Autonomous Vehicles Technological Trends

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 December 2022) | Viewed by 34597

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Special Issue Editors

Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca Romania, 400114 Cluj-Napoca, Romania
Interests: electric vehicles; electric vehicles; fuel cell vehicles; powertrain concept; electronic control unit; in-vehicle communication network; energy efficiency; autonomous vehicles; computer modeling and simulation in the automotive field
Special Issues, Collections and Topics in MDPI journals
Department of Automotive Engineering and Transports, Technical University of Cluj-Napoca, 400001 Cluj-Napoca, Romania
Interests: electric vehicles; hybrid vehicles; electric vehicle battery, fuel cell vehicles; autonomous vehicles; general powertrain efficiency; OBD diagnostics protocols; powertrain simulation; virtual vehicle testing
Special Issues, Collections and Topics in MDPI journals
IPG Automotive GmbH, Bannwaldallee 60, 76185 Karlsruhe, Germany
Interests: vehicle simulation platforms; ADAS; HIL; ECU control and simulation; vehicle dynamics; virtual vehicle testing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The automotive industry has always gone hand in hand with research and innovation, but nowadays the industry is adding pressure and is establishing the agenda of the researchers from the field. The vision is launched; the hardware and the software exist; the only question remaining is: “who is going to deliver”? To answer this question, we provoke scientists, researchers, and industry specialists together with academics to share their vision of the autonomous vehicle. What will the platform look like, what kind of hardware and software is most suitable, who will make the link and the connection between these two interdependent environments (and how) so that in the end the AI will define the process: all these are the hot themes of the moment and this Special Issue will help all those interested in the topic to promote their vision and ideas. Since the automotive field does not belong to a classic scientific field but has become an independent self-made scientific branch, all those who feel that they can bring their contribution to this highly dynamic environment are requested to join in promoting their particular research or reviews in this Special Issue coordinated from both academia and the industry.

Dr. Calin Iclodean
Prof. Dr. Bogdan Ovidiu Varga
Dr. Felix Pfister
Guest Editors

Manuscript Submission Information

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Keywords

  • autonomous vehicle (AV)
  • autonomous driving systems (ADS)
  • advanced vehicle control
  • driver assistance systems
  • automotive computing platform
  • adaptive AUTOSAR for ADS
  • autonomous vehicular clouds and edges
  • internet of vehicles (IoV)
  • advanced vehicular networks
  • neural networks for ADS
  • V2X communications
  • big data for connected vehicles
  • human-vehicle interface
  • cyber security in autonomous driving
  • 5G/6G applications in autonomous driving
  • smart sensors for AV
  • multi-sensor data fusion
  • multi-sensor data processing
  • AV perception
  • target perception and recognition

Published Papers (12 papers)

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Editorial

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5 pages, 192 KiB  
Editorial
Autonomous Vehicles Technological Trends
Electronics 2023, 12(5), 1149; https://doi.org/10.3390/electronics12051149 - 27 Feb 2023
Viewed by 1004
Abstract
Twenty years ago, only the most adventurous scientist might have been in the position of dreaming up such a dramatic change for the automotive industry, where fossil fuels are in a position of being banned and vehicles are driverless [...] Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)

Research

Jump to: Editorial

16 pages, 8566 KiB  
Article
Neurofuzzy Data Aggregation in a Multisensory System for Self-Driving Car Steering
Electronics 2023, 12(2), 314; https://doi.org/10.3390/electronics12020314 - 07 Jan 2023
Cited by 2 | Viewed by 1151
Abstract
In the self-driving vehicles domain, steering control is a process that transforms information obtained from sensors into commands that steer the vehicle on the road and avoid obstacles. Although a greater number of sensors improves perception and increases control precision, it also increases [...] Read more.
In the self-driving vehicles domain, steering control is a process that transforms information obtained from sensors into commands that steer the vehicle on the road and avoid obstacles. Although a greater number of sensors improves perception and increases control precision, it also increases the computational cost and the number of processes. To reduce the cost and allow data fusion and vehicle control as a single process, this research proposes a data fusion approach by formulating a neurofuzzy aggregation deep learning layer; this approach integrates aggregation using fuzzy measures μ as fuzzy synaptic weights, hidden state using the Choquet fuzzy integral, and a fuzzy backpropagation algorithm, creating a data processing from different sources. In addition, implementing a previous approach, a self-driving neural model is proposed based on the aggregation of a steering control model and another for obstacle detection. This was tested in an ROS simulation environment and in a scale prototype. Experimentation showed that the proposed approach generates an average autonomy of 95% and improves driving smoothness by 9% compared to other state-of-the-art methods. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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15 pages, 3234 KiB  
Article
Design and Analysis of the Trajectory of an Overtaking Maneuver Performed by Autonomous Vehicles Operating with Advanced Driver-Assistance Systems (ADAS) and Driving on a Highway
Electronics 2023, 12(1), 51; https://doi.org/10.3390/electronics12010051 - 23 Dec 2022
Cited by 2 | Viewed by 1902
Abstract
Overtaking is a maneuver that consists of passing another vehicle traveling on the same trajectory, but at a slower speed. Overtaking is considered one of the most dangerous, delicate and complex maneuvers performed by a vehicle, as it requires a quick assessment of [...] Read more.
Overtaking is a maneuver that consists of passing another vehicle traveling on the same trajectory, but at a slower speed. Overtaking is considered one of the most dangerous, delicate and complex maneuvers performed by a vehicle, as it requires a quick assessment of the distance and speed of the vehicle to be overtaken, and also the estimation of the available space for the maneuver. In particular, most drivers have difficulty overtaking a vehicle in the presence of vehicles coming on other trajectories. To solve these overtaking problems, this article proposes a method of performing safe, autonomous-vehicle maneuvers through the PreScan simulation program. In this environment, the overtaking-maneuver scenario (OMS) is composed of highway infrastructure, vehicles and sensors. The proposed OMS is based on the solution of minimizing the risks of collision in the presence of any oncoming vehicle during the overtaking maneuver. It is proven that the overtaking maneuver of an autonomous vehicle is safe to perform through the use of advanced driver-assistance systems (ADAS) such as adaptive cruise control (ACC) and technology-independent sensors (TIS) that detect the driving environment of the maneuver. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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24 pages, 8198 KiB  
Article
PG-Based Vehicle-In-the-Loop Simulation for System Development and Consistency Validation
Electronics 2022, 11(24), 4073; https://doi.org/10.3390/electronics11244073 - 07 Dec 2022
Cited by 3 | Viewed by 1857
Abstract
The concern over safety features in autonomous vehicles is increasing due to the rapid development and increasing use of autonomous driving technology. The safety evaluations performed for an autonomous driving system cannot depend only on existing safety verification methods, due to the lack [...] Read more.
The concern over safety features in autonomous vehicles is increasing due to the rapid development and increasing use of autonomous driving technology. The safety evaluations performed for an autonomous driving system cannot depend only on existing safety verification methods, due to the lack of scenario reproducibility and the dynamic characteristics of the vehicle. Vehicle-In-the-Loop Simulation (VILS) utilizes both real vehicles and virtual simulations for the driving environment to overcome these drawbacks and is a suitable candidate for ensuring reproducibility. However, there may be differences between the behavior of the vehicle in the VILS and vehicle tests due to the implementation level of the virtual environment. This study proposes a novel VILS system that displays consistency with the vehicle tests. The proposed VILS system comprises virtual road generation, synchronization, virtual traffic manager generation, and perception sensor modeling, and implements a virtual driving environment similar to the vehicle test environment. Additionally, the effectiveness of the proposed VILS system and its consistency with the vehicle test is demonstrated using various verification methods. The proposed VILS system can be applied to various speeds, road types, and surrounding environments. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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21 pages, 8429 KiB  
Article
Efficient Deep Reinforcement Learning for Optimal Path Planning
Electronics 2022, 11(21), 3628; https://doi.org/10.3390/electronics11213628 - 07 Nov 2022
Cited by 6 | Viewed by 3460
Abstract
In this paper, we propose a novel deep reinforcement learning (DRL) method for optimal path planning for mobile robots using dynamic programming (DP)-based data collection. The proposed method can overcome the slow learning process and improve training data quality inherently in DRL algorithms. [...] Read more.
In this paper, we propose a novel deep reinforcement learning (DRL) method for optimal path planning for mobile robots using dynamic programming (DP)-based data collection. The proposed method can overcome the slow learning process and improve training data quality inherently in DRL algorithms. The main idea of our approach is as follows. First, we mapped the dynamic programming method to typical optimal path planning problems for mobile robots, and created a new efficient DP-based method to find an exact, analytical, optimal solution for the path planning problem. Then, we used high-quality training data gathered using the DP method for DRL, which greatly improves training data quality and learning efficiency. Next, we established a two-stage reinforcement learning method where, prior to the DRL, we employed extreme learning machines (ELM) to initialize the parameters of actor and critic neural networks to a near-optimal solution in order to significantly improve the learning performance. Finally, we illustrated our method using some typical path planning tasks. The experimental results show that our DRL method can converge much easier and faster than other methods. The resulting action neural network is able to successfully guide robots from any start position in the environment to the goal position while following the optimal path and avoiding collision with obstacles. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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23 pages, 2960 KiB  
Article
Performance Evaluation of VANET Routing Protocols in Madinah City
Electronics 2022, 11(5), 777; https://doi.org/10.3390/electronics11050777 - 02 Mar 2022
Cited by 11 | Viewed by 3713
Abstract
Traffic management challenges in peak seasons for popular destinations such as Madinah city have accelerated the need for and introduction of autonomous vehicles and Vehicular ad hoc networks (VANETs) to assist in communication and alleviation of traffic congestions. The primary goal of this [...] Read more.
Traffic management challenges in peak seasons for popular destinations such as Madinah city have accelerated the need for and introduction of autonomous vehicles and Vehicular ad hoc networks (VANETs) to assist in communication and alleviation of traffic congestions. The primary goal of this study is to evaluate the performance of communication routing protocols in VANETs between autonomous and human-driven vehicles in Madinah city in varying traffic conditions. A simulation of assorted traffic distributions and densities were modeled in an extracted map of Madinah city and then tested in two application scenarios with three ad hoc routing protocols using a combination of traffic and network simulation tools working in tandem. The results measured for the average trip time show that opting for a fully autonomous vehicle scenario reduces the trip time of vehicles by approximately 7.1% in high traffic densities and that the reactive ad hoc routing protocols induce the least delay for network packets to reach neighboring VANET vehicles. From these observations, it can be asserted that autonomous vehicles provide a significant reduction in travel time and that either of the two reactive ad hoc routing protocols could be implemented for the VANET implementation in Madinah city. Furthermore, we perform an ANOVA test to examine the effects of the factors that are considered in our study on the variation of the results. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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15 pages, 16725 KiB  
Article
Microphone Array for Speaker Localization and Identification in Shared Autonomous Vehicles
Electronics 2022, 11(5), 766; https://doi.org/10.3390/electronics11050766 - 02 Mar 2022
Cited by 9 | Viewed by 3044
Abstract
With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With [...] Read more.
With the current technological transformation in the automotive industry, autonomous vehicles are getting closer to the Society of Automative Engineers (SAE) automation level 5. This level corresponds to the full vehicle automation, where the driving system autonomously monitors and navigates the environment. With SAE-level 5, the concept of a Shared Autonomous Vehicle (SAV) will soon become a reality and mainstream. The main purpose of an SAV is to allow unrelated passengers to share an autonomous vehicle without a driver/moderator inside the shared space. However, to ensure their safety and well-being until they reach their final destination, active monitoring of all passengers is required. In this context, this article presents a microphone-based sensor system that is able to localize sound events inside an SAV. The solution is composed of a Micro-Electro-Mechanical System (MEMS) microphone array with a circular geometry connected to an embedded processing platform that resorts to Field-Programmable Gate Array (FPGA) technology to successfully process in the hardware the sound localization algorithms. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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19 pages, 11964 KiB  
Article
A Change of Paradigm for the Design and Reliability Testing of Touch-Based Cabin Controls on the Seats of Self-Driving Cars
Electronics 2022, 11(1), 21; https://doi.org/10.3390/electronics11010021 - 22 Dec 2021
Cited by 4 | Viewed by 2342
Abstract
The current design paradigm of car cabin components assumes seats aligned with the driving direction. All passengers are aligned with the driver that, until recently, was the only element in charge of controlling the vehicle. The new paradigm of self-driving cars eliminates several [...] Read more.
The current design paradigm of car cabin components assumes seats aligned with the driving direction. All passengers are aligned with the driver that, until recently, was the only element in charge of controlling the vehicle. The new paradigm of self-driving cars eliminates several of those requirements, releasing the driver from control duties and creating new opportunities for entertaining the passengers during the trip. This creates the need for controlling functionalities that must be closer to each user, namely on the seat. This work proposes the use of low-cost capacitive touch sensors for controlling car functions, multimedia controls, seat orientation, door windows, and others. In the current work, we have reached a proof of concept that is functional, as shown for several cabin functionalities. The proposed concept can be adopted by current car manufacturers without changing the automobile construction pipeline. It is flexible and can adopt a variety of new functionalities, mostly software-based, added by the manufacturer, or customized by the end-user. Moreover, the newly proposed technology uses a smaller number of plastic parts for producing the component, which implies savings in terms of production cost and energy, while increasing the life cycle of the component. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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22 pages, 4439 KiB  
Article
Deep and Transfer Learning Approaches for Pedestrian Identification and Classification in Autonomous Vehicles
Electronics 2021, 10(24), 3159; https://doi.org/10.3390/electronics10243159 - 18 Dec 2021
Cited by 6 | Viewed by 2200
Abstract
Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in [...] Read more.
Pedestrian detection is at the core of autonomous road vehicle navigation systems as they allow a vehicle to understand where potential hazards lie in the surrounding area and enable it to act in such a way that avoids traffic-accidents, which may result in individuals being harmed. In this work, a review of the convolutional neural networks (CNN) to tackle pedestrian detection is presented. We further present models based on CNN and transfer learning. The CNN model with the VGG-16 architecture is further optimised using the transfer learning approach. This paper demonstrates that the use of image augmentation on training data can yield varying results. In addition, a pre-processing system that can be used to prepare 3D spatial data obtained via LiDAR sensors is proposed. This pre-processing system is able to identify candidate regions that can be put forward for classification, whether that be 3D classification or a combination of 2D and 3D classifications via sensor fusion. We proposed a number of models based on transfer learning and convolutional neural networks and achieved over 98% accuracy with the adaptive transfer learning model. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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25 pages, 16747 KiB  
Article
smartPlastic: Innovative Touch-Based Human-Vehicle Interface Sensors for the Automotive Industry
Electronics 2021, 10(11), 1233; https://doi.org/10.3390/electronics10111233 - 22 May 2021
Cited by 3 | Viewed by 2220
Abstract
Environmental concern regularly leads to the study and improvement of manufacturing processes and the development of new industrial products. The purpose of this work is to optimize the amount of injected plastic and reduce the number of parts used in the production of [...] Read more.
Environmental concern regularly leads to the study and improvement of manufacturing processes and the development of new industrial products. The purpose of this work is to optimize the amount of injected plastic and reduce the number of parts used in the production of entrance panels to control features inside the car cabin. It focuses on a particular case study, namely the control of opening and closing windows and rotation of the rear-view mirrors of a car, maintaining all of the functionality and introducing a futuristic and appealing design inline with new autonomous driving vehicles. For this purpose, distinct low-cost touch sensor technologies were evaluated and the performance of several types of sensors that were integrated with plastic polymers of distinct thickness was analyzed. Discrete sensors coupled to the plastic part were tested and integrated in the injected plastic procedure. In the former, sensitivity tests were performed for finding the maximum plastic thickness detectable by the different sensors. For the latter, experiments were carried out on the sensors subject to very high pressure and temperature inside the molds—the two most relevant characteristics of industrial plastic injection in this context—and functional results were observed later. We conclude that, by changing the way the user interacts with the car cabin, the replacement of conventional mechanical buttons—composed of dozens of parts—by a component consisting of a single plastic part that is associated with conventional low-cost electronics allows the control of a more diversified set of features, including many that are not yet usual in the interior of automobiles today, but that will eventually be required in the near future of autonomous driving, in which the user will interact less with driving and more with other people or services around her/him, namely of the multimedia type. Additionally, the economic factor was considered, namely regarding the cost of the new technology as well as its manufacturing, replacement, and subsequent recycling processes. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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16 pages, 902 KiB  
Article
Lane-Merging Strategy for a Self-Driving Car in Dense Traffic Using the Stackelberg Game Approach
Electronics 2021, 10(8), 894; https://doi.org/10.3390/electronics10080894 - 08 Apr 2021
Cited by 17 | Viewed by 3222
Abstract
This paper presents the lane-merging strategy for self-driving cars in dense traffic using the Stackelberg game approach. From the perspective of the self-driving car, in order to make sufficient space to merge into the next lane, a self-driving car should interact with the [...] Read more.
This paper presents the lane-merging strategy for self-driving cars in dense traffic using the Stackelberg game approach. From the perspective of the self-driving car, in order to make sufficient space to merge into the next lane, a self-driving car should interact with the vehicles in the next lane. In heavy traffic, where the possible actions of the vehicle are pretty limited, it is possible to conjecture the driving intentions of the vehicles from their behaviors. For example, by observing the speed changes of the human-driver in the next lane, the self-driving car can estimate its driving intention in real time, much in the same way of a human driver. We use the principle of Stackelberg competition to make the optimal decision for the self-driving car based on the predicted reaction of the interacting vehicles in the next lane. In this way, according to the traffic circumstances, a self-driving car can decide whether to merge or not. In addition, by limiting the number of interacting vehicles, the computational burden is manageable enough to be implemented in production vehicles. We verify the efficiency of the proposed method through the case studies for different test scenarios, and the test results show that our approach is closer to the human-like decision-making strategy, as compared to the conventional rule-based method. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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17 pages, 821 KiB  
Article
Continuous Automotive Software Updates through Container Image Layers
Electronics 2021, 10(6), 739; https://doi.org/10.3390/electronics10060739 - 20 Mar 2021
Cited by 12 | Viewed by 5620
Abstract
The vehicle-embedded system also known as the electronic control unit (ECU) has transformed the humble motorcar, making it more efficient, environmentally friendly, and safer, but has led to a system which is highly dependent on software. As new technologies and features are included [...] Read more.
The vehicle-embedded system also known as the electronic control unit (ECU) has transformed the humble motorcar, making it more efficient, environmentally friendly, and safer, but has led to a system which is highly dependent on software. As new technologies and features are included with each new vehicle model, the increased reliance on software will no doubt continue. It is an undeniable fact that all software contains bugs, errors, and potential vulnerabilities, which when discovered must be addressed in a timely manner, primarily through patching and updates, to preserve vehicle and occupant safety and integrity. However, current automotive software updating practices are ad hoc at best and often follow the same inefficient fix mechanisms associated with a physical component failure of return or recall. Increasing vehicle connectivity heralds the potential for over the air (OtA) software updates, but rigid ECU hardware design does not often facilitate or enable OtA updating. To address the associated issues regarding automotive ECU-based software updates, a new approach in how automotive software is deployed to the ECU is required. This paper presents how lightweight virtualisation technologies known as containers can promote efficient automotive ECU software updates. ECU functional software can be deployed to a container built from an associated image. Container images promote efficiency in download size and times through layer sharing, similar to ECU difference or delta flashing. Through containers, connectivity and OtA future software updates can be completed without inconveniences to the consumer or incurring expense to the manufacturer. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technological Trends)
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