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Dr. Arun Ulahannan
Coventry University

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0 Automotive
0 Electric Vehicles
0 Human Factors
0 Interface Design
0 Wireless Charging for EV

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Conference paper
Published: 27 June 2021 in Cyber-Physical Systems and Control
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ACS Style

Arun Ulahannan; Matthew Knight; Robert Doel; Stewart Birrell. Look, No Cables! An Interview Study into Guiding the Practical Implementation of Wireless Chargers for Electric Taxis. Cyber-Physical Systems and Control 2021, 269 -276.

AMA Style

Arun Ulahannan, Matthew Knight, Robert Doel, Stewart Birrell. Look, No Cables! An Interview Study into Guiding the Practical Implementation of Wireless Chargers for Electric Taxis. Cyber-Physical Systems and Control. 2021; ():269-276.

Chicago/Turabian Style

Arun Ulahannan; Matthew Knight; Robert Doel; Stewart Birrell. 2021. "Look, No Cables! An Interview Study into Guiding the Practical Implementation of Wireless Chargers for Electric Taxis." Cyber-Physical Systems and Control , no. : 269-276.

Journal article
Published: 18 June 2021 in IEEE Transactions on Intelligent Transportation Systems
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Partially automated vehicles present a large range of information to the driver in order to keep them in-the-loop and engaged with monitoring the vehicle's actions. However, existing research shows that this causes cognitive overload and disengagement from the monitoring task. Adaptive Human Machine Interfaces (HMIs) are an emerging technology that might address this problem, by prioritising the information presented. To date, research aiming to define the driver's glance fixation behaviour in a partially automated vehicle to contribute towards an adaptive interface is scarce. This study used a unique three-day longitudinal driving simulator study design to explore which information drivers in a partially automated vehicle require. Twenty-seven participants experienced nine partially automated driving simulations over three consecutive days. Nine information types, developed from standards, previous studies and industry collaboration, were displayed as discrete icons and presented on a surrogate in-vehicle display. Unique to the literature, this study showed that the recorded eye-tracking data demonstrated that usage of the information types changed with longitudinal driving simulator use. This study provides three key contributions: first, the longitudinal study design suggest that single exposure HMI evaluations may be limited in their assessment. Secondly, this study has methodologically shortlisted a list of nine information types that can be used in future studies to represent future partially automated vehicle interfaces. Finally, this is one of the first studies to characterise glance behaviour for partially automated vehicles. With this knowledge, this study contributes important design recommendations for the development of adaptive interfaces.

ACS Style

Arun Ulahannan; Simon Thompson; Paul Jennings; Stewart Birrell. Using Glance Behaviour to Inform the Design of Adaptive HMI for Partially Automated Vehicles. IEEE Transactions on Intelligent Transportation Systems 2021, PP, 1 -16.

AMA Style

Arun Ulahannan, Simon Thompson, Paul Jennings, Stewart Birrell. Using Glance Behaviour to Inform the Design of Adaptive HMI for Partially Automated Vehicles. IEEE Transactions on Intelligent Transportation Systems. 2021; PP (99):1-16.

Chicago/Turabian Style

Arun Ulahannan; Simon Thompson; Paul Jennings; Stewart Birrell. 2021. "Using Glance Behaviour to Inform the Design of Adaptive HMI for Partially Automated Vehicles." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-16.

Journal article
Published: 23 October 2020 in Sustainability
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The shift to electric vehicles has brought about the potential to reduce the environmental damage caused by road transport. However, several challenges prevent wider adoption of electric vehicles, such as: a lack of charging facilities, long charging times, limited range, and the inconvenience of cable charging. These barriers are more pronounced for taxis, which generally cover longer distances than regular cars and have fewer opportunities for recharging. This research aims to evaluate wireless charging for range extended electric taxis, as a strategy to minimise these challenges and facilitate the electrification of fleets. A mixed methods approach, combining quantitative vehicle tracking with qualitative interviews and focus groups with drivers and local authority representatives, provided an understanding of ‘facilitators’ and ‘barriers’ to the introduction of wireless chargers in London and Nottingham, UK. Results indicated that current wired charging infrastructure does not facilitate recharging opportunities during taxi working hours, causing longer shifts or lower earnings. Drivers reported running on a range extender petrol engine once the battery is depleted, limiting the environmental benefits of electric taxis. We conclude that wireless chargers could facilitate the increased driving range of existing electric taxis if installed where drivers stop more often. The results support the implementation of opportunistic, short but frequent charging boosts (known as choko-choko) as part of policies to alleviate the barriers to the introduction of wireless charging of electric taxis, and foster more sustainable means of road transportation.

ACS Style

Luis Oliveira; Arun Ulahannan; Matthew Knight; Stewart Birrell. Wireless Charging of Electric Taxis: Understanding the Facilitators and Barriers to Its Introduction. Sustainability 2020, 12, 8798 .

AMA Style

Luis Oliveira, Arun Ulahannan, Matthew Knight, Stewart Birrell. Wireless Charging of Electric Taxis: Understanding the Facilitators and Barriers to Its Introduction. Sustainability. 2020; 12 (21):8798.

Chicago/Turabian Style

Luis Oliveira; Arun Ulahannan; Matthew Knight; Stewart Birrell. 2020. "Wireless Charging of Electric Taxis: Understanding the Facilitators and Barriers to Its Introduction." Sustainability 12, no. 21: 8798.

Journal article
Published: 15 January 2020 in IEEE Access
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While partially automated vehicles can provide a range of benefits, they also bring about new Human Machine Interface (HMI) challenges around ensuring the driver remains alert and is able to take control of the vehicle when required. While humans are poor monitors of automated processes, specifically during ‘steady state’ operation, presenting the appropriate information to the driver can help. But to date, interfaces of partially automated vehicles have shown evidence of causing cognitive overload. Adaptive HMIs that automatically change the information presented (for example, based on workload, time or physiologically), have been previously proposed as a solution, but little is known about how information should adapt during steady-state driving. This study aimed to classify information usage based on driver experience to inform the design of a future adaptive HMI in partially automated vehicles. The unique feature of this study over existing literature is that each participant attended for five consecutive days; enabling a first look at how information usage changes with increasing familiarity and providing a methodological contribution to future HMI user trial study design. Seventeen participants experienced a steady-state automated driving simulation for twenty-six minutes per day in a driving simulator, replicating a regularly driven route, such as a work commute. Nine information icons, representative of future partially automated vehicle HMIs, were displayed on a tablet and eye tracking was used to record the information that the participants fixated on. The results found that information usage did change with increased exposure, with significant differences in what information participants looked at between the first and last trial days. With increasing experience, participants tended to view information as confirming technical competence rather than the future state of the vehicle. On this basis, interface design recommendations are made, particularly around the design of adaptive interfaces for future partially automated vehicles.

ACS Style

Arun Ulahannan; Paul Jennings; Luis Oliveira; Stewart Birrell. Designing an Adaptive Interface: Using Eye Tracking to Classify How Information Usage Changes Over Time in Partially Automated Vehicles. IEEE Access 2020, 8, 16865 -16875.

AMA Style

Arun Ulahannan, Paul Jennings, Luis Oliveira, Stewart Birrell. Designing an Adaptive Interface: Using Eye Tracking to Classify How Information Usage Changes Over Time in Partially Automated Vehicles. IEEE Access. 2020; 8 (99):16865-16875.

Chicago/Turabian Style

Arun Ulahannan; Paul Jennings; Luis Oliveira; Stewart Birrell. 2020. "Designing an Adaptive Interface: Using Eye Tracking to Classify How Information Usage Changes Over Time in Partially Automated Vehicles." IEEE Access 8, no. 99: 16865-16875.

Journal article
Published: 07 October 2019 in Applied Ergonomics
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Partially automated vehicles present interface design challenges in ensuring the driver remains alert should the vehicle need to hand back control at short notice, but without exposing the driver to cognitive overload. To date, little is known about driver expectations of partial driving automation and whether this affects the information they require inside the vehicle. Twenty-five participants were presented with five partially automated driving events in a driving simulator. After each event, a semi-structured interview was conducted. The interview data was coded and analysed using grounded theory. From the results, two groupings of driver expectations were identified: High Information Preference (HIP) and Low Information Preference (LIP) drivers; between these two groups the information preferences differed. LIP drivers did not want detailed information about the vehicle presented to them, but the definition of partial automation means that this kind of information is required for safe use. Hence, the results suggest careful thought as to how information is presented to them is required in order for LIP drivers to safely using partial driving automation. Conversely, HIP drivers wanted detailed information about the system's status and driving and were found to be more willing to work with the partial automation and its current limitations. It was evident that the drivers' expectations of the partial automation capability differed, and this affected their information preferences. Hence this study suggests that HMI designers must account for these differing expectations and preferences to create a safe, usable system that works for everyone.

ACS Style

Arun Ulahannan; Rebecca Cain; Simon Thompson; Lee Skrypchuk; Alex Mouzakitis; Paul Jennings; Stewart Birrell. User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle. Applied Ergonomics 2019, 82, 102969 .

AMA Style

Arun Ulahannan, Rebecca Cain, Simon Thompson, Lee Skrypchuk, Alex Mouzakitis, Paul Jennings, Stewart Birrell. User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle. Applied Ergonomics. 2019; 82 ():102969.

Chicago/Turabian Style

Arun Ulahannan; Rebecca Cain; Simon Thompson; Lee Skrypchuk; Alex Mouzakitis; Paul Jennings; Stewart Birrell. 2019. "User expectations of partial driving automation capabilities and their effect on information design preferences in the vehicle." Applied Ergonomics 82, no. : 102969.

Conference paper
Published: 01 June 2019 in 2019 IEEE Intelligent Vehicles Symposium (IV)
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Understanding how best to present information inside a semi-automated vehicle is a prevalent challenge in HMI design. There is an understanding that a driver's trust and previous driving experience can affect the information they require inside a semi-automated vehicle. However, to date little is known about how these predispositions specifically affect the types of information that should be presented and importantly, how this changes with increased exposure to an automated system. In this paper, seventeen participants experienced twenty-six minutes of an automated driving simulation once every day for a week. The information to display was carefully chosen in accordance with the Skills, Rules, Knowledge model. The information was synchronized to the driving simulation and presented on a tablet in the driving simulator. Eye tracking was used to measure the information looked at. The results showed that trust increased significantly with increased exposure, but this had no correlation to any specific piece of information viewed. Drivers who were more prone to making lapses or errors (as measured by the Driver Behavior Questionnaire) tended towards using information that was less cognitively demanding. Finally, a driver's propensity to making lapses was found to be a potential early predictor of trust, but this became less accurate with increased exposure to the semi-automated vehicle.

ACS Style

Arun Ulahannan; Stewart Birrell; Simon Thomson; Lee Skyrpchuk; Alex Mouzakitis; Paul Jennings. The interface challenge for semi-automated vehicles: how driver behavior and trust influence information requirements over time. 2019 IEEE Intelligent Vehicles Symposium (IV) 2019, 96 -101.

AMA Style

Arun Ulahannan, Stewart Birrell, Simon Thomson, Lee Skyrpchuk, Alex Mouzakitis, Paul Jennings. The interface challenge for semi-automated vehicles: how driver behavior and trust influence information requirements over time. 2019 IEEE Intelligent Vehicles Symposium (IV). 2019; ():96-101.

Chicago/Turabian Style

Arun Ulahannan; Stewart Birrell; Simon Thomson; Lee Skyrpchuk; Alex Mouzakitis; Paul Jennings. 2019. "The interface challenge for semi-automated vehicles: how driver behavior and trust influence information requirements over time." 2019 IEEE Intelligent Vehicles Symposium (IV) , no. : 96-101.

Proceedings article
Published: 28 June 2018 in DRS2018: Catalyst
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ACS Style

Arun Ulahannan; University of Warwick; Rebecca Cain; Gunwant Dhadyalla; Paul Jennings; Stewart Birrell; Mike Waters. The Ideas Café: engaging the public in design research. DRS2018: Catalyst 2018, 1 .

AMA Style

Arun Ulahannan, University of Warwick, Rebecca Cain, Gunwant Dhadyalla, Paul Jennings, Stewart Birrell, Mike Waters. The Ideas Café: engaging the public in design research. DRS2018: Catalyst. 2018; ():1.

Chicago/Turabian Style

Arun Ulahannan; University of Warwick; Rebecca Cain; Gunwant Dhadyalla; Paul Jennings; Stewart Birrell; Mike Waters. 2018. "The Ideas Café: engaging the public in design research." DRS2018: Catalyst , no. : 1.

Conference paper
Published: 24 June 2018 in Advances in Intelligent Systems and Computing
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Trust has been shown to play a key role in our ability to safely use autonomous vehicles; hence the authors used the Ideas Café to explore the factors affecting trust in autonomous vehicles. The Ideas Café is an informal collaborative event that brings the public together with domain experts for exploratory research. The authors structured the event around factors affecting trust in the technology, privacy and societal impact. The event followed a mixed methods approach using: table discussions, spectrum lines and line ups. 36 participants attended the Ideas Café event held at the Coventry Transport Museum in June 2017. Table discussions provided the key findings for Thematic Analysis as part of Grounded Theory; which found, contrary to current research trends, designing for the technology’s integration with society as equally important for trust as the vehicle design itself. The authors also reported on the emergent high level interface guidelines.

ACS Style

Arun Ulahannan; Rebecca Cain; Gunwant Dhadyalla; Paul Jennings; Stewart Birrell; Mike Waters; Alex Mouzakitis. Using the Ideas Café to Explore Trust in Autonomous Vehicles. Advances in Intelligent Systems and Computing 2018, 3 -14.

AMA Style

Arun Ulahannan, Rebecca Cain, Gunwant Dhadyalla, Paul Jennings, Stewart Birrell, Mike Waters, Alex Mouzakitis. Using the Ideas Café to Explore Trust in Autonomous Vehicles. Advances in Intelligent Systems and Computing. 2018; ():3-14.

Chicago/Turabian Style

Arun Ulahannan; Rebecca Cain; Gunwant Dhadyalla; Paul Jennings; Stewart Birrell; Mike Waters; Alex Mouzakitis. 2018. "Using the Ideas Café to Explore Trust in Autonomous Vehicles." Advances in Intelligent Systems and Computing , no. : 3-14.