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Intelligent Transportation Systems (ITS), Volume II
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Intelligent Transportation Systems (ITS), Volume II

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electrical and Autonomous Vehicles".

Deadline for manuscript submissions: closed (31 March 2022) | Viewed by 30942

Special Issue Editor

Prof. Dr. Beatriz L. Boada

E-Mail Website
Guest Editor
Mechanical Engineering Department, Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, 28911 Leganés, Madrid, Spain
Interests: vehicle control; vehicle safety; Internet of things; sensor fusion; intelligent vehicles
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the last few years, the growth of the number of vehicles in cities has caused problems of mobility, environmental pollution, and road safety. The intelligent transportation system (ITS) concept includes many advanced technologies, such as, communication, sensing, and control, which are used for managing a high amount of information, in order to face these challenges. ITS is a multidisciplinary field that comprises a large number of research areas.

Although tremendous advancements have been made in the last decade in this field, there are still aspects that need to be addressed in order to improve the transportation safety, efficiency, and sustainability.

The topics of interest include, but are not limited to, the following:

  • Internet of things/connected vehicles
  • Big data
  • Electric/autonomous vehicles
  • Vehicle control
  • Traffic control/traffic management
  • Smart sensors
  • Smart mobility systems
  • Reliability and security in transport

Prof. Dr. Beatriz L. Boada
Guest Editor

Manuscript Submission Information

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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. Electronics 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 2400 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

  • Internet of things/connected vehicles
  • Big data
  • Electric/autonomous vehicles
  • Vehicle control
  • Traffic control/traffic management
  • Smart sensors
  • Smart mobility systems
  • Reliability and security in transport

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Published Papers (7 papers)

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Research

16 pages, 3429 KiB  
Article
Simulation-Based Testing of Subsystems for Autonomous Vehicles at the Example of an Active Suspension Control System
by Volker Landersheim, Matthias Jurisch, Riccardo Bartolozzi, Georg Stoll, Riccardo Möller and Heiko Atzrodt
Electronics 2022, 11(9), 1469; https://doi.org/10.3390/electronics11091469 - 3 May 2022
Cited by 3 | Viewed by 2116
Abstract
Automated driving functions are expected to increase both the safety and ride comfort of future vehicles. Ensuring their functional safety and optimizing their performance requires thorough testing. Costs and duration of tests can be reduced if more tests can be performed numerically in [...] Read more.
Automated driving functions are expected to increase both the safety and ride comfort of future vehicles. Ensuring their functional safety and optimizing their performance requires thorough testing. Costs and duration of tests can be reduced if more tests can be performed numerically in a feasible simulation framework. This simulation setup must include all subsystems of the autonomous vehicle, which significantly interact with the system under test. In this paper, a modular model chain is presented, which is developed for testing systems with an impact on vehicle motion. It includes trajectory planning, motion control, and a model of the vehicle dynamics in a closed loop. Each subsystem can easily be exchanged to adapt the model chain with respect to the simulation objectives. As a use case, the testing of an active suspension control system is discussed. It is designed directly for use in autonomous cars and uses inputs from the vehicle motion planning subsystem for planning the suspension actuator motion. Using the presented closed-loop model chain, the effect of different actuator control strategies on ride comfort is compared, such as curve tilting. Furthermore, the impact of the active suspension system on lateral vehicle motion is shown. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS), Volume II)
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22 pages, 3724 KiB  
Article
A Framework for Lane-Change Maneuvers of Connected Autonomous Vehicles in a Mixed-Traffic Environment
by Runjia Du, Sikai Chen, Yujie Li, Majed Alinizzi and Samuel Labi
Electronics 2022, 11(9), 1350; https://doi.org/10.3390/electronics11091350 - 24 Apr 2022
Cited by 5 | Viewed by 2090
Abstract
In the transition era towards connected autonomous vehicles (CAVs), the sharing of the roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected to pose safety and flow efficiency concerns even though CAVs may tend to adopt rather conservative maneuvering [...] Read more.
In the transition era towards connected autonomous vehicles (CAVs), the sharing of the roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected to pose safety and flow efficiency concerns even though CAVs may tend to adopt rather conservative maneuvering policies. Unfortunately, this will likely cause HDV drivers to unduly exploit such conservativeness by driving in ways that imperil safety. A context of this situation is lane-changing by the CAV, a potential major source of traffic disturbance at multi-lane highways that could impair their traffic flow efficiency. In dense, high-speed traffic conditions, it will be extremely unsafe for the CAV to change lanes without cooperation from neighboring vehicles in the traffic stream. To help address this issue, this paper developed a framework through which connected HDVs (CHDVs) could cooperate to facilitate safe and efficient lane-changing by the CAV. A numerical experiment was carried out to demonstrate the efficacy of the framework. The results indicated the CAVs’ lane-changing feasibility and the overall duration of the lane-changing if the CAV carries out that maneuver. It was observed that throughout the lane-changing process, the safety of not only the CAV but also of all neighboring vehicles, was promoted through the framework’s collision avoidance mechanism. The overall traffic flow efficiency was analyzed in terms of the ambient level of CHDV–CAV cooperation. Overall, the results of the study present evidence of how CHDV–CAV cooperation can help enhance the overall system efficiency. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS), Volume II)
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20 pages, 8674 KiB  
Article
Cooperative Automotive Radars with Multi-Aperture Multiplexing MIMO Sparse Array Design
by Can Liang, Yanhua Wang, Zhuxi Yang, Xueyao Hu, Qiubo Pei, Wei Gu and Liang Zhang
Electronics 2022, 11(8), 1198; https://doi.org/10.3390/electronics11081198 - 9 Apr 2022
Cited by 7 | Viewed by 2388
Abstract
In this paper, a multi-aperture multiplexing multiple-input multiple-output (MAM-MIMO) sparse array is presented for cooperative automotive radars (CARs). The proposed sparse array composed of multiple subarrays can simultaneously cover a wide field-of-view (FOV) and achieve the required azimuth resolution at different ranges. To [...] Read more.
In this paper, a multi-aperture multiplexing multiple-input multiple-output (MAM-MIMO) sparse array is presented for cooperative automotive radars (CARs). The proposed sparse array composed of multiple subarrays can simultaneously cover a wide field-of-view (FOV) and achieve the required azimuth resolution at different ranges. To validate this idea, an optimization model for the MAM-MIMO sparse array is derived based on the example of CARs. This optimization model has been found by combining the peak-to-sidelobe ratio (PSLR) at all beams pointing within the constraints of different detection ranges. In addition, a hierarchical genetic algorithm based on the multi-objective decomposition method has been developed to obtain the optimized sparse array. The proposed method has been evaluated through both simulations and experiments. It is demonstrated that the optimized MAM-MIMO sparse array can effectively suppress sidelobes of its subarrays, yet with reasonably high azimuth resolutions and large FOVs. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS), Volume II)
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17 pages, 5715 KiB  
Article
IoT-Enabled Vehicle Speed Monitoring System
by Shafi Ullah Khan, Noor Alam, Sana Ullah Jan and In Soo Koo
Electronics 2022, 11(4), 614; https://doi.org/10.3390/electronics11040614 - 16 Feb 2022
Cited by 10 | Viewed by 11154
Abstract
Millions of people lose their lives each year worldwide due to traffic law violations, specifically, over speeding. The existing systems fail to report most of such violations due to their respective flaws. For instance, speed guns work in isolation and cannot measure speed [...] Read more.
Millions of people lose their lives each year worldwide due to traffic law violations, specifically, over speeding. The existing systems fail to report most of such violations due to their respective flaws. For instance, speed guns work in isolation and cannot measure speed of all vehicles on roads at all spatial points. They can only detect the speed of the vehicle the line of sight of the camera. A solution is to deploy a huge number of speed guns at different locations on the road to detect and report vehicles that are over speeding. However, this solution is not feasible because it demands a large amount of equipment and computational resources to process such a big amount of data. In this paper, a speed detection framework is developed to detect vehicles’ speeds with only two speed guns, which can report speed even when the vehicle is not within the camera’s line of sight. The system is specifically designed for an irregular traffic scenario such as that of Pakistan, where it is inconvenient to install conventional systems. The idea is to calculate the average speed of vehicles traveling in a specific region, for instance, between two spatial points. A low-cost Raspberry Pi (RPi) module and an ordinary camera are deployed to detect the registration numbers on vehicle license plates. This hardware presents a more stable system since it is powered by a low consumption Raspberry Pi that can operate for hours without crashing or malfunctioning. More specifically, the entrance and exit locations and the time taken to get from one point to another are recorded. An automatic alert to traffic authorities is generated when a driver is over speeding. A detailed explanation of the hardware prototype and the algorithms is given, along with the setup configurations of the hardware prototype, the website, and the mobile device applications. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS), Volume II)
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17 pages, 1571 KiB  
Article
Cooperative- and Eco-Driving: Impact on Fuel Consumption for Heavy Trucks on Hills
by Juergen Hauenstein, Jan Cedric Mertens, Frank Diermeyer and Andreas Zimmermann
Electronics 2021, 10(19), 2373; https://doi.org/10.3390/electronics10192373 - 28 Sep 2021
Cited by 5 | Viewed by 2588
Abstract
Greenhouse gas emissions are the cause of climate change, which in turn has a negative impact on people and the environment. Reducing the fuel consumption of conventional engines reduces climate-damaging emissions and can, thus, contribute to achieving climate protection goals. In addition, fuel [...] Read more.
Greenhouse gas emissions are the cause of climate change, which in turn has a negative impact on people and the environment. Reducing the fuel consumption of conventional engines reduces climate-damaging emissions and can, thus, contribute to achieving climate protection goals. In addition, fuel costs are a major cost factor for long-haul trucking. Eco-driving helps to reduce fuel costs when driving on inclines and declines. Due to the high mass and, therefore, high kinetic and potential energy of heavy trucks, fuel can be saved by coasting before slopes and before speed limits. However, energy-efficient and non-cooperative driving, i.e., without considering other road users, can lead to increased fuel consumption as vehicles impede each other. To resolve conflicts in road traffic, a variety of methods that enable cooperative driving exist. In general, vehicles communicate with vehicle-to-everything (V2X) and negotiate a joint driving strategy. This paper presents a method that combines cooperative and energy-efficient driving and examines the impact on fuel consumption during uphill driving. The method relies on the exchange of trajectories for cooperative maneuver coordination. By computing a strategic trajectory, energy-efficient driving with long coasting maneuvers is enabled. In the simulative evaluation, travel over hills with two and three trucks is investigated. It is shown that the combination of cooperative and eco-driving reduces the fuel costs for traffic. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS), Volume II)
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32 pages, 9659 KiB  
Article
Traffic Signal Control System Based on Intelligent Transportation System and Reinforcement Learning
by Julián Hurtado-Gómez, Juan David Romo, Ricardo Salazar-Cabrera, Álvaro Pachón de la Cruz and Juan Manuel Madrid Molina
Electronics 2021, 10(19), 2363; https://doi.org/10.3390/electronics10192363 - 28 Sep 2021
Cited by 11 | Viewed by 6712
Abstract
Traffic congestion has several causes, including insufficient road capacity, unrestricted demand and improper scheduling of traffic signal phases. A great variety of efforts have been made to properly program such phases. Some of them are based on traditional transportation assumptions, and others are [...] Read more.
Traffic congestion has several causes, including insufficient road capacity, unrestricted demand and improper scheduling of traffic signal phases. A great variety of efforts have been made to properly program such phases. Some of them are based on traditional transportation assumptions, and others are adaptive, allowing the system to learn the control law (signal program) from data obtained from different sources. Reinforcement Learning (RL) is a technique commonly used in previous research. However, properly determining the states and the reward is key to obtain good results and to have a real chance to implement it. This paper proposes and implements a traffic signal control system (TSCS), detailing its development stages: (a) Intelligent Transportation System (ITS) architecture design for the TSCS; (b) design and development of a system prototype, including an RL algorithm to minimize the vehicle queue at intersections, and detection and calculation of such queues by adapting a computer vision algorithm; and (c) design and development of system tests to validate operation of the algorithms and the system prototype. Results include the development of the tests for each module (vehicle queue measurement and RL algorithm) and real-time integration tests. Finally, the article presents a system simulation in the context of a medium-sized city in a developing country, showing that the proposed system allowed reduction of vehicle queues by 29%, of waiting time by 50%, and of lost time by 50%, when compared to fixed phase times in traffic signals. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS), Volume II)
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Graphical abstract

15 pages, 21633 KiB  
Article
Object Classification with Roadside LiDAR Data Using a Probabilistic Neural Network
by Jiancheng Zhang, Rendong Pi, Xiaohong Ma, Jianqing Wu, Hongtao Li and Ziliang Yang
Electronics 2021, 10(7), 803; https://doi.org/10.3390/electronics10070803 - 28 Mar 2021
Cited by 9 | Viewed by 2667
Abstract
Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of [...] Read more.
Object classification is important information for different transportation areas. This research developed a probabilistic neural network (PNN) classifier for object classification using roadside Light Detection and Ranging (LiDAR). The objective was to classify the road user on the urban road into one of four classes: Pedestrian, bicycle, passenger car, and truck. Five features calculated from the point cloud generated from the roadside LiDAR were selected to represent the difference between different classes. A total of 2736 records (2062 records for training, and 674 records for testing) were manually marked for training and testing the PNN algorithm. The data were collected at three different sites representing different scenarios. The performance of the classification was evaluated by comparing the result of the PNN with those of the support vector machine (SVM) and the random forest (RF). The comparison results showed that the PNN can provide the results of classification with the highest accuracy among the three investigated methods. The overall accuracy of the PNN for object classification was 97.6% using the testing database. The errors in the classification results were also diagnosed. Discussions about the direction of future studies were also provided at the end of this paper. Full article
(This article belongs to the Special Issue Intelligent Transportation Systems (ITS), Volume II)
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