Automatic Lane Recognition for Surveillance at Road Intersections

2020 ◽  
pp. 751-759
Author(s):  
Fanlei Min ◽  
Guan Wang ◽  
Liantao Wang ◽  
Jing Liu
Keyword(s):  
Lane Recognition ◽  
Road Intersections

A dual U-Net algorithm for automating feature extraction from satellite imagery

2021 ◽  
pp. 154851292098354
Author(s):  
Samuel Humphries ◽  
Trevor Parker ◽  
Bryan Jonas ◽  
Bryan Adams ◽  
Nicholas J Clark
Keyword(s):  
Neural Networks ◽  
Object Detection ◽  
Satellite Images ◽  
Detection Algorithm ◽  
Military Operations ◽  
Detection Algorithms ◽  
Us Military ◽  
Standard Product ◽  
Different Types ◽  
Road Intersections

Quick identification of building and roads is critical for execution of tactical US military operations in an urban environment. To this end, a gridded, referenced, satellite images of an objective, often referred to as a gridded reference graphic or GRG, has become a standard product developed during intelligence preparation of the environment. At present, operational units identify key infrastructure by hand through the work of individual intelligence officers. Recent advances in Convolutional Neural Networks, however, allows for this process to be streamlined through the use of object detection algorithms. In this paper, we describe an object detection algorithm designed to quickly identify and label both buildings and road intersections present in an image. Our work leverages both the U-Net architecture as well the SpaceNet data corpus to produce an algorithm that accurately identifies a large breadth of buildings and different types of roads. In addition to predicting buildings and roads, our model numerically labels each building by means of a contour finding algorithm. Most importantly, the dual U-Net model is capable of predicting buildings and roads on a diverse set of test images and using these predictions to produce clean GRGs.

scholarly journals Association of Infrastructure and Route Environment Factors with Cycling Injury Risk at Intersection and Non-Intersection Locations: A Case-Crossover Study of Britain

2021 ◽  
Vol 18 (6) ◽  
pp. 3060
Author(s):  
Rachel Aldred ◽  
Georgios Kapousizis ◽  
Anna Goodman
Keyword(s):  
Injury Risk ◽  
Conditional Logistic Regression ◽  
Traffic Signals ◽  
Control Groups ◽  
Class A ◽  
Case Crossover ◽  
Main Class ◽  
Road Intersections ◽  
Matched Case ◽  
Safety In Numbers

Objective: This paper examines infrastructural and route environment correlates of cycling injury risk in Britain for commuters riding in the morning peak. Methods: The study uses a case-crossover design which controls for exposure. Control sites from modelled cyclist routes (matched on intersection status) were compared with sites where cyclists were injured. Conditional logistic regression for matched case–control groups was used to compare characteristics of control and injury sites. Results: High streets (defined by clustering of retail premises) raised injury odds by 32%. Main (Class A or primary) roads were riskier than other road types, with injury odds twice that for residential roads. Wider roads, and those with lower gradients increased injury odds. Guard railing raised injury odds by 18%, and petrol stations or car parks by 43%. Bus lanes raised injury odds by 84%. As in other studies, there was a ‘safety in numbers’ effect from more cyclists. Contrary to other analysis, including two recent studies in London, we did not find a protective effect from cycle infrastructure and the presence of painted cycle lanes raised injury odds by 54%. At intersections, both standard and mini roundabouts were associated with injury odds several times higher than other intersections. Presence of traffic signals, with or without an Advanced Stop Line (‘bike box’), had no impact on injury odds. For a cyclist on a main road, intersections with minor roads were riskier than intersections with other main roads. Conclusions: Typical cycling environments in Britain put cyclists at risk, and infrastructure must be improved, particularly on busy main roads, high streets, and bus routes.

scholarly journals Spatial Distribution and Morphological Identification of Regional Urban Settlements Based on Road Intersections

2021 ◽  
Vol 10 (4) ◽  
pp. 201
Author(s):  
Liang Kong ◽  
Zhengwei He ◽  
Zhongsheng Chen ◽  
Mingliang Luo ◽  
Zhong Du ◽  
...  
Keyword(s):  
Urban Expansion ◽  
Vital Role ◽  
Morphological Identification ◽  
Spatial Form ◽  
Rapid Urbanization ◽  
Distance Threshold ◽  
Optimal Distance ◽  
Expansion Curve ◽  
Urban Settlements ◽  
Road Intersections

To measure and present urban size urban spatial forms, in solving problems in the rapid urbanization of China, urban territorial scope identification is essential. Although current commonly used methods can quantitatively identify urban territorial scopes to a certain extent, the results are displayed using a continuous and closed curve with medium- and low-resolution images. This makes the acquisition and interpretation of data challenging. In this paper, by extracting discretely distributed urban settlements, road intersections in OpenStreetMap (OSM), electronic maps, and urban expansion curve based on fractal thoughts have been used to present urban territorial scope and spatial form. Guangzhou, Chengdu, Nanjing, and Shijiazhuang cities were chosen as the identification targets. The results showed that the distance threshold corresponding to the principal curvature point of the urban expansion curve plays a vital role in the extraction of urban settlements. Moreover, from the analysis, the optimal distance thresholds of urban settlements in Guangzhou, Chengdu, Nanjing, and Shijiazhuang were 132 m, 204 m, 157 m, and 124 m, respectively, and the corresponding areas of urban territorial scopes were 1099.36 km2, 1076.78 km2, 803.07 km2, and 353.62 km2, respectively. These metrics are consistent with those for the built-up areas.

scholarly journals Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

2021 ◽  
Vol 11 (7) ◽  
pp. 101
Author(s):  
Andrew Paul Morris ◽  
Narelle Haworth ◽  
Ashleigh Filtness ◽  
Daryl-Palma Asongu Nguatem ◽  
Laurie Brown ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Red Light ◽  
Road User ◽  
Red Light Running ◽  
Crash Data ◽  
Passenger Vehicles ◽  
Road Users ◽  
Vulnerable Road User ◽  
Vulnerable Road Users ◽  
Road Intersections

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

The Integrated Design of Urban Road Intersections: A Case Study

ICSDC 2011 ◽  
2012 ◽  
Author(s):  
Giuseppe Cantisani ◽  
Giuseppe Loprencipe ◽  
Francesco Primieri
Keyword(s):  
Integrated Design ◽  
Urban Road ◽  
Road Intersections

scholarly journals Survey Analysis of Robust and Real-Time Multi-Lane and Single Lane Detection in Indian Highway Scenarios

2021 ◽  
Vol 309 ◽  
pp. 01117
Author(s):  
A. Sai Hanuman ◽  
G. Prasanna Kumar
Keyword(s):  
Neural Network ◽  
System Integration ◽  
Large Scale ◽  
Feature Learning ◽  
Semantic Segmentation ◽  
Lane Detection ◽  
Learning Approaches ◽  
Survey Analysis ◽  
Lane Recognition ◽  
Continuous Frames

Studies on lane detection Lane identification methods, integration, and evaluation strategies square measure all examined. The system integration approaches for building a lot of strong detection systems are then evaluated and analyzed, taking into account the inherent limits of camera-based lane detecting systems. Present deep learning approaches to lane detection are inherently CNN's semantic segmentation network the results of the segmentation of the roadways and the segmentation of the lane markers are fused using a fusion method. By manipulating a huge number of frames from a continuous driving environment, we examine lane detection, and we propose a hybrid deep architecture that combines the convolution neural network (CNN) and the continuous neural network (CNN) (RNN). Because of the extensive information background and the high cost of camera equipment, a substantial number of existing results concentrate on vision-based lane recognition systems. Extensive tests on two large-scale datasets show that the planned technique outperforms rivals' lane detection strategies, particularly in challenging settings. A CNN block in particular isolates information from each frame before sending the CNN choices of several continuous frames with time-series qualities to the RNN block for feature learning and lane prediction.

scholarly journals Digital diagnostics of assessing social risk with killed at single-level road intersections in Russia

2021 ◽  
Vol 18 (2) ◽  
pp. 232-239
Author(s):  
A. M. Plotnikov ◽  
◽  
D. O. Gurin ◽  
M. R. Vasyukhin ◽  
◽  
...  
Keyword(s):  
Digital Technology ◽  
Road Safety ◽  
Diagnostic Assessment ◽  
Social Risk ◽  
Road Accidents ◽  
High Quality ◽  
National Project ◽  
Single Level ◽  
Conflict Situations ◽  
Road Intersections

The performers of the federal project «Road Safety» and the national project «Safe and High-Quality Highways» are offered an effective, digital technology necessary to achieve the targets of «social risk» with numbers of killed as a result of road accidents by 2024 per 100 thousand people, on the example of St. Petersburg. This technology uses a tool for diagnostic assessment of road safety at single-level road intersections with a mechanism for visualizing the causes of conflict situations in the wayside crosses of traffic and pedestrian flows that cause accidents.

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