Learning to drive: learners' self-reported cognitive failure level predicts driving instructor's observation rating of driving performance

OBJECTIVES Evidence increases that cognitive failure may be used to screen for drivers at risk. Until now, most studies have relied on driving learners. This exploratory pilot study examines self-report of cognitive failure in driving beginners and error during real driving as observed by driving instructors. METHODS Forty-two driving learners of 14 driving instructors filled out a work-related cognitive failure questionnaire. Driving instructors observed driving errors during the next driving lesson. In multiple linear regression analysis, driving errors were regressed on cognitive failure with the number of driving lessons as an estimator of driving experience controlled. RESULTS Higher cognitive failure predicted more driving errors (p < .01) when age, gender and driving experience were controlled in analysis. CONCLUSIONS Cognitive failure was significantly associated with observed driving errors. Systematic research on cognitive failure in driving beginners is recommended.

L'habitude en matière de conduite automobile : une analyse de ses composantes et du rôle qu'elle joue dans le maintien et la régulation des comportements de conduite

« La grande majorité des accidents demeure liée aux comportements dangereux des usagers de la route ». Cet énoncé, lapidaire, fait aujourd’hui figure d’évidence au sein de la communauté des intervenants en sécurité routière. Il repose pourtant sur des prémisses discutables. Le problème le plus fondamental réside dans le fait que la recherche des dernières décennies s’est presque toute entière vouée à l’analyse des seuls aspects défaillants de la conduite (l’accident, les infractions, les erreurs et les fautes de conduite, les conducteurs à risque ou dysfonctionnels, les attitudes et traits de caractère incitant à la conduite dangereuse, etc.). Ce faisant, on a fini par oublier qu’il nous restait encore beaucoup de choses à apprendre sur le fonctionnement usuel de la conduite automobile. Comment, en effet, peut-on escompter élucider tous les rouages de la dynamique accidentelle sans avoir au préalable cerné, et bien compris, les mécanismes de la conduite « ordinaire » ? Comment peut-on parvenir à approfondir notre compréhension des comportements de conduite si l’on fait totalement abstraction de toutes les activités courantes, « normales », auxquelles se livrent les conducteurs lorsqu’ils sont au volant de leur véhicule ? C’est dans la perspective de mieux comprendre les comportements de conduite, dans leur complexité et dans leur diversité, que la présente thèse a été réalisée. Y a plus spécifiquement été examinée la question des habitudes en raison de leur prédominance dans l’activité de la conduite, mais également en raison de leur résistance, des obstacles bien concrets qu’elles sont susceptibles d’opposer à tous ceux qui tentent de modifier les comportements individuels de conduite. Des entrevues en profondeur, menées auprès de trente conducteurs et conductrices âgé(e)s de 17 à 54 ans, devaient permettre de répondre, entre autres, aux questions suivantes : De quoi sont constituées les différentes habitudes en matière de conduite automobile ? Quelle place occupent-elles dans la conduite des individus ? En quoi constituent-elles un frein au changement, à la modification des pratiques adoptées ? Quelles sont les dimensions qui participent à leur installation ? Les résultats de l’analyse ont permis de jeter les bases d’un modèle des comportements de conduite où les sensations corporelles se voient conférer un rôle des plus centraux, et où l’habitude – bien davantage caractérisée par la notion de confort que par celles d’automatisme ou de répétition – concourt non seulement à la pérennité, mais également à la régulation des conduites adoptées sur la route.

Prevalence and Distribution of Young Driver Distraction Errors in Naturalistic Driving, TPF-5(207), 2014

Naturalistic driving studies are the latest resource for gathering data associated with driver behavior. The University of Iowa has been studying teen driving using naturalistic methods since 2006. By instrumenting teen drivers’ vehicles with event-triggered video recorders (ETVR), we are able to record a 12-second video clip every time a vehicle exceeds a pre-set g-force threshold. Each of these video clips contains valuable data regarding the frequency and types of distractions present in vehicles driven by today’s young drivers. The 16-year old drivers who participated in the study had a distraction present in nearly half of the events that were captured. While a lot of attention has been given to the distractions associated with technology in the vehicle (cell phones, navigation devices, entertainment systems, etc.), the most frequent type of distraction coded was the presence of teen passengers engaging in conversation (45%). Cognitive distractions, such as singing along with the radio, were the second most common distraction. Cell phone use was the third most common distraction, detected in only 10% of the events containing distraction.

Quantum error correction for continuously detected errors

We show that quantum feedback control can be used as a quantum-error-correction process for errors induced by a weak continuous measurement. In particular, when the error model is restricted to one, perfectly measured, error channel per physical qubit, quantum feedback can act to perfectly protect a stabilizer codespace. Using the stabilizer formalism we derive an explicit scheme, involving feedback and an additional constant Hamiltonian, to protect an (n-1)-qubit logical state encoded in n physical qubits. This works for both Poisson (jump) and white-noise (diffusion) measurement processes. Universal quantum computation is also possible in this scheme. As an example, we show that detected-spontaneous emission error correction with a driving Hamiltonian can greatly reduce the amount of redundancy required to protect a state from that which has been previously postulated [e.g., Alber , Phys. Rev. Lett. 86, 4402 (2001)].

Measurement Errors Should Always Be Incorporated in Phylogenetic Comparative Analysis

The evolution of continuous traits is the central component of comparative analyses in phylogenetics, and the comparison of alternative models of trait evolution has greatly improved our understanding of the mechanisms driving phenotypic differentiation. Several factors influence the comparison of models, and we explore the effects of random errors in trait measurement on the accuracy of model selection. We simulate trait data under a Brownian motion model (BM) and introduce different magnitudes of random measurement error. We then evaluate the resulting statistical support for this model against two alternative models: Ornstein-Uhlenbeck (OU) and accelerating/decelerating rates (ACDC). Our analyses show that even small measurement errors (10%) consistently bias model selection towards erroneous rejection of BM in favour of more parameter-rich models (most frequently the OU model). Fortunately, methods that explicitly incorporate measurement errors in phylogenetic analyses considerably improve the accuracy of model selection. Our results call for caution in interpreting the results of model selection in comparative analyses, especially when complex models garner only modest additional support. Importantly, as measurement errors occur in most trait data sets, we suggest that estimation of measurement errors should always be performed during comparative analysis to reduce chances of misidentification of evolutionary processes.

A Realization Of An Fpga Sub System For Reducing Odometric Localization Errors In Wheeled Mobile Robots

This paper introduces a simple and efficient method and its implementation in an FPGA for reducing the odometric localization errors caused by over count readings of an optical encoder based odometric system in a mobile robot due to wheel-slippage and terrain irregularities. The detection and correction is based on redundant encoder measurements. The method suggested relies on the fact that the wheel slippage or terrain irregularities cause more count readings from the encoder than what corresponds to the actual distance travelled by the vehicle. The standard quadrature technique is used to obtain four counts in each encoder period. In this work a three-wheeled mobile robot vehicle with one driving-steering wheel and two-fixed rear wheels in-axis, fitted with incremental optical encoders is considered. The CORDIC algorithm has been used for the computation of sine and cosine terms in the update equations. The results presented demonstrate the effectiveness of the technique

Tri-level study: modification. Task 5: an examination of driver characteristics and collision producing errors of accident and traffic violation repeaters. Final report.

National Highway Traffic Safety Administration, Washington, D.C.

The effect of auditory and visual distracters on the useful field of view: Implications for the driving task

PURPOSE. The driving environment is becoming increasingly complex, including both visual and auditory distractions within the in- vehicle and external driving environments. This study was designed to investigate the effect of visual and auditory distractions on a performance measure that has been shown to be related to driving safety, the useful field of view. METHODS. A laboratory study recorded the useful field of view in 28 young visually normal adults (mean 22.6 +/- 2.2 years). The useful field of view was measured in the presence and absence of visual distracters (of the same angular subtense as the target) and with three levels of auditory distraction (none, listening only, listening and responding). RESULTS. Central errors increased significantly (P < 0.05) in the presence of auditory but not visual distracters, while peripheral errors increased in the presence of both visual and auditory distracters. Peripheral errors increased with eccentricity and were greatest in the inferior region in the presence of distracters. CONCLUSIONS. Visual and auditory distracters reduce the extent of the useful field of view, and these effects are exacerbated in inferior and peripheral locations. This result has significant ramifications for road safety in an increasingly complex in-vehicle and driving environment.

Older Adult Injury Risk Assessment in the Driving and Occupational Environments

Thesis (Ph.D.)--University of Washington, 2016-08

Prevalence of refractive errors and ocular disorders in preschool and school children of Ibiporã - PR, Brazil (1989 to 1996)

Purpose: To establish the prevalence of refractive errors and ocular disorders in preschool and schoolchildren of Ibiporã, Brazil. Methods: A survey of 6 to 12-year-old children from public and private elementary schools was carried out in Ibiporã between 1989 and 1996. Visual acuity measurements were performed by trained teachers using Snellen's chart. Children with visual acuity <0.7 in at least one eye were referred to a complete ophthalmologic examination. Results: 35,936 visual acuity measurements were performed in 13,471 children. 1.966 children (14.59%) were referred to an ophthalmologic examination. Amblyopia was diagnosed in 237 children (1.76%), whereas strabismus was observed in 114 cases (0.84%). Cataract (n=17) (0.12%), chorioretinitis (n=38) (0.28%) and eyelid ptosis (n=6) (0.04%) were also diagnosed. Among the 614 (4.55%) children who were found to have refractive errors, 284 (46.25%) had hyperopia (hyperopia or hyperopic astigmatism), 206 (33.55%) had myopia (myopia or myopic astigmatism) and 124 (20.19%) showed mixed astigmatism. Conclusions: The study determined the local prevalence of amblyopia, refractive errors and eye disorders among preschool and schoolchildren.

Knowledge acquisition by networks of interacting agents in the presence of observation errors

In this work we investigate knowledge acquisition as performed by multiple agents interacting as they infer, under the presence of observation errors, respective models of a complex system. We focus the specific case in which, at each time step, each agent takes into account its current observation as well as the average of the models of its neighbors. The agents are connected by a network of interaction of Erdos-Renyi or Barabasi-Albert type. First, we investigate situations in which one of the agents has a different probability of observation error (higher or lower). It is shown that the influence of this special agent over the quality of the models inferred by the rest of the network can be substantial, varying linearly with the respective degree of the agent with different estimation error. In case the degree of this agent is taken as a respective fitness parameter, the effect of the different estimation error is even more pronounced, becoming superlinear. To complement our analysis, we provide the analytical solution of the overall performance of the system. We also investigate the knowledge acquisition dynamic when the agents are grouped into communities. We verify that the inclusion of edges between agents (within a community) having higher probability of observation error promotes the loss of quality in the estimation of the agents in the other communities.

The impact of measurement errors in the identification of regulatory networks

Background: There are several studies in the literature depicting measurement error in gene expression data and also, several others about regulatory network models. However, only a little fraction describes a combination of measurement error in mathematical regulatory networks and shows how to identify these networks under different rates of noise. Results: This article investigates the effects of measurement error on the estimation of the parameters in regulatory networks. Simulation studies indicate that, in both time series (dependent) and non-time series (independent) data, the measurement error strongly affects the estimated parameters of the regulatory network models, biasing them as predicted by the theory. Moreover, when testing the parameters of the regulatory network models, p-values computed by ignoring the measurement error are not reliable, since the rate of false positives are not controlled under the null hypothesis. In order to overcome these problems, we present an improved version of the Ordinary Least Square estimator in independent (regression models) and dependent (autoregressive models) data when the variables are subject to noises. Moreover, measurement error estimation procedures for microarrays are also described. Simulation results also show that both corrected methods perform better than the standard ones (i.e., ignoring measurement error). The proposed methodologies are illustrated using microarray data from lung cancer patients and mouse liver time series data. Conclusions: Measurement error dangerously affects the identification of regulatory network models, thus, they must be reduced or taken into account in order to avoid erroneous conclusions. This could be one of the reasons for high biological false positive rates identified in actual regulatory network models.

Antimicrobial drug administration errors identified in Brazilian multicentric study

Medication administration errors (MAE) are the most frequent kind of medication errors. Errors with antimicrobial drugs (AD) are relevant because they may interfere inpatient safety and in the development of microbial resistance. The aim of this study is to analyze the AD errors detected in a Brazilian multicentric study of MAE. It was a devcriptive and explorotory study carried out in clinical units in five Brazilian teaching hospitals. The hospitals were investigated during 30 days. MAE were detected by observation technique. MAE were classified in categories: wrong route(WR), wrong patient(WP), wrong dose(WD) wrong time (WT) and unordered drug (UD). AD with MA E were classified by Anatomical-Therapeutical-Chemical Classification System. AD with narrow therapeutic index (NTI) wet-e identified A descriptive statistical analysis was performed using SPSS version 11.5 software. A total of 1500 errors were observed, 277 (18.5%) of them were error with AD. The hopes of AD error were: WT87.7%, QD 6.9%, WR 1.5%, UD 3.2% and WP 0.7%. The number of AD found was 36. The mostly ATC class were fluoroquinolones 13.9%, combinations of penicillin 13.9%, macrolides 8.3% and third-generation cephalosporines 5.6%. The parenteral drug dosage form was associated with 55.6% of AD. 16.7% of AD were NTI. 47.4% of WD and 21.8% WT were with NTI drugs. This study shows that these errors should be considered potential areas for improvement in the medication process and patient safety plus there is requirement to develop rational drug use of AD.

Offline Detection, Identification, and Correction of Branch Parameter Errors Based on Several Measurement Snapshots

This paper proposes a three-stage offline approach to detect, identify, and correct series and shunt branch parameter errors. In Stage 1 the branches suspected of having parameter errors are identified through an Identification Index (II). The II of a branch is the ratio between the number of measurements adjacent to that branch, whose normalized residuals are higher than a specified threshold value, and the total number of measurements adjacent to that branch. Using several measurement snapshots, in Stage 2 the suspicious parameters are estimated, in a simultaneous multiple-state-and-parameter estimation, via an augmented state and parameter estimator which increases the V - theta state vector for the inclusion of suspicious parameters. Stage 3 enables the validation of the estimation obtained in Stage 2, and is performed via a conventional weighted least squares estimator. Several simulation results (with IEEE bus systems) have demonstrated the reliability of the proposed approach to deal with single and multiple parameter errors in adjacent and non-adjacent branches, as well as in parallel transmission lines with series compensation. Finally the proposed approach is confirmed on tests performed on the Hydro-Quebec TransEnergie network.