Lee Skrypchuk - Academia.edu (original) (raw)

Papers by Lee Skrypchuk

Increased levels of automation in driving can reduce drivers’ situation-awareness and cause errat... more Increased levels of automation in driving can reduce drivers’ situation-awareness and cause erratic changes to workload and skills degradation following prolonged exposure. In addition, drivers (particularly those who are vulnerable to the onset of boredom/fatigue) may engage in non-driving related, and potentially distracting, secondary tasks. Understanding the behavioural cues associated with this change in driver state can assist in the design and development of future driver monitoring systems that intervene in instances where a driver exhibits ‘high’ levels of distraction. The aim of this study was to explore the behavioural cues associated with distraction caused by a non-driving related secondary task (pseudo-text reading) during manual, partially-automated and highly-automated driving in a medium- fidelity driving simulator. Results from thirty drivers show that highly-automated driving was characterised by reduced workload, increased secondary task times and longer in-vehic...

Usability and User Experience

As in-vehicle infotainment systems become increasingly complex, and as manufacturers increasingly... more As in-vehicle infotainment systems become increasingly complex, and as manufacturers increasingly move functions and features into the in-vehicle screen, interacting with these systems is resulting in increased demand, eyes-off-the-road time, and task completion time. To combat this complexity, some manufacturers have incorporated voice assistants into their vehicles, allowing drivers to speak to their vehicles to perform tasks rather than use touch. However, these assistants currently offer a limited feature set, and are generally passive, requiring manual activation. Here we outline early, but on-going work looking at techniques that can be used to nudge users towards using voice. Participants were presented with 6 prototype in-vehicle infotainment systems (IVIS), which varied in terms of how they nudged participants towards using voice and asked to perform a series of representative in-vehicle tasks. Data shows the most effective method for nudging was automatic activation of the...

Driver Acceptance of New Technology, 2018

Designing Interaction and Interfaces for Automated Vehicles, 2021

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, 2017

In order to develop an advanced haptic take-over system for highly automated vehicles, research i... more In order to develop an advanced haptic take-over system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analysed. In order to identify the key parameters of the driver-steering-wheel coupled system and investigate the system properties under different situations, experiments with drive-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the test results, key parameters of the transfer function and system properties are identified and investigated. The data and characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These test results identified system properties that provide a good foundation for the development of a haptic take-over control system for automated vehicles.

Computational Science and Its Applications – ICCSA 2017, 2017

Automatic classification of drivers' mental states is an important yet relatively unexplored topi... more Automatic classification of drivers' mental states is an important yet relatively unexplored topic. In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We present our video segmentation and annotation methodology of a spontaneous dataset of natural driving videos from 10 different drivers. We also present our real-time annotation tool used for labelling the dataset via an emotion perception experiment and discuss the challenges faced in obtaining the ground truth labels. Finally, we present a methodology for automatic classification of drivers' mental states. We compare SVM models trained on our dataset with an existing nearest neighbour model pre-trained on posed dataset, using facial Action Units as input features. We demonstrate that our temporal SVM approach yields better results. The dataset's extracted features and validated emotion labels, together with the annotation tool, will be made available to the research community.

Designing Interaction and Interfaces for Automated Vehicles, 2021

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017

Driving is a cognitively demanding task, and many current navigation systems present confusing gu... more Driving is a cognitively demanding task, and many current navigation systems present confusing guidance instructions that add to the distraction. Human navigators, by contrast, schedule their advice to minimise distraction, and phrase instructions in terms of visible landmarks to avoid confusion. In this paper, we present the basis for a 'natural navigation' system which interprets distances as references to landmarks. We use Extended Kalman Filtering to integrate visual odometry with other sensor data in order to obtain precise vehicle motion, then, based on the filtered motion parameters, we characterize recognised visual landmarks as locations on the navigational map. The navigation system can then use references to these landmarks in its driver instructions rather than absolute distances. Experimental results show that landmarks can be located on the navigational map with sufficient accuracy using normal vehicle telemetry and a dashboard camera.

Advances in Intelligent Systems and Computing, 2018

Drawing together the strengths of Ecological Interface Design (EID) and inclusive User Centred De... more Drawing together the strengths of Ecological Interface Design (EID) and inclusive User Centred Design (UCD), UCEID is a novel Human Factors (HF) method for complex sociotechnical systems. This method ensures user needs are appropriately represented in the constraints based models provided by the Cognitive Work Analysis (CWA) framework. The range of methodological approaches adopted, the advantages, disadvantages, tools and training times of UCEID are described. Examples of how to apply the method to the domain of automated driving to produce design concepts are provided, focusing on interactions between drivers and semi autonomous vehicles for a planned BASt level 3 vehicle to driver handover.

Automotive Human Centred Design Methods, 2021

Designing Interaction and Interfaces for Automated Vehicles, 2021

Designing Interaction and Interfaces for Automated Vehicles, 2021

Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2018

This paper reports on two studies investigating how following a lead vehicle could act as a metap... more This paper reports on two studies investigating how following a lead vehicle could act as a metaphor for an Augmented Reality (AR) system to support navigation tasks. For the first formative study, 34 participants completed a video-based evaluation of the role of a real lead vehicle when navigating a coherent journey. Verbal protocols indicated that a lead vehicle may be a valuable navigation aid at a range of different junction types, but not where drivers may desire a preview of upcoming steps or their overall orientation. A subsequent driving simulator study with 22 participants examined whether an AR lead vehicle may support drivers when navigating at complex junctions, specifically large multi-exit roundabouts. The virtual car led to good navigation and driving performance, which was comparable to a more traditional screen-fixed interface. Overall, this work demonstrates that a virtual lead vehicle may be beneficial within AR navigation devices.

Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2018

Predictive touch is an HMI technology that relies on inferring, early in the pointing gesture, th... more Predictive touch is an HMI technology that relies on inferring, early in the pointing gesture, the interface item a driver or passenger intends to select on an in-vehicle display [1, 2]. It simplifies and expedites the selection task, thereby reducing the associated interaction effort. This paper presents two studies on drivers using predictive touch and focuses on evaluating the best means to facilitate selecting the intended on-display item. This includes immediate midair selection with the system autonomously auto-selecting the predicted interface component, hover/dwell and drivers pressing a button on the steering wheel to execute the selection action. These were arrived at in an expert workshop study with twelve participants. The results of the subsequent evaluation study with twenty four participants demonstrate, using quantitative and qualitative measures, that immediate mid-air selection is a promising assistive scheme, where drivers need not touch a physical surface to select interface components, thus touch-free control.

Increased levels of automation in driving can reduce drivers’ situation-awareness and cause errat... more Increased levels of automation in driving can reduce drivers’ situation-awareness and cause erratic changes to workload and skills degradation following prolonged exposure. In addition, drivers (particularly those who are vulnerable to the onset of boredom/fatigue) may engage in non-driving related, and potentially distracting, secondary tasks. Understanding the behavioural cues associated with this change in driver state can assist in the design and development of future driver monitoring systems that intervene in instances where a driver exhibits ‘high’ levels of distraction. The aim of this study was to explore the behavioural cues associated with distraction caused by a non-driving related secondary task (pseudo-text reading) during manual, partially-automated and highly-automated driving in a medium- fidelity driving simulator. Results from thirty drivers show that highly-automated driving was characterised by reduced workload, increased secondary task times and longer in-vehic...

Usability and User Experience

As in-vehicle infotainment systems become increasingly complex, and as manufacturers increasingly... more As in-vehicle infotainment systems become increasingly complex, and as manufacturers increasingly move functions and features into the in-vehicle screen, interacting with these systems is resulting in increased demand, eyes-off-the-road time, and task completion time. To combat this complexity, some manufacturers have incorporated voice assistants into their vehicles, allowing drivers to speak to their vehicles to perform tasks rather than use touch. However, these assistants currently offer a limited feature set, and are generally passive, requiring manual activation. Here we outline early, but on-going work looking at techniques that can be used to nudge users towards using voice. Participants were presented with 6 prototype in-vehicle infotainment systems (IVIS), which varied in terms of how they nudged participants towards using voice and asked to perform a series of representative in-vehicle tasks. Data shows the most effective method for nudging was automatic activation of the...

Driver Acceptance of New Technology, 2018

Designing Interaction and Interfaces for Automated Vehicles, 2021

IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, 2017

In order to develop an advanced haptic take-over system for highly automated vehicles, research i... more In order to develop an advanced haptic take-over system for highly automated vehicles, research into the driver's neuromuscular dynamics is needed. In this paper a dynamic model of drivers' neuromuscular interaction with a steering wheel is firstly established. The transfer function and the natural frequency of the systems are analysed. In order to identify the key parameters of the driver-steering-wheel coupled system and investigate the system properties under different situations, experiments with drive-in-the-loop are carried out. For each test subject, two steering tasks, namely the passive and active steering tasks, are instructed to be completed. Furthermore, during the experiments, subjects manipulated the steering wheel with two distinct postures and three different hand positions. Based on the test results, key parameters of the transfer function and system properties are identified and investigated. The data and characteristics of the driver neuromuscular system are discussed and compared with respect to different steering tasks, hand positions and driver postures. These test results identified system properties that provide a good foundation for the development of a haptic take-over control system for automated vehicles.

Computational Science and Its Applications – ICCSA 2017, 2017

Automatic classification of drivers' mental states is an important yet relatively unexplored topi... more Automatic classification of drivers' mental states is an important yet relatively unexplored topic. In this paper, we define a taxonomy of a set of complex mental states that are relevant to driving, namely: Happy, Bothered, Concentrated and Confused. We present our video segmentation and annotation methodology of a spontaneous dataset of natural driving videos from 10 different drivers. We also present our real-time annotation tool used for labelling the dataset via an emotion perception experiment and discuss the challenges faced in obtaining the ground truth labels. Finally, we present a methodology for automatic classification of drivers' mental states. We compare SVM models trained on our dataset with an existing nearest neighbour model pre-trained on posed dataset, using facial Action Units as input features. We demonstrate that our temporal SVM approach yields better results. The dataset's extracted features and validated emotion labels, together with the annotation tool, will be made available to the research community.

Designing Interaction and Interfaces for Automated Vehicles, 2021

2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017

Driving is a cognitively demanding task, and many current navigation systems present confusing gu... more Driving is a cognitively demanding task, and many current navigation systems present confusing guidance instructions that add to the distraction. Human navigators, by contrast, schedule their advice to minimise distraction, and phrase instructions in terms of visible landmarks to avoid confusion. In this paper, we present the basis for a 'natural navigation' system which interprets distances as references to landmarks. We use Extended Kalman Filtering to integrate visual odometry with other sensor data in order to obtain precise vehicle motion, then, based on the filtered motion parameters, we characterize recognised visual landmarks as locations on the navigational map. The navigation system can then use references to these landmarks in its driver instructions rather than absolute distances. Experimental results show that landmarks can be located on the navigational map with sufficient accuracy using normal vehicle telemetry and a dashboard camera.

Advances in Intelligent Systems and Computing, 2018

Drawing together the strengths of Ecological Interface Design (EID) and inclusive User Centred De... more Drawing together the strengths of Ecological Interface Design (EID) and inclusive User Centred Design (UCD), UCEID is a novel Human Factors (HF) method for complex sociotechnical systems. This method ensures user needs are appropriately represented in the constraints based models provided by the Cognitive Work Analysis (CWA) framework. The range of methodological approaches adopted, the advantages, disadvantages, tools and training times of UCEID are described. Examples of how to apply the method to the domain of automated driving to produce design concepts are provided, focusing on interactions between drivers and semi autonomous vehicles for a planned BASt level 3 vehicle to driver handover.

Automotive Human Centred Design Methods, 2021

Designing Interaction and Interfaces for Automated Vehicles, 2021

Designing Interaction and Interfaces for Automated Vehicles, 2021

Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2018

This paper reports on two studies investigating how following a lead vehicle could act as a metap... more This paper reports on two studies investigating how following a lead vehicle could act as a metaphor for an Augmented Reality (AR) system to support navigation tasks. For the first formative study, 34 participants completed a video-based evaluation of the role of a real lead vehicle when navigating a coherent journey. Verbal protocols indicated that a lead vehicle may be a valuable navigation aid at a range of different junction types, but not where drivers may desire a preview of upcoming steps or their overall orientation. A subsequent driving simulator study with 22 participants examined whether an AR lead vehicle may support drivers when navigating at complex junctions, specifically large multi-exit roundabouts. The virtual car led to good navigation and driving performance, which was comparable to a more traditional screen-fixed interface. Overall, this work demonstrates that a virtual lead vehicle may be beneficial within AR navigation devices.

Proceedings of the 10th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 2018

Predictive touch is an HMI technology that relies on inferring, early in the pointing gesture, th... more Predictive touch is an HMI technology that relies on inferring, early in the pointing gesture, the interface item a driver or passenger intends to select on an in-vehicle display [1, 2]. It simplifies and expedites the selection task, thereby reducing the associated interaction effort. This paper presents two studies on drivers using predictive touch and focuses on evaluating the best means to facilitate selecting the intended on-display item. This includes immediate midair selection with the system autonomously auto-selecting the predicted interface component, hover/dwell and drivers pressing a button on the steering wheel to execute the selection action. These were arrived at in an expert workshop study with twelve participants. The results of the subsequent evaluation study with twenty four participants demonstrate, using quantitative and qualitative measures, that immediate mid-air selection is a promising assistive scheme, where drivers need not touch a physical surface to select interface components, thus touch-free control.