scholarly journals A Novel Algorithm Based on the Pixel-Entropy for Automatic Detection of Number of Lanes, Lane Centers, and Lane Division Lines Formation

Entropy ◽  
2018 ◽  
Vol 20 (10) ◽  
pp. 725 ◽  
Author(s):  
Fernando Hermosillo-Reynoso ◽  
Deni Torres-Roman ◽  
Jayro Santiago-Paz ◽  
Julio Ramirez-Pacheco
Keyword(s):  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Moving Objects ◽  
Color Space ◽  
Automatic Detection ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Lane Detection ◽  
Computational Time ◽  
The Road

Lane detection for traffic surveillance in intelligent transportation systems is a challenge for vision-based systems. In this paper, a novel pixel-entropy based algorithm for the automatic detection of the number of lanes and their centers, as well as the formation of their division lines is proposed. Using as input a video from a static camera, each pixel behavior in the gray color space is modeled by a time series; then, for a time period τ , its histogram followed by its entropy are calculated. Three different types of theoretical pixel-entropy behaviors can be distinguished: (1) the pixel-entropy at the lane center shows a high value; (2) the pixel-entropy at the lane division line shows a low value; and (3) a pixel not belonging to the road has an entropy value close to zero. From the road video, several small rectangle areas are captured, each with only a few full rows of pixels. For each pixel of these areas, the entropy is calculated, then for each area or row an entropy curve is produced, which, when smoothed, has as many local maxima as lanes and one more local minima than lane division lines. For the purpose of testing, several real traffic scenarios under different weather conditions with other moving objects were used. However, these background objects, which are out of road, were filtered out. Our algorithm, compared to others based on trajectories of vehicles, shows the following advantages: (1) the lowest computational time for lane detection (only 32 s with a traffic flow of one vehicle/s per-lane); and (2) better results under high traffic flow with congestion and vehicle occlusion. Instead of detecting road markings, it forms lane-dividing lines. Here, the entropies of Shannon and Tsallis were used, but the entropy of Tsallis for a selected q of a finite set achieved the best results.

scholarly journals L*a*b* color model based road lane detection in autonomous vehicles

2017 ◽  
Vol 52 (4) ◽  
pp. 273-280
Author(s):  
M Kazemi ◽  
Y Baleghi
Keyword(s):  
Intelligent Transportation Systems ◽  
Autonomous Vehicles ◽  
Color Space ◽  
Main Idea ◽  
Transportation Systems ◽  
Intelligent Vehicles ◽  
Lane Detection ◽  
The Road ◽  
Robotic Vehicle ◽  
Video Sensors

Autonomous vehicles, as a main part of Intelligent Transportation Systems (ITS), will have great impact on transportation in near future. They could navigate autonomously in specific areas or highways and city streets using maps, GPS, video sensors and so on. To navigate autonomously or follow a road, intelligent vehicles need to detect lanes. This paper presents a method for lane detection in image sequences of a camera on top of a robotic vehicle. The main idea is to find the road area using the L*a*b* color space in consecutive frames. Subsequently, by applying this model in road area and equalization of histogram and calculation of gradient image using Sobel operator, the parameters of the lane can be calculated using a Hough transform. The proposed method is tested under various illumination conditions and experimental results indicate the good performance of the proposed method.Bangladesh J. Sci. Ind. Res. 52(4), 273-280, 2017

scholarly journals Type of communication between driver and car, based on the augmented reality: "new trend" in building intelligent transportation systems

2017 ◽  
Vol 6 (1) ◽  
pp. 6-14 ◽  
Author(s):  
S.B. Efremov
Keyword(s):  
Augmented Reality ◽  
Transport System ◽  
Intelligent Transportation Systems ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Driving Safety ◽  
The Road ◽  
Advantages And Disadvantages ◽  
Road Signs ◽  
On The Road

In order to increase safety while driving and to minimize the burden on the driver, the information should be transmitted to him/her in such a way that the driver needn’t spent time on its recognition and comprehension. Projecting and visualization of information on the windshield can help simplify the dialogue between a car and a driver ("operator") and expand the influence of intellectual transport system using projection information about traffic jams in the field of perception of the driver, so that it does not interfere with the driver on the road. This article discusses the possible advantages and disadvantages of using "hints", created within the framework of the "augmented reality" to increase driving safety by treating them as a new form of communication between a car and a driver. So, it seems to be a new approach to the utilization of the system, based on performances in the field of augmented reality to recognize road signs, which impose virtual objects on the field of perception in all types of traffic situations including the uncomfortable weather conditions. This approach can be used to increase accuracy of intellectual transport system with the augmented reality to support the driver in various driving situations, increasing comfort and reducing the number of accidents

scholarly journals Multispectral Fusion Approach for Traffic Target Detection in Bad Weather

Algorithms ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 271
Author(s):  
Yajing Han ◽  
Dean Hu
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Cost Effective ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Surveillance Systems ◽  
Traffic Surveillance ◽  
Thermal Images ◽  
The Road ◽  
Bad Weather

Visual traffic surveillance using computer vision techniques can be noninvasive, automated and cost effective. Traffic surveillance systems with the ability to detect, count and classify vehicles can be employed in gathering traffic statistics and achieving better traffic control in intelligent transportation systems. This works well in daylight when the road users are clearly visible to the camera, but it often struggles when the visibility of the scene is impaired by insufficient lighting or bad weather conditions such as rain, snow, haze and fog. Therefore, in this paper, we design a dual input faster region-based convolutional neural network (RCNN) to make full use of the complementary advantages of color and thermal images to detect traffic objects in bad weather. Different from the previous detector, we used halfway fusion to fuse color and thermal images for traffic object detection. Besides, we adopt the polling from multiple layers method to adapt the characteristics of large size differences between objects of traffic targets to accurately identify targets of different sizes. The experimental results show that the present method improves the target recognition accuracy by 7.15% under normal weather conditions and 14.2% under bad weather conditions. This exhibits promising potential for implementation with real-world applications.

scholarly journals Urban Traffic Flow Forecast Based on FastGCRNN

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ya Zhang ◽  
Mingming Lu ◽  
Haifeng Li
Keyword(s):  
Computational Complexity ◽  
Traffic Flow ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Geometric Graph ◽  
Transportation Systems ◽  
Urban Traffic ◽  
The Road ◽  
Traffic Forecasting ◽  
Traffic Flow Forecast

Traffic forecasting is an important prerequisite for the application of intelligent transportation systems in urban traffic networks. The existing works adopted RNN and CNN/GCN, among which GCRN is the state-of-the-art work, to characterize the temporal and spatial correlation of traffic flows. However, it is hard to apply GCRN to the large-scale road networks due to high computational complexity. To address this problem, we propose abstracting the road network into a geometric graph and building a Fast Graph Convolution Recurrent Neural Network (FastGCRNN) to model the spatial-temporal dependencies of traffic flow. Specifically, we use FastGCN unit to efficiently capture the topological relationship between the roads and the surrounding roads in the graph with reducing the computational complexity through importance sampling, combine GRU unit to capture the temporal dependency of traffic flow, and embed the spatiotemporal features into Seq2Seq based on the Encoder-Decoder framework. Experiments on large-scale traffic data sets illustrate that the proposed method can greatly reduce computational complexity and memory consumption while maintaining relatively high accuracy.

Make More Connections: Urban Traffic Flow Forecasting with Spatiotemporal Adaptive Gated Graph Convolution Network

2022 ◽  
Vol 13 (2) ◽  
pp. 1-25
Author(s):  
Bin Lu ◽  
Xiaoying Gan ◽  
Haiming Jin ◽  
Luoyi Fu ◽  
Xinbing Wang ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Road Network ◽  
Large Scale ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Propagation Mechanism ◽  
Urban Road ◽  
The Road ◽  
Traffic Flow Forecasting ◽  
Gating Mechanism

Urban traffic flow forecasting is a critical issue in intelligent transportation systems. Due to the complexity and uncertainty of urban road conditions, how to capture the dynamic spatiotemporal correlation and make accurate predictions is very challenging. In most of existing works, urban road network is often modeled as a fixed graph based on local proximity. However, such modeling is not sufficient to describe the dynamics of the road network and capture the global contextual information. In this paper, we consider constructing the road network as a dynamic weighted graph through attention mechanism. Furthermore, we propose to seek both spatial neighbors and semantic neighbors to make more connections between road nodes. We propose a novel Spatiotemporal Adaptive Gated Graph Convolution Network ( STAG-GCN ) to predict traffic conditions for several time steps ahead. STAG-GCN mainly consists of two major components: (1) multivariate self-attention Temporal Convolution Network ( TCN ) is utilized to capture local and long-range temporal dependencies across recent, daily-periodic and weekly-periodic observations; (2) mix-hop AG-GCN extracts selective spatial and semantic dependencies within multi-layer stacking through adaptive graph gating mechanism and mix-hop propagation mechanism. The output of different components are weighted fused to generate the final prediction results. Extensive experiments on two real-world large scale urban traffic dataset have verified the effectiveness, and the multi-step forecasting performance of our proposed models outperforms the state-of-the-art baselines.

scholarly journals ANALYSIS OF DIFFERENT VISUAL STRATEGIES OF ‘ISOLATED VEHICLE’ AND ‘DISTURBED VEHICLE’

Transport ◽  
2018 ◽  
Vol 33 (3) ◽  
pp. 853-860
Author(s):  
Nicola BONGIORNO ◽  
Gaetano BOSURGI ◽  
Orazio PELLEGRINO ◽  
Giuseppe SOLLAZZO
Keyword(s):  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Disturbed Condition ◽  
The Road ◽  
Driving Behaviour ◽  
Visual Behaviour ◽  
Road Geometry ◽  
Potential Applications ◽  
Road Characteristics

This paper analyses the driver’ visual behaviour in the different conditions of ‘isolated vehicle’ and ‘disturbed vehicle’. If the meaning of the former is clear, the latter condition considers the influence on the driving behaviour of various objects that could be encountered along the road. These can be classified in static (signage, stationary vehicles at the roadside, etc.) and dynamic objects (cars, motorcycles, bicycles). The aim of this paper is to propose a proper analysis regarding the driver’s visual behaviour. In particular, the authors examined the quality of the visually informa-tion acquired from the entire road environment, useful for detecting any critical safety condition. In order to guaran-tee a deep examination of the various possible behaviours, the authors combined the several test outcomes with other variables related to the road geometry and with the dynamic variables involved while driving. The results of this study are very interesting. As expected, they obviously confirmed better performances for the ‘isolated vehicle’ in a rural two-lane road with different traffic flows. Moreover, analysing the various scenarios in the disturbed condition, the proposed indices allow the authors to quantitatively describe the different influence on the visual field and effects on the visual behaviour, favouring critical analysis of the road characteristics. Potential applications of these results may contribute to improve the choice of the best maintenance strategies for a road, to select the optimal signage location, to define forecasting models for the driving behaviour and to develop useful instruments for intelligent transportation systems.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Potential Using Vehicle to Vehicle Communication Based on Wireless Fidelity (Wi-Fi) for Supporting Intelligent Transportation Systems (ITS)

2021 ◽  
pp. 61-76
Author(s):  
إسراء عصام بن موسى ◽  
عبدالسلام صالح الراشدي
Keyword(s):  
Intelligent Transportation Systems ◽  
Ad Hoc ◽  
Real Life ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
The Road ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Vehicle To Vehicle Communication ◽  
Wireless Fidelity

Vehicular Ad-hoc Network (VANET) becomes one of the most popular modern technologies these days, due to its contribution to the development and modernization of Intelligent Transportation Systems (ITS). The primary goal of these networks is to provide safety and comfort for drivers and passengers in roads. There are many types of VANET that are used in ITS, in this paper, we particularly focus on the Vehicle to Vehicle communication (V2V), which each vehicle can exchange information to inform drivers of other vehicles about the current state of the road flow, in the event of any emergency to avoid accidents, and reduce congestion on roads. We proposed V2V using Wi-Fi (wireless fidelity); the reason of its unique characteristics that distinguish it from other types. There are many difficulties and the challenges in implementing most types of V2V, and the reason is due to the lack of devices and equipment needed for real implementation. To prove the possibility of applying this type in real life, we made a prototype contains a modified toy car, a 12-volt power supply, sensors, visual, audible alarm, a visual “LED” devices, and finally a 12-volt DC relay unit. As a conclusion, the proposed implementation in spite of minimal requirements and use simple equipment, we have achieved the most important main objectives of the paper: preventing vehicles from collision, early warning, and avoiding congestion on the roads.

A Survey of Approaches for Estimating Meteorological Visibility Distance Under Foggy Weather Conditions

2020 ◽  
pp. 65-92 ◽  
Author(s):  
Faouzi Kamoun ◽  
Hazar Chaabani ◽  
Fatma Outay ◽  
Ansar-Ul-Haque Yasar
Keyword(s):  
Intelligent Transportation Systems ◽  
Situational Awareness ◽  
Wide Spectrum ◽  
Distance Estimation ◽  
Weather Conditions ◽  
Transportation Systems ◽  
Driver Assistance Systems ◽  
Research Issues ◽  
Visibility Distance ◽  
Assistance Systems

The immaturity of fog abatement technologies for highway usage has led to growing interest towards developing intelligent transportation systems that are capable of estimating meteorological visibility distance under foggy weather conditions. This capability is crucial to support next-generation cooperative situational awareness and collision avoidance systems as well as onboard driver assistance systems. This chapter presents a survey and a comprehensive taxonomy of daytime visibility distance estimation approaches based on a review and synthesis of the literature. The proposed taxonomy is both comprehensive (i.e., captures a wide spectrum of earlier contributions) and effective (i.e., enables easy comparison among previously proposed approaches). The authors also highlight some open research issues that warrant further investigation.

Sign in / Sign up

Export Citation Format

Share Document