scholarly journals An OSM Data-Driven Method for Road-Positive Sample Creation

2020 ◽  
Vol 12 (21) ◽  
pp. 3612
Author(s):  
Jiguang Dai ◽  
Chengcheng Li ◽  
Yuqiang Zuo ◽  
Haibin Ai
Keyword(s):  
Texture Feature ◽  
Positive Sample ◽  
Data Driven ◽  
Self Similarity ◽  
The Road ◽  
Network Training ◽  
Orientation Histogram ◽  
Iterative Interpolation ◽  
Road Width ◽  
Local Road

Determining samples is considered to be a precondition in deep network training and learning, but at present, samples are usually created manually, which limits the application of deep networks. Therefore, this article proposes an OpenStreetMap (OSM) data-driven method for creating road-positive samples. First, based on the OSM data, a line segment orientation histogram (LSOH) model is constructed to determine the local road direction. Secondly, a road homogeneity constraint rule and road texture feature statistical model are constructed to extract the local road line, and on the basis of the local road lines with the same direction, a polar constraint rule is proposed to determine the local road line set. Then, an iterative interpolation algorithm is used to connect the local road lines on both sides of the gaps between the road lines. Finally, a local texture self-similarity (LTSS) model is implemented to determine the road width, and the centerpoint autocorrection model and random sample consensus (RANSAC) algorithm are used to extract the road centerline; the road width and road centerline are used to complete the creation of the road-positive samples. Experiments are conducted on different scenes and different types of images to demonstrate the proposed method and compare it with other approaches. The results demonstrate that the proposed method for creating road-positive samples has great advantages in terms of accuracy and integrity.

Identification of Linear Tire Cornering Stiffness Using Subspace Methods

2014 ◽  
Vol 701-702 ◽  
pp. 492-497
Author(s):  
Teng Yue Ba ◽  
Xi Qiang Guan ◽  
Jian Wu Zhang
Keyword(s):  
Data Driven ◽  
Subspace Identification ◽  
Subspace Methods ◽  
Identification Methods ◽  
The Road ◽  
Strong Robustness ◽  
Subspace Algorithms ◽  
Double Lane Change ◽  
On The Road ◽  
Lane Change Test

In this paper, subspace identification methods are proposed to estimate the linear tire cornering stiffness, which are only based on the road tests data without any prior knowledge. This kind of data-driven method has strong robustness. In order to validate the feasibility and effectiveness of the algorithms, a series of standard road tests are carried out. Comparing with different subspace algorithms used in road tests, it can be concluded that the front tire cornering stiffness can be estimated accurately by the N4SID and CCA methods when the double lane change test data are taken into analysis.

scholarly journals Influence of road conditions on vehicles’ fuel consumption

2021 ◽  
pp. 158-164
Author(s):  
Kateryna Dodukh ◽  
◽  
Anton Palchyk ◽  
Keyword(s):  
Fuel Consumption ◽  
Weather Conditions ◽  
General Criterion ◽  
Average Speed ◽  
The Road ◽  
Horizontal Curves ◽  
Vehicle Type ◽  
Wet Snow ◽  
Northern Regions ◽  
Road Width

The work is devoted to the solution of the issue of economic and safe transportation of goods and passengers by road. This transportation depends on the condition of roads, road surface, vehicle type and weather conditions. Weather conditions are taken into account both in terms of visibility (meteorological) and in terms of the coefficient of adhesion. The general criterion for assessing all conditions is the average speed of the vehicle, taking into account weather and road conditions. Weather conditions are determined by the type of visibillity: clear weather, rain, snowfall, blizzards, rain. By the coefficient of adhesion: dry surface, normal, wet, snow, ice. By road conditions: category of road, width of the travel section, radii of horizontal curves, longitudinal slopes, width of the road, the state of surface (coefficient of solidity). According to weather conditions, the calendar year is divided into three periods according to the conditions of cars’ movement. The first (winter) - December, January, February, March; second (spring-summer) - April, May, July, June, August; third (autumn) - September, October, November. The use of weather conditions in the Northern regions of Ukraine is presented in this work.

scholarly journals The impact of agriculture drainage reconstruction on ground water recession close to the subgrade

2015 ◽  
Vol 10 (3) ◽  
pp. 230-238 ◽  
Author(s):  
Vilimantas Vaičiukynas ◽  
Saulius Vaikasas ◽  
Henrikas Sivilevičius ◽  
Audrius Grinys
Keyword(s):  
Agricultural Land ◽  
Drainage System ◽  
Hydraulic Permeability ◽  
Road Maintenance ◽  
Permeability Characteristics ◽  
The Road ◽  
The Impact ◽  
Good Drainage ◽  
Important Design ◽  
Local Road

Good drainage is the most important design consideration for a road, both to miniaturize road maintenance costs and maximize the time the road is operational. The lack of good drainage lead to the structural damages and costly repairs. Many of roads are built in intensively drained agricultural land. The effective way to drain subgrades is reconstruction of existing agricultural drainage. The impact of cross-subsurface drainage system on water level fluctuation was measured using Plane geofiltration mathematical model, one of 3D geofiltration modelling programs. The hydraulic permeability characteristics were determined in field of Pikeliai, close to local road in Kėdainiai district, Lithuania. This object is composed of clay and loamy soils. Subsurface cross drains trenches spacing of 20 m, 30 m and 40 m were simulated. The hydraulic permeability of cross drain trenches and lateral trenches modelled was from 0.006 m/a day to 6 m/a day. The simulation of cross drains trenches showed that the most effective distance between them are 20 m. The highest water depression occurs when the permeability of cross drain trenches and lateral trenches is ~ 6 m/day, at the distance of 20 m. The water recession is 20 cm lower compared to the drainage systems without cross drains trenches. By installing cross drains trenches every 30 m, water recession is 10 cm lower when the trench permeability is about 6 m/day. When increasing the distance between the cross drains up to 40 m their influence disappears.

Data-Driven Ground Models: The Road to Fully-Integrated Site Characterization and Design

2022 ◽  
pp. 3-21
Author(s):  
Maarten Vanneste ◽  
Guillaume Sauvin ◽  
Jean-Rémi Dujardin ◽  
Carl Fredrik Forsberg ◽  
Rasmus T. Klinkvort ◽  
...  
Keyword(s):  
Site Characterization ◽  
Data Driven ◽  
Fully Integrated ◽  
The Road

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

2019 ◽  
Vol 2673 (12) ◽  
pp. 367-379 ◽  
Author(s):  
Yalda Rahmati ◽  
Mohammadreza Khajeh Hosseini ◽  
Alireza Talebpour ◽  
Benjamin Swain ◽  
Christopher Nelson
Keyword(s):  
Autonomous Vehicles ◽  
Simulation Models ◽  
Driving Behavior ◽  
Data Driven ◽  
Microscopic Simulation ◽  
Car Following ◽  
The Road ◽  
Human Driver ◽  
Significant Difference ◽  
On The Road

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

scholarly journals Study on the Wind Environment of the Architecture Communities: Traditional Typical Min Nan Human Settlements’ Case

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Yuan Miao ◽  
Shang-Chia Chiou
Keyword(s):  
Architectural Design ◽  
Life Experience ◽  
Conservation Strategy ◽  
Building Structures ◽  
Rural Settlements ◽  
The Road ◽  
Short Period ◽  
Road Width ◽  
Planning And Construction

In contrast to the modern urban planning, which can be done in short period in terms of the spatial qualified design, the traditional tribe needs longer period in terms of the villagers’ sense of community. The selection of location, planning, and construction reveals the wisdom of the former people’s use of the resourceful life experience. First, the paper employs PHOENICS to simulate the wind environments of two most representative patterns of rural settlements in the southern area of Southern Fujian, China. This was made to compare the different conditions caused by settlements of various architectural groups. Second, the engineering and construction aspects of settlements—such as the width of roads and building structures—will be further analyzed and examined as case study in attempt to discover the favorable environmental factors for generating winds as well as the construction dimension of the settlement, such as the road width and the architectural design. Finally, the paper tends to conclude with an energy conservation strategy applied to the construction of modern communities which has low density and small group buildings.

scholarly journals Shot Boundary Detection Algorithm Based on Multi-Feature Fusion

2013 ◽  
Vol 347-350 ◽  
pp. 3866-3871
Author(s):  
Kai Jin ◽  
Hong Cai Feng ◽  
Qi Feng ◽  
Chi Zhang
Keyword(s):  
Feature Fusion ◽  
Texture Feature ◽  
Boundary Detection ◽  
Detection Algorithm ◽  
Shot Boundary Detection ◽  
Features Fusion ◽  
Orientation Histogram ◽  
Video Shot ◽  
Shot Boundary ◽  
The Ideal

To establish a general and robust shot boundary detection algorithm, according to characteristics of lens conversion and the ideal of multiple video features fusion, a shot boundary detection algorithm is proposed based on YUV histogram, texture feature and edge orientation histogram in the paper. Besides, global and self-adaptive threshold are combined to use so as to control the process of shot boundary detection and enhance the accuracy of threshold selection. The experiment results show that the algorithm can effectively realize video shot boundary detection and strengthen the robustness of the detection.

scholarly journals Towards Scale-Free Rain Streak Removal via Self-Supervised Fractal Band Learning

2020 ◽  
Vol 34 (07) ◽  
pp. 12629-12636 ◽  
Author(s):  
Wenhan Yang ◽  
Shiqi Wang ◽  
Dejia Xu ◽  
Xiaodong Wang ◽  
Jiaying Liu
Keyword(s):  
Deep Learning ◽  
Frequency Band ◽  
Data Driven ◽  
Constraint Forces ◽  
Paired Data ◽  
Scale Free ◽  
Network Training ◽  
Generalization Problem ◽  
Texture Characteristics ◽  
Highly Correlated

Data-driven rain streak removal methods, which most of rely on synthesized paired data, usually come across the generalization problem when being applied in real cases. In this paper, we propose a novel deep-learning based rain streak removal method injected with self-supervision to improve the ability to remove rain streaks in various scales. To realize this goal, we made efforts in two aspects. First, considering that rain streak removal is highly correlated with texture characteristics, we create a fractal band learning (FBL) network based on frequency band recovery. It integrates commonly seen band feature operations with neural modules and effectively improves the capacity to capture discriminative features for deraining. Second, to further improve the generalization ability of FBL for rain streaks in various scales, we add cross-scale self-supervision to regularize the network training. The constraint forces the extracted features of inputs in different scales to be equivalent after rescaling. Therefore, FBL can offer similar responses based on solely image content without the interleave of scale and is capable to remove rain streaks in various scales. Extensive experiments in quantitative and qualitative evaluations demonstrate the superiority of our FBL for rain streak removal, especially for the real cases where very large rain streaks exist, and prove the effectiveness of its each component. Our code will be public available at: https://github.com/flyywh/AAAI-2020-FBL-SS.

scholarly journals Road Extraction from High-Resolution SAR Images via Automatic Local Detecting and Human-Guided Global Tracking

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Jianghua Cheng ◽  
Wenxia Ding ◽  
Xishu Ku ◽  
Jixiang Sun
Keyword(s):  
High Resolution ◽  
Sar Images ◽  
The Road ◽  
Global Tracking ◽  
On The Road ◽  
Airborne Sar ◽  
Fitting In ◽  
Searching Method ◽  
Local Detection ◽  
Local Road

Because of existence of various kinds of disturbances, layover effects, and shadowing, it is difficult to extract road from high-resolution SAR images. A new road center-point searching method is proposed by two alternant steps: local detection and global tracking. In local detection step, double window model is set, which consists of the outer fixed square window and the inner rotary rectangular one. The outer window is used to obtain the local road direction by using orientation histogram, based on the fact that the surrounding objects always range along with roads. The inner window rotates its orientation in accordance with the result of local road direction calculation and searches the center points of a road segment. In global tracking step, particle filter of variable-step is used to deal with the problem of tracking frequently broken by shelters along the roadside and obstacles on the road. Finally, the center-points are linked by quadratic curve fitting. In 1 m high-resolution airborne SAR image experiment, the results show that this method is effective.

scholarly journals PENGENALAN TAS RANSEL PADA CITRA DIGITAL DENGAN EKSTRAKSI FITUR TEKSTUR MENGGUNAKAN METODE GRAY LEVEL CO-OCCURRENCE MATRIX

2018 ◽  
Vol 4 (4) ◽  
pp. 258
Author(s):  
Cahya Rahmad ◽  
Mungki Astiningrum ◽  
Ade Putra Lesmana
Keyword(s):  
Feature Extraction ◽  
Texture Feature ◽  
Gray Level ◽  
Significant Development ◽  
Average Percentage ◽  
The Road ◽  
The Third ◽  
Last Stage ◽  
On The Road ◽  
Occurrence Matrix

The Backpack is one type of bag that experienced significant development. Many people buy it for their needs. However, when assessing a backpack directly or on the road, he could not recognize the backpack. The generally people want to buy backpacks must look at the price, color, shape, features, and the main ingredients of manufacture. Therefore, in image processing, there is a feature extraction theory for the process of recognizing an object. The backpack itself has a different texture. So that the introduction of the object is better done texture feature extraction with the gray level Co-occurrence matrix method. After that, then get the uniqueness of the backpack image to the classification with the image of the backpack in the database. The last stage in this study the authors conducted trials in 3 conditions. The first condition is based on a backpack photo-taking background. The second condition is based on the pixel capacity of the camera to retrieve the backpack image. And the third condition is based on the brightness of the backpack image. Of these three conditions, a percentage of matching values was obtained in the first condition with an average percentage of 90%, the second condition with an average percentage of 80% and last on the third condition with an average percentage of 70%.

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