Special Issue "Autonomous Vehicles Technology"

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

Deadline for manuscript submissions: 31 March 2020.

Special Issue Editors

Prof. Dr. Jose Eugenio Naranjo
E-Mail Website
Guest Editor
School of Computer Science, Universidad Politécnica de Madrid, Campus Sur, Ctra. Valencia km. 7, 28031, Spain
Interests: Intelligent Transportation Systems, Intelligent Vehicles, Electromobility, Vehicular Communications, Autonomous Vehicles
Special Issues and Collections in MDPI journals
Dr. Edgar Talavera Muñoz
E-Mail Website
Guest Editor
Department of Informatic Systems, Universidad Politécnica de Madrid, Madrid 9220, Spain
Interests: VANETs, Cybersecurity, IA, deep learning, machine learning, Map-reduce techniques, Data Bases, ITS, Cooperative Systems
Dr. Alberto Díaz-Álvarez
E-Mail Website
Guest Editor
Universidad Politécnica de Madrid, E.T.S.I. de Ingenieros Informáticos, Ctra. Valencia, km. 7, 28031, Madrid, Spain
Interests: Artificial Intelligence, Deep Learning, Genetic Algorithms, Neural Networks, Fuzzy Logic, Driver Behavior
Prof. Dr. Cristina Olaverri-Monreal
E-Mail Website
Guest Editor
Chair for Sustainable Transport Logistics 4.0, Johannes Kepler University, Linz, Austria
Interests: Sustainable Transportation, Vehicle-to-X communication, Autonomous Driving, Driver Behaviour, Intelligent Transportation Systems

Special Issue Information

Dear Colleagues,

As years go by, self-driving continue to receive high levels of attention, both from academia and industry, not only in terms of components and sensors (e.g. cameras, laser scanners, radars, GPS, etc.), but also in terms of algorithms that use these data to further fine-tune their inferences and predictions. All this with special attention paid to artificial intelligence and big data, present in the vast majority of works.

These technologies cover a wide range of disciplines, from the identification and tracking of elements to the intelligent intervehicular connection, not to mention the optimization of routes, maps, driver behavior, interconnection and communication or traffic safety, among many others.

With this Special Issue we propose to cover the different technologies involved in the area of autonomous vehicles, in order to identify where we stand in terms of complete vehicle autonomy and what the years to come will bring. Thus, the topics of interest include, but are not limited to:

  • Advanced driver assistance systems (ADASs)
  • Artificial and computational intelligence
  • Driver behavior
  • Environment perception
  • Full and partial automatization
  • Sensor fusion techniques
  • Simulation techniques for autonomous driving
  • Special applications of autonomous vehicles
  • State-of-the-art sensors applied to autonomous driving
  • Traffic and flow optimization techniques
  • Vehicles and infrastructure cooperation

Prof. Dr. Jose Eugenio Naranjo
Dr. Edgar Talavera Muñoz
Dr. Alberto Díaz-Álvarez
Prof. Dr. Cristina Olaverri-Monreal
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. Electronics is an international peer-reviewed open access monthly 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 1400 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.

Published Papers (7 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle
Electronics 2020, 9(2), 364; https://doi.org/10.3390/electronics9020364 - 21 Feb 2020
Abstract
This paper describes a low-cost flight control system of a small aileron-less hand-launched solar-powered unmanned aerial vehicle (UAV). In order to improve the accuracy of the whole system model and quantify the influence of each subsystem, detailed modeling of UAV energy and a [...] Read more.
This paper describes a low-cost flight control system of a small aileron-less hand-launched solar-powered unmanned aerial vehicle (UAV). In order to improve the accuracy of the whole system model and quantify the influence of each subsystem, detailed modeling of UAV energy and a control system including a solar model, engine, energy storage, sensors, state estimation, control law, and actuator module are established in accordance with the experiment and component principles. A whole system numerical simulation combined with the 6 degree-of-freedom (DOF) simulation model is constructed based on the typical mission route, and the parameter precision sequence and energy balance are obtained. Then, a hardware-in-the-loop (HIL) experiment scheme based on the Stewart platform (SP) is proposed, and three modes of acceleration, angular velocity, and attitude are designed to verify the control system through the inner and boundary states of the flight envelope. The whole system scheme is verified by flight tests at different altitudes, and the aerodynamic force coefficient and sensor error are corrected by flight data. With the increase of altitude, the cruise power increases from 47 W to 78 W, the trajectory tracking precision increases from 23 m to 44 m, the sensor measurement noise increases, and the bias decreases. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
Show Figures

Figure 1

Open AccessArticle
An Urban Autodriving Algorithm Based on a Sensor-Weighted Integration Field with Deep Learning
Electronics 2020, 9(1), 158; https://doi.org/10.3390/electronics9010158 - 15 Jan 2020
Abstract
This paper proposes two algorithms for adaptive driving in urban environments: the first uses vision deep learning, which is named the sparse spatial convolutional neural network (SSCNN); and the second uses a sensor integration algorithm, named the sensor-weighted integration field (SWIF). These algorithms [...] Read more.
This paper proposes two algorithms for adaptive driving in urban environments: the first uses vision deep learning, which is named the sparse spatial convolutional neural network (SSCNN); and the second uses a sensor integration algorithm, named the sensor-weighted integration field (SWIF). These algorithms utilize three kinds of sensors, namely vision, Light Detection and Range (LiDAR), and GPS sensors, and decide critical motions for autonomous vehicle, such as steering angles and vehicle speed. SSCNN, which is used for lane recognition, has 2.7 times faster processing speed than the existing spatial CNN method. Additionally, the dataset for SSCNN was constructed by considering both normal and abnormal driving in 7 classes. Thus, lanes can be recognized by extending lanes for special characteristics in urban settings, in which the lanes can be obscured or erased, or the vehicle can drive in any direction. SWIF generates a two-dimensional matrix, in which elements are weighted by integrating both the object data from LiDAR and waypoints from GPS based on detected lanes. These weights are the integers, indicating the degree of safety. Based on the field formed by SWIF, the safe trajectories for two vehicles’ motions, steering angles, and vehicle speed are generated by applying the cost field. Additionally, to flexibly follow the desired steering angle and vehicle speed, the Proportional-Integral-Differential (PID) control is moderated by an integral anti-windup scheme. Consequently, as the dataset considers characteristics of the urban environment, SSCNN is able to be adopted for lane recognition on urban roads. The SWIF algorithm is also useful for flexible driving owing to the high efficiency of its sensor integration, including having a resolution of 2 cm per pixel and speed of 24 fps. Thus, a vehicle can be successfully maneuvered with minimized steering angle change, without lane or route departure, and without obstacle collision in the presence of diverse disturbances in urban road conditions. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
Show Figures

Figure 1

Open AccessArticle
Autonomous Driving in Roundabout Maneuvers Using Reinforcement Learning with Q-Learning
Electronics 2019, 8(12), 1536; https://doi.org/10.3390/electronics8121536 - 13 Dec 2019
Abstract
Navigating roundabouts is a complex driving scenario for both manual and autonomous vehicles. This paper proposes an approach based on the use of the Q-learning algorithm to train an autonomous vehicle agent to learn how to appropriately navigate roundabouts. The proposed learning algorithm [...] Read more.
Navigating roundabouts is a complex driving scenario for both manual and autonomous vehicles. This paper proposes an approach based on the use of the Q-learning algorithm to train an autonomous vehicle agent to learn how to appropriately navigate roundabouts. The proposed learning algorithm is implemented using the CARLA simulation environment. Several simulations are performed to train the algorithm in two scenarios: navigating a roundabout with and without surrounding traffic. The results illustrate that the Q-learning-algorithm-based vehicle agent is able to learn smooth and efficient driving to perform maneuvers within roundabouts. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
Show Figures

Figure 1

Open AccessArticle
FPGA-Based Mechatronic Design and Real-Time Fuzzy Control with Computational Intelligence Optimization for Omni-Mecanum-Wheeled Autonomous Vehicles
Electronics 2019, 8(11), 1328; https://doi.org/10.3390/electronics8111328 - 11 Nov 2019
Abstract
This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence [...] Read more.
This study presents a field-programmable gate array (FPGA)-based mechatronic design and real-time fuzzy control method with computational intelligence optimization for omni-Mecanum-wheeled autonomous vehicles. With the advantages of cuckoo search (CS), an evolutionary CS-based fuzzy system is proposed, called CS-fuzzy. The CS’s computational intelligence was employed to optimize the structure of fuzzy systems. The proposed CS-fuzzy computing scheme was then applied to design an optimal real-time control method for omni-Mecanum-wheeled autonomous vehicles with four wheels. Both vehicle model and CS-fuzzy optimization are considered to achieve intelligent tracking control of Mecanum mobile vehicles. The control parameters of the Mecanum fuzzy controller are online-adjusted to provide real-time capability. This methodology outperforms the traditional offline-tuned controllers without computational intelligences in terms of real-time control, performance, intelligent control and evolutionary optimization. The mechatronic design of the experimental CS-fuzzy based autonomous mobile vehicle was developed using FPGA realization. Some experimental results and comparative analysis are discussed to examine the effectiveness, performance, and merit of the proposed methods against other existing approaches. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
Show Figures

Figure 1

Open AccessArticle
Design and Implementation Procedure for an Advanced Driver Assistance System Based on an Open Source AUTOSAR
Electronics 2019, 8(9), 1025; https://doi.org/10.3390/electronics8091025 - 12 Sep 2019
Cited by 1
Abstract
In this paper, we present the detailed design and implementation procedures for an advanced driver assistance system (ADAS) based on an open source automotive open system architecture (AUTOSAR). Due to the increasing software complexity of ADAS, portability, component interoperability, and maintenance are becoming [...] Read more.
In this paper, we present the detailed design and implementation procedures for an advanced driver assistance system (ADAS) based on an open source automotive open system architecture (AUTOSAR). Due to the increasing software complexity of ADAS, portability, component interoperability, and maintenance are becoming essential development factors. AUTOSAR satisfies these demands by defining system architecture standards. Although commercial distributions of AUTOSAR are well established, accessibility is a huge concern as they come with very expensive licensing fees. Open source AUTOSAR addresses this issue and can also minimize the overall cost of development. However, the development procedure has not been well established and could be difficult for engineers. The contribution of this paper is divided into two main parts: First, we provide the complete details on developing a collision warning system using an open source AUTOSAR. This includes the simplified basic concepts of AUTOSAR, which we have organized for easier understanding. Also, we present an improvement of the existing AUTOSAR development methodology focusing on defining the underlying tools at each development stage with clarity. Second, as the performance of open source software has not been proven and requires benchmarking to ensure the stability of the system, we propose various evaluation methods measuring the real-time performance of tasks and the overall latency of the communication stack. These performance metrics are relevant to confirm whether the entire system has deterministic behavior and responsiveness. The evaluation results can help developers to improve the overall safety of the vehicular system. Experiments are conducted on an AUTOSAR evaluation kit integrated with our self-developed collision warning system. The procedures and evaluation methods are applicable not only on detecting obstacles but other variants of ADAS and are very useful in integrating open source AUTOSAR to various vehicular applications. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
Show Figures

Figure 1

Open AccessArticle
Preceding Vehicle Detection Using Faster R-CNN Based on Speed Classification Random Anchor and Q-Square Penalty Coefficient
Electronics 2019, 8(9), 1024; https://doi.org/10.3390/electronics8091024 - 12 Sep 2019
Cited by 1
Abstract
At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host [...] Read more.
At present, preceding vehicle detection remains a challenging problem for autonomous vehicle technologies. In recent years, deep learning has been shown to be successful for vehicle detection, such as the faster region with a convolutional neural network (Faster R-CNN). However, when the host vehicle speed increases or there is an occlusion in front, the performance of the Faster R-CNN algorithm usually degrades. To obtain better performance on preceding vehicle detection when the speed of the host vehicle changes, a speed classification random anchor (SCRA) method is proposed. The reasons for degraded detection accuracy when the host vehicle speed increases are analyzed, and the factor of vehicle speed is introduced to redesign the anchors. Redesigned anchors can adapt to changes of the preceding vehicle size rule when the host vehicle speed increases. Furthermore, to achieve better performance on occluded vehicles, a Q-square penalty coefficient (Q-SPC) method is proposed to optimize the Faster R-CNN algorithm. The experimental validation results show that compared with the Faster R-CNN algorithm, the SCRA and Q-SPC methods have certain significance for improving preceding vehicle detection accuracy. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
Show Figures

Figure 1

Open AccessArticle
Large-Scale Outdoor SLAM Based on 2D Lidar
Electronics 2019, 8(6), 613; https://doi.org/10.3390/electronics8060613 - 31 May 2019
Cited by 3
Abstract
For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve the accuracy and robustness of the [...] Read more.
For autonomous driving, it is important to navigate in an unknown environment. In this paper, we propose a fully automated 2D simultaneous localization and mapping (SLAM) system based on lidar working in large-scale outdoor environments. To improve the accuracy and robustness of the scan matching module, an improved Correlative Scan Matching (CSM) algorithm is proposed. For efficient place recognition, we design an AdaBoost based loop closure detection algorithm which can efficiently reject false loop closures. For the SLAM back-end, we propose a light-weight graph optimization algorithm based on incremental smoothing and mapping (iSAM). The performance of our system is verified on various large-scale datasets including our real-world datasets and the KITTI odometry benchmark. Further comparisons to the state-of-the-art approaches indicate that our system is competitive with established techniques. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
Show Figures

Figure 1

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Could pairs of multi-focal, gaze-controllable ‘Eyes’ be optimal for vehicles too?

Ernst D. Dickmanns

Abstract: Starting from the differences between well-developed eyes in vertebrate biological vision systems and actually observed technical vision systems for vehicles, the question is discussed, whether the predominant solution in biological vision systems, namely pairs of multi-focal, gaze-controllable eyes, could be a useful or even optimal solution for vehicles too. One potential candidate, briefly proposed two years ago, is analyzed in more detail and extended. Increasingly general realizations of these types of systems may take the full 21st century. Such systems are seen as mandatory, if human performance levels in dynamic real-time vision and scene understanding is the goal. The big challenge for systems with the capability of learning will be more on the software side than on the hardware for sensing and computing.

Back to TopTop