- Title
- Attentional allocation and automation: reconstruction of operator situation awareness during takeover
- Creator
- McKerral, Angus John Alexander
- Relation
- University of Newcastle Research Higher Degree Thesis
- Resource Type
- thesis
- Date
- 2023
- Description
- Research Doctorate - Doctor of Philosophy (PhD)
- Description
- Increasing vehicle automation is changing the way the driving task is executed on the road, with human drivers serving an increasingly supervisory role. However, we humans are poor supervisors, and deciding when to take back control is a key challenge for the driver. Similarly, manufacturers are searching for metrics to determine whether the driver is ready to accept control when a system boundary is reached. In recent years, extensive research has identified a range of factors that influence takeover performance, however the underlying cognitive mechanisms that predict performance outcomes are poorly understood. This thesis employs a series of experiments to characterise ‘expert’ cognition in a manual environment – namely, higher situation awareness – and investigate the factors that contribute to enhancing situation awareness (SA) in an automated context. The first and second chapters will provide an overview of the literature and set out the problem of cognitive re-engagement during takeover from automated driving systems. The following experimental chapters will examine both contextual features of the automated driving environment (passive fatigue and non-driving related task [NDRT] engagement) and individual factors unique to the driver (driving anxiety and physiological response metrics). A novel measure of situation awareness is adapted from the Perceptual Cycle Model (PCM) to differentiate expert and non-expert drivers, which is then applied to assess novice automated vehicle-operator SA in critical simulated takeover scenarios. Passive fatigue is shown to impair SA construction while NDRT engagement is found to ameliorate this effect. The interaction between these factors and individual driver characteristics are assessed, with driving anxiety shown to influence SA construction, highlighting the need to tailor human-machine interactions to the characteristics of each operator. Finally, ECG (electrocardiogram signal) and GSR (galvanic skin response) measures were assessed for their utility in predicting situation awareness construction, with the aim of promoting user-aware automated vehicle systems that are capable of adapting to variability in driver state. This thesis makes multiple contributions to the extant literature (including manuscripts published and under review) and emphasises the utility of SA as a core component of cognitive performance underpinning automated vehicle takeover.
- Subject
- attention; cognition; situation awareness; automation; automated vehicles
- Identifier
- http://hdl.handle.net/1959.13/1477721
- Identifier
- uon:50022
- Rights
- Copyright 2023 Angus John Alexander McKerral
- Language
- eng
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| View Details Download | ATTACHMENT01 | Thesis | 65 MB | Adobe Acrobat PDF | View Details Download | ||
| View Details Download | ATTACHMENT02 | Abstract | 381 KB | Adobe Acrobat PDF | View Details Download |