Analyzing Driver Strategy for Scheduled Takeovers Under the Effect of Multi-stage Takeover Request Design

Tan, Xiaomei; Zhang, Yiqi

doi:10.1177/21695067231192422

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The four designs were tested in the experiment following a Latin square design. Details of the experiment can be found in Tan (2023). The data was sourced from a driving simulator STISIM Drive M300WS-Console system) and a wearable eye-tracking pair of glasses (Tobii Pro Glasses 2).…”

Section: Methodsmentioning

confidence: 99%

Predicting Driver Takeover Decisions in Conditionally Automated Vehicles with a Gaze-based Deep Learning Model

Gupta,

Tan,

Zhang

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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This study proposes a novel methodology for modeling driver takeover behavior in conditionally automated vehicles (AVs) when exiting a freeway using deep learning (DL) network architectures. While previous research has focused on modeling takeover time in emergency scenarios, which require quick responses, these models may not be applicable to scheduled, non-time-critical takeovers. In such situations, drivers may employ varying strategies and take longer to resume control of the vehicle when there is no time pressure. To address this problem, a deep learning architecture based on a convolutional neural network (CNN) was implemented to predict drivers’ takeover behaviors in scheduled takeovers. The model was trained on drivers’ driving data and eye gaze with varying time windows, facilitating an analysis of drivers’ takeover decisions to various takeover request designs. The model achieved good performance metrics, with an F1 Score of 0.993, a recall of 0.996, and a precision of 0.991. The application of these models holds substantial potential for refining the design of the human-machine interface, specifically in calibrating the takeover request (ToR) lead time, thereby promoting safe freeway exiting takeovers in conditionally AVs.

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Section: Methodsmentioning

confidence: 99%

Predicting Driver Takeover Decisions in Conditionally Automated Vehicles with a Gaze-based Deep Learning Model

Gupta,

Tan,

Zhang

2023

Proceedings of the Human Factors and Ergonomics Society Annual

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Driver Situation Awareness for Regaining Control from Conditionally Automated Vehicles: A Systematic Review of Empirical Studies

Tan,

Zhang

2024

Hum Factors

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Objective An up-to-date and thorough literature review is needed to identify factors that influence driver situation awareness (SA) during control transitions in conditionally automated vehicles (AV). This review also aims to ascertain SA components required for takeovers, aiding in the design and evaluation of human–vehicle interfaces (HVIs) and the selection of SA assessment methodologies. Background Conditionally AVs alleviate the need for continuous road monitoring by drivers yet necessitate their reengagement during control transitions. In these instances, driver SA is crucial for effective takeover decisions and subsequent actions. A comprehensive review of influential SA factors, SA components, and SA assessment methods will facilitate driving safety in conditionally AVs but is still lacking. Method A systematic literature review was conducted. Thirty-four empirical research articles were screened out to meet the criteria for inclusion and exclusion. Results A conceptual framework was developed, categorizing 23 influential SA factors into four clusters: task/system, situational, individual, and nondriving-related task factors. The analysis also encompasses an examination of pertinent SA components and corresponding HVI designs for specific takeover events, alongside an overview of SA assessment methods for conditionally AV takeovers. Conclusion The development of a conceptual framework outlining influential SA factors, the examination of SA components and their suitable design of presentation, and the review of SA assessment methods collectively contribute to enhancing driving safety in conditionally AVs. Application This review serves as a valuable resource, equipping researchers and practitioners with insights to guide their efforts in evaluating and enhancing driver SA during conditionally AV takeovers.

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Analyzing Driver Strategy for Scheduled Takeovers Under the Effect of Multi-stage Takeover Request Design

Cited by 2 publications

References 13 publications

Predicting Driver Takeover Decisions in Conditionally Automated Vehicles with a Gaze-based Deep Learning Model

Predicting Driver Takeover Decisions in Conditionally Automated Vehicles with a Gaze-based Deep Learning Model

Driver Situation Awareness for Regaining Control from Conditionally Automated Vehicles: A Systematic Review of Empirical Studies

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