A Hybrid Intelligent Multisensor Positioning Methodology for Reliable Vehicle Navigation

With the rapid development of intelligent transportation systems worldwide, it becomes more important to realize accurate and reliable vehicle positioning in various environments whether GPS is available or not. This paper proposes a hybrid intelligent multisensor positioning methodology fusing the information from low-cost sensors including GPS, MEMS-based strapdown inertial navigation system (SINS) and electronic compass, and velocity constraint, which can achieve a significant performance improvement over the integration scheme only including GPS and MEMS-based SINS. First, the filter model of SINS aided by multiple sensors is presented in detail and then an improved Kalman filter with sequential measurement-update processing is developed to realize the filtering fusion. Further, a least square support vector machine- (LS SVM-) based intelligent module is designed and augmented with the improved KF to constitute the hybrid positioning system. In case of GPS outages, the LS SVM-based intelligent module trained recently is used to predict the position error to achieve more accurate positioning performance. Finally, the proposed hybrid positioning method is evaluated and compared with traditional methods through real field test data. The experimental results validate the feasibility and effectiveness of the proposed method.

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A Multi-Loop Vehicle-Counting Method under Gray Mode and RGB Mode

Applied Sciences ◽

10.3390/app11156831 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6831

Author(s):

Yue Chen ◽

Jian Lu

Keyword(s):

Intelligent Transportation Systems ◽

High Efficiency ◽

Road Traffic ◽

Low Cost ◽

Rapid Development ◽

Intelligent Transportation ◽

Transportation Systems ◽

Counting Method ◽

Moving Vehicle ◽

Available Information

With the rapid development of road traffic, real-time vehicle counting is very important in the construction of intelligent transportation systems (ITSs). Compared with traditional technologies, the video-based method for vehicle counting shows great importance and huge advantages in its low cost, high efficiency, and flexibility. However, many methods find difficulty in balancing the accuracy and complexity of the algorithm. For example, compared with traditional and simple methods, deep learning methods may achieve higher precision, but they also greatly increase the complexity of the algorithm. In addition to that, most of the methods only work under one mode of color, which is a waste of available information. Considering the above, a multi-loop vehicle-counting method under gray mode and RGB mode was proposed in this paper. Under gray and RGB modes, the moving vehicle can be detected more completely; with the help of multiple loops, vehicle counting could better deal with different influencing factors, such as driving behavior, traffic environment, shooting angle, etc. The experimental results show that the proposed method is able to count vehicles with more than 98.5% accuracy while dealing with different road scenes.

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Perception advances in outdoor vehicle detection for automatic cruise control

Robotica ◽

10.1017/s0263574709990464 ◽

2009 ◽

Vol 28 (5) ◽

pp. 765-779 ◽

Cited By ~ 10

Author(s):

S. Álvarez ◽

M. Á. Sotelo ◽

M. Ocaña ◽

D. F. Llorca ◽

I. Parra ◽

...

Keyword(s):

Intelligent Transportation Systems ◽

Detection System ◽

Low Cost ◽

Vehicle Detection ◽

Basic Feature ◽

Transportation Systems ◽

Support Vector ◽

Cruise Control ◽

Vehicle To Vehicle ◽

Time Gap

SUMMARYThis paper describes a vehicle detection system based on support vector machine (SVM) and monocular vision. The final goal is to provide vehicle-to-vehicle time gap for automatic cruise control (ACC) applications in the framework of intelligent transportation systems (ITS). The challenge is to use a single camera as input, in order to achieve a low cost final system that meets the requirements needed to undertake serial production in automotive industry. The basic feature of the detected objects are first located in the image using vision and then combined with a SVM-based classifier. An intelligent learning approach is proposed in order to better deal with objects variability, illumination conditions, partial occlusions and rotations. A large database containing thousands of object examples extracted from real road scenes has been created for learning purposes. The classifier is trained using SVM in order to be able to classify vehicles, including trucks. In addition, the vehicle detection system described in this paper provides early detection of passing cars and assigns lane to target vehicles. In the paper, we present and discuss the results achieved up to date in real traffic conditions.

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Optimization and Evaluation of Platooning Car-Following Models in a Connected Vehicle Environment

Sustainability ◽

10.3390/su13063474 ◽

2021 ◽

Vol 13 (6) ◽

pp. 3474

Author(s):

Guang Yu ◽

Shuo Liu ◽

Qiangqiang Shangguan

Keyword(s):

Communication Technology ◽

Intelligent Transportation Systems ◽

Rapid Development ◽

Safety Margin ◽

Practical Method ◽

Transportation Systems ◽

Systematic Evaluation ◽

Connected Vehicle ◽

Car Following ◽

Cooperative Driving

With the rapid development of information and communication technology, future intelligent transportation systems will exhibit a trend of cooperative driving of connected vehicles. Platooning is an important application technique for cooperative driving. Herein, optimized car-following models for platoon control based on intervehicle communication technology are proposed. On the basis of existing indicators, a series of evaluation methods for platoon safety, stability, and energy consumption is constructed. Numerical simulations are used to compare the effects of three traditional models and their optimized counterparts on the car-following process. Moreover, the influence of homogenous and heterogeneous attributes on the platoon is analyzed. The optimized model proposed in this paper can improve the stability and safety of vehicle following and reduce the total fuel consumption. The simulation results show that a homogenous platoon can enhance the overall stability of the platoon and that the desired safety margin (DSM) model is better suited for heterogeneous platoon control than the other two models. This paper provides a practical method for the design and systematic evaluation of a platoon control strategy, which is one of the key focuses in the connected and autonomous vehicle industry.

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Multi-Target Detection Method Based on Variable Carrier Frequency Chirp Sequence

Sensors ◽

10.3390/s18103386 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3386 ◽

Cited By ~ 6

Author(s):

Wei Wang ◽

Jinsong Du ◽

Jie Gao

Keyword(s):

Intelligent Transportation Systems ◽

Simple Structure ◽

Low Cost ◽

Doppler Frequency ◽

Transportation Systems ◽

Practical Application ◽

Frequency Aliasing ◽

Application Requirements ◽

Relationship Of ◽

Cw Radar

Continuous waveform (CW) radar is widely used in intelligent transportation systems, vehicle assisted driving, and other fields because of its simple structure, low cost and high integration. There are several waveforms which have been developed in the last years. The chirp sequence waveform has the ability to extract the range and velocity parameters of multiple targets. However, conventional chirp sequence waveforms suffer from the Doppler ambiguity problem. This paper proposes a new waveform that follows the practical application requirements, high precision requirements, and low system complexity requirements. The new waveform consists of two chirp sequences, which are intertwined to each other. Each chirp signal has the same frequency modulation, the same bandwidth and the same chirp duration. The carrier frequencies are different and there is a frequency shift which is large enough to ensure that the Doppler frequencies for the same moving target are different. According to the sign and numerical relationship of the Doppler frequencies (possibly frequency aliasing), the Doppler frequency ambiguity problem is solved in eight cases. Theoretical analysis and simulation results verify that the new radar waveform is capable of measuring range and radial velocity simultaneously and unambiguously, with high accuracy and resolution even in multi-target situations.

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Analysis of Space-Time Variation of Passenger Flow and Commuting Characteristics of Residents Using Smart Card Data of Nanjing Metro

Sustainability ◽

10.3390/su11184989 ◽

2019 ◽

Vol 11 (18) ◽

pp. 4989 ◽

Cited By ~ 7

Author(s):

Wei Yu ◽

Hua Bai ◽

Jun Chen ◽

Xingchen Yan

Keyword(s):

Intelligent Transportation Systems ◽

Traffic Management ◽

Rapid Development ◽

Weather Conditions ◽

Transportation Systems ◽

Space Time ◽

Smart Cards ◽

The Sustainable Development ◽

Passenger Flow ◽

Challenges And Opportunities

The rapid development of cities has brought new challenges and opportunities to traditional traffic management. The usage of smart cards promotes the upgrading of intelligent transportation systems, and also produces considerable big data. As an important part of the urban comprehensive transportation system, Nanjing metro has more than 1 million inbound and outbound records of traffic smart cards used by residents every day. How to process these traffic data and present them visually is an urgent problem in modern traffic management. In this study, five working days with normal weather conditions in Nanjing were selected, and the swiping records of the smart cards were extracted, and the space–time characteristics were analyzed. In terms of time analysis, this research analyzed the 24-h fluctuation of daily average passenger flow, peak hour coefficient of passenger flow, 24-h fluctuation of passenger flow on different metro lines, passenger flow intensity on different metro lines and passenger flow comparison at different stations. In spatial analysis, this study uses thermodynamic charts to represent the inflow and outflow of passengers at different stations during early and evening peak periods. The analysis results and visualized images directly reflect the area where Nanjing metro congestion is located, and also shows the commuting characteristics of residents. It can solve the problem of urban congestion, carry out the rational layout of urban functional areas, and promote the sustainable development of people and cities.

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Eco-Efficient Mobility in Smart City Scenarios

Sustainability ◽

10.3390/su12208443 ◽

2020 ◽

Vol 12 (20) ◽

pp. 8443

Author(s):

Ramon Sanchez-Iborra ◽

Luis Bernal-Escobedo ◽

José Santa

Keyword(s):

Intelligent Transportation Systems ◽

Smart City ◽

Smart Cities ◽

Low Cost ◽

Transportation Systems ◽

Area Network ◽

Wide Area Network ◽

Fully Integrated ◽

Personal Mobility ◽

Traffic Efficiency

Cooperative-Intelligent Transportation Systems (C-ITS) have brought a technological revolution, especially for ground vehicles, in terms of road safety, traffic efficiency, as well as in the experience of drivers and passengers. So far, these advances have been focused on traditional transportation means, leaving aside the new generation of personal vehicles that are nowadays flooding our streets. Together with bicycles and motorcycles, personal mobility devices such as segways or electric scooters are firm sustainable alternatives that represent the future to achieve eco-friendly personal mobility in urban settings. In a near future, smart cities will become hyper-connected spaces where these vehicles should be integrated within the underlying C-ITS ecosystem. In this paper, we provide a wide overview of the opportunities and challenges related to this necessary integration as well as the communication solutions that are already in the market to provide these moving devices with low-cost and efficient connectivity. We also present an On-Board Unit (OBU) prototype with different communication options based on the Low Power Wide Area Network (LPWAN) paradigm and several sensors to gather environmental information to facilitate eco-efficiency services. As the attained results suggest, this module allows personal vehicles to be fully integrated in smart city environments, presenting the possibilities of LoRaWAN and Narrow Band-Internet of Things (NB-IoT) communication technologies to provide vehicle connectivity and enable mobile urban sensing.

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A Low-Cost Real-World Planning Strategy for Deploying a Dedicated Short-Range Communications Roadside Unit on a Highway Off-Ramp

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118776813 ◽

2018 ◽

Vol 2672 (19) ◽

pp. 124-134 ◽

Cited By ~ 1

Author(s):

Jonathan B. Walker ◽

Kevin Heaslip

Keyword(s):

Real World ◽

Intelligent Transportation Systems ◽

Short Range ◽

Environmental Changes ◽

Low Cost ◽

Transportation Systems ◽

Planning Strategy ◽

Dedicated Short Range Communications ◽

Roadside Unit ◽

World Environment

The deployment of dedicated short-range communications (DSRC) roadside units (RSUs) allows a connected or automated vehicle to acquire information from the surrounding environment, such as a traffic light’s signal phase and timing, using vehicle-to-infrastructure communication. Several scholarly papers exist on planning strategies for DSRC RSU deployments using simulation without accounting for wireless communication constraints and environmental changes. This paper proposes an empirical-based planning strategy for a highway off-ramp in a real-world environment. The research goal focuses on developing a low-cost and structured deployment plan for DSRC RSUs with the following objectives: use free planning tools; apply the deployment strategy in a real-world environment; utilize publicly available DSRC RSU data measurements; and leverage existing intelligent transportation systems infrastructure when possible. The proposed planning strategy includes three steps: (1) conduct a virtual site survey, (2) gather baseline performance data for the DSRC RSU equipment, and (3) generate a predictive radio frequency signal. The planning strategy was successfully applied on a highway off-ramp at exit 19A of the Capital Beltway, which encircles Washington, DC.

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Big Data analysis: Intelligent Transportation Development Engine

Urban Transportation & Construction ◽

10.18686/utc.v1i1.3 ◽

2014 ◽

Vol 1 (1) ◽

pp. 11

Author(s):

Qin Xiao

Keyword(s):

Big Data ◽

Intelligent Transportation Systems ◽

Low Cost ◽

Information Age ◽

Large Data ◽

Intelligent Transportation ◽

Transportation Systems ◽

Transportation Demand ◽

Advantages And Disadvantages ◽

Transportation Development

<p>With the development of the times, people have unwittingly entered the information age. The information age has become a large amount of data bursting characteristics of the new era. In this feature people still seek to improve the production and quality of life. For the development of intelligent transportation needs of people's lives and make the real world, but in the construction of intelligent transportation among a large number of information data also adds to its change and difficulty, how to build an intelligent era of big data, security, low-cost, efficient and convenient of intelligent transportation systems become today people study. From the era of big data to intelligent traffic changes brought advantages and disadvantages, the era of big data to bring intelligent traffic problems and challenges, as well as the integration platform for massive data intelligent transportation intelligent transportation demand and large data structures has done a simple elaborate, it can provide some suggestions for areas of research that scientists.</p>

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TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b4-243-2016 ◽

2016 ◽

Vol XLI-B4 ◽

pp. 243-246

Author(s):

Victor J. D. Tsai ◽

Jyun-Han Chen ◽

Hsun-Sheng Huang

Keyword(s):

Intelligent Transportation Systems ◽

Low Cost ◽

Autonomous Vehicle ◽

Transportation Systems ◽

Traffic Sign ◽

Google Street View ◽

Street View ◽

Sign Detection ◽

Traffic Sign Detection ◽

Detection And Recognition

Traffic sign detection and recognition (TSDR) has drawn considerable attention on developing intelligent transportation systems (ITS) and autonomous vehicle driving systems (AVDS) since 1980’s. Unlikely to the general TSDR systems that deal with real-time images captured by the in-vehicle cameras, this research aims on developing techniques for detecting, extracting, and positioning of traffic signs from Google Street View (GSV) images along user-selected routes for low-cost, volumetric and quick establishment of the traffic sign infrastructural database that may be associated with Google Maps. The framework and techniques employed in the proposed system are described.

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An adaptive k nearest neighbour method for imputation of missing traffic data based on two similarity

Archives of Transport ◽

10.5604/01.3001.0014.2968 ◽

2020 ◽

Vol 54 (2) ◽

pp. 59-73

Author(s):

Yang Wang ◽

Yu Xiao ◽

Jianhui Lai ◽

Yanyan Chen

Keyword(s):

Missing Data ◽

Traffic Flow ◽

Intelligent Transportation Systems ◽

Domain Knowledge ◽

Rapid Development ◽

Transportation Systems ◽

Nearest Neighbour ◽

Flow Data ◽

Fundamental Parameters ◽

Determination Of Parameters

Traffic flow is one of the fundamental parameters for traffic analysis and planning. With the rapid development of intelligent transportation systems, a large number of various detectors have been deployed in urban roads and, consequently, huge amount of data relating to the traffic flow are accumulatively available now. However, the traffic flow data detected through various detectors are often degraded due to the presence of a number of missing data, which can even lead to erroneous analysis and decision if no appropriate process is carried out. To remedy this issue, great research efforts have been made and subsequently various imputation techniques have been successively proposed in recent years, among which the k nearest neighbour algorithm (kNN) has received a great popularity as it is easy to implement and impute the missing data effectively. In the work presented in this paper, we firstly analyse the stochastic effect of traffic flow, to which the suffering of the kNN algorithm can be attributed. This motivates us to make an improvement, while eliminating the requirement to predefine parameters. Such a parameter-free algorithm has been realized by introducing a new similarity metric which is combined with the conventional metric so as to avoid the parameter setting, which is often determined with the requirement of adequate domain knowledge. Unlike the conventional version of the kNN algorithm, the proposed algorithm employs the multivariate linear regression model to estimate the weights for the final output, based on a set of data, which is smoothed by a Wavelet technique. A series of experiments have been performed, based on a set of traffic flow data reported from serval different countries, to examine the adaptive determination of parameters and the smoothing effect. Additional experiments have been conducted to evaluate the competent performance for the proposed algorithm by comparing to a number of widely-used imputing algorithms.

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