User interface considerations to prevent self-driving carsickness

Self-driving cars have the potential to bring significant benefits to drivers and society at large. However, all envisaged scenarios are predicted to increase the risk of motion sickness. This will negatively affect user acceptance and uptake and hence negate the benefits of this technology. Here we discuss the impact of the user interface design in particular, focusing on display size, position, and content and the relationship with the degree of sensory conflict and ability to anticipate the future motion trajectory of the vehicle, two key determinants of motion sickness in general. Following initial design recommendations, we provide a research agenda to accelerate our understanding of self-driving cars in the context of the scenarios currently proposed. We conclude that basic perceptual mechanisms need to be considered in the design process whereby self-driving cars cannot simply be thought of as living rooms, offices, or entertainment venues on wheels

HapWheel: in-car infotainment system feedback using haptic and hovering techniques

Abstract—In-car devices are growing both in complexity and capacity, integrating functionalities that used to be divided among other controls in the vehicles. These systems appear increasingly in the form of touchscreens as a cost-saving measure. Screens lack the physicality of traditional buttons or switches, requiring drivers to look away from the road to operate them. This paper presents the design, implementation, and two studies that evaluated HapWheel, a system that provides the driver with haptic feedback in the steering wheel while interacting with an Infotainment System. Results show that the proposed system reduced both the duration of and the number of times a driver looked away from the road. HapWheel was also successful at reducing the number of mistakes during the interaction.info:eu-repo/semantics/publishedVersio

Towards Sustainable Research Data Management in Human-Computer Interaction

We discuss important aspects of HCI research regarding Research Data Management (RDM) to achieve better publication processes and higher reuse of HCI research results. Various context elements of RDM for HCI are discussed, including examples of existing and emerging infrastructures for RDM. We briefly discuss existing approaches and come up with additional aspects which need to be addressed. This is to apply the so-called FAIR principle fully, which -- besides being findable and accessible -- also includes interoperability and reusability. We also discuss briefly the kind of research data types that play a role here and propose to build on existing work and involve the HCI scientific community to improve current practices

A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions

In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers’ multi- modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach

Warwick-JLR driver monitoring dataset (DMD) : statistics and early findings

Driving is a safety critical task that requires a high levels of attention and workload from the driver. Despite this, people often also perform secondary tasks such as eating or using a mobile phone, which increase workload levels and divert cognitive and physical attention from the primary task of driving. If a vehicle is aware that the driver is currently under high workload, the vehicle functionality can be changed in order to minimize any further demand. Traditionally, workload measurements have been performed using intrusive means such as physiological sensors. Another approach may be to monitor workload online using readily available and robust sensors accessible via the vehicle's Controller Area Network (CAN). In this paper, we present details of the Warwick-JLR Driver Monitoring Dataset (DMD) collected for this purpose, and to announce its publication for driver monitoring research. The collection protocol is briefly introduced, followed by statistical analysis of the dataset to describe its structure. Finally, the public release of the dataset, for use in both driver monitoring and data mining research, is announced

Investigating the Effect of Tactile Input and Output Locations for Drivers’ Hands on In-Car Tasks Performance

This paper reports a study investigating the effects of tactile input and output from the steering wheel and the centre console on non-driving task performance. While driving, participants were asked to perform list selection tasks using tactile switches and to experience tactile feedback on either the non-dominant, dominant or both hands as they were browsing the list. Our results show the average duration for selecting an item is 30% shorter when interacting with the steering wheel. They also show a 20% increase in performance when tactile feedback is provided. Our findings reveal that input prevails over output location when designing interaction for drivers. However, tactile feedback on the steering wheel is beneficial when provided at the same location as the input or to both hands. The results will help designers understand the trade-offs of using different interaction locations in the car

Measuring driving styles: A validation of the multidimensional driving style inventory,"

ABSTRACT The aim of this study was to validate the stability of the different factors of the Multidimensional Driving Style Inventory (MDSI

A comparative study of speculative retrieval for multi-modal data trails: towards user-friendly Human-Vehicle interactions

In the era of growing developments in Autonomous Vehicles, the importance of Human-Vehicle Interaction has become apparent. However, the requirements of retrieving in-vehicle drivers’ multi- modal data trails, by utilizing embedded sensors, have been consid- ered user unfriendly and impractical. Hence, speculative designs, for in-vehicle multi-modal data retrieval, has been demanded for future personalized and intelligent Human-Vehicle Interaction. In this paper, we explore the feasibility to utilize facial recog- nition techniques to build in-vehicle multi-modal data retrieval. We first perform a comprehensive user study to collect relevant data and extra trails through sensors, cameras and questionnaire. Then, we build the whole pipeline through Convolution Neural Net- works to predict multi-model values of three particular categories of data, which are Heart Rate, Skin Conductance and Vehicle Speed, by solely taking facial expressions as input. We further evaluate and validate its effectiveness within the data set, which suggest the promising future of Speculative Designs for Multi-modal Data Retrieval through this approach