Special Issue "Intelligent Transportation Systems: Beyond Intelligent Vehicles"

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 October 2020.

Special Issue Editors

Prof. Dr. Javier Alonso Ruiz

Guest Editor
Computer Engineering Department. INVETT Research Group. Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: Intelligent Transportation Systems, Autonomous Vehicles, Control Systems, Driver Assistance Systems, Artificial Vision
Special Issues and Collections in MDPI journals
Dr.ir. Jeroen Ploeg
Website
Guest Editor
(1) Lead Cooperative Driving, 2getthere B.V., Utrecht, The Netherlands;
(2) Associate Professor (part-time), Mechanical Engineering Department, Dynamics and Control group, Eindhoven University of Technology, Eindhoven, The Netherlands
Interests: networked control, string stability, agent-based control, vehicle automation, platooning
Special Issues and Collections in MDPI journals
Dr. Martin Lauer
Website
Guest Editor
Institute of Measurement and Control Systems,Karlsruhe Institute of Technology, Germany
Interests: autonomous vehicles, machine vision, machine learning
Special Issues and Collections in MDPI journals
Dr. Angel Llamazares Llamazares
Website
Guest Editor
Postdoctoral Researcher, INVETT Research Group, Computer Engineering Department, Universidad de Alcalá, Alcalá de Henares, Spain
Interests: Robotics; Intelligent Transportation Systems
Special Issues and Collections in MDPI journals
Prof. Dr. Noelia Hernández Parra
Website
Guest Editor
Assistant professor, Computer Engineering Department. INVETT Research Group. Universidad de Alcalá, Alcalá de Henares, Madrid, Spain
Interests: Accurate Indoor and Outdoor Global Positioning; Vehicle Localization; Autonomous Vehicles; Driver Assistance Systems; Imaging and Image Analysis
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The development of intelligent vehicles is essential for improving urban mobility and for contributing to the development of smart cities. Also, the intelligent vehicle is the central pillar of the future of intelligent transport systems (ITS). Within the area of intelligent vehicles research there are still many challenges/areas for improvement: perception systems, scene understanding, localization and mapping, navigation, path planning, trajectory planning, vehicle control, etc.

If you look at the equipment of the vehicle, there are a variety of sensors. GPS, IMU, cameras, radars, and lidars are the most common. Lidars are the least preferred option in the industry, to avoid anti-aesthetic effects on the cars’ appearance. Cameras and lidars have experienced a small revolution thanks to the application of convolutional neural networks to the image processing. These sensors are used for localization (visual odometry, lidar odometry, 3D maps, map matching, etc.), perception (trajectory planning, scene understanding, traffic sign detection, drive-able space detection, obstacle avoidance, etc.), and so on. The aim of this Special Issue is to get a view of the latest works in these fields, and to give the reader a clear picture on the advances that are to come. Welcome topics include, but are not strictly limited to, the following:

  • Computer vision and image processing;
  • Lidar and 3D sensors;
  • Radar and other proximity sensors;
  • Advanced driver assistance systems onboard vehicles;
  • Self-driving car perception and navigation systems;
  • Navigation and path planning;
  • Automatic vehicle trajectory planning and control.

Prof. Dr. Javier Alonso Ruiz
Dr.ir. Jeroen Ploeg
Dr. Martin Lauer
Dr. Angel Llamazares Llamazares
Prof. Dr. Noelia Hernández Parra
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Computer vision
  • Lidar
  • Radar
  • 3D perception systems
  • Convolutional neural networks
  • Traffic light detection
  • Collision mitigation brake systems
  • Driving monitoring system
  • Visual odometry
  • Lidar odometry
  • 3D maps construction and localization
  • Scene understanding
  • Traffic sign detection
  • Drivable space detection
  • Obstacle detection.

Published Papers (28 papers)

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Open AccessArticle
Vision-Based Distance Measurement in Advanced Driving Assistance Systems
Appl. Sci. 2020, 10(20), 7276; https://doi.org/10.3390/app10207276 - 17 Oct 2020
Abstract
As the forward-looking depth information plays a considerable role in advanced driving assistance systems, in this paper, we first propose a method of depth map estimation based on semi-supervised learning, which uses the left and right views of binocular vision and sparse depth [...] Read more.
As the forward-looking depth information plays a considerable role in advanced driving assistance systems, in this paper, we first propose a method of depth map estimation based on semi-supervised learning, which uses the left and right views of binocular vision and sparse depth values as inputs to train a deep learning network with an encoding–decoding structure. Compared with unsupervised networks without sparse depth labels, the proposed semi-supervised network improves the estimation accuracy of depth maps. Secondly, this paper combines the estimated depth map with the results of instance segmentation to measure the distance between the subject vehicle and the target vehicle or pedestrian. Specifically, for measuring the distance between the subject vehicle and a pedestrian, this paper proposes a depth histogram-based method that calculates the average depth values of all pixels whose depth values are in the peak range of the depth histogram of this pedestrian. To measure the distance between the subject vehicle and the target vehicle, this paper proposes a method that first fits a 3-D plane based on the locations of target points in the camera body coordinate using RANSAC (RANdom SAmple Consensus), it then projects all the pixels of the target to this plane, and finally uses the minimum depth value of these projected points to calculate the distance to the target vehicle. The results of the quantitative and qualitative comparisons on the KITTI dataset show that the proposed method can effectively estimate depth maps. The experimental results in real road scenarios and the KITTI dataset confirm the accuracy of the proposed distance measurement methods. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Modeling the Accuracy of Estimating a Neighbor’s Evolving Position in VANET
Appl. Sci. 2020, 10(19), 6814; https://doi.org/10.3390/app10196814 - 28 Sep 2020
Abstract
Accurate estimation of a neighbor’s evolving position is essential to enhancing safety in intelligent transport systems. A vehicle can estimate a neighbor’s evolving position via periodic beaconing wherein each vehicle periodically broadcasts a beacon including its own kinematic data (e.g., position, speed, and [...] Read more.
Accurate estimation of a neighbor’s evolving position is essential to enhancing safety in intelligent transport systems. A vehicle can estimate a neighbor’s evolving position via periodic beaconing wherein each vehicle periodically broadcasts a beacon including its own kinematic data (e.g., position, speed, and acceleration). Many researchers have proposed analytic models to describe periodic beaconing in vehicular ad-hoc networks (VANETs). However, those models have focused only on network performance, e.g., packet delivery ratio (PDR), or a delay, which fail to evaluate the accuracy of estimating a neighbor’s evolving position. In this paper, we present a new analytic model capable of providing an estimation error of a neighbor’s evolving position in VANET to assess the accuracy of the estimation. This model relies on a vehicle system using periodic beaconing and a constant speed and position estimator (CSPE) to estimate a neighbor’s evolving position. To derive an estimation error, we first calculate the estimation error using a simple equation, which is associated with a probability of successful reception. Then, we derive the probability of successful reception that is applied onto the error model. To our knowledge, this is the first paper to establish a mathematical model to assess the accuracy of estimating a neighbor’s evolving position. To validate the proposed model, we compared the numerical results of the model with those of the NS-2 simulation. We observed that numerical results of the proposed model were located within the 95% confidential intervals of simulations results. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Compression of Vehicle Trajectories with a Variational Autoencoder
Appl. Sci. 2020, 10(19), 6739; https://doi.org/10.3390/app10196739 - 26 Sep 2020
Abstract
The perception and prediction of the surrounding vehicles’ trajectories play a significant role in designing safe and optimal control strategies for connected and automated vehicles. The compression of trajectory data and the drivers’ strategic behavior’s classification is essential to communicate in vehicular ad-hoc [...] Read more.
The perception and prediction of the surrounding vehicles’ trajectories play a significant role in designing safe and optimal control strategies for connected and automated vehicles. The compression of trajectory data and the drivers’ strategic behavior’s classification is essential to communicate in vehicular ad-hoc networks (VANETs). This paper presents a Variational Autoencoder (VAE) solution to solve the compression problem, and as an added benefit, it also provides classification information. The input is the time series of vehicle positions along actual real-world trajectories obtained from a dataset containing highway measurements, which also serves as the target. During training, the autoencoder learns to compress and decompress this data and produces a small, few element context vector that can represent vehicle behavior in a probabilistic manner. The experiments show how the size of this context vector affects the performance of the method. The method is compared to other approaches, namely, Bidirectional LSTM Autoencoder and Sparse Convolutional Autoencoder. According to the results, the Sparse Autoencoder fails to converge to the target for the specific tasks. The Bidirectional LSTM Autoencoder could provide the same performance as the VAE, though only with double context vector length, proving that the compression capability of the VAE is better. The Support Vector Machine method is used to prove that the context vector can be used for maneuver classification for lane changing behavior. The utilization of this method, considering neighboring vehicles, can be extended for maneuver prediction using a wider, more complex network structure. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
An Intelligent System-on-a-Chip for a Real-Time Assessment of Fuel Consumption to Promote Eco-Driving
Appl. Sci. 2020, 10(18), 6549; https://doi.org/10.3390/app10186549 - 19 Sep 2020
Abstract
Pollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people’s health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that [...] Read more.
Pollution that originates from automobiles is a concern in the current world, not only because of global warming, but also due to the harmful effects on people’s health and lives. Despite regulations on exhaust gas emissions being applied, minimizing unsuitable driving habits that cause elevated fuel consumption and emissions would achieve further reductions. For that reason, this work proposes a self-organized map (SOM)-based intelligent system in order to provide drivers with eco-driving-intended driving style (DS) recommendations. The development of the DS advisor uses driving data from the Uyanik instrumented car. The system classifies drivers regarding the underlying causes of non-optimal DSs from the eco-driving viewpoint. When compared with other solutions, the main advantage of this approach is the personalization of the recommendations that are provided to motorists, comprising the handling of the pedals and the gearbox, with potential improvements in both fuel consumption and emissions ranging from the 9.5% to the 31.5%, or even higher for drivers that are strongly engaged with the system. It was successfully implemented using a field-programmable gate array (FPGA) device of the Xilinx ZynQ programmable system-on-a-chip (PSoC) family. This SOM-based system allows for real-time implementation, state-of-the-art timing performances, and low power consumption, which are suitable for developing advanced driving assistance systems (ADASs). Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Stationary Target Identification in a Traffic Monitoring Radar System
Appl. Sci. 2020, 10(17), 5838; https://doi.org/10.3390/app10175838 - 23 Aug 2020
Abstract
Recently, as one of the intelligent transportation systems, radar systems that monitor traffic on the road have received attention. To ensure the reliable detection performance of the traffic monitoring radar, it is necessary to distinguish stationary road structures from moving vehicles. Therefore, in [...] Read more.
Recently, as one of the intelligent transportation systems, radar systems that monitor traffic on the road have received attention. To ensure the reliable detection performance of the traffic monitoring radar, it is necessary to distinguish stationary road structures from moving vehicles. Therefore, in this paper, we propose a method for discriminating stationary targets in traffic monitoring radar systems. First, we install a frequency-modulated continuous wave radar system using a center frequency of 24.15 GHz on an overpass to monitor multiple lanes on the road. Then, we process the raw data obtained by the radar sensor to extract target information such as the distance, angle, velocity, and radar cross-section. Finally, we analyze the target characteristics in the angle-velocity domain to classify stationary targets and moving vehicles. In this domain, stationary targets appear as points lying around a straight line, and if we estimate that line, we can extract the stationary targets among all targets. To find the trend line, we use a random sample consensus-based estimation method, which can extract a dominant line component from a set of sample points. Through the proposed method, we can effectively remove the stationary targets in the field of view of the radar system. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Vehicle Detection with Self-Training for Adaptative Video Processing Embedded Platform
Appl. Sci. 2020, 10(17), 5763; https://doi.org/10.3390/app10175763 - 20 Aug 2020
Abstract
Traffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware [...] Read more.
Traffic monitoring from closed-circuit television (CCTV) cameras on embedded systems is the subject of the performed experiments. Solving this problem encounters difficulties related to the hardware limitations, and possible camera placement in various positions which affects the system performance. To satisfy the hardware requirements, vehicle detection is performed using a lightweight Convolutional Neural Network (CNN), named SqueezeDet, while, for tracking, the Simple Online and Realtime Tracking (SORT) algorithm is applied, allowing for real-time processing on an NVIDIA Jetson Tx2. To allow for adaptation of the system to the deployment environment, a procedure was implemented leading to generating labels in an unsupervised manner with the help of background modelling and the tracking algorithm. The acquired labels are further used for fine-tuning the model, resulting in a meaningful increase in the traffic estimation accuracy, and moreover, adding only minimal human effort to the process allows for further accuracy improvement. The proposed methods, and the results of experiments organised under real-world test conditions are presented in the paper. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
A Clustering Refinement Approach for Revealing Urban Spatial Structure from Smart Card Data
Appl. Sci. 2020, 10(16), 5606; https://doi.org/10.3390/app10165606 - 13 Aug 2020
Abstract
Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with [...] Read more.
Facilitated by rapid development of the data-intensive techniques together with communication and sensing technology, we can take advantage of smart card data collected through Automatic Fare Collection (AFC) systems to establish connections between public transit and urban spatial structure. In this paper, with a case study on Shenzhen metro system in China, we investigate the agglomeration pattern of passenger flow among subway stations. Specifically, leveraging inbound and outbound passenger flows at subway stations, we propose a clustering refinement approach based on cluster member stability among multiple clusterings produced by isomorphic or heterogeneous clusterers. Furthermore, we validate and elaborate five clusters of subway stations in terms of regional functionality and urban planning by comparing station clusters with reference to government planning policies and regulations of Shenzhen city. Additionally, outlier stations with ambiguous functionalities are detected using proposed clustering refinement framework. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Economic Aspects of Driving Various Types of Vehicles in Intelligent Urban Transport Systems, Including Car-Sharing Services and Autonomous Vehicles
Appl. Sci. 2020, 10(16), 5580; https://doi.org/10.3390/app10165580 - 12 Aug 2020
Cited by 3
Abstract
Nowadays, the concept of new mobility solutions like shared mobility systems is becoming more and more popular in current transport systems. The next technological step will be the idea of replacing traditional vehicles with autonomous ones. Because autonomous vehicles are a new concept [...] Read more.
Nowadays, the concept of new mobility solutions like shared mobility systems is becoming more and more popular in current transport systems. The next technological step will be the idea of replacing traditional vehicles with autonomous ones. Because autonomous vehicles are a new concept in the automotive market, we dedicated this article to the idea of using autonomous vehicles as a part of car-sharing systems in intelligent, urban transport systems. The research herein is focused on the economic aspects of using autonomous vehicles in comparison to the classic car fleet available in car-sharing systems and to vehicles that belong to individual owners. We present our method for appropriate fleet selection based on the Delphi method and the calculations made through a scientific experiment performed based on Hartley’s plan. The results indicate the relation of travel parameters (including vehicle type) to the total cost of travel in urban transport systems. We also present the main terms related to autonomous vehicles. This article provides support for people who want to deepen knowledge about autonomous vehicles and new mobility solutions used in urban transport systems. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Driver Attention Area Extraction Method Based on Deep Network Feature Visualization
Appl. Sci. 2020, 10(16), 5474; https://doi.org/10.3390/app10165474 - 07 Aug 2020
Abstract
The current intelligent driving technology based on image data is being widely used. However, the analysis of traffic accidents occurred in intelligent driving vehicles shows that there is an explanatory difference between the intelligent driving system based on image data and the driver’s [...] Read more.
The current intelligent driving technology based on image data is being widely used. However, the analysis of traffic accidents occurred in intelligent driving vehicles shows that there is an explanatory difference between the intelligent driving system based on image data and the driver’s understanding of the target information in the image. In addition, driving behavior is the driver’s response based on the analysis of road information, which is not available in the current intelligent driving system. In order to solve this problem, our paper proposes a driver attention area extraction method based on deep network feature visualization. In our method, we construct a Driver Behavior Information Network (DBIN) to map the relation between image information and driving behavior. Then we use the Deep Network Feature Visualization method (DNFV) to determine the driver’s attention area. The experimental results show that our method can extract effective road information from a real traffic scene picture and obtain the driver’s attention area. Our method can provide a useful theoretical basis and related technology of visual perception for future intelligent driving systems, driving training and assisted driving systems. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Smart Route: Internet-of-Vehicles (IoV)-Based Congestion Detection and Avoidance (IoV-Based CDA) Using Rerouting Planning
Appl. Sci. 2020, 10(13), 4541; https://doi.org/10.3390/app10134541 - 30 Jun 2020
Cited by 1
Abstract
Massive traffic jam is the top concern of multiple disciplines (Civil Engineering, Intelligent Transportation Systems (ITS), and Government Policy) presently. Although literature constitutes several IoT-based congestion detection schemes, the existing schemes are costly (money and time) and, as well as challenging to deploy [...] Read more.
Massive traffic jam is the top concern of multiple disciplines (Civil Engineering, Intelligent Transportation Systems (ITS), and Government Policy) presently. Although literature constitutes several IoT-based congestion detection schemes, the existing schemes are costly (money and time) and, as well as challenging to deploy due to its complex structure. In the same context, this paper proposes a smart route Internet-of-Vehicles (IoV)-based congestion detection and avoidance (IoV-based CDA) scheme for a particular area of interest (AOI), i.e., road intersection point. The proposed scheme has two broad parts: (1) IoV-based congestion detection (IoV-based CD); and (2) IoV-based congestion avoidance (IoV-based CA). In the given area of interest, the congestion detection phase sets a parametric approach to calculate the capacity of each entry point for real-time traffic congestion detection. On each road segment, the installed roadside unit (RSU) assesses the traffic status concerning two factors: (a) occupancy rate and (b) occupancy time. If the values of these factors (either a or b) exceed the threshold limits, then congestion will be detected in real time. Next, IoV-based congestion avoidance triggers rerouting using modified Evolving Graph (EG)-Dijkstra, if the number of arriving vehicles or the occupancy time of an individual vehicle exceeds the thresholds. Moreover, the rerouting scheme in IoV-based congestion avoidance also considers the capacity of the alternate routes to avoid the possibility of moving congestion from one place to another. From the experimental results, we determine that proposed IoV-based congestion detection and avoidance significantly improves (i.e., 80%) the performance metrics (i.e., path cost, travel time, travelling speed) in low segment size scenarios than the previous microscopic congestion detection protocol (MCDP). Although in the case of simulation time, the performance increase depends on traffic congestion status (low, medium, high, massive), the performance increase varies from 0 to 100%. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
A Deep Learning-Based Perception Algorithm Using 3D LiDAR for Autonomous Driving: Simultaneous Segmentation and Detection Network (SSADNet)
Appl. Sci. 2020, 10(13), 4486; https://doi.org/10.3390/app10134486 - 29 Jun 2020
Abstract
In this paper, we propose a deep learning-based perception method in autonomous driving systems using a Light Detection and Ranging(LiDAR) point cloud data, which is called a simultaneous segmentation and detection network (SSADNet). SSADNet can be used to recognize both drivable areas and [...] Read more.
In this paper, we propose a deep learning-based perception method in autonomous driving systems using a Light Detection and Ranging(LiDAR) point cloud data, which is called a simultaneous segmentation and detection network (SSADNet). SSADNet can be used to recognize both drivable areas and obstacles, which is necessary for autonomous driving. Unlike the previous methods, where separate networks were needed for segmentation and detection, SSADNet can perform segmentation and detection simultaneously based on a single neural network. The proposed method uses point cloud data obtained from a 3D LiDAR for network input to generate a top view image consisting of three channels of distance, height, and reflection intensity. The structure of the proposed network includes a branch for segmentation and a branch for detection as well as a bridge connecting the two parts. The KITTI dataset, which is often used for experiments on autonomous driving, was used for training. The experimental results show that segmentation and detection can be performed simultaneously for drivable areas and vehicles at a quick inference speed, which is appropriate for autonomous driving systems. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
End-to-End Automated Guided Modular Vehicle
Appl. Sci. 2020, 10(12), 4400; https://doi.org/10.3390/app10124400 - 26 Jun 2020
Cited by 2
Abstract
Autonomous Vehicles (AVs) have caught people’s attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial [...] Read more.
Autonomous Vehicles (AVs) have caught people’s attention in recent years, not only from an academic or developmental viewpoint but also because of the wide range of applications that these vehicles may entail, such as intelligent mobility and logistics, as well as for industrial purposes, among others. The open literature contains a variety of works related to the subject. They employ a diversity of techniques ranging from probabilistic to ones based on Artificial Intelligence. The increase in computing capacity, well known to many, has opened plentiful opportunities for the algorithmic processing needed by these applications, making way for the development of autonomous navigation, in many cases with astounding results. The following paper presents a low-cost but high-performance minimal sensor open architecture implemented in a modular vehicle. It was developed in a short period of time, surpassing many of the currently available solutions found in the literature. Diverse experiments were carried out in the controlled and circumscribed environment of an autonomous circuit that demonstrates the efficiency of the applicability of the developed solution. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Motion Planning for Autonomous Vehicles Considering Longitudinal and Lateral Dynamics Coupling
Appl. Sci. 2020, 10(9), 3180; https://doi.org/10.3390/app10093180 - 02 May 2020
Cited by 1
Abstract
Vehicles are highly coupled and multi-degree nonlinear systems. The establishment of an appropriate vehicle dynamical model is the basis of motion planning for autonomous vehicles. With the development of autonomous vehicles from L2 to L3 and beyond, the automatic driving system is required [...] Read more.
Vehicles are highly coupled and multi-degree nonlinear systems. The establishment of an appropriate vehicle dynamical model is the basis of motion planning for autonomous vehicles. With the development of autonomous vehicles from L2 to L3 and beyond, the automatic driving system is required to make decisions and plans in a wide range of speeds and on bends with large curvature. In order to make precise and high-quality control maneuvers, it is important to account for the effects of dynamical coupling in these working conditions. In this paper, a new single-coupled dynamical model (SDM) is proposed to deal with the various dynamical coupling effects by identifying and simplifying the complicated one. An autonomous vehicle motion planning problem is then formulated using the nonlinear model predictive control theory (NMPC) with the SDM constraint (NMPC-SDM). We validated the NMPC-SDM with hardware-in-the-loop (HIL) experiments to evaluate improvements to control performance by comparing with the planners original design, using the kinematic and single-track models. The comparative results show the superiority of the proposed motion planning algorithm in improving the maneuverability and tracking performance. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Railway Lines across the Alps: Analysis of Their Usage through a New Railway Link Cost Function
Appl. Sci. 2020, 10(9), 3120; https://doi.org/10.3390/app10093120 - 29 Apr 2020
Cited by 2
Abstract
In this paper, the usage of railway lines across the Alps is evaluated, both at present and after the new lines and base tunnels will be in operation. The railway network of a large part of Europe has been modelled through a graph, [...] Read more.
In this paper, the usage of railway lines across the Alps is evaluated, both at present and after the new lines and base tunnels will be in operation. The railway network of a large part of Europe has been modelled through a graph, and the best routes between some of the most important origin/destination pairs in Italy and Europe have been determined. A new cost function has been developed for the links of the network. The proposed cost function is an improvement of those existing in the literature, because all cost components are taken into account in detail, while the traction cost and the number of locomotives utilized explicitly depend on the geometrical characteristics of rail lines. This last aspect is crucial in analyzing the rail lines across the Alps, as they are often operated in double or triple traction. The results of the study show the importance of new Alpine rail lines and base tunnels: the Ceneri base tunnel will remove a bottleneck on the Gotthard line, while the Brenner and Frejus base tunnels will take up a quota of demand currently served by other lines. Moreover, the new Alpine lines will create an east–west rail connection, through the Italian Padan Plain, alternative to the rail route which currently bypasses the Alps to the north. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
A Hybrid Dispatch Strategy Based on the Demand Prediction of Shared Bicycles
Appl. Sci. 2020, 10(8), 2778; https://doi.org/10.3390/app10082778 - 16 Apr 2020
Abstract
With the advent of pile-less shared bicycles, the techniques initially used for public bicycle dispatching were unable to fulfill the routine dispatch tasks, resulting in constant bicycle crowding. In this paper, to alleviate the mess of shared bicycles, we propose a hybrid dispatching [...] Read more.
With the advent of pile-less shared bicycles, the techniques initially used for public bicycle dispatching were unable to fulfill the routine dispatch tasks, resulting in constant bicycle crowding. In this paper, to alleviate the mess of shared bicycles, we propose a hybrid dispatching algorithm based on bicycle demand data. We take the bicycle stations’ imbalance as an optimization index and use greedy ideas to ensure that after each dispatch all stations get the smallest imbalance. In addition, it is suggested that two assessment metrics evaluate the efficiency of the dispatching technique from the users and operators’ perspectives. It is shown that the proposed dispatching algorithm performs better in terms of user satisfaction and operator revenue, and is less affected by bicycle distribution compared with the traditional manual scheduling algorithm. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Real-Time, Deep Learning Based Wrong Direction Detection
Appl. Sci. 2020, 10(7), 2453; https://doi.org/10.3390/app10072453 - 03 Apr 2020
Abstract
In this paper, we develop a real-time intelligent transportation system (ITS) to detect vehicles traveling the wrong way on the road. The concept of this wrong-way system is to detect such vehicles as soon as they enter an area covered by a single [...] Read more.
In this paper, we develop a real-time intelligent transportation system (ITS) to detect vehicles traveling the wrong way on the road. The concept of this wrong-way system is to detect such vehicles as soon as they enter an area covered by a single closed-circuit television (CCTV) camera. After detection, the program alerts the monitoring center and triggers a warning signal to the drivers. The developed system is based on video imaging and covers three aspects: detection, tracking, and validation. To locate a car in a video frame, we use a deep learning method known as you only look once version 3 (YOLOv3). Therefore, we use a custom dataset for training to create a deep learning model. After estimating a car’s position, we implement linear quadratic estimation (also known as Kalman filtering) to track the detected vehicle during a certain period. Lastly, we apply an “entry-exit” algorithm to identify the car’s trajectory, achieving 91.98% accuracy in wrong-way driver detection. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
On the Impact of the Rules on Autonomous Drive Learning
Appl. Sci. 2020, 10(7), 2394; https://doi.org/10.3390/app10072394 - 01 Apr 2020
Abstract
Autonomous vehicles raise many ethical and moral issues that are not easy to deal with and that, if not addressed correctly, might be an obstacle to the advent of such a technological revolution. These issues are critical because autonomous vehicles will interact with [...] Read more.
Autonomous vehicles raise many ethical and moral issues that are not easy to deal with and that, if not addressed correctly, might be an obstacle to the advent of such a technological revolution. These issues are critical because autonomous vehicles will interact with human road users in new ways and current traffic rules might not be suitable for the resulting environment. We consider the problem of learning optimal behavior for autonomous vehicles using Reinforcement Learning in a simple road graph environment. In particular, we investigate the impact of traffic rules on the learned behaviors and consider a scenario where drivers are punished when they are not compliant with the rules, i.e., a scenario in which violation of traffic rules cannot be fully prevented. We performed an extensive experimental campaign, in a simulated environment, in which drivers were trained with and without rules, and assessed the learned behaviors in terms of efficiency and safety. The results show that drivers trained with rules enforcement are willing to reduce their efficiency in exchange for being compliant to the rules, thus leading to higher overall safety. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Highly Curved Lane Detection Algorithms Based on Kalman Filter
Appl. Sci. 2020, 10(7), 2372; https://doi.org/10.3390/app10072372 - 30 Mar 2020
Abstract
The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road [...] Read more.
The purpose of the self-driving car is to minimize the number casualties of traffic accidents. One of the effects of traffic accidents is an improper speed of a car, especially at the road turn. If we can make the anticipation of the road turn, it is possible to avoid traffic accidents. This paper presents a cutting edge curve lane detection algorithm based on the Kalman filter for the self-driving car. It uses parabola equation and circle equation models inside the Kalman filter to estimate parameters of a using curve lane. The proposed algorithm was tested with a self-driving vehicle. Experiment results show that the curve lane detection algorithm has a high success rate. The paper also presents simulation results of the autonomous vehicle with the feature to control steering and speed using the results of the full curve lane detection algorithm. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
An Adaptive Approach for Multi-National Vehicle License Plate Recognition Using Multi-Level Deep Features and Foreground Polarity Detection Model
Appl. Sci. 2020, 10(6), 2165; https://doi.org/10.3390/app10062165 - 22 Mar 2020
Cited by 1
Abstract
License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent [...] Read more.
License plate recognition system (LPR) plays a vital role in intelligent transport systems to build up smart environments. Numerous country specific methods have been proposed successfully for an LPR system, but there is a need to find a generalized solution that is independent of license plate layout. The proposed architecture is comprised of two important LPR stages: (i) License plate character segmentation (LPCS) and (ii) License plate character recognition (LPCR). A foreground polarity detection model is proposed by using a Red-Green-Blue (RGB) channel-based color map in order to segment and recognize the LP characters effectively at both LPCS and LPCR stages respectively. Further, a multi-channel CNN framework with layer aggregation module is proposed to extract deep features, and support vector machine is used to produce target labels. Multi-channel processing with merged features from different-level convolutional layers makes output feature map more expressive. Experimental results show that the proposed method is capable of achieving high recognition rate for multinational vehicles license plates under various illumination conditions. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
An Integrated Algorithm for Intersection Queue Length Estimation Based on IoT in a Mixed Traffic Scenario
Appl. Sci. 2020, 10(6), 2078; https://doi.org/10.3390/app10062078 - 19 Mar 2020
Cited by 4
Abstract
Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by [...] Read more.
Nowadays, traffic infrastructures and vehicles are connected through the network benefiting from the development of Internet of Things (IoT). Connected automated cars can provide some useful traffic information. An architecture and algorithm of mobile service computing are proposed for traffic state sensing by integration between IoT and transport system models (TSMs). The formation process of queue at this intersection is analyzed based on the state information of connected vehicles and the velocity of shockwave is calculated to predict queue length. The computing results can be delivered to the traffic information edge server. However, not all the vehicles are capable of connecting to the network and will affect the queue length estimation accuracy. At the same time, traffic cameras transmit the traffic image to the edge server and a deep neuron network (DNN) is constructed on the edge server to tackle the traffic image. It can recognize and classify the vehicles in the image but takes several seconds to work with the complex DNN. At last, the final queue length is determined according to the weight of the two computing results. The integrated result is delivered to the traffic light controller and traffic monitoring center cloud. It reveals that the estimation from DNN can compensate the estimation from shockwave when the penetration rate of connected vehicles is low. A testbed is built based on VISSIM, and the evaluation results demonstrate the availability and accuracy of the integrated queue length estimation algorithm. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
An LSTM-Based Autonomous Driving Model Using a Waymo Open Dataset
Appl. Sci. 2020, 10(6), 2046; https://doi.org/10.3390/app10062046 - 18 Mar 2020
Cited by 2
Abstract
The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking. While the dataset provides a large amount of high-quality and multi-source driving information, people in academia are [...] Read more.
The Waymo Open Dataset has been released recently, providing a platform to crowdsource some fundamental challenges for automated vehicles (AVs), such as 3D detection and tracking. While the dataset provides a large amount of high-quality and multi-source driving information, people in academia are more interested in the underlying driving policy programmed in Waymo self-driving cars, which is inaccessible due to AV manufacturers’ proprietary protection. Accordingly, academic researchers have to make various assumptions to implement AV components in their models or simulations, which may not represent the realistic interactions in real-world traffic. Thus, this paper introduces an approach to learn a long short-term memory (LSTM)-based model for imitating the behavior of Waymo’s self-driving model. The proposed model has been evaluated based on Mean Absolute Error (MAE). The experimental results show that our model outperforms several baseline models in driving action prediction. In addition, a visualization tool is presented for verifying the performance of the model. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Broadband Wireless Communication Systems for Vacuum Tube High-Speed Flying Train
Appl. Sci. 2020, 10(4), 1379; https://doi.org/10.3390/app10041379 - 18 Feb 2020
Cited by 1
Abstract
A vactrain (or vacuum tube high-speed flying train) is considered as a novel proposed rail transportation approach in the ultra-high-speed scenario. The maglev train can run with low mechanical friction, low air resistance, and low noise mode at a speed exceeding 1000 km/h [...] Read more.
A vactrain (or vacuum tube high-speed flying train) is considered as a novel proposed rail transportation approach in the ultra-high-speed scenario. The maglev train can run with low mechanical friction, low air resistance, and low noise mode at a speed exceeding 1000 km/h inside the vacuum tube regardless of weather conditions. Currently, there is no research on train-to-ground wireless communication system for vactrain. In this paper, we first summarize a list of the unique challenges and opportunities associated with the wireless communication for vactrain, then analyze the bandwidth and Quality of Service (QoS) requirements of vactrain’s train-to-ground communication services quantitatively. To address these challenges and utilize the unique opportunities, a leaky waveguide solution with simple architecture but excellent performance is proposed for wireless coverage for vactrains. The simulation of the leaky waveguide is conducted, and the results show the uniform phase distribution along the horizontal direction of the tube, but also the smooth field distribution at the point far away from the leaky waveguide, which can suppress Doppler frequency shift, indicating that the time-varying frequency-selective fading channel could be approximated as a stationary channel. Furthermore, the train-to-ground wireless access architectures based on leaky waveguide are studied and analyzed. Finally, the moving scheme is adopted based on centralized, cooperative, cloud Radio Access Network (C-RAN), so as to deal with the extremely frequent handoff issue. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessArticle
Supervised Learning of Natural-Terrain Traversability with Synthetic 3D Laser Scans
Appl. Sci. 2020, 10(3), 1140; https://doi.org/10.3390/app10031140 - 07 Feb 2020
Cited by 1
Abstract
Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from [...] Read more.
Autonomous navigation of ground vehicles on natural environments requires looking for traversable terrain continuously. This paper develops traversability classifiers for the three-dimensional (3D) point clouds acquired by the mobile robot Andabata on non-slippery solid ground. To this end, different supervised learning techniques from the Python library Scikit-learn are employed. Training and validation are performed with synthetic 3D laser scans that were labelled point by point automatically with the robotic simulator Gazebo. Good prediction results are obtained for most of the developed classifiers, which have also been tested successfully on real 3D laser scans acquired by Andabata in motion. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessFeature PaperArticle
Efficient Management of Road Intersections for Automated Vehicles—The FRFP System Applied to the Various Types of Intersections and Roundabouts
Appl. Sci. 2020, 10(1), 316; https://doi.org/10.3390/app10010316 - 31 Dec 2019
Cited by 2
Abstract
In the last decade, automatic driving systems for vehicles circulating on public roads have become increasingly closer to reality. There is always a strong interest in this topic among research centers and car manufacturers. One of the most critical aspects is the management [...] Read more.
In the last decade, automatic driving systems for vehicles circulating on public roads have become increasingly closer to reality. There is always a strong interest in this topic among research centers and car manufacturers. One of the most critical aspects is the management of intersections, i.e., who will have to go first and in what ways? This is the question we want to answer through this research. Clearly, the goal is to manage the intersection safely, making it possible to reduce road congestion, travel time, emissions, and fuel consumption as much as possible. The research is conducted by comparing a new management system with the systems already known in the state of the art for different types of intersections. The new system proposed by us is called FRFP (first to reach the end of the intersection first to pass). In particular, vehicles will increase or decrease their speed in collaboration with each other by making the right decision. The vehicle that can potentially reach the intersection exit first. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Review

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Open AccessReview
Discriminative Parameter Training of the Extended Particle-Aided Unscented Kalman Filter for Vehicle Localization
Appl. Sci. 2020, 10(18), 6260; https://doi.org/10.3390/app10186260 - 09 Sep 2020
Abstract
Location is one of the most important parameters of a self-driving car. To filter the sensor noise, we proposed the extended particle-aided unscented Kalman filter (PAUKF). Although the performance of the PAUKF improved, it still needed parameter tuning as other Kalman filter applications [...] Read more.
Location is one of the most important parameters of a self-driving car. To filter the sensor noise, we proposed the extended particle-aided unscented Kalman filter (PAUKF). Although the performance of the PAUKF improved, it still needed parameter tuning as other Kalman filter applications do. The characteristic of noise is important to the filter’s performance; the most important parameters therefore are the variances of the measurement. In most Kalman filter research, the variance of the filter is tuned manually, costing researchers plenty of time and yielding non-optimized results in most applications. In this paper, we propose a method that improves the performance of the extended PAUKF based on the coordinate descent algorithm by learning the most appropriate measurement variances. The results show that the performance of the extended PAUKF improved compared to the manually tuned extended PAUKF. By using the proposed training algorithm, practicability, training time efficiency and the estimation precision of the PAUKF improved compared to previous research. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessReview
Extended Particle-Aided Unscented Kalman Filter Based on Self-Driving Car Localization
Appl. Sci. 2020, 10(15), 5045; https://doi.org/10.3390/app10155045 - 22 Jul 2020
Cited by 1
Abstract
The location of the vehicle is a basic parameter for self-driving cars. The key problem of localization is the noise of the sensors. In previous research, we proposed a particle-aided unscented Kalman filter (PAUKF) to handle the localization problem in non-Gaussian noise environments. [...] Read more.
The location of the vehicle is a basic parameter for self-driving cars. The key problem of localization is the noise of the sensors. In previous research, we proposed a particle-aided unscented Kalman filter (PAUKF) to handle the localization problem in non-Gaussian noise environments. However, the previous basic PAUKF only considers the infrastructures in two dimensions (2D). This previous PAUKF 2D limitation rendered it inoperable in the real world, which is full of three-dimensional (3D) features. In this paper, we have extended the previous basic PAUKF’s particle weighting process based on the multivariable normal distribution for handling 3D features. The extended PAUKF also raises the feasibility of fusing multisource perception data into the PAUKF framework. The simulation results show that the extended PAUKF has better real-world applicability than the previous basic PAUKF. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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Open AccessReview
Survey of Smart Parking Systems
Appl. Sci. 2020, 10(11), 3872; https://doi.org/10.3390/app10113872 - 02 Jun 2020
Abstract
The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, [...] Read more.
The large number of vehicles constantly seeking access to congested areas in cities means that finding a public parking place is often difficult and causes problems for drivers and citizens alike. In this context, strategies that guide vehicles from one point to another, looking for the most optimal path, are needed. Most contributions in the literature are routing strategies that take into account different criteria to select the optimal route required to find a parking space. This paper aims to identify the types of smart parking systems (SPS) that are available today, as well as investigate the kinds of vehicle detection techniques (VDT) they have and the algorithms or other methods they employ, in order to analyze where the development of these systems is at today. To do this, a survey of 274 publications from January 2012 to December 2019 was conducted. The survey considered four principal features: SPS types reported in the literature, the kinds of VDT used in these SPS, the algorithms or methods they implement, and the stage of development at which they are. Based on a search and extraction of results methodology, this work was able to effectively obtain the current state of the research area. In addition, the exhaustive study of the studies analyzed allowed for a discussion to be established concerning the main difficulties, as well as the gaps and open problems detected for the SPS. The results shown in this study may provide a base for future research on the subject. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)

Other

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Open AccessLetter
High Definition Map-Based Localization Using ADAS Environment Sensors for Application to Automated Driving Vehicles
Appl. Sci. 2020, 10(14), 4924; https://doi.org/10.3390/app10144924 - 17 Jul 2020
Abstract
This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical [...] Read more.
This paper presents high definition (HD) map-based localization using advanced driver assistance system (ADAS) environment sensors for application to automated driving vehicles. A variety of autonomous driving technologies are being developed using expensive and high-performance sensors, but limitations exist due to several practical issues. In respect of the application of autonomous driving cars in the near future, it is necessary to ensure autonomous driving performance by effectively utilizing sensors that are already installed for ADAS purposes. Additionally, the most common localization algorithm, which is usually used lane information only, has a highly unstable disadvantage in the absence of that information. Therefore, it is essential to ensure localization performance with other road features such as guardrails when there are no lane markings. In this study, we would like to propose a localization algorithm that could be implemented in the near future by using low-cost sensors and HD maps. The proposed localization algorithm consists of several sections: environment feature representation with low-cost sensors, digital map analysis and application, position correction based on map-matching, designated validation gates, and extended Kalman filter (EKF)-based localization filtering and fusion. Lane information is detected by monocular vision in front of the vehicle. A guardrail is perceived by radar by distinguishing low-speed object measurements and by accumulating several steps to extract wall features. These lane and guardrail information are able to correct the host vehicle position by using the iterative closest point (ICP) algorithm. The rigid transformation between the digital high definition map (HD map) and environment features is calculated through ICP matching. Each corrected vehicle position by map-matching is selected and merged based on EKF with double updating. The proposed algorithm was verified through simulation based on actual driving log data. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems: Beyond Intelligent Vehicles)
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