road surface condition detection

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Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

Hyperspectral Imaging for Autonomous Inspection of Road Pavement Defects

surface inspection to detect cracks and anomalies. Hyperspectral imaging, HSI, has been used previously to classify road conditions from satellite images [4] [5] [6] [7] [8]. The research was intended to classify road conditions in general and the spatial resolution can not detect road cracks or defects. Only few papers considered the detection of pavement cracks based on hyperspectral data [1] [9] [10] . In such case HSC were fitted on drones of low altitude flights to have higher spatial resolutions to enable observing cracks. The previous studies considered using descriptors of the spectrum such as the VIS2 (intensity difference between 830nm and 490nm-showing metal oxide content) and Short Wave Infra Red, SWIR (Intensity difference between 2120nm and 2340 showing hydrocarbon content). The metrics measure the rise and decay of spectral response curve at the wavelength regions for metal oxides and hydrocarbon which usually characterizes road conditions. These metrics have also been linked [11] to the Pavement Condition Index, PCI,(A standard metric by ASTM D6433 and D5340, used to indicate the condition of road pavement and ranges 0-100) and is usually computed using visual surveys [5].
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Trend Deviation Analysis for Automated Detection of Defects in GPR Data for Road Condition Surveys

Trend Deviation Analysis for Automated Detection of Defects in GPR Data for Road Condition Surveys

III. D ISCUSSION AND F UTURE R ESEARCH D IRECTIONS In addition to the example demonstrated in the previous section, the outputs of processing of GPR scans of two road segments with the different degree of deterioration are given in Figure 8. In Case I, there is an evident presence of deviations in the layer interfaces as well as material property changes. The automated analysis successfully resulted in detection of a series of delaminations in the HMA/base layer interface, areas of higher reflection intensity in the surface layer, and variations in the base layer structure profile. On the other hand, in Case II (good structural condition), only one significant deviation “alert” was mapped, which corresponds to the change in the base layer thickness.
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Road Lane Detection and Tracking

Road Lane Detection and Tracking

Therefore this algorithm is not much suitable. IPM (Inverse Perspective Mapping) method is used for detecting all kinds of obstacles on the road such as pedestrians and other vehicles systems. The principle of vision-based detection is one of the methods which is used in lane detection. It is a process of recognizing as well as detecting the lanes where the ground traffic circulates. To perform the functionalities of lane detection there must use some specific terms that implies the certain algorithms, certain processing units and the utilization of certain perceptive sensors. The lane detection is effected by many factors and it is also an essential function of advanced driving assistant system. The shadows of trees buildings, other aid boards can be affect the good quality of lane. Also the change of light condition, the dirt left on the road surface, the existence of the surrounding objects, etc can cause the damage to the quality of lane. There are some problems regarding with the detection of the road lane marks which are still existing.
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An approach to produce a GIS database for road surface monitoring

An approach to produce a GIS database for road surface monitoring

The detection and measurement of the pavement cracking provides valuable information to the local authorities on the road network condition and reduces maintenance costs. Significant progress has been made in recent years in using a variety of techniques for assessing the pavement road surface. For efficient collection of pavement condition data, different approaches have been proposed, and various automated systems have been developed world widely since the 1980s. Previous approaches to pavement condition involved a labor-intensive, time consuming, and risky process of data collection [8]. A quick, easy to use, and cost-effective method for establishing a GIS database is proposed in this paper.
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Introducing Road Surface Conditions into a Microscopic Traffic Simulation

Introducing Road Surface Conditions into a Microscopic Traffic Simulation

The introduction of highly automated driving functions is one of the main research and development efforts in the automotive industry worldwide. In the early stages of the development process, suppliers and manufacturers often wonder whether and to what extend the potential of the systems under development can be estimated in a cheap and timely manner. In the context of a current research project, a sensor system for the detection of the road surface condition is to be developed and it is to be investigated how such a system can be used to improve higher level driving functions. This paper presents how road surface conditions are introduced in various elements of the microscopic traffic simulation such as the actual network, the network editor, a device for detection, and an adaptation of the standard Krauß car following model. It is also shown how the adaptations can subsequently affect traffic scenarios. Furthermore, a summary is given how this preliminary work integrates into the larger scope of using SUMO as a tool in the process of analyzing the effectiveness of a road surface condition sensor.
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A novel approach for road surface wetness detection with planar capacitive sensors

A novel approach for road surface wetness detection with planar capacitive sensors

The experimental results for the comparison of the sensor system and the reference measuring instrument are presented in Fig. 8. It shows the mean capacitance of every condition, plotted consecutively, which indicates similar behavior for both the sensor system and the impedance analyzer for ev- ery condition. While the mean x of each condition is simi- lar, there are differences in standard deviation σ, as summa- rized in Table 3. Besides variations among the condition due to stochastic effects of impinging water drops, there are dif- ferences between the sensor system and the impedance an- alyzer. Although in the dry condition σ is more than 94 % lower for the impedance analyzer, the sensor system’s stan- dard deviation with 0.59 pF is still low enough when consid- ering the much higher differences in capacitance, even for small quantities of water drops impinging upon the trans- ducer. While σ increases with wetness, the difference be- tween the output of the sensor system and the impedance analyzer decreases. In the wet condition, the difference is about 49 %, and in the very wet condition, it is about 38 %. As already stated for the dry condition, even here, σ is low enough to reliably distinguish between different conditions. Therefore, our proposed sensor system is suitable for practi- cal tests in a vehicle.
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Assessment Of Potential Impact On Environment Due To Upgradation Of Highway Work From Padubidri To Karkala.”-A Case Study

Assessment Of Potential Impact On Environment Due To Upgradation Of Highway Work From Padubidri To Karkala.”-A Case Study

Some stretches of the project corridor constitutes productive agricultural land which may be affected. Loss of agricultural land may result from the establishment of construction camp, labour camp, concrete batching plant; hot mix plants borrow areas, quarries near to subproject roads and access roads. Suspended particulate matter from quarries and crushers lead to decrease in productivity of the soil; degeneration of plant species and retards the plant growth found. After the construction of road is over, some of the land use changes are envisaged due to improved accessibility to far off distances and changes in socio economic activities, especially linear developments at the villages and towns will increase. The agricultural and horticultural activities will be positively benefited from better roads and connectivity.
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ROAD NETWORK ASSESSMENT IN TRANS-AMADI, PORT HARCOURT IN NIGERIA USING GIS

ROAD NETWORK ASSESSMENT IN TRANS-AMADI, PORT HARCOURT IN NIGERIA USING GIS

walking and cycling hence, priority must be given to the maintenance and improvement of roadways, sidewalks and arterial roads. The survey also shows that the state of Nigerian roads remains poor for a number of reasons such as faulty designs, lack of drainage and very thin coatings that were easily washed away, excessive use of the road network given the under-developed nature of waterways and railways among others. The most important role of both government and individual citizen is to find a solution for managing existing roads even before new ones are constructed but managing modern roadways is a complex business especially at this time when economic growth has come close to a halt in Nigeria. Thus, the creation, updating, maintenance and general management of road information and network in terms of spatial and non-spatial data are needed but the voluminous nature of data involved for proper record keeping is indeed cumbersome, and cannot effectively be handled by a traditional system of record keeping. The analogue system means remain inflexible resulting in data storage in fixed forms and formats; however, the system becomes less useful for many purposes and is rarely updated because of the costs implication. The maps are easily displaced or destroyed because many different people at different locations use them.
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Diurnal variation of tropospheric ozone 
		(O3) and its precursors (Co and No2) due to transportation activity in 
		the roadside areas in Padang city, Indonesia

Diurnal variation of tropospheric ozone (O3) and its precursors (Co and No2) due to transportation activity in the roadside areas in Padang city, Indonesia

Sampling locations were set at the three-point which differentiated by the classification of roads according to its function, namely local road, collector road and arterial road. Based on its condition, the road that can be used as the location of sampling is Ganting Road representing local road, Bagindo Aziz Chan Road representing collector road and Khatib Sulaiman Road representing arterial road. Monitoring was conducted

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The road weather model RoadSurf (v6.60b) driven by the regional climate model HCLIM38: evaluation over Finland

The road weather model RoadSurf (v6.60b) driven by the regional climate model HCLIM38: evaluation over Finland

model employs observations from road weather stations, me- teorological SYNOP (surface synoptic observations) weather stations, and radar precipitation networks to initialize road conditions while the road weather is predicted for the up- coming days utilizing forecasts produced by NWP models. In this study, we did not include any forecasted periods im- plying that no in situ observations were used to initialize and force RoadSurf. Instead, RoadSurf was modified so that it utilizes the RCM data, in this case the output of reanalysis- driven HCLIM38-ALARO. In addition to the abovemen- tioned inputs needed by RoadSurf, we utilized the bottom layer ground temperature (at the depth of 4.28 m) produced by HCLIM38-ALARO. Using the simulated ground temper- ature instead of the climatological one was motivated by the fact that although in the original RoadSurf version this tem- perature is assumed to vary sinusoidally, it is estimated by an equation in which some of the parameter values are based on measurements retrieved from only one FMI observatory located in Southern Finland. RoadSurf’s main outputs are T road and a traffic index describing driving conditions, but the
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IMPACT OF VEHICULAR TRAFFIC ON AMBIENT AIR QUALITY IN SELECTED JUNCTIONS IN PORT HARCOURT, NIGERIA

IMPACT OF VEHICULAR TRAFFIC ON AMBIENT AIR QUALITY IN SELECTED JUNCTIONS IN PORT HARCOURT, NIGERIA

The poor traffic flow and congestions are major problems sighted in many cities due to obstructions and increase vehicular population. A study by Kayode (2015), stated that the causes of congestion on road are frequent break-down of vehicle, road traffic crashes, low road network capacity, road condition, narrow lane, and violation of traffic rules.

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Design, Analysis And Fabrication Of Regenerative Automotive Suspension System Test RIG Module

Design, Analysis And Fabrication Of Regenerative Automotive Suspension System Test RIG Module

The suspension system allows the vehicle to travel over rough surfaces with a minimum of up and down body movement. Besides that, it will also allow the vehicle to corner with minimum roll to lose the traction between the road surfaces and the tires. This provides a cushioning action so road shocks have a minimal effect on the occupants and load in the vehicle. Road shock is the result of action from tires moving up and down as they meet bumps or holes in the road.

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Vision-based vehicle detection and counting system using deep learning in highway scenes

Vision-based vehicle detection and counting system using deep learning in highway scenes

The traditional machine vision method has a faster speed when detecting the vehicle but does not produce a good result when the image changes in brightness, there is periodic motion in the background, and where there are slow moving vehicles or complex scenes. Advanced CNN has achieved good results in object detection; however, CNN is sensitive to scale changes in object detection [17, 18]. The one stage method uses grids to predict objects, and the grid’s spatial constraints make it impossible to have higher precision with the two-stage approach, espe- cially for small objects. The two stage method uses region of interest pooling to segment candidate regions into blocks according to given parameters, and if the candidate region is smaller than the size of the given parameters, the candidate region is padded to the size of the given parameters. In this way, the characteristic structure of a small object is destroyed and its detection accuracy is low. The existing methods do not distinguish if large and small objects belong to the same category. The same method is used to deal with the same type of object, which will also lead to inaccurate detection. The use of image pyramids or multi-scale input images can solve the above problems, although the calculation requirements are large.
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The impact of vibration on comfort and bodily stress while cycling

The impact of vibration on comfort and bodily stress while cycling

There has been an increase in commuter cycling in a number of localities within the UK over the decade to the 2011 census. Bristol, for example has seen a 3 percentage point increase to 8%, and thirteen of the thirty three London Boroughs now demonstrate more than 5% more share for cycling to work, with Hackney at 15% (Goodman, 2013). The mean journey length for those using the Cycle Superhighways in London is in the order of eight miles. As more commuters adopt cycling as their preferred mode of transport, and also as ‘last mile’ delivery and courier work is increasingly being undertaken by bicycle, it is important to understand problems entailed in prolonged physical effort and exposure to vibration. The catalyst of the research presented here has been the personal experiences of the authors where new (ESC) and longer (JP) commuting journeys on a new bicycle led to a severe permanent headache (ESC), and neck discomfort (JP). This is presumed to result at least partly from vibration caused by vertical impact of the bicycle on the irregularities of road surfaces.
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Neural Network based Road Sign Recognition

Neural Network based Road Sign Recognition

parallel position before reading the sign. De La Escalera et al. [6] started with color matching, in which they looked for patterns in specific relationships that correspond to triangular, rectangular or circular signs. Lauziere et al. [7] used a physics–based approach for sign recognition, but this approach required keeping in memory the changes in the model parameter to accommodate the natural variation of illumination. Template matching methods that were used face some problems in case of noise in analog or gray scale images, improper illumination in the images, sampling and quantization noise and presence of the shadows in the images. After surveying different research works, the objective of our thesis is to represent a fast and robust system for road sign recognition which is a combination of a local image sample representation and a hybrid network.
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Road  Detection and Tracking  For UAVs

Road Detection and Tracking For UAVs

distributions using Gaussian mixture models (GMMs) from given sample images, and then determine road pixels in each frame by checking the probabilities of pixels that fit the GMMs. Gaussian and gamma distributions are used to represent color and gradient models. Vanishing points are calculated by detecting pairs of line segments, and used to correct the image in order to obtain rectified parallel scans. Road boundary are then identified by finding large strength changes in the example profile of each horizontal scan. The clustering technique based on prior hue and texture information is used to categorize each image pixel into target and background, and then boundary lines are fitted to refine the desired region. The author Srikanth Saripalli et. al [2] (2012) has proposed hough line transformation on the detected road regions. The minimum length of a line segment that needs to be detected is set close to half of image’s height. The division between collinear segments is set to 1/40 of image’s height. This assumes that the road consists of a relatively long straight line. This allows to detect straight long roads but will fail on curved roads. To detect curved roads author modify the algorithm to detect “chains” of intersecting lines. The George K. Siogkas et. al [3] (2013) has proposed fast and robust random-walker algorithm (RWA) and using an automatic seed generator that draws information from a customized version of the Horn–Schunck optical flow algorithm , a priori topological knowledge, and previous frame segmentation results to discriminate road from nonroad pixels. The spatiotemporal information required for this discrimination is derived from an optical flow estimator applied in the c1c2c3 color space to achieve robustness to shadows
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EXTRACTION OF RIDGES TO DETECT ROAD NETWORK FROM TOPOGRAPHIC IMAGE

EXTRACTION OF RIDGES TO DETECT ROAD NETWORK FROM TOPOGRAPHIC IMAGE

The ridges (or the ridge set) of a smooth function of two variables are a set of curves whose points are, in one or more ways to be made precise below, local maxima of the function in at least one dimension. For a function of variables, its ridges are a set of curves whose points are local maxima in dimensions. In this respect, the notion of ridge points extends the concept of a local maximum. Correspondingly, the notion of valleys for a function can be defined by replacing the condition of a local maximum with the condition of a local minimum. The union of ridge sets and valley sets, together with a related set of points called the connector set form a connected set of curves that partition, intersect, or meet at the critical points of the function. This union of sets together is called the function’s relative critical set [8] and Miller [9].
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Towards automatic near real-time traffic monitoring with an airborne wide angle camera system

Towards automatic near real-time traffic monitoring with an airborne wide angle camera system

A society that relies on individual mobility day to day requires sufficient methods for traffic monitoring and guidance. Especially daily commuters want to know travel times for their way to work. Moreover, relief forces are interested in precise travel times for their routing in case of emergencies, mass events, and disasters. However, precise travel time prediction on road networks is one of the most important concerns and challenges in modern transportation and traffic sciences. In order to determine traffic flow on different road types automatically, several approaches are possible. In general, traffic monitoring is mainly based on data from conventional stationary ground measurement systems such as inductive loops, radar sensors or terrestrial cameras. All ground measurement systems embedded in road infrastructure deliver precise traffic data punctually with high temporal resolution, but their spatial distribution is still limited to selected motorways and main roads. The low spatial resolution of these systems makes area-wide traffic monitoring difficult. New approaches collect data by means of mobile measurement units which flow with the traffic. The so called floating car data (FCD, [4, 17]) obtained from taxicabs can deliver useful traffic informa- tion within cities, but they are only available in few big cities today. Furthermore, the traffic information available from this source depends on the routes taxicabs drive, but taxi drivers tend to avoid busy roads during rush hours. Hence, only few or no data will be available on roads burdened with commuter traffic. In order to contribute to area-wide traffic monitoring by remote sensing, several projects, based on airborne optical and SAR sensors as well
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Online Full Text

Online Full Text

A monocular lane-vehicle detection and tracking system has been presented in this paper with an integration of three components, i.e. (i) lane boundary detection, (ii) lane region tracking, and (iii) vehicle detection with a proposed vertical asymmetry measurement. The advantages of lane-vehicle detection and tracking system are, (i) the reduction of vehicle searching time, and (ii) the increase performance of the vehicle detection based on the priori knowledge regardless to the environmental interference caused by non-vehicle structures and sky region. At the same time, lane detection and tracking system without any camera parameter can be applied to other driver assistance function for the determination of ROIs. Furthermore, horizontal symmetry analysis with an assistance of vertical asymmetry analysis can easily obtain the center part of vehicle and this center point can be used for vehicle tracking in future. In the future, further investigation will be carried out on lane-vehicle detection and tracking system under various road conditions.
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Strategies for handling black spot area to 
		increased road safety

Strategies for handling black spot area to increased road safety

The deficiency of road infrastructure reduces or decreases its efficiency, thereby, resulting in inadequate functioning, capable of triggering traffic accidents. This is also one of the major causes of traffic accident on roads. Interaction between human errors and road infrastructure cause road traffic accident 34%. Road accidents are not only caused by the condition of vehicle and driver, but also due to many factors, including its design (horizontal and vertical alignments), preceding and stop sight distance, pavement conditions, and completeness of road equipment facilities. The aim of this research was analyzing road infrastructure deficiency at black spot area to increased road safety. The study location was in Brobot Street, Bojongsari, Purbalingga and Central Java, Indonesia. Three parameters that analyzed in road infrastructure deficiency i.e.: condition of road geometric, condition of road pavement, and harmonization of road equipment facilities. The result showed that some road infrastructures must be repaired to reduce traffic accidents. Road geometric aspect involves the shoulder width, (improve the shoulder to 1.5 m wide), while its pavement condition involve lane or shoulder drop off. To harmonize these inconsistencies equipment such as install maximum speed limit sign (40 km/h), warning sign along black spot location should be perfectly located along these roads and install traffic sign.
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