Driving Into the Twenty-First Century: Technology Solutions to Transportation Problems Symposium, IISTPS Report S-99-I

Driving Into the Twenty-First Century: Technology Solutions to Transportation Problems is the transcript of a symposium held on November 16, 1998. The symposium was sponsored by the Norman Y. Mineta International Institute for Surface Transportation Policy Studies, the Silicon Valley Manufacturing Group, Hewlett-Packard and Lockheed Martin. Numerous industry leaders and innovators were invited to participate in the open forum, and several vendors of electric and alternative power vehicles were on hand for participants to view and test drive. Topics of discussion included new technologies which will make commute times more pleasant for the 21st century worker. These possibilities include high-tech user-friendly highways, electronic toll collections, quicker response times for emergency vehicles, and the Intelligent Vehicles of tomorrow will help ease time spent in traffic thus making commute time less stressful, and perhaps even productive

Towards Computer-Assisted Regulation of Emotions

Tunteet ovat keskeinen ja erottamaton osa ihmisen toimintaa, ajattelua ja yksilöiden välistä vuorovaikutusta. Tunteet luovat perustan mielekkäälle, toimivalle ja tehokkaalle toiminnalle. Joskus tunteiden sävy tai voimakkuus voi kuitenkin olla epäedullinen henkilön tavoitteiden ja hyvinvoinnin kannalta. Tällöin taidokas tunteiden säätely voi auttaa saavuttamaan terveen ja menestyksellisen elämän. Väitöstyön tavoitteena oli muodostaa perusta tulevaisuuden tietokoneille, jotka auttavat säätelemään tunteita. Tietokoneiden tunneälyä on toistaiseksi kehitetty kahdella alueella: ihmisen tunnereaktioiden mittaamisessa ja tietokoneen tuottamissa tunneilmaisuissa. Viimeisimmät teknologiat antavat tietokoneille jo mahdollisuuden tunnistaa ja jäljitellä ihmisen tunneilmaisuja hyvinkin tarkasti. Väitöstyössä toimistotuoliin asennetuilla paineantureilla kyettiin huomaamattomasti havaitsemaan muutoksia kehon liikkeissä: osallistujat nojautuivat kohti heille esitettyjä tietokonehahmoja. Tietokonehahmojen esittämät kasvonilmeet ja kehollinen etäisyys vaikuttivat merkittävästi osallistujien tunne- ja tarkkaavaisuuskokemuksiin sekä sydämen, ihon hikirauhasten ja kasvon lihasten toimintaan. Tulokset osoittavat että keinotekoiset tunneilmaisut voivat olla tehokkaita henkilön kokemusten ja kehon toiminnan säätelyssä. Väitöstyössä laadittiin lopulta vuorovaikutteinen asetelma, jossa tunneilmaisujen automaattinen tarkkailu liitettiin tietokoneen tuottamien sosiaalisten ilmaisujen ohjaamiseen. Osallistujat pystyivät säätelemään välittömiä fysiologisia reaktioitaan ja tunnekokemuksiaan esittämällä tahdonalaisia kasvonilmeitä (mm. ikään kuin hymyilemällä) heitä lähestyvälle tietokonehahmolle. Väitöstyön tuloksia voidaan hyödyntää laajasti, muun muassa uudenlaisten, ihmisen luonnollisia vuorovaikutustapoja paremmin tukevien tietokoneiden suunnittelussa.Emotions are intimately connected with our lives. They are essential in motivating behaviour, for reasoning effectively, and in facilitating interactions with other people. Consequently, the ability to regulate the tone and intensity of emotions is important for leading a life of success and well-being. Intelligent computer perception of human emotions and effective expression of virtual emotions provide a basis for assisting emotion regulation with technology. State-of-the-art technologies already allow computers to recognize and imitate human social and emotional cues accurately and in great detail. For example, in the present work a regular looking office chair was used to covertly measure human body movement responses to artifical expressions of proximity and facial cues. In general, such artificial cues from visual agents were found to significantly affect heart, sweat gland, and facial muscle activities, as well as subjective experiences of emotion and attention. The perceptual and expressive capabilities were combined in a setup where a person regulated her or his more spontaneous reactions by either smiling or frowning voluntarily to a virtual humanlike character. These results highlight the potential of future emotion-sensitive technologies for creating supportive and even healthy interactions between humans and computers

Behavioural adaptation to in-vehicle navigation systems

This PhD investigates driver behavioural adaptation to in-vehicle navigation systems (IVNS). Behavioural adaptation is receiving an increasing amount of research attention in traffic psychology, but few studies have directly considered the concept in relation to IVNS. The thesis aims were addressed using a range of quantitative and qualitative methodologies. Using two online surveys, over 1300 drivers (including over 1000 IVNS users) were sampled, to identify a range of positive, neutral and negative aspects of end-user behavioural adaptation to IVNS in terms of both safety and navigational efficiency. The first survey (N=450) aimed at drivers in general, showed that IVNS users believe they commit some common driving errors (e.g. misreading signs when leaving a roundabout) significantly less frequently than ordinary drivers who do not use these systems, but that they also feel they drive without fully attending to the road ahead significantly more frequently. The second survey (N=872) was aimed at IVNS users only, and further explored distracted driving. This survey found that the majority of IVNS users have interacted with their system while driving (e.g. to enter a destination), and that some do so frequently. It also showed that system reliability is a key issue affecting most current IVNS users, revealing that some drivers have followed inaccurate as well as illegal and potentially dangerous, system-generated route guidance information in a range of different contexts. A longitudinal diary study (N=20) then collected rich qualitative data from a sample of worker drivers who regularly used their IVNS in unfamiliar areas. The data collected illustrated the diverse contexts in which drivers experience aspects of behavioural adaptation to IVNS identified in the surveys. Both the IVNS user-survey and diary study also identified key demographic individual difference variables (most notably age and computing skill) that were associated with the extent to which driver’s experienced different manifestations of behavioural adaptation to IVNS. Moreover, other individual difference variables (e.g. complacency potential, system-trust, confidence) were found to be associated with more specific behavioural adaptations. Two simulator studies investigated system interaction while driving. The first (N=24) demonstrated the poor degree of correspondence between drivers’ perceptions of driving performance when entering destinations while driving (relative to normal driving) and objective performance differences between these conditions. The second simulator study (N=24) showed that safety and training based interventions designed to reduce the extent to which drivers use IVNS while driving or to improve their performance if they do had only a modest effect on dependent measures. This thesis represents the first attempt in the literature to bring together research from diverse areas of human factors and traffic psychology to consider behavioural adaptation to in-vehicle navigation systems. By associating a range of these issues with behavioural adaptation to IVNS, it has indirectly increased the scope of several salient, previous research findings. Moreover, by investigating many of these issues in depth, using both quantitative and qualitative methodological approaches, it has set the foundation for future work. Such work should aim to explore many of the issues raised, and develop effective remediating or mitigating intervention strategies for negative behavioural adaptations that could adversely affect driving safety, as well as to encourage and support those which may be considered more positive

Developing Intelligent Multimodal IVI Systems to Reduce Driver Distraction

acceptedVersionPeer reviewe

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF