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Author ORCID Identifier

https://orcid.org/0000-0002-5151-9860

AccessType

Open Access Dissertation

Document Type

dissertation

Degree Name

Doctor of Philosophy (PhD)

Degree Program

Industrial Engineering & Operations Research

Year Degree Awarded

2024

Month Degree Awarded

February

First Advisor

Anuj K. Pradhan

Second Advisor

Jenna Marquard

Third Advisor

Michael Knodler Jr.

Subject Categories

Ergonomics | Industrial Engineering | Operational Research | Operations Research, Systems Engineering and Industrial Engineering | Other Engineering | Other Operations Research, Systems Engineering and Industrial Engineering | Transportation Engineering

Abstract

Advanced Driver Assistance Systems (ADAS) are vehicle automation systems that have become more accessible and prevalent in vehicles in recent years. But the introduction of such technologies introduces new human factors challenges. Past literature suggests that users of vehicle automation lack the necessary and appropriate knowledge about their automation system. This may play a negative role in their hazard avoidance abilities when driving with automation features. Improving mental models and knowledge could generally lead to safer interactions with vehicle automation systems, but any effort to develop hazard avoidance skills when driving with vehicle automation is impeded by the lack of literature regarding the subject. Moreover, it is possible hazard avoidance for vehicle automation may actually differ from that for traditional driving. For vehicle automation, system-related changes occurring internally inside one’s vehicle also impact how the system responds and controls the vehicle. Failure to recognize certain critical system changes may have disastrous consequences. Hence, it is imperative that a new framework for hazard avoidance in the new context of vehicle automation, especially for ADAS features, is conceptualized. Initially, the research focused on realizing exactly this by proposing a conceptual framework for hazard avoidance in the context of vehicle automation by making use of past literary sources on hazard avoidance for traditional driving. Next, the relationship between mental models, training, and hazard avoidance was mapped and each new behavioral construct of hazard avoidance focusing on awareness, detection, and responses based on internal events was assigned potential outcome measure. Next, an observational study was conducted with ten experienced users of Adaptive Cruise Control (ACC). Among them, five were assigned to an eye movements group and five others to a verbal responses group. The eye movement observations gave us insights into how experienced users detect and respond to hazards and how these affect their interactions and responses using their ACC systems. The verbal group also provided insights about the participants’ awareness during the drive which featured several edge-case and normal events. These observations imply that hazard avoidance behaviors actually differ in the context of ADAS compared to traditional driving. The findings from the observational study were leveraged when designing and developing a new training program where drivers would receive an immersive and realistic training experience through a Virtual Reality (VR) headset. The main objective of the training program was to improve the user’s mental models about ACC and also equip them with the necessary skills to avoid hazard during edge case events of ACC. Finally, an evaluation study was conducted with 36 novice ACC users on a driving simulator capable of simulating ACC operations. The participants were equally and randomly assigned to one of three group – the VR group that received the newly designed VR training program; the SD group that received training material with state diagram visualization of ACC and other information derived from owner’s manuals; or the BI group that received basic textual information about ACC. The participants’ mental models before and after training were measured using a mental models survey, and the simulator drive was designed to collect valuable data about the participants interactions with ACC and their hazard avoidance behaviors. Findings revealed that although the VR training program had some impact on the participants' mental models and hazard avoidance behaviors, the impact was not statistically significant. However, the VR training did show significantly positive influences on the participants’ internal glance activities that detect and assess system states, during edge case events. This finding is important since one of the modules of the VR training program was carefully curated to improve driver’s glance behavior when encountering edge case events of ACC. The results also establish the relationships between training and mental models although no significant correlations were found between the participants’ mental models and their hazard avoidance behaviors. However, this does fill a major gap in literature about our understanding about hazard avoidance in the context of vehicle automation and ADAS and could be extended for ADAS features other than ACC or even higher levels of automation. The VR training program can be built upon to include more ADAS features as well leading to better training practices in a rapidly developing world where vehicle automation has become a mainstay.

DOI

https://doi.org/10.7275/36459251

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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