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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 July 2020) | Viewed by 53323

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Special Issue Editor

Prof. Dr. Beatriz L. Boada

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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
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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

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 submissions that pass pre-check are 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.

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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

Published Papers (12 papers)

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Research

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18 pages, 1652 KiB

Open AccessEditor’s ChoiceArticle

A Predictive Fleet Management Strategy for On-Demand Mobility Services: A Case Study in Munich

by Michael Wittmann, Lorenz Neuner and Markus Lienkamp

Electronics 2020, 9(6), 1021; https://doi.org/10.3390/electronics9061021 - 19 Jun 2020

Cited by 6 | Viewed by 4975

Abstract

The global market for MoD services is in a state of rapid and challenging transformation, with new market entrants in Europe, such as Uber, MOIA, and CleverShuttle, competing with traditional taxi providers. Rapid developments in available algorithms, data sources, and real-time information systems offer new possibilities of maximizing the efficiency of MoD services. In particular, the use of demand predictions is expected to contribute to a reduction in operational costs and an increase in overall service quality. This paper examines the potential of predictive fleet management strategies applied to a large-scale real-world taxi dataset for the city of Munich. A combination of state-of-the art dispatching algorithms and a predictive RHC optimization for idle vehicle rebalancing was developed to determine the scale by which a fleet size can be reduced without affecting service quality. A simulation study was conducted over a one-week period in Munich, which showed that predictive fleet strategies clearly outperform the present strategy in terms of both service quality and costs. Furthermore, the results showed that current taxi fleets could be reduced to 70% of their original size without any decrease in performance. In addition, the results indicated that the reduced fleet size of the predictive strategy was still 20% larger compared to the theoretical optimum resulting from a bipartite matching approach. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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22 pages, 8501 KiB

Open AccessArticle

The Multi-Station Based Variable Speed Limit Model for Realization on Urban Highway

by Soobin Jeon, Chongmyung Park and Dongmahn Seo

Electronics 2020, 9(5), 801; https://doi.org/10.3390/electronics9050801 - 13 May 2020

Cited by 4 | Viewed by 2210

Abstract

Intelligent transport systems (ITS) are a convergence of information technology and transportation systems as seen in the variable speed limit (VSL) system. Since the VSL system controls the speed limit according to the traffic conditions, it can improve the safety and efficiency of a transport network. Many researchers have studied the real-time VSL (RVSL) algorithm based on real-time traffic information from multiple stations recording traffic data. However, this method can suffer from inaccurate selection of the VSL start station (VSS), incorrect VSL calculations, and is unable to quickly react to the changing traffic conditions. Unstable VSL systems result in more congestion on freeways. In this study, an enhanced VSL algorithm (EVSL) is proposed to address the limitations of the existing RVSL algorithm. This selects preliminary VSL start stations (pVSS), which is expected to end congestion using acceleration and allocates final VSSs for each congestion interval using selected pVSS. This controls the vehicles that entered the congestion area based on the selected VSS. We used four metrics to evaluate the performance of the proposed VSL (VSS stability assessment, speed control stability assessment, travel time, and shockwave), which were all enhanced when compared to the standard RVSL algorithm. In addition, the EVSL algorithm showed stable VSL performance, which is critical for road safety. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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19 pages, 1621 KiB

Open AccessArticle

The Need for Cooperative Automated Driving

by Jan Cedric Mertens, Christian Knies, Frank Diermeyer, Svenja Escherle and Sven Kraus

Electronics 2020, 9(5), 754; https://doi.org/10.3390/electronics9050754 - 04 May 2020

Cited by 20 | Viewed by 3781

Abstract

In this paper we describe cooperation and social dilemmas in multiagent systems, with an analogy applied to road traffic. Cooperative human drivers, based on their perception of trust and fairness, find efficient solutions for such dilemmas. In the development of automated vehicles (AVs) it is therefore important to ensure that this cooperative ability is maintained even without a human driver. Therefore, the topic of cooperative intelligent transport systems (C-ITSs) is discussed in detail and different characteristics of cooperation and their implementation are derived. Further, three planning levels with the corresponding communication techniques are discussed and several methods for maneuver planning are listed. All in all, we hope that this paper will allow us to better classify different cooperative scenarios, develop novel approaches for cooperative AVs (CAVs), and emphasize the need for cooperative driving. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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19 pages, 3999 KiB

Open AccessEditor’s ChoiceCommunication

Floating Car Data Adaptive Traffic Signals: A Description of the First Real-Time Experiment with “Connected” Vehicles

by Vittorio Astarita, Vincenzo Pasquale Giofré, Demetrio Carmine Festa, Giuseppe Guido and Alessandro Vitale

Electronics 2020, 9(1), 114; https://doi.org/10.3390/electronics9010114 - 07 Jan 2020

Cited by 17 | Viewed by 6107

Abstract

The future of traffic management will be based on “connected” and “autonomous” vehicles. With connected vehicles it is possible to gather real-time information. The main potential application of this information is in real-time adaptive traffic signal control. Despite the feasibility of using Floating Car Data (FCD), for signal control, there have been practically no real experiments with all “connected” vehicles to regulate traffic signals in real-time. Most of the research in this field has been carried out with simulations. The purpose of this study is to present a dedicated system that was implemented in the first experiment of an FCD-based adaptive traffic signal. For the first time in the history of traffic management, a traffic signal has been regulated in real time with real “connected” vehicles. This paper describes the entire path of software and system development that has allowed us to make the steps from just simulation test to a real on-field implementation. Results of the experiments carried out with the presented system prove the feasibility of FCD adaptive traffic signals with commonly-used technologies and also establishes a test-bed that may help others to develop better regulation algorithms for these kinds of new “connected” intersections. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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23 pages, 6238 KiB

Open AccessArticle

Machine Learning-Based Driving Style Identification of Truck Drivers in Open-Pit Mines

by Qun Wang, Ruixin Zhang, Yangting Wang and Shuaikang Lv

Electronics 2020, 9(1), 19; https://doi.org/10.3390/electronics9010019 - 24 Dec 2019

Cited by 16 | Viewed by 4263

Abstract

The significance in constructing a driving style identification model for open-pit mine truck drivers is to reduce diesel consumption and improve training. First, we developed a driving behavior and mining truck condition monitoring system for an open-pit mine. Under heavy-load and no-load conditions of a mining truck, based on the same experimental truck and haulage road, the data of driving behavior and truck status of different drivers were collected. The driving style characteristic parameters of mining trucks under heavy-load and no-load conditions were constructed through Pearson correlation analysis. Using a k-means clustering algorithm, driving style can be divided into three types: normal type, soft type, and aggressive type, and we verified the validity of this driving style classification with a box plot. On this basis, the parameters of random forest, k-nearest neighbor, support vector machine, and neural network models were optimized and the accuracy was compared through a cross-validation grid search, and then a driving style identification model based on the random forest method was finally proposed. Driving style parameter weight values were obtained based on the Gini coefficient. Last, the fuel consumption characteristics of different driving styles were calculated. The results show that the driving style identification models based on random forest can effectively identify different driving styles when the mining truck is operating under heavy load and no load, and the overall accuracy of the model is 95.39% and 90.74% respectively. The fuel consumption of the aggressive driving style was the largest and was 10% higher than the average fuel consumption. The research results provide data support and new ideas for operation training and fuel-saving driving of mining trucks in open-pit mines. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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34 pages, 1039 KiB

Open AccessArticle

Masivo: Parallel Simulation Model Based on OpenCL for Massive Public Transportation Systems’ Routes

by Juan Ruiz-Rosero, Gustavo Ramirez-Gonzalez and Rahul Khanna

Electronics 2019, 8(12), 1501; https://doi.org/10.3390/electronics8121501 - 08 Dec 2019

Cited by 5 | Viewed by 3188

Abstract

There is a large number of tools for the simulation of traffic and routes in public transport systems. These use different simulation models (macroscopic, microscopic, and mesoscopic). Unfortunately, these simulation tools are limited when simulating a complete public transport system, which includes all its buses and routes (up to 270 for the London Underground). The processing times for these type of simulations increase in an unmanageable way since all the relevant variables that are required to simulate consistently and reliably the system behavior must be included. In this paper, we present a new simulation model for public transport routes’ simulation called Masivo. It runs the public transport stops’ operations in OpenCL work items concurrently, using a multi-core high performance platform. The performance results of Masivo show a speed-up factor of 10.2 compared with the simulator model running with one compute unit and a speed-up factor of 278 times faster than the validation simulator. The real-time factor achieved was 3050 times faster than the 10 h simulated duration, for a public transport system of 300 stops, 2400 buses, and 456,997 passengers. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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19 pages, 1331 KiB

Open AccessArticle

Deep, Consistent Behavioral Decision Making with Planning Features for Autonomous Vehicles

by Lilin Qian, Xin Xu, Yujun Zeng and Junwen Huang

Electronics 2019, 8(12), 1492; https://doi.org/10.3390/electronics8121492 - 06 Dec 2019

Cited by 13 | Viewed by 3254

Abstract

Autonomous driving promises to be the main trend in the future intelligent transportation systems due to its potentiality for energy saving, and traffic and safety improvements. However, traditional autonomous vehicles’ behavioral decisions face consistency issues between behavioral decision and trajectory planning and shows a strong dependence on the human experience. In this paper, we present a planning-feature-based deep behavior decision method (PFBD) for autonomous driving in complex, dynamic traffic. We used a deep reinforcement learning (DRL) learning framework with the twin delayed deep deterministic policy gradient algorithm (TD3) to exploit the optimal policy. We took into account the features of topological routes in the decision making of autonomous vehicles, through which consistency between decision making and path planning layers can be guaranteed. Specifically, the features of a route extracted from path planning space are shared as the input states for the behavioral decision. The actor-network learns a near-optimal policy from the feasible and safe candidate emulated routes. Simulation tests on three typical scenarios have been performed to demonstrate the performance of the learning policy, including the comparison with a traditional rule-based expert algorithm and the comparison with the policy considering partial information of a contour. The results show that the proposed approach can achieve better decisions. Real-time test on an HQ3 (HongQi the third ) autonomous vehicle also validated the effectiveness of PFBD. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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17 pages, 11271 KiB

Open AccessArticle

A Traffic Flow Prediction Method Based on Road Crossing Vector Coding and a Bidirectional Recursive Neural Network

by Shuanfeng Zhao, Qingqing Zhao, Yunrui Bai and Shijun Li

Electronics 2019, 8(9), 1006; https://doi.org/10.3390/electronics8091006 - 08 Sep 2019

Cited by 7 | Viewed by 2880

Abstract

Aiming at the problems that current predicting models are incapable of extracting the inner rule of the traffic flow sequence in traffic big data, and unable to make full use of the spatio-temporal relationship of the traffic flow to improve the accuracy of prediction, a Bi-directional Regression Neural Network (BRNN) is proposed in this paper, which can fully apply the context information of road intersections both in the past and the future to predict the traffic volume, and further to make up the deficiency that the current models can only predict the next-moment output according to the time series information in the previous moment. Meanwhile, a vectorized code to screen out the intersections related to the predicting point in the road network and to train and predict through inputting the track data of the selected intersections into BRNN, is designed. In addition, the model is testified through the true traffic data in partial area of Shen Zhen. The results indicate that, compared with current traffic predicting models, the model in this paper is capable of providing the necessary evidence for traffic guidance and control due to its excellent performance in extracting the spatio-temporal feature of the traffic flow series, which can enhance the accuracy by 16.298% on average. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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25 pages, 11682 KiB

Open AccessArticle

Efficiency Analysis and Improvement of an Intelligent Transportation System for the Application in Greenhouse

by Tianfan Zhang, Weiwen Zhou, Fei Meng and Zhe Li

Electronics 2019, 8(9), 946; https://doi.org/10.3390/electronics8090946 - 28 Aug 2019

Cited by 13 | Viewed by 3771

Abstract

In view of the future lack of human resources due to the aging of the population, the automatic, Intelligent Mechatronic Systems (IMSs) and Intelligent Transportation Systems (ITSs) have broad application prospects. However, complex application scenarios and limited open design resources make designing highly efficient ITS systems still a challenging task. In this paper, the optimal load factor solving solution is established. By converting the three user requirements including working distance, time and load into load-related factors, the optimal result can be obtained among system complexity, efficiency and system energy consumption. A specialized visual navigation and motion control system has been proposed to simplify the path planning, navigation and motion control processes and to be accurately calculated in advance, thereby further improving the efficiency of the ITS system. The validity of the efficiency calculation formula and navigation control method proposed in this paper is verified. Under optimal conditions, the actual working mileage is expected to be 99.7%, and the energy consumption is 83.5% of the expected value, which provides sufficient redundancy for the system. In addition, the individual ITS reaches the rated operating efficiency of 95.86%; in other words, one ITS has twice the ability of a single worker. This proves the accuracy and efficiency of the designed ITS system. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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13 pages, 3736 KiB

Open AccessArticle

Decision-Making System for Lane Change Using Deep Reinforcement Learning in Connected and Automated Driving

by HongIl An and Jae-il Jung

Electronics 2019, 8(5), 543; https://doi.org/10.3390/electronics8050543 - 14 May 2019

Cited by 38 | Viewed by 7549

Abstract

Lane changing systems have consistently received attention in the fields of vehicular communication and autonomous vehicles. In this paper, we propose a lane change system that combines deep reinforcement learning and vehicular communication. A host vehicle, trying to change lanes, receives the state information of the host vehicle and a remote vehicle that are both equipped with vehicular communication devices. A deep deterministic policy gradient learning algorithm in the host vehicle determines the high-level action of the host vehicle from the state information. The proposed system learns straight-line driving and collision avoidance actions without vehicle dynamics knowledge. Finally, we consider the update period for the state information from the host and remote vehicles. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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16 pages, 3754 KiB

Open AccessArticle

A Vehicle Type Dependent Car-following Model Based on Naturalistic Driving Study

by Ping Wu, Feng Gao and Keqiang Li

Electronics 2019, 8(4), 453; https://doi.org/10.3390/electronics8040453 - 23 Apr 2019

Cited by 19 | Viewed by 3920

Abstract

In this paper, a car-following model considering the preceding vehicle type is proposed to describe the longitudinal driving behavior closer to reality. Based on the naturalistic driving data sampled in real traffic for more than half a year, the relation between ego vehicle velocity and relative distance was analyzed by a multi-variable Gaussian Mixture model, from which it is found that the driver following behavior is influenced by the type of leading vehicle. Then a Hidden Markov model was designed to identify the vehicle type. This car-following model was trained and tested by using the naturalistic driving data. It can identify the leading vehicle type, i.e., passenger car, bus, and truck, and predict the ego vehicle velocity and relative distance based on a series of limited historical data in real time. The experimental validation results show that the identification accuracy of vehicle type under the static and dynamical conditions are 96.6% and 83.1%, respectively. Furthermore, comparing the results with the well-known collision avoidance model and intelligent driver model show that this new model is more accurate and can be used to design advanced driver assist systems for better adaptability to traffic conditions. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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Review

Jump to: Research

25 pages, 1991 KiB

Open AccessReview

Fleet Management and Control System for Medium-Sized Cities Based in Intelligent Transportation Systems: From Review to Proposal in a City

by Beimar Rojas, Cristhian Bolaños, Ricardo Salazar-Cabrera, Gustavo Ramírez-González, Álvaro Pachón de la Cruz and Juan Manuel Madrid Molina

Electronics 2020, 9(9), 1383; https://doi.org/10.3390/electronics9091383 - 27 Aug 2020

Cited by 13 | Viewed by 6189

Abstract

In medium-sized cities in developing countries, transit services without dedicated lanes have issues related to route compliance, schedules, speed control, and safety. An efficient way for dealing with this issue is the use of Information and Communication Technologies (ICT), to implement a Fleet Management and Control Systems (FMCS). Such implementation can be performed using Intelligent Transportation Systems (ITSs), which allow integration of services and adequate standardization. This article features: (a) a literature review, related to FMCS based on ITS and enabling technologies, (b) design of the ITS architecture of an FMCS, and (c) some advances in the development of the proposed FMCS in a Colombian city (Popayán). The results of the literature review allowed identifying the most important requirements of FMCS in order to design the ITS architecture and build a prototype featuring the suggested technologies. Finally, some experiments were performed to evaluate the operation of the developed prototype. The results showed evidence of adequate operation in sending and receiving messages from and to four prototypes developed for the vehicles, also complying with the established requirements of location, tracking, exchanged data, and security. This allows continuing the development of the proposed FMCS, with some adjustments. Full article

(This article belongs to the Special Issue Intelligent Transportation Systems (ITS))

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