Poor Visibility Conditions Research Articles

INVESTIGATION OF THE EFFECT OF IONOSPHERIC GRADIENTS ON GPS SIGNALS IN THE CONTEXT OF LAAS

Local Area Augmentation System (LAAS) is expected to enable the pilots to guide the aircraft more precisely and safely into busy airports even in poor visibility conditions. The anomalous low and equatorial latitude Ionosphere is severe threat to the LAAS system. To characterize the anomalous ionospheric gradients, the performance of an ionospheric threat model is evaluated. In our investigation, in contrast to the reported work available in the open literature, smoothed code phase measurements are used in the threat model to obtain precise ionospheric time delay. The three key parameters of the threat model gradient slope (mm/km), width (km) and front speed (m/s) are used in the analysis. Further, geometry screening using Maximum Ionosphere Induced Error in Vertical (MIEV) as a key parameter is carried out to identify the stationary gradients and its impact on system performance for CAT-I operations. A maximum ionospheric gradient of 355.74mm/km over a distance of approximately 75km is reported at mid latitudes. Whereas, in our flndings at low/equatorial latitudes even within a distance of approximately 4km a maximum gradient of 460mm/km is observed, which is comparatively very high. Our results show that, there is necessity to enhance upper bound for the ionospheric gradients threat space over low latitudes.

Enhancement of Underwater Image using Fuzzy Histogram Equalization

Image enhancement is a process of improving the quality of image by improving its feature. The underwater images suffer from low contrast and resolution due to scattering of light and poor visibility conditions. In this paper we proposed an image enhancement technique to enhance the quality of underwater images. The proposed technique comprises a combination of classical contrast enhancement techniques and fuzzyhistogram equalization techniques. For comparing the performance, some statistics parameter is used. The proposed technique comprises a combination of classical contrast enhancement techniques and fuzzy-histogram equalization techniques. Experiment results demonstrate the effectiveness of fuzzy logic techniques to enhance the quality of the underwater images.

Socio-spatial cognition in vervet monkeys

Safety in numbers is thought to be the principal advantage of living in groups for many species. The group can only provide protection against predators, however, when group cohesion is maintained. Vocalisations are used to monitor inter-individual distances, especially under conditions of poor visibility, but should be avoided in the presence of predators. Mentally tracking the movements of silent and invisible group members would allow animals foraging in dense vegetation to stay close to their group members while reducing the use of vocal contact. We tested the socio-spatial cognitive abilities of wild vervet monkeys (Chlorocebus pygerythrus) by comparing their reactions to plausible and implausible displacements of group members simulated by sound playbacks. Our methods are comparable to those used in studies of 'object permanence' and 'invisible displacements' of inanimate objects. Our results show that vervets can track the whereabouts of invisibly and silently moving group members, at least over short periods of time.

A Comparative Analysis between Active and Passive Techniques for Underwater 3D Reconstruction of Close-Range Objects

In some application fields, such as underwater archaeology or marine biology, there is the need to collect three-dimensional, close-range data from objects that cannot be removed from their site. In particular, 3D imaging techniques are widely employed for close-range acquisitions in underwater environment. In this work we have compared in water two 3D imaging techniques based on active and passive approaches, respectively, and whole-field acquisition. The comparison is performed under poor visibility conditions, produced in the laboratory by suspending different quantities of clay in a water tank. For a fair comparison, a stereo configuration has been adopted for both the techniques, using the same setup, working distance, calibration, and objects. At the moment, the proposed setup is not suitable for real world applications, but it allowed us to conduct a preliminary analysis on the performances of the two techniques and to understand their capability to acquire 3D points in presence of turbidity. The performances have been evaluated in terms of accuracy and density of the acquired 3D points. Our results can be used as a reference for further comparisons in the analysis of other 3D techniques and algorithms.

Robust Vehicle Detection under Various Environmental Conditions Using an Infrared Thermal Camera and Its Application to Road Traffic Flow Monitoring

We have already proposed a method for detecting vehicle positions and their movements (henceforth referred to as “our previous method”) using thermal images taken with an infrared thermal camera. Our experiments have shown that our previous method detects vehicles robustly under four different environmental conditions which involve poor visibility conditions in snow and thick fog. Our previous method uses the windshield and its surroundings as the target of the Viola-Jones detector. Some experiments in winter show that the vehicle detection accuracy decreases because the temperatures of many windshields approximate those of the exterior of the windshields. In this paper, we propose a new vehicle detection method (henceforth referred to as “our new method”). Our new method detects vehicles based on tires' thermal energy reflection. We have done experiments using three series of thermal images for which the vehicle detection accuracies of our previous method are low. Our new method detects 1,417 vehicles (92.8%) out of 1,527 vehicles, and the number of false detection is 52 in total. Therefore, by combining our two methods, high vehicle detection accuracies are maintained under various environmental conditions. Finally, we apply the traffic information obtained by our two methods to traffic flow automatic monitoring, and show the effectiveness of our proposal.

Non–linear analysis of short term variations in ambient visibility

Ambient visibility is a complex manifestation arising out of interactions among many atmospheric variables, including ambient aerosol load, and region specific geophysical characteristics. To functionally relate visibility impairment in Delhi region during winter months-months marred with poor visibility conditions–a novel experiment was designed to relate visibility with ambient aerosol load (PM2.5), and relevant meteorological variables: dew point temperature (Dp), height of planetary boundary layer (PBL), ambient temperature (T), relative humidity (RH), wind speed (WS) and wind direction (WD). Time series data sets of Visibility(t) and other variables were subjected to non–linear decomposition using Empirical Mode Decomposition Method (EMD), enabling to obtain total cyclic and acyclic–trend components embedded in all data–sets. Extracted total cyclic visibility components were functionally related with the corresponding components associated with PM2.5 load and meteorological variables. Decomposed acyclic–trend component of the visibility, representing time dependent acyclic trend (AT), was separately related with the corresponding AT components of the considered meteorological variables. The decomposed components of the visibility (total cyclic and AT) were subjected to multiple linear regression to establish a functional relationship between them and a set of variables among the considered variables. The analysis suggests that acyclic–trend associated with Visibility(t) can be predicted better as opposed to the Visibility(t)cyclic component.

A Fusion of Real and Virtual Information for Aiding Aircraft Pilotage in Low Visibility

Low visibility is the single most critical factor affecting both the safety and capacity of worldwide aviation operations. Enhanced Vision System (EVS) and Synthetic Vision System (SVS) are new assistant systems designed with the goal of eliminating poor visibility conditions as a causal factor to civil aircraft accidents and delays. Due to their great potential, both EVS and SVS have been drawing dramatic attention from academia to industry all over the world and they are moving from laboratory to practical application. In China, however, the progress is comparatively slow because of the costly and limited experimental resources. This paper reports on the preliminary work for developing EVS and SVS including the pioneer flight test in China for civil aviation use and its data analysis. A novel concept of merging real and virtual vision together is put forward in this paper. Index Terms—Low visibility, EVS, SVS, Fusion

Real-Time Fog Warning System for the Abu Dhabi Emirate (UAE)

Based on historical evidence, driving in heavy fog conditions is one of the most serious causes that lead to massive highway accidents. For example, the Abu Dhabi Dubai Highway (E10) faced two record accidents in recent times. The first accident was in March 2008 in which more than 200 vehicles were involved in a mass collision. The second was in April 2011 and it involved 130 vehicles. These two massive accidents, and several other relatively minor ones, were due to poor visibility conditions caused by dense fog. Vehicles driving at high speed suddenly enter road sectors covered by dense fog without warning and are then implicated in mass collisions. The main objective of this research is to improve road safety in Abu Dhabi when drivers face poor visibility conditions caused by dense fog. This is achieved by sending early real-time warning signals to all drivers who are about to enter poor visibility sections of the highway of the dangers ahead, using radio signals or cell phone voice-based short messages. Warning signals can also be displayed to the drivers using Changeable Message Signs (CMS) installed along the highway. The concept and architecture of a novel, modular fog warning system are

Unidirectional Sound Signage for Speech Frequency Range Using Multiple-Loudspeaker Reproduction System

Human safety is the most important issue in disaster management. Speech is a sound signal containing information that is easily and quickly understood by humans. Using speech as sound signage in emergency systems can effectively increase human safety in low or poor visibility conditions such as in smoke-filled situations. However, reflections of sound through walls, floor surfaces, and ceilings will affect clarity of speech. Unfortunately, because of the characteristics of sound reproduction systems, a single loudspeaker propagates sound waves omni-directionally at low frequencies. This paper proposes a simple multiple-loudspeaker system for reproducing sound with uni-directional characteristics. The proposed system consists mainly of a primary loudspeaker for introducing sound in the desired beam, a secondary loudspeaker for reducing gain in the undesired direction, and digital filters. An adaptive finite-impulse-response (FIR) filter is used to produce the controlling sound by implementing a filtered-x least-mean-square algorithm, and a delay filter for adjusting the time alignment of sound propagation between primary and secondary sources at the control point. Several operational conditions for illustrating real situations and reflections were considered in an anechoic chamber. Experimental results show the directivity patterns of the proposed multiple-loudspeaker system for the required conditions. In a low frequency range, the system is able to control unidirectional propagation; there is a sound beam in the desired direction and, conversely, reduction of gain in the undesired direction around the control point.

Oil/Gas Pipeline Leak Inspection and Repair in Underwater Poor Visibility Conditions: Challenges and Perspectives

Mechanical pressure clamps are examples of innovative tools commonly used in the oil and gas industry for arresting leaks from damaged oil and gas pipelines. However, if leaks result from pipeline rupture, clamps are not usually recommended. It is therefore obvious that inspection of the leaking pipeline is very crucial in deciding the strategy for repair. For subsea pipelines where underwater poor visibility is pronounced, this important aspect of the pipeline repair process becomes difficult to implement. The result is a repair-leak-repair cycle. This challenge is commonly found in repairs of old pipelines in unclear water conditions. Old pipelines and their vulnerability to fractures that often lead to ruptures are discussed. In this paper, the challenges and technologies available for visualisation and examination in such unclear water conditions are discussed. There appears to be a gap in the existing pipeline integrity management system with respect to inspection and repair of pipelines in unclear water conditions. This gap needs to be filled in order to minimise spills and pollution. For pipelines installed in unclear water condition, a perspective is suggested to extend the capability of existing remotely operated vehicles to employ the use of clear laminar water system or a related technique to provide integrity engineers and operators with close visual assess to inspect leaking pipelines and effect adequate repairs. This paper suggests that the use of optical eye as the main tool for examination remains valuable in managing the challenges in underwater pipeline repairs in unclear water condition.

Robust vehicle detection even in poor visibility conditions using infrared thermal images and its application to road traffic flow monitoring

We propose an algorithm for detecting vehicle positions and their movements by using thermal images obtained through an infrared thermography camera. The infrared thermography camera offers high contrast images even in poor visibility conditions like snow and thick fog. The proposed algorithm specifies the area of moving vehicles based on the standard deviations of pixel values along the time direction of spatio-temporal images. It also specifies vehicle positions by applying the pattern recognition algorithm which uses Haar-like features per frame of the images. Moreover, to increase the accuracy of vehicle detection, correction procedures for misrecognition of vehicles are employed. The results of our experiments at three different temperatures show that the information about both vehicle positions and their movements can be obtained by combining those two kinds of detection, and the vehicle detection accuracy is 96.2%. Moreover, the proposed algorithm detects the vehicles robustly in the 222 continuous frames taken in poor visibility conditions like snow and thick fog. As an application of the algorithm, we also propose a method for estimating traffic flow conditions based on the results obtained by the algorithm. By using the method for estimating traffic flow conditions, automatic traffic flow monitoring can be achieved.

Bistatic Forward-Looking SAR: Results of a Spaceborne–Airborne Experiment

Forward-looking radar imaging continues to gain in significance due to a variety of convenient applications, like landing assistance for aircraft in poor visibility conditions. Synthetic aperture radar (SAR) techniques are typically used to achieve a high azimuth resolution, but conventional monostatic SAR is not applicable in forward direction because of azimuth ambiguities and poor Doppler resolution. To improve the Doppler resolution and to avoid azimuth ambiguities, bistatic SAR configurations can be used to obtain high-resolution radar images. This is demonstrated for the first time in a spaceborne-airborne SAR experiment by using TerraSAR-X as the illuminator and the Phased Array Multifunctional Imaging Radar as the receiver. For convenience, the receiver's SAR antenna was mounted on the aircraft's loading ramp and looked backward. Due to identical image properties and the same challenges for forward- and backward-looking sensors, this configuration also demonstrates the feasibility of forward-looking bistatic SAR. This letter describes the experimental setup, analyzes the performance, and presents the imaging results.

Legibility of Orienteering Maps: Evolution and Influences

Paper maps are generally planned for use under normal circumstances. One of the exceptions is orienteering maps, which are used in special circumstances, namely, at sport events such as running, when poor visibility and bad weather conditions may considerably reduce the legibility. When the drawing specifications of orienteering maps were created and when later new forms and disciplines of orienteering events were invented, the clear legibility of printed maps was one of the most important aspects. The International Orienteering Federation keeps the aim of preserving the characteristics of the different event and discipline formats by ensuring the legibility of the event maps.Les cartes papiers sont généralement conçues pour un usage standard. Une exception existe pour les cartes d’orientation qui sont utilisées dans des conditions spéciales. En effet lors d’un événement sportif, une visibilité réduite ou de mauvaises conditions météorologiques peuvent réduire considérablement la lisibilité de la carte. Lorsque les spécifications des cartes d’orientation ont été produites, et lorsque plus tard de nouvelles formes d’activité d’orientation ont été inventées, la bonne lisibilité des cartes imprimées était un des critères essentiel lors des choix de conception. La fédération internationale d’Orientation garde l’objectif de conserver les caractéristiques des différents événements et de la discipline en assurant une lisibilité des cartes.

Retinex-Like Method for Image Enhancement in Poor Visibility Conditions

Nowadays, still image and video systems are typically of limited use in poor visibility conditions such as in rain, fog, smoke, and at dawn. These situations severely limit the range and effectiveness of imaging systems because of the severe reduction in contrast. Retinex algorithm has been very popular area of research used to image enhancement. Basic idea of Retinex algorithm is to separate illumination from the reflectance in a given obtained image. In this paper, I have studied and implemented one particular Retinex-like algorithm based on bilateral filter with some supporting technique, this method associates de-noising method with Retinex algorithm and has been used in cases of severe image turbidity in air as well as image blurring with dramatic results.

Construction of Passing-Maneuver Model on Two-Lane Highway with Consideration of Road Surface and Visibility Conditions

Most national highways in rural parts of Hokkaido, Japan, are two-way, two-lane roads. On these highways, passing maneuvers that involve the use of the oncoming lane are frequently observed. Such passing is observed even when road surfaces are covered with compacted snow. To optimize speed and safety, simulated passing maneuvers have been conducted to make use of the oncoming lane of a two-way, two-lane highway. No such traffic simulations, however, have taken into consideration the surface conditions of roads in winter (e.g., snow-covered, icy, with poor visibility caused by snowfall and fog). In this study, a traffic flow simulation to model passing maneuvers that involved the oncoming lane was created through the use of values measured in a passing-maneuver field survey conducted on a two-lane road in summer and winter. The field values for the number of passing maneuvers, traffic volume, and speed distribution were compared with the simulation results. The outcomes confirmed that the difference between them was small. The sensitivity analysis results revealed that the number of passing maneuvers on surfaces covered with compacted snow under conditions of poor visibility did not increase as much as they did on dry surfaces, even when the traffic volume in the original lane was higher. The passing success rate tended to decrease with higher volumes of traffic in the oncoming lane, regardless of surface conditions.

Ensemble Kalman Filter Data Assimilation in a 1D Numerical Model Used for Fog Forecasting

Abstract Because poor visibility conditions have a considerable influence on airport traffic, a need exists for accurate and updated fog and low-cloud forecasts. Couche Brouillard Eau Liquide (COBEL)-Interactions between Soil, Biosphere, and Atmosphere (ISBA), a boundary layer 1D numerical model, has been developed for the very short-term forecast of fog and low clouds. This forecast system assimilates local observations to produce initial profiles of temperature and specific humidity. The initial conditions have a great impact on the skill of the forecast. In this work, the authors first estimated the background error statistics; they varied greatly with time, and cross correlations between temperature and humidity in the background were significant. This led to the implementation of an ensemble Kalman filter (EnKF) within COBEL-ISBA. The new assimilation system was evaluated with temperature and specific humidity scores, as well as in terms of its impact on the quality of fog forecasts. Simulated observations were used and focused on the modeling of the atmosphere before fog formation and also on the simulation of the life cycle of fog and low clouds. For both situations, the EnKF brought a significant improvement in the initial conditions and the forecasts. The forecast of the onset and burn-off times of fogs was also improved. The EnKF was also tested with real observations and gave good results. The size of the ensemble did not have much impact when simulated observations were used, thanks to an adaptive covariance inflation algorithm, but the impact was greater when real observations were used.

Developmental changes in behavioral and retinomotor responses of Pacific bluefin tuna on exposure to sudden changes in illumination

Schooling behavior traits during the process of retinomotor response from scotopic to photopic vision were examined in cultivated juvenile Pacific bluefin tuna (PBT) at 3 different ages. After a sudden change in illumination from darkness to 300 lx, retinal adaptations changed from scotopic to photopic vision. Retinomotor and schooling indices showed strong agreement, with juvenile PBTs forming polarized schools upon complete retinal adaptation to photopic vision. The behavioral and retinal adaptation to sudden illumination took 20, 15, and 10 min after illumination in PBT 25, 40, and 55 days after hatching (dah). At 40 dah, PBT took a longer time to adapt than fish aged 55 dah and showed the highest swimming speed, including momentary bursts of swimming immediately after illumination. This suggested that these fish were swimming at high speed under poor visibility conditions. In contrast, PBT at 55 dah showed a gradual increase in swimming speed that correlated with their retinal adaptation. Therefore, behavioral and retinal adaptation traits changed during growth, suggesting that the high mortality in PBT around 40 dah, due to collisions with the tank and net walls at dawn, may be because these adapt more slowly than fish at 55 dah and were swimming at a relatively high speed under conditions of poor visibility.

Driver's visual attention as a function of driving experience and visibility. Using a driving simulator to explore drivers’ eye movements in day, night and rain driving

Road crashes are the main cause of death of young people in the developed world. The reasons that cause traffic crashes are numerous; however, most researchers agree that a lack of driving experience is one of the major contributing factors. In addition it has been demonstrated that environmental factors such as driving during night and rain increases the risk of a crash. Both of these factors may be related to drivers’ visual search strategies that become more efficient with increased experience. In the present study we recorded the eye movements of driving instructors and learner drivers while they drove three virtual routes that included day, night and rain routes in a driving simulator. The results showed that driving instructors had an increased sampling rate, shorter processing time and broader scanning of the road than learner drivers. This broader scanning of the road could be possibly explained by the mirror inspection pattern which revealed that driving instructors fixated more on the side mirrors than learner drivers. Also it was found that poor visibility conditions, especially rain, decrease the effectiveness of drivers’ visual search. The lack of interaction between driving experience and visibility suggests that some aspects of visual search are affected by general rather than situation specific driving experience. The present findings support the effect of driving experience in modifying eye movement strategies. The high accident risk of night and rain driving could be partly explained by the decrement in visual search strategies during these conditions. Finally it is argued that the use of driving simulators can provide valuable insights regarding driving safety.

Novel machine vision methods for outdoor and built environments

Machine vision is potentially the most powerful sensing technique for intelligent automation. However, the existing techniques and algorithms only partially satisfy needs of complex environments (e.g. outdoor construction sites) mainly because of poor and/or unpredictably changing visibility conditions frequently encountered there (weather phenomena, mud and silt, partially buried objects, burst of intensive illumination, etc.). In this paper, we discuss principles of two methods that can significantly improve performances of vision system in such difficult conditions. The first method (based on gated imaging) is a specialized image acquisition technique while the second method addresses an important problem of object detection in complex environments. Both methods have been tested using images captured under visual conditions typically expected in such environments (e.g. many images are captured in high-turbidity water). The methods can be incorporated into more complex sensing systems, depending on needs and constraints of particular applications.

People Tracking with UWB Radar Using a Multiple-Hypothesis Tracking of Clusters (MHTC) Method

This paper presents a method to track multiple moving humans using Ultra-Wideband (UWB) radar. UWB radar can complement other human tracking technologies, as it works well in poor visibility conditions. Our tracking approach is based on a point process interpretation of the multi-path UWB radar scattering model for moving humans. Based on this model, we present a multiple hypothesis tracking (MHT) framework for tracking the ranges and velocities of a variable number of moving human targets. The multi-target tracking (MTT) problem for UWB radar differs from traditional applications because of the complex multipath scattering observations per target. We develop an MHT framework for UWB radar-based multiple human target tracking, which can simultaneously solve the complex observation clustering and data association problems using Bayesian inference. We present experimental results in which a monostatic UWB radar tracks both individual and multiple human targets to estimate target ranges and velocities, even with changing numbers of targets across radar scans.