Using augmented video to test in-car user experiences of context analog HUDs

Automotive interactive technologies represent an exemplar challenge for user experience (UX) designers, as the concerns for aesthetics, functionality and usability add up to the compelling issues of safety and cognitive demand. This extended abstract presents a methodology for the user-centred creation and evaluation of novel in-car applications, involving real users in realistic use settings. As a case study, we present the methodologies of an ideation workshop in a simulated environment and the evaluation of six design idea prototypes for in-vehicle head up display (HUD) applications using a semi-naturalistic drive. Both methods rely on video recordings of real traffic situations that the users are familiar with and/or experienced themselves. The extended abstract presents experiences and results from the evaluation and reflection on our methods.

Parallel attentive visual tracking

The research reported here addresses the problem of detecting and tracking independently moving objects from a moving observer in real-time, using corners as object tokens. Corners are detected using the Harris corner detector, and local image-plane constraints are employed to solve the correspondence problem. The approach relaxes the restrictive static-world assumption conventionally made, and is therefore capable of tracking independently moving and deformable objects. Tracking is performed without the use of any 3-dimensional motion model. The technique is novel in that, unlike traditional feature-tracking algorithms where feature detection and tracking is carried out over the entire image-plane, here it is restricted to those areas most likely to contain-meaningful image structure. Two distinct types of instantiation regions are identified, these being the “focus-of-expansion” region and “border” regions of the image-plane. The size and location of these regions are defined from a combination of odometry information and a limited knowledge of the operating scenario. The algorithms developed have been tested on real image sequences taken from typical driving scenarios. Implementation of the algorithm using T800 Transputers has shown that near-linear speedups are achievable, and that real-time operation is possible (half-video rate has been achieved using 30 processing elements).

Modeling and control of a robotic power line inspection vehicle

Power line inspection is a vital function for electricity supply companies but it involves labor-intensive and expensive procedures which are tedious and error-prone for humans to perform. A possible solution is to use an unmanned aerial vehicle (UAV) equipped with video surveillance equipment to perform the inspection. This paper considers how a small, electrically driven rotorcraft conceived for this application could be controlled by visually tracking the overhead supply lines. A dynamic model for a ducted-fan rotorcraft is presented and used to control the action of an Air Vehicle Simulator (AVS), consisting of a cable-array robot. Results show how visual data can be used to determine, and hence regulate in closed loop, the simulated vehicle’s position relative to the overhead lines.

Impact of mobile phone use on car-following behaviour of young drivers

Multitasking, such as the concurrent use of a mobile phone and operating a motor vehicle, is a significant distraction that impairs driving performance and is becoming a leading cause of motor vehicle crashes. This study investigates the impact of mobile phone conversations on car-following behaviour. The CARRS-Q Advanced Driving Simulator was used to test a group of young Australian drivers aged 18–26 years on a car-following task in three randomised phone conditions: baseline (no phone conversation), hands-free and handheld. Repeated measure ANOVA was applied to examine the effect of mobile phone distraction on selected car-following variables such as driving speed, spacing, and time headway. Overall, drivers tended to select slower driving speeds, larger vehicle spacings, and longer time headways when they were engaged in either hands-free or handheld phone conversations, suggesting possible risk compensatory behaviour. In addition, phone conversations while driving influenced car-following behaviour such that variability was increased in driving speeds, vehicle spacings, and acceleration and decelerations. To further investigate car-following behaviour of distracted drivers, driver time headways were modelled using Generalized Estimation Equation (GEE). After controlling for various exogenous factors, the model predicts an increase of 0.33 s in time headway when a driver is engaged in hands-free phone conversation and a 0.75 s increase for handheld phone conversation. The findings will improve the collective understanding of distraction on driving performance, in particular car following behaviour which is most critical in the determination of rear-end crashes.

Stone baby: An exploration of trembling and its potential for visual art

Informed by Kristeva's formulation of affect and Winnicott's Holding Environment, this practice-led visual art project is an exploration into how sensitivity to the physical sensation of trembling can sustain a creative practice. Building upon this is a further enquiry into what the significance of the affective experience of trembling is for an ethics of affect in contemporary art. I have done this through object and video-based installations informed by my own experience of trembling. This has been further informed by the work of artists like Louise Bourgeois, Dennis Del Favero and Willie Doherty. The creative outcomes contribute to the discourse around ethical responses to affect by extending and developing on the works of these artists.

Revisiting the Task-Capability interface model for incorporating human factors into car-following models

Human factors such as distraction, fatigue, alcohol and drug use are generally ignored in car-following (CF) models. Such ignorance overestimates driver capability and leads to most CF models’ inability in realistically explaining human driving behaviors. This paper proposes a novel car-following modeling framework by introducing the difficulty of driving task measured as the dynamic interaction between driving task demand and driver capability. Task difficulty is formulated based on the famous Task Capability Interface (TCI) model, which explains the motivations behind driver’s decision making. The proposed method is applied to enhance two popular CF models: Gipps’ model and IDM, and named as TDGipps and TDIDM respectively. The behavioral soundness of TDGipps and TDIDM are discussed and their stabilities are analyzed. Moreover, the enhanced models are calibrated with the vehicle trajectory data, and validated to explain both regular and human factor influenced CF behavior (which is distraction caused by hand-held mobile phone conversation in this paper). Both the models show better performance than their predecessors, especially in presence of human factors.

Comparison of visual status of Iranian military and commercial drivers

Background There is no legal requirement for Iranian military truck drivers to undergo regular visual checkups as compared to commercial truck drivers. Objectives This study aimed to evaluate the impact of drivers’ visual checkups by comparing the visual function of Iranian military and commercial truck drivers. Patients and Methods In this comparative cross-sectional study, two hundred military and 200 commercial truck drivers were recruited and their Visual Acuity (VA), Visual Field (VF), color vision and Contrast Sensitivity (CS) were assessed and compared using the Snellen chart, confrontation screening method, D15 test and Pelli-Robson letter chart, respectively. A questionnaire regarding driving exposure and history of motor-vehicle crashes (MVCs) was also filled by drivers. Results were analyzed using an independent samples t-test, one-way ANOVA (assessing difference in number of MVCs across different age groups), chi-square test and Pearson correlation at statistical significance level of P < 0.05. Results Mean age was 41.6 ± 9.2 for the military truck drivers and 43.4 ± 10.9 for commercial truck drivers (P > 0.05). No significant difference between military and commercial drivers was found in terms of driving experience, number of MVCs, binocular VA, frequency of color vision defects and CS scores. In contrast, the last ocular examination was significantly earlier in military drivers than commercial drivers (P < 0.001). In addition, 4% of military drivers did not meet the national standards to drive as opposed to 2% of commercial drivers. There was a significant but weak correlation between binocular VA and age (r = 0.175, P < 0.001). However, CS showed a significantly moderate correlation with age (r = -0.488, P < 0.001). Conclusions The absence of legal requirement for regular eye examination in military drivers caused the incompetent drivers to be missed in contrast to commercial drivers. The need for scientific revision of VA standard for Iranian drivers is also discussed. The CS measurement in visual checkups of older drivers deserves to be investigated more thoroughly.

The root locus method: application to linear stability analysis and design of cooperative car-following models

A global framework for linear stability analyses of traffic models, based on the dispersion relation root locus method, is presented and is applied taking the example of a broad class of car-following (CF) models. This approach is able to analyse all aspects of the dynamics: long waves and short wave behaviours, phase velocities and stability features. The methodology is applied to investigate the potential benefits of connected vehicles, i.e. V2V communication enabling a vehicle to send and receive information to and from surrounding vehicles. We choose to focus on the design of the coefficients of cooperation which weights the information from downstream vehicles. The coefficients tuning is performed and different ways of implementing an efficient cooperative strategy are discussed. Hence, this paper brings design methods in order to obtain robust stability of traffic models, with application on cooperative CF models

Systematic literature review of incidence rates of low-speed vehicle run-over incidents in children

Aim: To systematically review the literature investigating the incidence of fatal and or nonfatal low-speed vehicle run-over (LSVRO) incidents in children aged 0–15 years. Methods: The following databases were searched using specific search terms, from their date of conception up to June 2011: Cochrane Library, Medline, CINAHL, Embase, AMI, Sociological Abstracts, ERIC, PsycArticles, PsycInfo, Urban Studies and Planning; Australian Criminology Database; Dissertations and Thesis; Academic Research Library; Social Services Abstracts; Family and Society; Scopus; and Web of Science. A total of 128 articles were identified in the databases (33 found by hand searching). The title and abstract of these were read, and 102 were removed because they were not primary research articles relating to LSVRO-type injuries. Twenty-six articles were assessed against the inclusion (reporting population level incidence rates) and exclusion criteria, 19 of which were excluded, leaving a total of five articles for inclusion in the review. Findings: Five studies were identified that met the inclusion criteria. The incidence rate in nonfatal LSVRO events varied in the range of 7.09 to 14.79 per 100,000 and from 0.63 to 3.2 per 100,000 in fatal events. Discussion: Using International Classification of Diseases codes for classifying fatal or nonfatal LSVRO incidents is problematic as there is no specific code for LSVRO. The current body of research is void of a comprehensive secular population data analysis. Only with an improved spectrum of incidence rates will appropriate evaluation of this problem be possible, and this will inform nursing prevention interventions. The effect of LSVRO incidents is clearly understudied. More research is required to address incidence rates in relation to culture, environment, risk factors, car design, and injury characteristics. Conclusions: Thevlack of nursing research or policy around this area of injury, most often to children, indicates a field of inquiry and policy development that needs attention.

Linear and weakly nonlinear stability analyses of cooperative car-following models

Stability analyses have been widely used to better understand the mechanism of traffic jam formation. In this paper, we consider the impact of cooperative systems (a.k.a. connected vehicles) on traffic dynamics and, more precisely, on flow stability. Cooperative systems are emerging technologies enabling communication between vehicles and/or with the infrastructure. In a distributed communication framework, equipped vehicles are able to send and receive information to/from other equipped vehicles. Here, the effects of cooperative traffic are modeled through a general bilateral multianticipative car-following law that improves cooperative drivers' perception of their surrounding traffic conditions within a given communication range. Linear stability analyses are performed for a broad class of car-following models. They point out different stability conditions in both multianticipative and nonmultianticipative situations. To better understand what happens in unstable conditions, information on the shock wave structure is studied in the weakly nonlinear regime by the mean of the reductive perturbation method. The shock wave equation is obtained for generic car-following models by deriving the Korteweg de Vries equations. We then derive traffic-state-dependent conditions for the sign of the solitary wave (soliton) amplitude. This analytical result is verified through simulations. Simulation results confirm the validity of the speed estimate. The variation of the soliton amplitude as a function of the communication range is provided. The performed linear and weakly nonlinear analyses help justify the potential benefits of vehicle-integrated communication systems and provide new insights supporting the future implementation of cooperative systems.

Association between glaucoma and at–fault motor vehicle collision involvement among older drivers

Objective To examine the association between glaucoma and motor vehicle collision (MVC) involvement among older drivers, including the role of visual field impairment that may underlie any association found. Design A retrospective population-based study Participants A sample of 2,000 licensed drivers aged 70 years and older who reside in north central Alabama. Methods At-fault MVC involvement for five years prior to enrollment was obtained from state records. Three aspects of visual function were measured: habitual binocular distance visual acuity, binocular contrast sensitivity and the binocular driving visual field constructed from combining the monocular visual fields of each eye. Poisson regression was used to calculate crude and adjusted rate ratios (RR) and 95% confidence intervals (CI). Main Outcomes Measures At-fault MVC involvement for five years prior to enrollment. Results Drivers with glaucoma (n = 206) had a 1.65 (95% confidence interval [CI] 1.20-2.28, p = 0.002) times higher MVC rate compared to those without glaucoma after adjusting for age, gender and mental status. Among those with glaucoma, drivers with severe visual field loss had higher MVC rates (RR = 2.11, 95% CI 1.09-4.09, p = 0.027), whereas no significant association was found among those with impaired visual acuity and contrast sensitivity. When the visual field was sub-divided into six regions (upper, lower, left, and right visual fields; horizontal and vertical meridians), we found that impairment in the left, upper or lower visual field was associated with higher MVC rates, and an impaired left visual field showed the highest RR (RR = 3.16, p = 0.001) compared to other regions. However, no significant association was found in deficits in the right side or along the horizontal or vertical meridian. Conclusions A population-based study suggests that older drivers with glaucoma are more likely to have a history of at-fault MVC involvement than those without glaucoma. Impairment in the driving visual field in drivers with glaucoma appears to have an independent association with at-fault MVC involvement, whereas visual acuity and contrast sensitivity impairments do not.

Motorcycle riders' self-reported aggression when riding compared with car driving

Aggressive driving has been shown to be related to increased crash risk for car driving. However, less is known about aggressive behaviour and motorcycle riding and whether there are differences in on-road aggression as a function of vehicle type. If such differences exist, these could relate to differences in perceptions of relative vulnerability associated with characteristics of the type of vehicle such as level of protection and performance. Specifically, the relative lack of protection offered by motorcycles may cause riders to feel more vulnerable and therefore to be less aggressive when they are riding compared to when they are driving. This study examined differences in self-reported aggression as a function of two vehicle types: passenger cars and motorcycles. Respondents (n = 247) were all motorcyclists who also drove a car. Results were that scores for the composite driving aggression scale were significantly higher than on the composite riding aggression scale. Regression analyses identified different patterns of predictors for driving aggression from those for riding aggression. Safety attitudes followed by thrill seeking tendencies were the strongest predictors for driving aggression, with more positive safety attitudes being protective while greater thrill seeking was associated with greater self-reported aggressive driving behaviour. For riding aggression, thrill seeking was the strongest predictor (positive relationship), followed by self-rated skill, such that higher self rated skill was protective against riding aggression. Participants who scored at the 85th percentile or above for the aggressive driving and aggressive riding indices had significantly higher scores on thrill seeking, greater intentions to engage in future risk taking, and lower safety attitude scores than other participants. In addition participants with the highest aggressive driving scores also had higher levels of self-reported past traffic offences than other participants. Collectively, these findings suggest that people are less likely to act aggressively when riding a motorcycle than when driving a car, and that those who are the most aggressive drivers are different from those who are the most aggressive riders. However, aggressive riders and drivers appear to present a risk to themselves and others on road. Importantly, the underlying influences for aggressive riding or driving that were identified in this study may be amenable to education and training interventions.

Water bottles from China: Decoding visual grammar

On the ALEA Study Tour to China, Beryl Exley and her roomie Kathryn O’Sullivan pondered over their first night dilemma whilst staying at a hotel in Beijing. They read the room service guide (in English) which advised against drinking the tap water and confirming the supply of one bottle of complementary water per guest per day. The room service guide listed ‘special’ bottled water was the equivalent of $AUS7 per bottle. However the dilemma was this: sitting on the shelf above the fridge were three different kinds of water-like bottles. Each had a different label, written mainly in Chinese characters. Not wanting to mistake the bottles, Beryl and Kathryn set about decoding the text of the three bottles in question.

A Probabilistic Approach to the Primary Visual Cortex

What can the statistical structure of natural images teach us about the human brain? Even though the visual cortex is one of the most studied parts of the brain, surprisingly little is known about how exactly images are processed to leave us with a coherent percept of the world around us, so we can recognize a friend or drive on a crowded street without any effort. By constructing probabilistic models of natural images, the goal of this thesis is to understand the structure of the stimulus that is the raison d etre for the visual system. Following the hypothesis that the optimal processing has to be matched to the structure of that stimulus, we attempt to derive computational principles, features that the visual system should compute, and properties that cells in the visual system should have. Starting from machine learning techniques such as principal component analysis and independent component analysis we construct a variety of sta- tistical models to discover structure in natural images that can be linked to receptive field properties of neurons in primary visual cortex such as simple and complex cells. We show that by representing images with phase invariant, complex cell-like units, a better statistical description of the vi- sual environment is obtained than with linear simple cell units, and that complex cell pooling can be learned by estimating both layers of a two-layer model of natural images. We investigate how a simplified model of the processing in the retina, where adaptation and contrast normalization take place, is connected to the nat- ural stimulus statistics. Analyzing the effect that retinal gain control has on later cortical processing, we propose a novel method to perform gain control in a data-driven way. Finally we show how models like those pre- sented here can be extended to capture whole visual scenes rather than just small image patches. By using a Markov random field approach we can model images of arbitrary size, while still being able to estimate the model parameters from the data.

Learning Nonlinear Visual Processing from Natural Images

The paradigm of computational vision hypothesizes that any visual function -- such as the recognition of your grandparent -- can be replicated by computational processing of the visual input. What are these computations that the brain performs? What should or could they be? Working on the latter question, this dissertation takes the statistical approach, where the suitable computations are attempted to be learned from the natural visual data itself. In particular, we empirically study the computational processing that emerges from the statistical properties of the visual world and the constraints and objectives specified for the learning process. This thesis consists of an introduction and 7 peer-reviewed publications, where the purpose of the introduction is to illustrate the area of study to a reader who is not familiar with computational vision research. In the scope of the introduction, we will briefly overview the primary challenges to visual processing, as well as recall some of the current opinions on visual processing in the early visual systems of animals. Next, we describe the methodology we have used in our research, and discuss the presented results. We have included some additional remarks, speculations and conclusions to this discussion that were not featured in the original publications. We present the following results in the publications of this thesis. First, we empirically demonstrate that luminance and contrast are strongly dependent in natural images, contradicting previous theories suggesting that luminance and contrast were processed separately in natural systems due to their independence in the visual data. Second, we show that simple cell -like receptive fields of the primary visual cortex can be learned in the nonlinear contrast domain by maximization of independence. Further, we provide first-time reports of the emergence of conjunctive (corner-detecting) and subtractive (opponent orientation) processing due to nonlinear projection pursuit with simple objective functions related to sparseness and response energy optimization. Then, we show that attempting to extract independent components of nonlinear histogram statistics of a biologically plausible representation leads to projection directions that appear to differentiate between visual contexts. Such processing might be applicable for priming, \ie the selection and tuning of later visual processing. We continue by showing that a different kind of thresholded low-frequency priming can be learned and used to make object detection faster with little loss in accuracy. Finally, we show that in a computational object detection setting, nonlinearly gain-controlled visual features of medium complexity can be acquired sequentially as images are encountered and discarded. We present two online algorithms to perform this feature selection, and propose the idea that for artificial systems, some processing mechanisms could be selectable from the environment without optimizing the mechanisms themselves. In summary, this thesis explores learning visual processing on several levels. The learning can be understood as interplay of input data, model structures, learning objectives, and estimation algorithms. The presented work adds to the growing body of evidence showing that statistical methods can be used to acquire intuitively meaningful visual processing mechanisms. The work also presents some predictions and ideas regarding biological visual processing.