Objective: This study aimed at identifying and predicting in advance the point in time with a high risk of a virtual accident before a virtual accident actually occurs using the change of behavioral measures and subjective rating on drowsiness over time and the trend analysis of each behavioral measure.
Methods: Behavioral measures such as neck bending angle and tracking error in steering maneuvering during the simulated driving task were recorded under the low arousal condition of all participants who stayed up all night without sleeping. The trend analysis of each evaluation measure was conducted using a single regression model where time and each measure of drowsiness corresponded to an independent variable and a dependent variable, respectively. Applying the trend analysis technique to the experimental data, we proposed a method to predict in advance the point in time with a high risk of a virtual accident (in a real-world driving environment, this corresponds to a crash) before the point in time when the participant would have encountered a crucial accident if he or she continued driving a vehicle (we call this the point in time of a virtual accident).
Results: On the basis of applying the proposed trend analysis method to behavioral measures, we found that the proposed approach could predict in advance the point in time with a high risk of a virtual accident before the point in time of a virtual accident.
Conclusion: The proposed method is a promising technique for predicting in advance the time zone with potentially high risk (probability) of being involved in an accident due to drowsy driving and for warning drivers of such a drowsy and risky state. 相似文献
Abstract: Species’ assessments must frequently be derived from opportunistic observations made by volunteers (i.e., citizen scientists). Interpretation of the resulting data to estimate population trends is plagued with problems, including teasing apart genuine population trends from variations in observation effort. We devised a way to correct for annual variation in effort when estimating trends in occupancy (species distribution) from faunal or floral databases of opportunistic observations. First, for all surveyed sites, detection histories (i.e., strings of detection–nondetection records) are generated. Within‐season replicate surveys provide information on the detectability of an occupied site. Detectability directly represents observation effort; hence, estimating detectablity means correcting for observation effort. Second, site‐occupancy models are applied directly to the detection‐history data set (i.e., without aggregation by site and year) to estimate detectability and species distribution (occupancy, i.e., the true proportion of sites where a species occurs). Site‐occupancy models also provide unbiased estimators of components of distributional change (i.e., colonization and extinction rates). We illustrate our method with data from a large citizen‐science project in Switzerland in which field ornithologists record opportunistic observations. We analyzed data collected on four species: the widespread Kingfisher (Alcedo atthis) and Sparrowhawk (Accipiter nisus) and the scarce Rock Thrush (Monticola saxatilis) and Wallcreeper (Tichodroma muraria). Our method requires that all observed species are recorded. Detectability was <1 and varied over the years. Simulations suggested some robustness, but we advocate recording complete species lists (checklists), rather than recording individual records of single species. The representation of observation effort with its effect on detectability provides a solution to the problem of differences in effort encountered when extracting trend information from haphazard observations. We expect our method is widely applicable for global biodiversity monitoring and modeling of species distributions.相似文献
This paper summarizes, from a global perspective, the major progress in the implementation of the Agenda 21 since the UN Conference on Environment and Development. The results show that global economy has achieved a substantial growth, and positive progress has been made in poverty eradication, urbanization, and conservation and intensive use of natural resources. However, relevant international conventions and commitments have not yet been completely fulfilled. The paper further analyzes the current major challenges and future trends of global sustainable development. It is argued that there are three major challenges:1) fatal global environmental issues posing an increasing threat to human survival; 2) more and more severe global competition for developing spaces; and 3) issues highlighting global people’s livelihood. There are four trends of global sustainable development:1) sustainable development will further turn from concept into global action; 2) green will be the main trend of global development; 3) emerging developing countries will become the main driving force of global sustainable development; and 4) international relations in the field of sustainable development will turn to competitive co-operation. 相似文献