Effectiveness of traffic light vs. boom barrier controls at road–rail level crossings: A simulator study

Although collisions at level crossings are relatively uncommon occurrences, the potential severity of their consequences make them a top priority among safety authorities. Twenty-five fully-licensed drivers aged between 20 and 50 years participated in a driving simulator study that compared the efficacy, and drivers’ subjective perception, of two active level crossing traffic control devices: flashing lights with boom barriers and standard traffic lights. Because of its common usage in most states in Australia, a stop sign-controlled level crossing served as the passive referent. Although crossing violations were less likely at the level crossings controlled by active devices than at those controlled by stop signs, both kinds of active control were associated with a similar number of violations. Further, the majority (72%) of drivers reported preferring flashing lights to traffic lights. Collectively, results indicate that the installation of traffic lights at real-world level crossings would not be likely to offer safety benefits over and above those provided already by flashing lights with boom barriers. Furthermore, the high rate of violations at passively controlled crossings strongly supports the continued practice of upgrading level crossings with active traffic control devices.     

How do drivers behave during indecision zone maneuvers?

Yellow signal indications at intersections are well-known to be a contributor to traffic crashes. This study examined drivers’ behavior during yellow signal indication (i.e., indecision zone) maneuvers. Data from a driving simulator study was used, which included drivers’ performance data when they encountered a yellow signal indication at intersections under different secondary-task conditions. This study calculated drivers’ likelihood to go through a yellow signal indication and examined factors that are related to drivers’ decision making on intersection traversing. The results showed that drivers’ decision on stopping or not at a yellow signal indication was associated with different variables including age, distraction, pedal conditions, and time to stop line. Distracted drivers’ insensitive behavior was also captured from the significant interaction effect between time to stop line and distraction conditions, which implied that intersection related crash risk may increase when drivers were distracted.     

Adolescent exposure to drink driving as a predictor of young adults’ drink driving

The purpose of this study was to investigate the influence of exposure to others’ drink driving during adolescence on self-reported driving under the influence (DUI) of alcohol in young adulthood. Data were drawn from 1956 participants with a driving license enrolled in the International Youth Development Study from Victoria, Australia. During 2003 and 2004, adolescents in Grades 7, 9 and 10 (aged 12–17) completed questionnaires examining whether they had ridden in a vehicle with a driver who had been drinking, as well as other demographic, individual, peer and family risk factors for DUI. In 2010, the same participants (aged 18–24) then reported on their own DUI behaviour. 18% of young adults with a driving license reported DUI in the past 12 months. Exposure to others’ drink driving during adolescence was associated with an increased likelihood of DUI as a young adult (OR = 2.13, 95% CI 1.68–2.69). This association remained after accounting for the effects of other potential confounding factors from the individual, peer and family domains (OR = 1.62, 95% CI 1.23–2.13). Observing the drink driving behaviours of others during adolescence may increase the likelihood of DUI as a young adult. Strategies to reduce youth exposure to drink driving are warranted.     

Monitoring by Remote Sensing of Habitat Quality Changes in Xianyang City

With the accelerating process of society，economy and urbanization，land use and landscape changes have gradually become important to make effects on regional habitat quality.It is necessary to further investigate those two effects，the result of which can provide a scientific basis for regional habitat conservation and reasonable utilization of land，then will be of great importance in habitat protection and development of the region.In this paper，the study area was located in Xianyang city，Shaanxi Province，which had the frequent human activities and obvious land use changes.Based on the classification of land use data which interpreted by remote sensing，supported by ArcGIS software，the land use transfer matrix of Xianyang from 2000 to 2010 was analyzed.Landscape metrics were calculated by the Fragstats software，which represented for the landscape pattern changes and spatial characteristics.The InVEST model was selected to evaluate habitat quality in study area.The habitat quality changes was monitored.The results indicate that the integrated land use dynamics of Xianyang city is 2.34%，and the changes of land use rate is slow.The main transition from cultivated land and grassland to forest and construction land，which cause the area of first two land use types reduced，and the latter two types increased.The degree of fragmentation and the complexity of structure in landscape are higher than before.Habitat quality improved slightly，and its overall spatial pattern is that central and north areas are relatively higher than the south.Area percentage of excellent，good and poor grades increase，while the habitat quality of medium grade significantly decline.Among 14 districts and counties of habitat quality in Xianyang city，Xunyi County is the best，and the improved magnitude of Liquan county is the most significant.The main driving force of habitat quality change is the transform of land use pattern.Therefore，the relevant departments of Xianyang city should continue to implement the ecological protection measures，and increase the intensity about the protection and management of environment.Thereby to promote the coordinated sustainable development of land use and habitats.     

Precognitive control: open- and closed-loop steering in a lane-change manoeuvre

Abstract

This paper presents two experiments in which steering a lane-change manoeuvre is analysed as a precognitive control task. The suggestion that the dependence on instantaneous visual feedback is low during such a task was tested by analysing steering performance under conditions both with and without visual occlusion. Steering force served as the main independent variable in experiment I, which was performed in a fixed-base driving simulator. A 1 s visual occlusion period during the initial steering-wheel movement to the left was given every other manoeuvre. The results indicate that steering force reduces steering-wheel amplitude variability, this effect also being reflected in the maximum heading angle variability.

Steering-wheel angle amplitude was the main independent variable in experiment II, which was carried out in the instrumented car. In this experiment the duration of the occlusion period was 3 s, i.e. covering the complete pull-out phase of the manoeuvre. The results show that variability in steering-wheel movement amplitude increases about linearly with the amplitude: standard deviations are about 9% of the amplitude. For both experiments I and II the results correspond quite well with those of an earlier reproduction experiment. Regarding the withdrawal of visual feedback it appears that occlusion affects the mutual tuning of steering amplitude and timing, rather than affecting these quantities separately. The vehicle-motion effects resulting from occlusion are relatively small and it can be concluded therefore that a temporary withdrawal of visual feedback has no dramatic effects in the pre-programmed task considered here.     

Driving behaviors in early stage dementia: A study using in-vehicle technology

According to the Alzheimer's Association (2011), (1) in 8 people age 65 and older, and about one-half of people age 85 and older, have Alzheimer's disease in the United States (US). There is evidence that drivers with Alzheimer's disease and related dementias are at an increased risk for unsafe driving. Recent advances in sensor, computer, and telecommunication technologies provide a method for automatically collecting detailed, objective information about the driving performance of drivers, including those with early stage dementia. The objective of this project was to use in-vehicle technology to describe a set of driving behaviors that may be common in individuals with early stage dementia (i.e., a diagnosis of memory loss) and compare these behaviors to a group of drivers without cognitive impairment. Seventeen drivers with a diagnosis of early stage dementia, who had completed a comprehensive driving assessment and were cleared to drive, participated in the study. Participants had their vehicles instrumented with a suite of sensors and a data acquisition system, and drove 1–2 months as they would under normal circumstances. Data from the in-vehicle instrumentation were reduced and analyzed, using a set of algorithms/heuristics developed by the research team. Data from the early stage dementia group were compared to similar data from an existing dataset of 26 older drivers without dementia. The early stage dementia group was found to have significantly restricted driving space relative to the comparison group. At the same time, the early stage dementia group (which had been previously cleared by an occupational therapist as safe to drive) drove as safely as the comparison group. Few safety-related behavioral errors were found for either group. Wayfinding problems were rare among both groups, but the early stage dementia group was significantly more likely to get lost.     

Assessment of driver stopping prediction models before and after the onset of yellow using two driving simulator datasets

Accurate modeling of driver decisions in dilemma zones (DZ), where drivers are not sure whether to stop or go at the onset of yellow, can be used to increase safety at signalized intersections. This study utilized data obtained from two different driving simulator studies (VT-SCORES and NADS datasets) to investigate the possibility of developing accurate driver-decision prediction/classification models in DZ. Canonical discriminant analysis was used to construct the prediction models, and two timeframes were considered. The first timeframe used data collected during green immediately before the onset of yellow, and the second timeframe used data collected during the first three seconds after the onset of yellow. Signal protection algorithms could use the results of the prediction model during the first timeframe to decide the best time for ending the green signal, and could use the results of the prediction model during the first three seconds of yellow to extend the clearance interval. It was found that the discriminant model using data collected during the first three seconds of yellow was the most accurate, at 99% accuracy. It was also found that data collection should focus on variables that are related to speed, acceleration, time, and distance to intersection, as opposed to secondary variables, such as pavement conditions, since secondary variables did not significantly change the accuracy of the prediction models. The results reveal a promising possibility for incorporating the developed models in traffic-signal controllers to improve DZ-protection strategies.     

Hazard prediction discriminates between novice and experienced drivers

Typical hazard perception tests often confound multiple processes in their responses. The current study tested hazard prediction in isolation to assess whether this component can discriminate between novice and experienced drivers. A variant of the hazard perception test, based on the Situation Awareness Global Assessment Technique, found experienced drivers to outperform novices across three experiments suggesting that the act of predicting an imminent hazard is a crucial part of the hazard-perception process. Furthermore three additional hypotheses were tested in these experiments. First, performance was compared across clips of different length. There was marginal evidence that novice drivers’ performance suffered with the longest clips, but experienced drivers’ performance did not, suggesting that experienced drivers find hazard prediction less effortful. Secondly, predictive accuracy was found to be dependent on the temporal proximity of visual precursors to the hazard. Thirdly the relationship between the hazard and its precursor was found to be important, with less obvious precursors improving the discrimination between novice and experience drivers. These findings demonstrate that a measure of hazard prediction, which is less confounded by the influence of risk appraisal than simple response time measures, can still discriminate between novice and experienced drivers. Application of this methodology under different conditions can produce insights into the underlying processes that may be at work, whilst also providing an alternative test of driver skill in relation to the detection of hazards.     

如何用visual foxpro6.0编写驾考考试模拟考试系统

在可视化数据库编程中,大部分的书本都是从数据库、表、字段等着手开始讲述具体的内容,本文则通过一个实际的项目"驾考考试模拟软件"的开发过程,从一个开发者的角度一步一步地引导分析编程过程所需的知识,对于广大程序开发者而言具有一定的借鉴作用。     

Efficient driver drowsiness detection at moderate levels of drowsiness

Previous research on driver drowsiness detection has focused primarily on lane deviation metrics and high levels of fatigue. The present research sought to develop a method for detecting driver drowsiness at more moderate levels of fatigue, well before accident risk is imminent. Eighty-seven different driver drowsiness detection metrics proposed in the literature were evaluated in two simulated shift work studies with high-fidelity simulator driving in a controlled laboratory environment. Twenty-nine participants were subjected to a night shift condition, which resulted in moderate levels of fatigue; 12 participants were in a day shift condition, which served as control. Ten simulated work days in the study design each included four 30-min driving sessions, during which participants drove a standardized scenario of rural highways. Ten straight and uneventful road segments in each driving session were designated to extract the 87 different driving metrics being evaluated. The dimensionality of the overall data set across all participants, all driving sessions and all road segments was reduced with principal component analysis, which revealed that there were two dominant dimensions: measures of steering wheel variability and measures of lateral lane position variability. The latter correlated most with an independent measure of fatigue, namely performance on a psychomotor vigilance test administered prior to each drive. We replicated our findings across eight curved road segments used for validation in each driving session. Furthermore, we showed that lateral lane position variability could be derived from measured changes in steering wheel angle through a transfer function, reflecting how steering wheel movements change vehicle heading in accordance with the forces acting on the vehicle and the road. This is important given that traditional video-based lane tracking technology is prone to data loss when lane markers are missing, when weather conditions are bad, or in darkness. Our research findings indicated that steering wheel variability provides a basis for developing a cost-effective and easy-to-install alternative technology for in-vehicle driver drowsiness detection at moderate levels of fatigue.