In the context of automated vehicles, transparency of in-vehicle intelligent agents (IVIAs) is an important contributor to drivers' 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. The experiments in this Thesis focused on different aspects of IVIA's transparency, such as interaction modes and information levels, and explored their impact on drivers considering different system reliability. In Experiment 1, a 2 x 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, twenty-seven young drivers drove with two types of in-vehicle agents during Level 3 automated driving. Results suggested that participants generally preferred the Push-type agent, as it conveyed a sense of intelligence and competence. The high-reliability agent was associated with higher situation awareness and less workload, compared to the low-reliability agent.
Although Experiment 1 explored the effects of transparency by changing the interaction mode and the accuracy of the information, a theoretical framework was not well outlined regarding how much information should be conveyed and how unreliable information influenced drivers. Thus, Experiment 2 further studied the transparency regrading information level, and the impact of reliability on its effect. A 3 x 2 mixed factorial design was used in this study, with transparency (T1, T2, T3) as a between-subject variable and reliability (high vs. low) as a within-subjects variable. Fifty-three participants were recruited. Results suggested that transparency influenced drivers' takeover time, lane keeping, and jerk. The high-reliability agent was associated with the higher perception of system accuracy and response speed, and longer takeover time than the low-reliability agent. Participants in T2 transparency showed higher cognitive trust, lower workload, and higher situation awareness only when system reliability was high. The results of this study may have significant effects on the ongoing creation and advancement of intelligent agent design in automated vehicles. / Master of Science / This thesis explores the effects of system's transparency and reliability of the in-vehicle intelligent agents (IVIAs) on drivers' performance and perception in the context of automated vehicles. Transparency is defined as the amount of information and the way to be shared with the operator about the function of the system. Reliability refers to the accuracy of the agent's statements. The experiments focused on different aspects of IVIA's transparency, such as interaction modes (proactive vs. on-demand) and information composition (small vs. medium vs. large), and how they impact drivers considering different system reliability. In the experiment, participants were required to drive in the driving simulator and follow the voice command from the IVIAs. A theoretical model called Situation Awareness-based Agent Transparency Model was adopted to build the agent's interactive scripts.
In Experiment 1, 27 young drivers drove with two types of in-vehicle agents during Level 3 automated driving. Results suggested that participants generally preferred the agent that provided information proactively, and it conveyed a sense of intelligence and competence. Also, when the system's reliability is high, participants were found to have higher situation awareness of the environment and spent less effort on the driving tasks, compared to when the system's reliability is low. Our result also showed that these two factors can jointly influence participants' driving performance when they need to take over control from the automated system.
Experiment 2 further studied the transparency regarding the information composition of the agent's voice prompt and the impact of reliability on its effect. A total of 53 participants were recruited, and the results suggested that transparency influenced drivers' takeover time, lane keeping, and jerk. The high-reliability agent was associated with a higher perception of system accuracy and response speed and a longer time to take over when requested than the low-reliability agent. Participants in the medium transparency condition showed higher cognitive trust toward the system, perceived lower workload when driving, and higher situation awareness only when system reliability was high.
Overall, this research highlights the importance of transparency in IVIAs for improving drivers' performance, perception, and situation awareness. The results may have significant implications for the design and advancement of intelligent agents in automated vehicles.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/115652 |
| Date | 05 July 2023 |
| Creators | Zang, Jing |
| Contributors | Industrial and Systems Engineering, Jeon, Myounghoon, Klauer, Charlie, Patrick, Rafael |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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