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: closed (31 December 2020).

Special Issue Editors

Prof. Dr. Jose Eugenio Naranjo
E-Mail Website
Guest Editor
Instituto Universitario de Investigación del Automóvil (INSIA), Universidad Politécnica de Madrid, 28040 Madrid, Spain
Interests: Connected and Autonomous Driving; Intelligent Transport Systems; Electromobility; Cooperative Services; Electromobility; Vehicular Communications
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
E.T.S.I. of Computer Engineers, Polytechnic University of Madrid, Ctra. Valencia, km. 7, 28031 Madrid, Spain
Interests: artificial intelligence; deep learning; genetic algorithms; neural networks; fuzzy logic; driver behavior
Special Issues and Collections in MDPI journals
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

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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.

Published Papers (31 papers)

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Open AccessArticle
A Lightweight Motional Object Behavior Prediction System Harnessing Deep Learning Technology for Embedded ADAS Applications
Electronics 2021, 10(6), 692; https://doi.org/10.3390/electronics10060692 - 16 Mar 2021
Viewed by 332
Abstract
This paper proposes a lightweight moving object prediction system to detect and recognize pedestrian crossings, vehicles cutting-in, and vehicles ahead applying emergency brakes based on a 3D Convolution network for behavior prediction. The proposed design significantly improves the performance of the conventional 3D [...] Read more.
This paper proposes a lightweight moving object prediction system to detect and recognize pedestrian crossings, vehicles cutting-in, and vehicles ahead applying emergency brakes based on a 3D Convolution network for behavior prediction. The proposed design significantly improves the performance of the conventional 3D convolution network (C3D) adapted to predict the behaviors employing behavior recognition network capable of performing object localization, which is pivotal in detecting the numerous moving objects’ behaviors, combining and verifying the detected objects with the results of the YOLO v3 detection model with that of the proposed C3D model. Since the proposed system is a lightweight CNN model requiring far lesser parameters, it can be efficiently realized on an embedded system for real-time applications. The proposed lightweight C3D model achieves 10 frames per second (FPS) on a NVIDIA Jetson AGX Xavier and yields over 92.8% accuracy in recognizing pedestrian crossing, over 94.3% accuracy in detecting vehicle cutting-in behavior, and over 95% accuracy for vehicles applying emergency brakes. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
State Estimation for Cooperative Lateral Vehicle Following Using Vehicle-to-Vehicle Communication
Electronics 2021, 10(6), 651; https://doi.org/10.3390/electronics10060651 - 11 Mar 2021
Cited by 1 | Viewed by 285
Abstract
A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to [...] Read more.
A cooperative state estimation framework for automated vehicle applications is presented and demonstrated via simulations, the estimation framework is used to estimate the state of a lead and following vehicle simultaneously. Recent developments in the field of cooperative driving require novel techniques to ensure accurate and stable vehicle following behavior. Control schemes for the cooperative control of longitudinal and lateral vehicle dynamics generally require vehicle state information about the lead vehicle, which in some cases cannot be accurately measured. Including vehicle-to-vehicle communication in the state estimation process can provide the required input signals for the practical implementation of cooperative control schemes. This study is focused on demonstrating the benefits of using vehicle-to-vehicle communication in the state estimation of a lead and following vehicle via simulations. The state estimator, which uses a cascaded Kalman filtering process, takes the operating frequencies of different sensors into account in the estimation process. Simulation results of three different driving scenarios demonstrate the benefits of using vehicle-to-vehicle communication as well as the attenuation of measurement noise. Furthermore, in contrast to relying on low frequency measurement data for the input signals of cooperative control schemes, the state estimator provides a state estimate at every sample. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Machine Learning-Based Vehicle Trajectory Prediction Using V2V Communications and On-Board Sensors
Electronics 2021, 10(4), 420; https://doi.org/10.3390/electronics10040420 - 09 Feb 2021
Viewed by 466
Abstract
Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models using machine learning algorithms [...] Read more.
Predicting the trajectories of surrounding vehicles is important to avoid or mitigate collision with traffic participants. However, due to limited past information and the uncertainty in future driving maneuvers, trajectory prediction is a challenging task. Recently, trajectory prediction models using machine learning algorithms have been addressed solve to this problem. In this paper, we present a trajectory prediction method based on the random forest (RF) algorithm and the long short term memory (LSTM) encoder-decoder architecture. An occupancy grid map is first defined for the region surrounding the target vehicle, and then the row and the column that will be occupied by the target vehicle at future time steps are determined using the RF algorithm and the LSTM encoder-decoder architecture, respectively. For the collection of training data, the test vehicle was equipped with a camera and LIDAR sensors along with vehicular wireless communication devices, and the experiments were conducted under various driving scenarios. The vehicle test results demonstrate that the proposed method provides more robust trajectory prediction compared with existing trajectory prediction methods. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Deadlock-Free Planner for Occluded Intersections Using Estimated Visibility of Hidden Vehicles
Electronics 2021, 10(4), 411; https://doi.org/10.3390/electronics10040411 - 08 Feb 2021
Viewed by 368
Abstract
A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped [...] Read more.
A common approach used for planning blind intersection crossings is to assume that hypothetical vehicles are approaching the intersection at a constant speed from the occluded areas. Such an assumption can result in a deadlock problem, causing the ego vehicle to remain stopped at an intersection indefinitely due to insufficient visibility. To solve this problem and facilitate safe, deadlock-free intersection crossing, we propose a blind intersection planner that utilizes both the ego vehicle and the approaching vehicle’s visibility. The planner uses a particle filter and our proposed visibility-dependent behavior model of approaching vehicles for predicting hidden vehicles. The behavior model is designed based on an analysis of actual driving data from multiple drivers crossing blind intersections. The proposed planner was tested in a simulation and found to be effective for allowing deadlock-free crossings at intersections where a baseline planner became stuck in a deadlock. The effects of perception accuracy and sensor position on output motion were also investigated. It was found that the proposed planner delayed crossing motion when the perception was imperfect. Furthermore, our results showed that the planner decelerated less while crossing the intersection with the front-mounted sensor configuration compared to the roof-mounted configuration due to the improved visibility. The minimum speed difference between the two sensor configurations was 1.82 m/s at an intersection with relatively poor visibility and 1.50 m/s at an intersection with good visibility. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
A Study on Re-Engagement and Stabilization Time on Take-Over Transition in a Highly Automated Driving System
Electronics 2021, 10(3), 344; https://doi.org/10.3390/electronics10030344 - 01 Feb 2021
Viewed by 472
Abstract
In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this [...] Read more.
In the case of level 3 automated vehicles, in order to safely and quickly transfer control authority rights to manual driving, it is necessary that a study be conducted on the characteristics of human factors affecting the transition of manual driving. In this study, we conducted three experiments to compare the characteristics of human factors that influence the driver’s quality of response when re-engaging and stabilizing manual driving. The three experiments were conducted sequentially by dividing them into a normal driving situation, an obstacle occurrence situation in front, and an obstacle and congestion on surrounding roads. We performed a statistical analysis and classification and regression tree (CART) analysis using experimental data. We found that as the number of trials increased, there was a learning effect that shortened re-engagement times and increased the proportion of drivers with good response times. We found that the stabilization time increased as the experiment progressed, as obstacles appeared in front and traffic density increased in the surrounding lanes. The results of the analysis are useful for vehicle developers designing safer human–machine interfaces and for governments developing guidelines for automated driving systems. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Ultra-Low Power and High-Throughput SRAM Design to Enhance AI Computing Ability in Autonomous Vehicles
Electronics 2021, 10(3), 256; https://doi.org/10.3390/electronics10030256 - 22 Jan 2021
Viewed by 376
Abstract
Power consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI) applications, such as autonomous vehicles and Internet of Things (IoT). Existing state-of-the-art SRAM architectures for AI computing are highly accurate and can provide [...] Read more.
Power consumption and data processing speed of integrated circuits (ICs) is an increasing concern in many emerging Artificial Intelligence (AI) applications, such as autonomous vehicles and Internet of Things (IoT). Existing state-of-the-art SRAM architectures for AI computing are highly accurate and can provide high throughput. However, these SRAMs have problems that they consume high power and occupy a large area to accommodate complex AI models. A carbon nanotube field-effect transistors (CNFET) device has been reported as a potential candidates for AI devices requiring ultra-low power and high-throughput due to their satisfactory carrier mobility and symmetrical, good subthreshold electrical performance. Based on the CNFET and FinFET device’s electrical performance, we propose novel ultra-low power and high-throughput 8T SRAMs to circumvent the power and the throughput issues in Artificial Intelligent (AI) computation for autonomous vehicles. We propose two types of novel 8T SRAMs, P-Latch N-Access (PLNA) 8T SRAM structure and single-ended (SE) 8T SRAM structure, and compare the performance with existing state-of-the-art 8T SRAM architectures in terms of power consumption and speed. In the SRAM circuits of the FinFET and CNFET, higher tube and fin numbers lead to higher operating speed. However, the large number of tubes and fins can lead to larger area and more power consumption. Therefore, we optimize the area by reducing the number of tubes and fins without compromising the memory circuit speed and power. Most importantly, the decoupled reading and writing of our new SRAMs cell offers better low-power operation due to the stacking of device in the reading part, as well as achieving better readability and writability, while offering read Static Noise Margin (SNM) free because of isolated reading path, writing path, and greater pull up ratio. In addition, the proposed 8T SRAMs show even better performance in delay and power when we combine them with the collaborated voltage sense amplifier and independent read component. The proposed PLNA 8T SRAM can save 96%, while the proposed SE 8T SRAM saves around 99% in writing power consumption compared with the existing state-of-the-art 8T SRAM in FinFET model, as well as 99% for writing operation in CNFET model. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Ego-Motion Estimation Using Recurrent Convolutional Neural Networks through Optical Flow Learning
Electronics 2021, 10(3), 222; https://doi.org/10.3390/electronics10030222 - 20 Jan 2021
Viewed by 383
Abstract
Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel [...] Read more.
Visual odometry (VO) refers to incremental estimation of the motion state of an agent (e.g., vehicle and robot) by using image information, and is a key component of modern localization and navigation systems. Addressing the monocular VO problem, this paper presents a novel end-to-end network for estimation of camera ego-motion. The network learns the latent subspace of optical flow (OF) and models sequential dynamics so that the motion estimation is constrained by the relations between sequential images. We compute the OF field of consecutive images and extract the latent OF representation in a self-encoding manner. A Recurrent Neural Network is then followed to examine the OF changes, i.e., to conduct sequential learning. The extracted sequential OF subspace is used to compute the regression of the 6-dimensional pose vector. We derive three models with different network structures and different training schemes: LS-CNN-VO, LS-AE-VO, and LS-RCNN-VO. Particularly, we separately train the encoder in an unsupervised manner. By this means, we avoid non-convergence during the training of the whole network and allow more generalized and effective feature representation. Substantial experiments have been conducted on KITTI and Malaga datasets, and the results demonstrate that our LS-RCNN-VO outperforms the existing learning-based VO approaches. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Design and Reliability Analysis of a Tunnel-Detection AUV Based on a Heterogeneous Dual CPU Hot Redundancy System
Electronics 2021, 10(1), 22; https://doi.org/10.3390/electronics10010022 - 25 Dec 2020
Viewed by 437
Abstract
A water conveyance tunnel is narrow and enclosed with a complex distribution of flow field. The performance of sensors such as Doppler log, magnetic compass, sonar, and depth gauge used by conventional underwater vehicles in the tunnel is greatly affected and can even [...] Read more.
A water conveyance tunnel is narrow and enclosed with a complex distribution of flow field. The performance of sensors such as Doppler log, magnetic compass, sonar, and depth gauge used by conventional underwater vehicles in the tunnel is greatly affected and can even fail. Aiming at the special operating environment and operational requirements of water conveyance tunnels, this paper designed an architecture suitable for pressurized water conveyance tunnel-detection autonomous underwater vehicles (AUVs). The tunnel-detection AUV (called AUV-T in this paper) with the architecture proposed in this paper could easily and smoothly complete inspection tasks in water conveyance tunnels, and field tests have verified the effectiveness of the architecture. Since an AUV in a water conveyance tunnel cannot go to the surface to rescue itself, in order to ensure its safety we designed the heterogeneous dual-CPU (Central Processing Unit) hot redundancy system based on dual communication lines. The reliability analysis showed that the system can significantly reduce the probability of AUV failure and ensure that the AUV can still be recovered even if it fails in the tunnel. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Calibration Venus: An Interactive Camera Calibration Method Based on Search Algorithm and Pose Decomposition
Electronics 2020, 9(12), 2170; https://doi.org/10.3390/electronics9122170 - 17 Dec 2020
Viewed by 452
Abstract
Cameras are widely used in many scenes such as robot positioning and unmanned driving, in which the camera calibration is a major task in this field. The interactive camera calibration method based on a plane board is becoming popular due to its stability [...] Read more.
Cameras are widely used in many scenes such as robot positioning and unmanned driving, in which the camera calibration is a major task in this field. The interactive camera calibration method based on a plane board is becoming popular due to its stability and handleability. However, most methods choose suggestions subjectively from a fixed pose dataset, which is error-prone and limited for different camera models. In addition, these methods do not provide clear guidelines on how to place the board in the specified pose. This paper proposes a new interactive calibration method, named ‘Calibration Venus’, including two main parts: pose search and pose decomposition. First, a pose search algorithm based on simulated annealing (SA) algorithm is proposed to select the optimal pose in the entire pose space. Second, an intuitive and easy-to-use user guidance method is designed to decompose the optimal pose into four sub-poses: translation, each rotation along X-, Y-, Z-axes. Thereby the users could follow the guide step by step to accurately complete the placement of the calibration board. Experimental results evaluated on simulated and real datasets show that the proposed method can reduce the difficulty of calibration, and improve the accuracy of calibration, as well as provide better guidance. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
A Low-Cost Platform for Modeling and Controlling the Yaw Dynamics of an Agricultural Tractor to Gain Autonomy
Electronics 2020, 9(11), 1826; https://doi.org/10.3390/electronics9111826 - 02 Nov 2020
Cited by 1 | Viewed by 709
Abstract
In this study, a low-cost proposed platform for training dynamics (PPTD) is proposed based on operational amplifiers to understand the dynamics and variables of the agricultural tractor John Deere tractor model 4430 to gain autonomy and analyze the behavior of control algorithms proposed [...] Read more.
In this study, a low-cost proposed platform for training dynamics (PPTD) is proposed based on operational amplifiers to understand the dynamics and variables of the agricultural tractor John Deere tractor model 4430 to gain autonomy and analyze the behavior of control algorithms proposed in real time by state feedback. The proposed platform uses commercial sensors and interacts with the Arduino Uno and/or Daq-6009 board from National Instruments. A mobile application (APP) was also developed for real-time monitoring of autonomous control signals, the local reference system, and physical and dynamic variables in the tractor; this platform can be used as a mobile alternative applied to a tractor in physically installed form. In the presented case, the PPTD was mounted on a John Deere tractor to test its behavior; moreover, it may be used on other tractor models similarly as established here. The established results of this platform were compared with models established in MATLAB, validating the proposal. All simulations and developments are shared through a web-link as open-source files so that anyone with basic knowledge of electronics and modeling of vehicles can reproduce the proposed platform. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Lateral-Acceleration-Based Vehicle-Models-Blending for Automated Driving Controllers
Electronics 2020, 9(10), 1674; https://doi.org/10.3390/electronics9101674 - 13 Oct 2020
Cited by 1 | Viewed by 515
Abstract
Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is [...] Read more.
Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral forces that differentiate kinematic and dynamic models, is a more appropriate model-switching enabler than the currently used longitudinal velocity. Moreover, the advanced procedure to define blending parameters is shown to be effective. Finally, a smooth blending method offers better tracking results versus sudden model switching ones and non-blending techniques. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessEditor’s ChoiceArticle
Integrating Driving Hardware-in-the-Loop Simulator with Large-Scale VANET Simulator for Evaluation of Cooperative Eco-Driving System
Electronics 2020, 9(10), 1645; https://doi.org/10.3390/electronics9101645 - 08 Oct 2020
Viewed by 647
Abstract
Recent advances in information and communication technology (ICT) have enabled interaction and cooperation between components of the transportation system, and cooperative eco-driving systems that apply ICT to eco-driving systems are receiving significant attention. A cooperative eco-driving system is a complex system that requires [...] Read more.
Recent advances in information and communication technology (ICT) have enabled interaction and cooperation between components of the transportation system, and cooperative eco-driving systems that apply ICT to eco-driving systems are receiving significant attention. A cooperative eco-driving system is a complex system that requires consideration of the electronic control unit (ECU) and vehicle-to-everything (V2X) communication. To evaluate these complex systems, it is needed to integrate simulators with expertise. Therefore, this study presents an integrated driving hardware-in-the-loop (IDHIL) simulator for the testing and evaluation of cooperative eco-driving systems. The IDHIL simulator is implemented by integrating the driving hardware-in-the-loop simulator and a vehicular ad hoc network simulator to develop and evaluate a hybrid control unit and cooperative eco-driving application for the connected hybrid electric vehicle (CHEV). A cooperative eco-driving speed guidance application is utilized to demonstrate the use of our simulator. The results of the evaluation show the improved fuel efficiency of the CHEV through a calculation of the optimal speed profile and the optimal distribution of power based on V2X communication. Finally, this paper concludes with a description of future directions for the testing and evaluation of cooperative eco-driving systems. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Power Supply Platform and Functional Safety Concept Proposals for a Powertrain Transmission Electronic Control Unit
Electronics 2020, 9(10), 1580; https://doi.org/10.3390/electronics9101580 - 27 Sep 2020
Viewed by 540
Abstract
In the last decade, modern vehicles have become very complex, being equipped with embedded electronic systems which include more than a thousand of electronic control units (ECUs). Therefore, it is mandatory to analyze the potential risk of automotive systems failure because it could [...] Read more.
In the last decade, modern vehicles have become very complex, being equipped with embedded electronic systems which include more than a thousand of electronic control units (ECUs). Therefore, it is mandatory to analyze the potential risk of automotive systems failure because it could have a significant impact on humans’ safety. This paper proposes a novel, functional safety concept at the power management level of a system basis chip (SBC), from the development phase to system design. In the presented case, the safety-critical application is represented by a powertrain transmission electronic control unit. A step-by-step design guideline procedure is presented, having as a focus the cost, safety, and performance to obtain a robust, cost-efficient, safe, and reliable design. To prove compliance with the ISO 26262 standard, quantitative worst-case evaluations of the hardware have been done. The assessment results qualify the proposed design with automotive safety integrity levels (ASIL, up to ASIL-D). The main contribution of this paper is to demonstrate how to apply the functional safety concept to a real, safety-critical system by following the proposed design methodology. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
UAV-Assisted Hybrid Scheme for Urban Road Safety Based on VANETs
Electronics 2020, 9(9), 1499; https://doi.org/10.3390/electronics9091499 - 12 Sep 2020
Cited by 1 | Viewed by 1089
Abstract
Traffic congestion control is becoming a popular field of research due to the rapid development of the automotive market. Vehicular ad hoc networks (VANETs) have become the core research technology for numerous application possibilities related to road safety. Road congestions have been a [...] Read more.
Traffic congestion control is becoming a popular field of research due to the rapid development of the automotive market. Vehicular ad hoc networks (VANETs) have become the core research technology for numerous application possibilities related to road safety. Road congestions have been a serious issue of all time since the nodes have high mobility and transmission range is limited, resulting in an interruption of communication. One of the significant technical challenges faced in implementing VANET is the design of the routing protocol, providing adequate information and a reliable source for the destination. We proposed a novel mechanism unmanned aerial vehicle (UAV)-assisted ad hoc on-demand distance vector (AODV) routing protocol (UAVa) for current-time traffic information accumulation and sharing to the entire traffic network and to control congestions before it happens. The UAV-assisted (UAVa) protocol is dedicated to urban environments, and its primary goal is to enhance the performance of routing protocols based on intersections. We compared the overall performance of existing routing protocols, namely ad hoc on-demand distance vector. The simulations were done by using OpenStreetMap (OSM), Network Simulator (NS-2.35), Simulation of Urban Mobility (SUMO), and VanetMobiSim. Furthermore, we compared the simulation results with AODV, and it shows that UAV-assisted (UAVa) AODV has significantly enhanced the packet delivery ratio, reduced the end-to-end delay, improved the average and instant throughput, and saved more energy. The results show that the UAVa is more robust and effective and we can conclude that UAVa is more suitable for VANETs. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Predictive Control Using Active Aerodynamic Surfaces to Improve Ride Quality of a Vehicle
Electronics 2020, 9(9), 1463; https://doi.org/10.3390/electronics9091463 - 07 Sep 2020
Viewed by 559
Abstract
This work presents a predictive control strategy for a four degrees of freedom (DOF) half-car model in the presence of active aerodynamic surfaces. The proposed control strategy consists of two parts: the feedback control deals with the tracking error while the feedforward control [...] Read more.
This work presents a predictive control strategy for a four degrees of freedom (DOF) half-car model in the presence of active aerodynamic surfaces. The proposed control strategy consists of two parts: the feedback control deals with the tracking error while the feedforward control handles the anticipated road disturbance and ensures the desired maneuvering. The desired roll and pitch angles are obtained by using disturbance, vehicle speed and radius of curvature. The proposed approach helps the vehicle to achieve better ride comfort by suppressing the amplitude of vibrations occurring in the vertical motion of the vehicle body, and enhances the road-holding capability by overcoming the amplitude of vibrations in tyre deflection. The control strategy also cancels out the hypothetical forces acting on the vehicle body to help the vehicle to track the desired attitude motion without compromising the ride comfort and road-holding capability. The simulations results show that the proposed control strategy successfully reduces the root mean square error (RMSE) values of sprung mass acceleration as well as tyre deflection. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Hydraulic Pressure-Flow Rate Control of a Pallet Handling Robot for an Autonomous Freight Delivery Vehicle
Electronics 2020, 9(9), 1370; https://doi.org/10.3390/electronics9091370 - 24 Aug 2020
Cited by 1 | Viewed by 769
Abstract
The current paper presents an upgrade of a pre-installed hydraulic system for the operation of a pallet handling robot for a freight delivery vehicle known as FURBOT (freight urban robotic vehicle). The automated forklift installed on FURBOT for loading/unloading of cargo is powered [...] Read more.
The current paper presents an upgrade of a pre-installed hydraulic system for the operation of a pallet handling robot for a freight delivery vehicle known as FURBOT (freight urban robotic vehicle). The automated forklift installed on FURBOT for loading/unloading of cargo is powered with the help of hydraulics. The previous hydraulic system worked via a classical approach with a fixed displacement pump and a bypass valve, making it work on full power when in use. An alternative design was proposed, simulated and installed on FURBOT; it uses a fixed displacement pump and changes the rotation speed in real time using a pressure sensor. Novelty was attained with the use of gear pumps for said scenario. A control algorithm is implemented in the processing unit for controlling the speed of the motor driving the pump. The main advantage of this approach is better use of energy for the vehicle’s battery. The aim of this research is to control both the speed and maximum force exerted by the actuators with the help of a single sensor and an inexpensive pump. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Battery Energy Management of Autonomous Electric Vehicles Using Computationally Inexpensive Model Predictive Control
Electronics 2020, 9(8), 1277; https://doi.org/10.3390/electronics9081277 - 09 Aug 2020
Cited by 1 | Viewed by 887
Abstract
With the emergence of vehicle-communication technologies, many researchers have strongly focused their interest in vehicle energy-efficiency control using this connectivity. For instance, the exploitation of preview traffic enables the vehicle to plan its speed and position trajectories given a prediction horizon so that [...] Read more.
With the emergence of vehicle-communication technologies, many researchers have strongly focused their interest in vehicle energy-efficiency control using this connectivity. For instance, the exploitation of preview traffic enables the vehicle to plan its speed and position trajectories given a prediction horizon so that energy consumption is minimized. To handle the strong uncertainties in the traffic model in the future, a constrained controller is generally employed in the existing researches. However, its expensive computational feature largely prevents its commercialization. This paper addresses computational burden of the constrained controller by proposing a computationally tractable model prediction control (MPC) for real-time implementation in autonomous electric vehicles. We present several remedies to achieve a computationally manageable constrained control, and analyze its real-time computation feasibility and effectiveness in various driving conditions. In particular, both warmstarting and move-blocking methods could relax the computations significantly. Through the validations, we confirm the effectiveness of the proposed approach while maintaining good performance compared to other alternative schemes. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Security Risk Analysis Approach for Safety-Critical Systems of Connected Vehicles
Electronics 2020, 9(8), 1242; https://doi.org/10.3390/electronics9081242 - 02 Aug 2020
Viewed by 1007
Abstract
Modern vehicles are no longer merely mechanical systems but are monitored and controlled by various electronic systems. Safety-critical systems of connected vehicles become vulnerable to cyberattacks because of increasing interconnection. At present, the security risk analysis of connected vehicles is mainly based on [...] Read more.
Modern vehicles are no longer merely mechanical systems but are monitored and controlled by various electronic systems. Safety-critical systems of connected vehicles become vulnerable to cyberattacks because of increasing interconnection. At present, the security risk analysis of connected vehicles is mainly based on qualitative methods, while these methods are usually subjective and lack consideration for functional safety. In order to solve this problem, we propose in this paper a security risk analysis framework for connected vehicles based on formal methods. Firstly, we introduce the electronic and electrical architecture of the connected vehicle and analyze the attack surfaces of the in-vehicle safety-critical systems from three levels of sensors, in-vehicle networks, and controllers. Secondly, we propose a method to model the target of evaluation (i.e., in-vehicle safety-critical system) as a Markov decision process and use probabilistic computation tree logic to formally describe its security properties. Then, a probabilistic model checker PRISM is used to analyze the security risk of target systems quantitatively according to security properties. Finally, we apply the proposed approach to analyze and compare the security risks of the collision warning system under a distributed and centralized electrical and electronic architecture. In addition, from a practical point of view, we propose a Markov model generation method based on a SysML activity diagram, which can simplify our modeling process. The evaluation results show that we can have a quantitative understanding of the security risks at the system level in the early stage of system design. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessEditor’s ChoiceArticle
Precise Positioning of Autonomous Vehicles Combining UWB Ranging Estimations with On-Board Sensors
Electronics 2020, 9(8), 1238; https://doi.org/10.3390/electronics9081238 - 01 Aug 2020
Cited by 2 | Viewed by 1473
Abstract
In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. [...] Read more.
In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessArticle
Simulated Sensor Based Strategies for Obstacle Avoidance Using Velocity Profiling for Autonomous Vehicle FURBOT
Electronics 2020, 9(6), 883; https://doi.org/10.3390/electronics9060883 - 26 May 2020
Cited by 4 | Viewed by 1192
Abstract
Freight Urban Robotic Vehicle (FURBOT) is an autonomous vehicle designed to transport last mile freight to designated urban stations. It is a slow vehicle designed to tackle urban environment with complete autonomy. A slow vehicle may have slightly different strategies for avoiding obstacles. [...] Read more.
Freight Urban Robotic Vehicle (FURBOT) is an autonomous vehicle designed to transport last mile freight to designated urban stations. It is a slow vehicle designed to tackle urban environment with complete autonomy. A slow vehicle may have slightly different strategies for avoiding obstacles. Unlike on a highway, it has to deal with pedestrians, traffic lights and slower vehicles while maintaining smoothness in its drive. To tackle obstacle avoidance for this vehicle, sensor feedback based strategies have been formulated for smooth drive and obstacle avoidance. A full mathematical model for the vehicle is formulated and simulated in MATLAB environment. The mathematical model uses velocity control for obstacle avoidance without steering control. The obstacle avoidance is attained through velocity control and strategies are formulated with velocity profiling. Innovative techniques are formulated in creating the simulated sensory feed-backs of the environment. Using these feed-backs, correct velocity profiling is autonomously created for giving velocity profile input to the velocity controller. Proximity measurements are assumed to be available for the vehicle in its given range of drive. Novelty is attained by manipulating velocity profile without prior knowledge of the environment. Four different type of obstacles are modeled for simulated environment of the vehicle. These obstacles are randomly placed in the path of the vehicle and autonomous velocity profiling is verified in simulated environment. The simulated results obtained show satisfactory velocity profiling for controller input. The current technique helps to tune the existing controller and in designing of a better velocity controller for the autonomous vehicle and bridges the gap between sensor feed-back and controller input. Moreover, accurate input profiling creates less strain on the system and brings smoothness in drive for an overall safer environment. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessFeature PaperArticle
May a Pair of ‘Eyes’ Be Optimal for Vehicles Too?
Electronics 2020, 9(5), 759; https://doi.org/10.3390/electronics9050759 - 05 May 2020
Viewed by 800
Abstract
Following a very brief look at the human vision system, an extended summary of our own elemental steps towards future vision systems for ground vehicles is given, leading to the proposal made in the main part. The question is raised of why the [...] Read more.
Following a very brief look at the human vision system, an extended summary of our own elemental steps towards future vision systems for ground vehicles is given, leading to the proposal made in the main part. The question is raised of why the predominant solution in biological vision systems, namely pairs of eyes (very often multi-focal and gaze-controllable), has not been found in technical systems up to now, though it may be a useful or even optimal solution for vehicles too. Two potential candidates with perception capabilities closer to the human sense of vision are discussed in some detail: one with all cameras mounted in a fixed way onto the body of the vehicle, and one with a multi-focal gaze-controllable set of cameras. Such compact systems are considered advantageous for many types of vehicles if a human level of performance in dynamic real-time vision and detailed scene understanding is the goal. Increasingly general realizations of these types of vision systems may take all of the 21st century to be developed. The big challenge for such systems with the capability of learning while seeing will be more on the software side than on the hardware side required for sensing and computing. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessFeature PaperArticle
Speed Control Optimization for Autonomous Vehicles with Metaheuristics
Electronics 2020, 9(4), 551; https://doi.org/10.3390/electronics9040551 - 26 Mar 2020
Cited by 4 | Viewed by 943
Abstract
The development of speed controllers under execution in autonomous vehicles within their dynamic driving task (DDT) is a traditional research area from the point of view of control techniques. In this regard, Proportional – Integral – Derivative (PID) controllers are the most widely [...] Read more.
The development of speed controllers under execution in autonomous vehicles within their dynamic driving task (DDT) is a traditional research area from the point of view of control techniques. In this regard, Proportional – Integral – Derivative (PID) controllers are the most widely used in order to meet the requirements of cruise control. However, fine tuning of the parameters associated with this type of controller can be complex, especially if it is intended to optimize them and reduce their characteristic errors. The objective of the work described in this paper is to evaluate the capacity of several metaheuristics for the adjustment of the parameters Kp, 1/Ti, and 1/Td of a PID controller to regulate the speed of a vehicle. To do this, an adjustment error function has been established from a linear combination of classic estimators of the goodness of the controller, such as overshoot, settling time (ts), steady-state error (ess), and the number of changes of sign of the signal (d). The error obtained when applying the controller has also been compared to a computational model of the vehicle after estimating the parameters Kp, Ki, and Kd, both for a setpoint sequence used in the adjustment of the system parameters and for a sequence not used during the adjustment, and therefore unknown by the system. The main novelty of the paper is to propose a new global error function, a function that enables the use of heuristic optimization methods for PID tuning. This optimization has been carried out by using three methods: genetic algorithms (GA), memetics algorithms (MA), and mesh adaptive direct search (MADS). The results of the application of the optimization methods using the proposed metric show that the accuracy of the PID controller is improved, compared with the classical optimization based on classical methods like the integral absolute error (IAE) or similar metrics, reducing oscillatory behaviours as well as minimizing the analysed performance indexes. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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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
Cited by 3 | Viewed by 843
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)
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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
Cited by 2 | Viewed by 925
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)
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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
Cited by 8 | Viewed by 1526
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)
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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
Cited by 2 | Viewed by 728
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)
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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 3 | Viewed by 1164
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)
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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 2 | Viewed by 1232
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)
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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 13 | Viewed by 2806
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)
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Review

Jump to: Research

Open AccessReview
A Survey on Deep Learning Based Approaches for Scene Understanding in Autonomous Driving
Electronics 2021, 10(4), 471; https://doi.org/10.3390/electronics10040471 - 15 Feb 2021
Cited by 1 | Viewed by 629
Abstract
As a prerequisite for autonomous driving, scene understanding has attracted extensive research. With the rise of the convolutional neural network (CNN)-based deep learning technique, research on scene understanding has achieved significant progress. This paper aims to provide a comprehensive survey of deep learning-based [...] Read more.
As a prerequisite for autonomous driving, scene understanding has attracted extensive research. With the rise of the convolutional neural network (CNN)-based deep learning technique, research on scene understanding has achieved significant progress. This paper aims to provide a comprehensive survey of deep learning-based approaches for scene understanding in autonomous driving. We categorize these works into four work streams, including object detection, full scene semantic segmentation, instance segmentation, and lane line segmentation. We discuss and analyze these works according to their characteristics, advantages and disadvantages, and basic frameworks. We also summarize the benchmark datasets and evaluation criteria used in the research community and make a performance comparison of some of the latest works. Lastly, we summarize the review work and provide a discussion on the future challenges of the research domain. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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Open AccessFeature PaperEditor’s ChoiceReview
Automated Driving: A Literature Review of the Take over Request in Conditional Automation
Electronics 2020, 9(12), 2087; https://doi.org/10.3390/electronics9122087 - 07 Dec 2020
Cited by 1 | Viewed by 699
Abstract
In conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the [...] Read more.
In conditional automation (level 3), human drivers can hand over the Driving Dynamic Task (DDT) to the Automated Driving System (ADS) and only be ready to resume control in emergency situations, allowing them to be engaged in non-driving related tasks (NDRT) whilst the vehicle operates within its Operational Design Domain (ODD). Outside the ODD, a safe transition process from the ADS engaged mode to manual driving should be initiated by the system through the issue of an appropriate Take Over Request (TOR). In this case, the driver’s state plays a fundamental role, as a low attention level might increase driver reaction time to take over control of the vehicle. This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process. It introduces the topic in the appropriate context describing as well a variety of concerns that are associated with the TOR. It also provides theoretical foundations on implemented designs, and report on concrete examples that are targeted towards designers and the general public. Moreover, it compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research, discussing also approaches and limitations and providing conclusions. Full article
(This article belongs to the Special Issue Autonomous Vehicles Technology)
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