Andrew L. Kun

Estimating cognitive load using remote eye tracking in a driving simulator

We report on the results of a study in which pairs of subjects were involved in spoken dialogues and one of the subjects also operated a simulated vehicle. We estimated the drivers cognitive load based on pupil size measurements from a remote eye tracker. We compared the cognitive load estimates based on the physiological pupillometric data and driving performance data. The physiological and performance measures show high correspondence suggesting that remote eye tracking might provide reliable driver cognitive load estimation, especially in simulators. We also introduced a new pupillometric cognitive load measure that shows promise in tracking cognitive load changes on time scales of several seconds.

Adaptive dynamic balance of a biped robot using neural networks

An adaptive dynamic balance scheme was implemented and tested on an experimental biped. The control scheme used pre-planned but adaptive motion sequences. CMAC neural networks were responsible for the adaptive control of side-to-side and front-to-back balance, as well as for maintaining good foot contact. Qualitative and quantitative test results show that the biped performance improved with neural network training. The biped is able to start and stop on demand, and to walk with continuous motion on flat surfaces at a rate of up to 100 steps per minute, with up to 6 cm long step.

Augmented reality vs. street views: a driving simulator study comparing two emerging navigation aids

Prior research has shown that when drivers look away from the road to view a personal navigation device (PND), driving performance is affected. To keep visual attention on the road, an augmented reality (AR) PND using a heads-up display could overlay a navigation route. In this paper, we compare the AR PND, a technology that does not currently exist but can be simulated, with two PND technologies that are popular today: an egocentric street view PND and the standard map-based PND. Using a high-fidelity driving simulator, we examine the effect of all three PNDs on driving performance in a city traffic environment where constant, alert attention is required. Based on both objective and subjective measures, experimental results show that the AR PND exhibits the least negative impact on driving. We discuss the implications of these findings on PND design as well as methods for potential improvement.

Glancing at personal navigation devices can affect driving: experimental results and design implications

Nowadays, personal navigation devices (PNDs) that provide GPS-based directions are widespread in vehicles. These devices typically display the real-time location of the vehicle on a map and play spoken prompts when drivers need to turn. While such devices are less distracting than paper directions, their graphical display may distract users from their primary task of driving. In experiments conducted with a high fidelity driving simulator, we found that drivers using a navigation system with a graphical display indeed spent less time looking at the road compared to those using a navigation system with spoken directions only. Furthermore, glancing at the display was correlated with higher variance in driving performance measures. We discuss the implications of these findings on PND design for vehicles.

Automotive user interfaces: human computer interaction in the car

Cars have become complex interactive systems. Mechanical controls and electrical systems are transformed to the digital realm. It is common that drivers operate a vehicle and, at the same time, interact with a variety of devices and applications. Texting while driving, looking up an address for the navigation system, and taking a phone call are just some common examples that add value for the driver, but also increase the risk of driving. Novel interaction technologies create many opportunities for designing useful and attractive in-car user interfaces. With technologies that assist the user in driving, such as assistive cruise control and lane keeping, the user interface is essential to the way people perceive the driving experience. New means for user interface development and interaction design are required as the number of factors influencing the design space for automotive user interfaces is increasing. In comparison to other domains, a trial and error approach while the product is already in the market is not acceptable as the cost of failure may be fatal. User interface design in the automotive domain is relevant across many areas ranging from primary driving control, to assisted functions, to navigation, information services, entertainment and games.

Computers in police cruisers

A typical police cruiser is filled with electronic devices, displays, and inputs, all competing for the officers attention. The Project54 system integrates those devices, and its speech-based user interface lets officers operate them without taking their eyes off the road.

Shifting Gears: User Interfaces in the Age of Autonomous Driving

The field of automotive user interfaces has developed rapidly over the last several years. To date, the field has primarily focused on creating user interfaces that promote safe driving, including when the driver is engaged in a secondary task in addition to operating the vehicle. However, researchers now need to prepare for a major change in the automotive domain: the automated driving revolution. The authors argue for a new research agenda that focuses on four challenges for automotive user interfaces: assuring safety in the age of automation, transforming vehicles into places for productivity and play, taking advantage of new mobility options made possible by automated vehicles, while throughout all this preserving user privacy and data security. This article is part of a special issue on smart vehicle spaces.

A Model Relating Pupil Diameter to Mental Workload and Lighting Conditions

In this paper, we present a proof-of-concept approach to estimating mental workload by measuring the users pupil diameter under various controlled lighting conditions. Knowing the users mental workload is desirable for many application scenarios, ranging from driving a car, to adaptive workplace setups. Typically, physiological sensors allow inferring mental workload, but these sensors might be rather uncomfortable to wear. Measuring pupil diameter through remote eye-tracking instead is an unobtrusive method. However, a practical eye-tracking-based system must also account for pupil changes due to variable lighting conditions. Based on the results of a study with tasks of varying mental demand and six different lighting conditions, we built a simple model that is able to infer the workload independently of the lighting condition in 75% of the tested conditions.

Exploring the effects of visual cognitive load and illumination on pupil diameter in driving simulators

Pupil diameter is an important measure of cognitive load. However, pupil diameter is also influenced by the amount of light reaching the retina. In this study we explore the interaction between these two effects in a simulated driving environment. Our results indicate that it is possible to separate the effects of illumination and visual cognitive load on pupil diameter, at least in certain situations.

Conventions in human-human multi-threaded dialogues: a preliminary study

In this paper, we explore the conventions that people use in managing multiple dialogue threads. In particular, we focus on where in a thread people interrupt when switching to another thread. We find that some subjects are able to vary where they switch depending on how urgent the interrupting task is. When time-allowed, they switched at the end of a discourse segment, which we hypothesize is less disruptive to the interrupted task when it is later resumed.