K. Liang
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4 records found
1
This thesis systematically develops an adaptive framework for designing takeover time budgets that account for diverse drivers and situational demands. First, a systematic review synthesises the takeover sequence, identifying factors influencing takeover time and performance, and introduces the concept of the takeover buffer as the safety margin between required and allocated takeover time. Building on this foundation, a driving simulator experiment is conducted to collect behavioural, physiological, operational, and subjective data during takeover situations. Using these data, machine learning models are developed to predict takeover time, revealing that drivers’ perceived Spare Capacity provides substantial predictive power, while extensive driver profiling offers limited additional benefit. The thesis then establishes a multidimensional framework for takeover performance assessment, demonstrating that Situational Awareness primarily influences response efficiency, whereas Spare Capacity has a stronger impact on takeover quality. Finally, these insights are integrated into an adaptive time budget framework that combines predicted takeover time with a preferred takeover buffer to dynamically allocate time budgets.
The proposed framework enables personalised takeover time prediction, multidimensional performance evaluation, and adaptive time budget allocation in conditionally automated driving. In practice, these contributions can support cognition-aware vehicle interfaces, personalised takeover assistance systems, and human-centred automated driving design. Together, they contribute to safer, more reliable, and more comfortable control transitions, supporting the broader deployment and acceptance of automated vehicles. ...
This thesis systematically develops an adaptive framework for designing takeover time budgets that account for diverse drivers and situational demands. First, a systematic review synthesises the takeover sequence, identifying factors influencing takeover time and performance, and introduces the concept of the takeover buffer as the safety margin between required and allocated takeover time. Building on this foundation, a driving simulator experiment is conducted to collect behavioural, physiological, operational, and subjective data during takeover situations. Using these data, machine learning models are developed to predict takeover time, revealing that drivers’ perceived Spare Capacity provides substantial predictive power, while extensive driver profiling offers limited additional benefit. The thesis then establishes a multidimensional framework for takeover performance assessment, demonstrating that Situational Awareness primarily influences response efficiency, whereas Spare Capacity has a stronger impact on takeover quality. Finally, these insights are integrated into an adaptive time budget framework that combines predicted takeover time with a preferred takeover buffer to dynamically allocate time budgets.
The proposed framework enables personalised takeover time prediction, multidimensional performance evaluation, and adaptive time budget allocation in conditionally automated driving. In practice, these contributions can support cognition-aware vehicle interfaces, personalised takeover assistance systems, and human-centred automated driving design. Together, they contribute to safer, more reliable, and more comfortable control transitions, supporting the broader deployment and acceptance of automated vehicles.
Beyond Beeps
Evaluating Soundscapes for Take-Over Situations in Automated Vehicles
In automated vehicles, beeps are widely used as alarms and feedback. However, as automation advances, there is a need to explore subtler, contextually sound-based notifications for non-urgent situations. While auditory interfaces for take-over requests have been studied, limited attention has been given to using soundscapes for such alerts. This paper designed and evaluated soundscapes using existing driving-related sounds–amplified road noise and/or dimmed background music–for scheduled take-over situations. A driving simulator study showed that these soundscapes enhanced reaction time, situation awareness, and acceptance without causing annoyance. Particularly, the combined condition (music dimming and road noise amplifying) supported higher driver awareness and responsiveness. These findings suggest that soundscapes can offer safer, more intuitive take-over alerts by embedding information into familiar audio cues. This study contributes to developing soundscapes as novel alert mechanisms that integrate seamlessly with the driving environment to enhance both safety and user experience in automated vehicles.
Predicting drivers’ takeover time for safe and comfortable vehicle control transitions
The role of spare capacity and driver characteristics
Conditionally automated driving requires drivers to resume vehicle control within constrained time budgets upon receiving takeover requests. Accurately predicting drivers’ takeover time (ToT) is essential for dynamically adjusting time budgets to individual needs across scenarios. This study addresses enduring challenges in reliability and interpretability of ToT prediction models by optimizing predictor selection. Using a driving simulator experiment, we examine the relationship between ToT, driver characteristics, and perceived Spare Capacity (pSC, a cognitive construct from Task-Capability Interface theory) using Category Boosting models. Results show that (i) incorporating 13 additional driver characteristics does not significantly improve prediction accuracy when pSC is already considered; and (ii) individual characteristics influence how drivers cognitively process takeover scenarios, and their predictive contribution likely overlaps with pSC. These findings suggest that monitoring cognitive states may be more effective for ToT prediction than extensive profiling of driver characteristics. This study provides a critical first step toward predictive frameworks for adaptive takeover strategies and offers guidance for designing personalized human–vehicle interactions.
Examining the Role of Driver Perception in Takeover Time
Application of Task-Capability Interface Theory
Conditionally automated driving enables drivers to engage in non-driving-related activities, with the responsibility to take over vehicle control upon request. This takeover process increases the risk of collisions, especially when drivers fail to safely complete takeovers within limited time budgets (i.e., the time offered by automation for takeovers). This phenomenon underlines the significance of providing time budgets that sufficiently accommodate drivers' takeover time (i.e., the time required by drivers to resume conscious control of vehicles). Considering that drivers' takeover time varies significantly across scenarios, this study centres on understanding the role of driver perception in takeover time using the Task-Capability Interface (TCI) theory. The TCI theory suggests that drivers adjust their behaviours based on their perceived task demands and driver capabilities. Accordingly, in a driving simulator experiment featuring diverse traffic densities and distractions, we investigated drivers' takeover time while capturing their perceived task demands and capabilities through a takeover-oriented questionnaire based on established instruments. The results show that drivers generally have longer takeover time as their perceived task demand rises, perceived driver capability diminishes, and perceived spare capacity (perceived driver capability minus perceived task demand) decreases. These patterns fluctuate under conditions of low perceived task demand or high perceived driver capability. When both conditions coincide, drivers necessitate a considerably longer time to regain vehicle control. Our findings on takeover time contribute to the development of strategies aimed at predicting drivers' takeover time, optimizing time budgets, fostering human-centred vehicle design, and enhancing the safety of conditionally automated driving.