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

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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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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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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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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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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The Application of the Precision Electron Transmission Chain in the Rotary Screen Printing Machine

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.574.577 ◽

2014 ◽

Vol 574 ◽

pp. 577-580

Author(s):

Xin Juan Shang ◽

Hong Rong Lu

Keyword(s):

Screen Printing ◽

Simple Structure ◽

Low Cost ◽

Synchronization Control ◽

Practical Application ◽

Control Effect ◽

Absolute Position ◽

Printing Machine ◽

Very High ◽

Control Accuracy

This paper introduces a high-precision, closed-loop and automatic checking marks synchronization control system based on STM32.It enhances the multi-axis motion accuracy as the goal synchronization control. The position synchronization arithmetic of cylinders and error compensation arithmetic for guide belt speed is to improve motion control accuracy of the synchronization between cylinders nodes, and realize absolute position synchronization control between cylinders. In the practical application, the system which will improve the grade of textile has simple structure ,low cost, highly accuracy and quickly dynamic response, and it has received satisfactory control effect. it is has the very high value of market popularization.

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Security Vulnerabilities in LPWANs—An Attack Vector Analysis for the IoT Ecosystem

Applied Sciences ◽

10.3390/app11073176 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3176

Author(s):

Nuno Torres ◽

Pedro Pinto ◽

Sérgio Ivan Lopes

Keyword(s):

Systematic Review ◽

Intelligent Transportation Systems ◽

Smart Grids ◽

Low Cost ◽

Methodological Approach ◽

Transportation Systems ◽

Vector Analysis ◽

Security Vulnerabilities ◽

Iot Applications ◽

Starting Point

Due to its pervasive nature, the Internet of Things (IoT) is demanding for Low Power Wide Area Networks (LPWAN) since wirelessly connected devices need battery-efficient and long-range communications. Due to its low-cost and high availability (regional/city level scale), this type of network has been widely used in several IoT applications, such as Smart Metering, Smart Grids, Smart Buildings, Intelligent Transportation Systems (ITS), SCADA Systems. By using LPWAN technologies, the IoT devices are less dependent on common and existing infrastructure, can operate using small, inexpensive, and long-lasting batteries (up to 10 years), and can be easily deployed within wide areas, typically above 2 km in urban zones. The starting point of this work was an overview of the security vulnerabilities that exist in LPWANs, followed by a literature review with the main goal of substantiating an attack vector analysis specifically designed for the IoT ecosystem. This methodological approach resulted in three main contributions: (i) a systematic review regarding cybersecurity in LPWANs with a focus on vulnerabilities, threats, and typical defense strategies; (ii) a state-of-the-art review on the most prominent results that have been found in the systematic review, with focus on the last three years; (iii) a security analysis on the recent attack vectors regarding IoT applications using LPWANs. Results have shown that LPWANs communication technologies contain security vulnerabilities that can lead to irreversible harm in critical and non-critical IoT application domains. Also, the conception and implementation of up-to-date defenses are relevant to protect systems, networks, and data.

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VLC Network Design for High Mobility Users in Urban Tunnels

Sensors ◽

10.3390/s22010088 ◽

2021 ◽

Vol 22 (1) ◽

pp. 88

Author(s):

Edmundo Torres-Zapata ◽

Victor Guerra ◽

Jose Rabadan ◽

Martin Luna-Rivera ◽

Rafael Perez-Jimenez

Keyword(s):

Network Design ◽

Intelligent Transportation Systems ◽

High Speed ◽

Vehicular Networks ◽

Low Cost ◽

High Mobility ◽

Visible Light Communication ◽

Transportation Systems ◽

System Level ◽

On The Road

Current vehicular systems require real-time information to keep drivers safer and more secure on the road. In addition to the radio frequency (RF) based communication technologies, Visible Light Communication (VLC) has emerged as a complementary way to enable wireless access in intelligent transportation systems (ITS) with a simple design and low-cost deployment. However, integrating VLC in vehicular networks poses some fundamental challenges. In particular, the limited coverage range of the VLC access points and the high speed of vehicles create time-limited links that the existing handover procedures of VLC networks can not be accomplished timely. Therefore, this paper addresses the problem of designing a vehicular VLC network that supports high mobility users. We first modify the traditional VLC network topology to increase uplink reliability. Then, a low-latency handover scheme is proposed to enable mobility in a VLC network. Furthermore, we validate the functionality of the proposed VLC network design method by using system-level simulations of a vehicular tunnel scenario. The analysis and the results show that the proposed method provides a steady connection, where the vehicular node is available more than 99% of the time regardless of the number of vehicular nodes on this network. Additionally, the system is able to achieve a Frame-Error-Rate (FER) performance lower than 10−3.

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Lane Discovery in Traffic Video

Journal of Computational Vision and Imaging Systems ◽

10.15353/vsnl.v2i1.125 ◽

1969 ◽

Vol 2 (1) ◽

Author(s):

Nicholas Miller ◽

David M. Swart ◽

Akshaya Mishra ◽

Andrew Achkar

Keyword(s):

Intelligent Transportation Systems ◽

Low Cost ◽

Transportation Systems ◽

Traffic Information ◽

Surveillance Video ◽

Counting Problem ◽

Moving Vehicle ◽

Non Invasive ◽

Vehicle Trajectories ◽

Reduced Space

Video sensing has become very important in Intelligent Transportation Systems (ITS) due to its relative low cost and non-invasive deployment. An effective ITS requires detailed traffic information, including vehicle volume counts for each lane in surveillance video of a highway or an intersection. The multiple-target, vehicle-tracking and counting problem is most reliably solved in a reduced space defined by the constraints of the vehicles driving within lanes. This requires lanes to be pre-specified. An off-line pre-processing method is presented which automatically discovers traffic lanes from vehicle motion in uncalibrated video from a stationary camera. A moving vehicle density map is constructed, then multiple lane curves are fitted. Traffic lanes are found without relying on possibly noisy tracked vehicle trajectories.

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