IVIS), various intelligent agents, such as robot agents, virtual agents, and voice‐only agents, have been integrated into vehicles. However, it is not yet clear which type of in‐vehicle agent is best suited to the driving context. This study aims to investigate the effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload. In a within‐subject simulated driving experiment, 22 participants interacted with three different in‐vehicle agents (smartphone agent, robot agent, and virtual agent). Functional near‐infrared spectroscopy and electrocardiogram (ECG) were used to record prefrontal cortex activation and electrical changes associated with cardiac activity during simulated driving, respectively. The results show that the smartphone agent had the lowest perceived usability scores, oxygenated hemoglobin concentration variation (ΔHbO), and maximum ECG signal variation compared to baseline. There were no statistical differences in cognitive workload, perceived usability scores, brain area activation, and ECG signals between the robot agent and the virtual agent. The research findings demonstrate the positive effects of the anthropomorphic appearance of in‐vehicle agents on perceived usability and contribute to improving the design of in‐vehicle intelligent agents.

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“…The use of in-vehicle agents (IVAs) plays a critical role in communication with drivers in CAVs. These IVAs are “anthropomorphized intelligent systems that can interact with drivers using natural human language” ( Lee and Jeon, 2022 ). Due to the current technology, these can vary in reliability which, along with emotions, can have a strong impact on the effectiveness of driver-agent interaction ( Lee and Jeon, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

“…These IVAs are “anthropomorphized intelligent systems that can interact with drivers using natural human language” ( Lee and Jeon, 2022 ). Due to the current technology, these can vary in reliability which, along with emotions, can have a strong impact on the effectiveness of driver-agent interaction ( Lee and Jeon, 2022 ). To investigate the influence of reliability of IVAs, we created two separate reliability levels based on the percentage of correctly presented information: high (100% reliability) and low (67% reliability) conditions.…”

Section: Introductionmentioning

confidence: 99%

Happiness and high reliability develop affective trust in in-vehicle agents

Zieger

Dong²,

Taylor³

et al. 2023

Front. Psychol.

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The advancement of Conditionally Automated Vehicles (CAVs) requires research into critical factors to achieve an optimal interaction between drivers and vehicles. The present study investigated the impact of driver emotions and in-vehicle agent (IVA) reliability on drivers’ perceptions, trust, perceived workload, situation awareness (SA), and driving performance toward a Level 3 automated vehicle system. Two humanoid robots acted as the in-vehicle intelligent agents to guide and communicate with the drivers during the experiment. Forty-eight college students participated in the driving simulator study. The participants each experienced a 12-min writing task to induce their designated emotion (happy, angry, or neutral) prior to the driving task. Their affective states were measured before the induction, after the induction, and after the experiment by completing an emotion assessment questionnaire. During the driving scenarios, IVAs informed the participants about five upcoming driving events and three of them asked for the participants to take over control. Participants’ SA and takeover driving performance were measured during driving; in addition, participants reported their subjective judgment ratings, trust, and perceived workload (NASA-TLX) toward the Level 3 automated vehicle system after each driving scenario. The results suggested that there was an interaction between emotions and agent reliability contributing to the part of affective trust and the jerk rate in takeover performance. Participants in the happy and high reliability conditions were shown to have a higher affective trust and a lower jerk rate than other emotions in the low reliability condition; however, no significant difference was found in the cognitive trust and other driving performance measures. We suggested that affective trust can be achieved only when both conditions met, including drivers’ happy emotion and high reliability. Happy participants also perceived more physical demand than angry and neutral participants. Our results indicated that trust depends on driver emotional states interacting with reliability of the system, which suggested future research and design should consider the impact of driver emotions and system reliability on automated vehicles.

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A systematic review of functions and design features of in-vehicle agents

Cited by 20 publications

References 50 publications

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

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

The effect of in‐vehicle agent embodiment on drivers' perceived usability and cognitive workload: Evidence from subjective reporting, ECG, and fNIRS

Happiness and high reliability develop affective trust in in-vehicle agents

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