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Advances in Automated Driving Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: closed (1 December 2021) | Viewed by 44573

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Special Issue Editors


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Guest Editor
Institute of Automotive Engineering, Graz University of Technology, Inffeldgasse 11/2, 8010 GRAZ, Austria
Interests: automated driving; automotive engineering; advanced driver assistance systems; road safety; multi-body simualtion; finite element analysis; computer-aided engineering; structural dynamics; product development

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Guest Editor
Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering, Budapest University of Technology and Economics, Building J, 6 Stoczek Street, 1111 Budapest, Hungary
Interests: autonomous vehicles; information and communication technology; security; intelligent transportation systems; 5G

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Guest Editor
Transport Planning and Traffic Engineering, Graz University of Technology, Inffeldgasse 11/2, 8010 GRAZ, Austria
Interests: transport modelling; transport planning; highway engineering; driving automation

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Guest Editor
Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA

Special Issue Information

Dear Colleagues,

Electrification, automation of vehicle control, digitalization and new mobility are the mega trends in automotive engineering and they are strongly connected to each other. Whereas many demonstrations for highly automated vehicles have been made worldwide, many challenges remain to bring automated vehicles on the market for private and commercial use.

The main challenges are: Reliable machine perception; accepted standards for vehicle approval and homologation; verification and validation of the functional safety especially at SAE level 3+ systems; legal and ethical implications; acceptance of vehicle automation by occupants and society; interaction between automated and human controlled vehicles in mixed traffic; human-machine-interaction and usability; manipulation, misuse and cyber-security; but also the system costs for hard- and software and development effort.

This special issue deals with recent advances related to the technological aspects of the aforementioned challenges, papers are welcomed for:

  • Machine perception for SAE L3+ driving automation;
  • Trajectory planning and decision making in complex traffic situations;
  • X-by-Wire system components;
  • Verification and validation of SAE L3+ systems;
  • Misuse, manipulation and cybersecurity;
  • Human-machine-interaction, driver monitoring and driver intention recognition;
  • Road infrastructure measures for introduction of SAE L3+ systems;
  • Solutions for interactions of vehicles human and machine controlled in mixed traffic.

Dr. Arno Eichberger
Dr. Zsolt Szalay
Prof. Dr. Martin Fellendorf
Prof. Dr. Henry Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • automated driving
  • machine perception
  • X-by-wire
  • verification and validation
  • human-machine-interaction
  • interaction of automated vehicles and road infrastructure

Published Papers (16 papers)

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Editorial

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5 pages, 327 KiB  
Editorial
Advances in Automated Driving Systems
by Arno Eichberger, Zsolt Szalay, Martin Fellendorf and Henry Liu
Energies 2022, 15(10), 3476; https://doi.org/10.3390/en15103476 - 10 May 2022
Cited by 3 | Viewed by 1776
Abstract
Electrification, automation of vehicle control, digitalization and new mobility are the mega trends in automotive engineering and they are strongly connected to each other [...] Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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Research

Jump to: Editorial

20 pages, 4502 KiB  
Article
Evaluation Methodology for Physical Radar Perception Sensor Models Based on On-Road Measurements for the Testing and Validation of Automated Driving
by Zoltan Ferenc Magosi, Christoph Wellershaus, Viktor Roland Tihanyi, Patrick Luley and Arno Eichberger
Energies 2022, 15(7), 2545; https://doi.org/10.3390/en15072545 - 31 Mar 2022
Cited by 8 | Viewed by 2730
Abstract
In recent years, verification and validation processes of automated driving systems have been increasingly moved to virtual simulation, as this allows for rapid prototyping and the use of a multitude of testing scenarios compared to on-road testing. However, in order to support future [...] Read more.
In recent years, verification and validation processes of automated driving systems have been increasingly moved to virtual simulation, as this allows for rapid prototyping and the use of a multitude of testing scenarios compared to on-road testing. However, in order to support future approval procedures for automated driving functions with virtual simulations, the models used for this purpose must be sufficiently accurate to be able to test the driving functions implemented in the complete vehicle model. In recent years, the modelling of environment sensor technology has gained particular interest, since it can be used to validate the object detection and fusion algorithms in Model-in-the-Loop testing. In this paper, a practical process is developed to enable a systematic evaluation for perception–sensor models on a low-level data basis. The validation framework includes, first, the execution of test drive runs on a closed highway; secondly, the re-simulation of these test drives in a precise digital twin; and thirdly, the comparison of measured and simulated perception sensor output with statistical metrics. To demonstrate the practical feasibility, a commercial radar-sensor model (the ray-tracing based RSI radar model from IPG) was validated using a real radar sensor (ARS-308 radar sensor from Continental). The simulation was set up in the simulation environment IPG CarMaker® 8.1.1, and the evaluation was then performed using the software package Mathworks MATLAB®. Real and virtual sensor output data on a low-level data basis were used, which thus enables the benchmark. We developed metrics for the evaluation, and these were quantified using statistical analysis. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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15 pages, 5834 KiB  
Article
Effects of Automated Vehicle Models at the Mixed Traffic Situation on a Motorway Scenario
by Xuan Fang, Hexuan Li, Tamás Tettamanti, Arno Eichberger and Martin Fellendorf
Energies 2022, 15(6), 2008; https://doi.org/10.3390/en15062008 - 09 Mar 2022
Cited by 11 | Viewed by 3065
Abstract
There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates [...] Read more.
There is consensus in industry and academia that Highly Automated Vehicles (HAV) and Connected Automated Vehicles (CAV) will be launched into the market in the near future due to emerging autonomous driving technology. In this paper, a mixed traffic simulation framework that integrates vehicle models with different automated driving systems in the microscopic traffic simulation was proposed. Currently, some of the more mature Automated Driving Systems (ADS) functions (e.g., Adaptive Cruise Control (ACC), Lane Keeping Assistant (LKA), etc.) are already equipped in vehicles, the very next step towards a higher automated driving is represented by Level 3 vehicles and CAV which show great promise in helping to avoid crashes, ease traffic congestion, and improve the environment. Therefore, to better predict and simulate the driving behavior of automated vehicles on the motorway scenario, a virtual test framework is proposed which includes the Highway Chauffeur (HWC) and Vehicle-to-Vehicle (V2V) communication function. These functions are implemented as an external driver model in PTV Vissim. The framework uses a detailed digital twin based on the M86 road network located in southwestern Hungary, which was constructed for autonomous driving tests. With this framework, the effect of the proposed vehicle models is evaluated with the microscopic traffic simulator PTV Vissim. A case study of the different penetration rates of HAV and CAV was performed on the M86 motorway. Preliminary results presented in this paper demonstrated that introducing HAV and CAV to the current network individually will cause negative effects on traffic performance. However, a certain ratio of mixed traffic, 60% CAV and 40% Human Driver Vehicles (HDV), could reduce this negative impact. The simulation results also show that high penetration CAV has fine driving stability and less travel delay. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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16 pages, 4342 KiB  
Article
Digitalize the Twin: A Method for Calibration of Reference Data for Transfer Real-World Test Drives into Simulation
by Martin Holder, Lukas Elster and Hermann Winner
Energies 2022, 15(3), 989; https://doi.org/10.3390/en15030989 - 28 Jan 2022
Cited by 3 | Viewed by 2217
Abstract
In the course of the development of automated driving, there has been increasing interest in obtaining ground truth information from sensor recordings and transferring road traffic scenarios to simulations. The quality of the “ground truth” annotation is dictated by its accuracy. This paper [...] Read more.
In the course of the development of automated driving, there has been increasing interest in obtaining ground truth information from sensor recordings and transferring road traffic scenarios to simulations. The quality of the “ground truth” annotation is dictated by its accuracy. This paper presents a method for calibrating the accuracy of ground truth in practical applications in the automotive context. With an exemplary measurement device, we show that the proclaimed accuracy of the device is not always reached. However, test repetitions show deviations, resulting in non-uniform reliability and limited trustworthiness of the reference measurement. A similar result can be observed when reproducing the trajectory in the simulation environment: the exact reproduction of the driven trajectory does not always succeed in the simulation environment shown as an example because deviations occur. This is particularly relevant for making sensor-specific features such as material reflectivities for lidar and radar quantifiable in dynamic cases. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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22 pages, 8399 KiB  
Article
Enhancing Acceptance and Trust in Automated Driving trough Virtual Experience on a Driving Simulator
by Philipp Clement, Omar Veledar, Clemens Könczöl, Herbert Danzinger, Markus Posch, Arno Eichberger and Georg Macher
Energies 2022, 15(3), 781; https://doi.org/10.3390/en15030781 - 21 Jan 2022
Cited by 10 | Viewed by 3264
Abstract
As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial [...] Read more.
As vehicle driving evolves from human-controlled to autonomous, human–machine interaction ensures intuitive usage as well as the feedback from vehicle occupants to the machine for optimising controls. The feedback also improves understanding of the user satisfaction with the system behaviour, which is crucial for determining user trust and, hence, the acceptance of the new functionalities that aim to improve mobility solutions and increase road safety. Trust and acceptance are potentially the crucial parameters for determining the success of autonomous driving deployment in wider society. Hence, there is a need to define appropriate and measurable parameters to be able to quantify trust and acceptance in a physically safe environment using dependable methods. This study seeks to support technical developments and data gathering with psychology to determine the degree to which humans trust automated driving functionalities. The primary aim is to define if the usage of an advanced driving simulator can improve consumer trust and acceptance of driving automation through tailor-made studies. We also seek to measure significant differences in responses from different demographic groups. The study employs tailor-made driving scenarios to gather feedback on trust, usability and user workload of 55 participants monitoring the vehicle behaviour and environment during the automated drive. Participants’ subjective ratings are gathered before and after the simulator session. Results show a significant increase in trust ensuing the exposure to the driving automation functionalities. We quantify this increase resulting from the usage of the driving simulator. Those less experienced with driving automation show a higher increase in trust and, therefore, profit more from the exercise. This appears to be linked to the demanded participant workload, as we establish a link between workload and trust. The findings provide a noteworthy contribution to quantifying the method of evaluating and ensuring user acceptance of driving automation. It is only through the increase of trust and consequent improvement of user acceptance that the introduction of the driving automation into wider society will be a guaranteed success. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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25 pages, 80025 KiB  
Article
Driver Monitoring of Automated Vehicles by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals
by Sadegh Arefnezhad, Arno Eichberger, Matthias Frühwirth, Clemens Kaufmann, Maximilian Moser and Ioana Victoria Koglbauer
Energies 2022, 15(2), 480; https://doi.org/10.3390/en15020480 - 10 Jan 2022
Cited by 17 | Viewed by 4080
Abstract
Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based [...] Read more.
Driver drowsiness is one of the leading causes of traffic accidents. This paper proposes a new method for classifying driver drowsiness using deep convolution neural networks trained by wavelet scalogram images of electrocardiogram (ECG) signals. Three different classes were defined for drowsiness based on video observation of driving tests performed in a simulator for manual and automated modes. The Bayesian optimization method is employed to optimize the hyperparameters of the designed neural networks, such as the learning rate and the number of neurons in every layer. To assess the results of the deep network method, heart rate variability (HRV) data is derived from the ECG signals, some features are extracted from this data, and finally, random forest and k-nearest neighbors (KNN) classifiers are used as two traditional methods to classify the drowsiness levels. Results show that the trained deep network achieves balanced accuracies of about 77% and 79% in the manual and automated modes, respectively. However, the best obtained balanced accuracies using traditional methods are about 62% and 64%. We conclude that designed deep networks working with wavelet scalogram images of ECG signals significantly outperform KNN and random forest classifiers which are trained on HRV-based features. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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17 pages, 15444 KiB  
Article
Phenomenological Modelling of Camera Performance for Road Marking Detection
by Hexuan Li, Kanuric Tarik, Sadegh Arefnezhad, Zoltan Ferenc Magosi, Christoph Wellershaus, Darko Babic, Dario Babic, Viktor Tihanyi, Arno Eichberger and Marcel Carsten Baunach
Energies 2022, 15(1), 194; https://doi.org/10.3390/en15010194 - 28 Dec 2021
Cited by 5 | Viewed by 1861
Abstract
With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination [...] Read more.
With the development of autonomous driving technology, the requirements for machine perception have increased significantly. In particular, camera-based lane detection plays an essential role in autonomous vehicle trajectory planning. However, lane detection is subject to high complexity, and it is sensitive to illumination variation, appearance, and age of lane marking. In addition, the sheer infinite number of test cases for highly automated vehicles requires an increasing portion of test and validation to be performed in simulation and X-in-the-loop testing. To model the complexity of camera-based lane detection, physical models are often used, which consider the optical properties of the imager as well as image processing itself. This complexity results in high efforts for the simulation in terms of modelling as well as computational costs. This paper presents a Phenomenological Lane Detection Model (PLDM) to simulate camera performance. The innovation of the approach is the modelling technique using Multi-Layer Perceptron (MLP), which is a class of Neural Network (NN). In order to prepare input data for our neural network model, massive driving tests have been performed on the M86 highway road in Hungary. The model’s inputs include vehicle dynamics signals (such as speed and acceleration, etc.). In addition, the difference between the reference output from the digital-twin map of the highway and camera lane detection results is considered as the target of the NN. The network consists of four hidden layers, and scaled conjugate gradient backpropagation is used for training the network. The results demonstrate that PLDM can sufficiently replicate camera detection performance in the simulation. The modelling approach improves the realism of camera sensor simulation as well as computational effort for X-in-the-loop applications and thereby supports safety validation of camera-based functionality in automated driving, which decreases the energy consumption of vehicles. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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27 pages, 4551 KiB  
Article
Automated Conflict Management Framework Development for Autonomous Aerial and Ground Vehicles
by David Sziroczák and Daniel Rohács
Energies 2021, 14(24), 8344; https://doi.org/10.3390/en14248344 - 10 Dec 2021
Cited by 3 | Viewed by 2593
Abstract
The number of aerial- and ground-based unmanned vehicles and operations is expected to significantly expand in the near future. While aviation traditionally has an excellent safety record in managing conflicts, the current approaches will not be able to provide safe and efficient operations [...] Read more.
The number of aerial- and ground-based unmanned vehicles and operations is expected to significantly expand in the near future. While aviation traditionally has an excellent safety record in managing conflicts, the current approaches will not be able to provide safe and efficient operations in the future. This paper presents the development of a novel framework integrating autonomous aerial and ground vehicles to facilitate short- and mid-term tactical conflict management. The methodology presents the development of a modular web service framework to develop new conflict management algorithms. This new framework is aimed at managing urban and peri-urban traffic of unmanned ground vehicles and assisting the introduction of urban air mobility into the same framework. A set of high-level system requirements is defined. The incremental development of two versions of the system prototype is presented. The discussions highlight the lessons learnt while implementing and testing the conflict management system and the introduced version of the stop-and-go resolution algorithm and defines the identified future development directions. Operation of the system was successfully demonstrated using real hardware. The developed framework implements short- and mid-term conflict management methodologies in a safe, resource efficient and scalable manner and can be used for the further development and the evaluation of various methods integrating aerial- and ground-based autonomous vehicles. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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19 pages, 758 KiB  
Article
Increasing the Safety of Adaptive Cruise Control Using Physics-Guided Reinforcement Learning
by Sorin Liviu Jurj, Dominik Grundt, Tino Werner, Philipp Borchers, Karina Rothemann and Eike Möhlmann
Energies 2021, 14(22), 7572; https://doi.org/10.3390/en14227572 - 12 Nov 2021
Cited by 8 | Viewed by 2414
Abstract
This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical knowledge. More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use [...] Read more.
This paper presents a novel approach for improving the safety of vehicles equipped with Adaptive Cruise Control (ACC) by making use of Machine Learning (ML) and physical knowledge. More exactly, we train a Soft Actor-Critic (SAC) Reinforcement Learning (RL) algorithm that makes use of physical knowledge such as the jam-avoiding distance in order to automatically adjust the ideal longitudinal distance between the ego- and leading-vehicle, resulting in a safer solution. In our use case, the experimental results indicate that the physics-guided (PG) RL approach is better at avoiding collisions at any selected deceleration level and any fleet size when compared to a pure RL approach, proving that a physics-informed ML approach is more reliable when developing safe and efficient Artificial Intelligence (AI) components in autonomous vehicles (AVs). Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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16 pages, 1428 KiB  
Article
Comparing Different Levels of Technical Systems for a Modular Safety Approval—Why the State of the Art Does Not Dispense with System Tests Yet
by Björn Klamann and Hermann Winner
Energies 2021, 14(22), 7516; https://doi.org/10.3390/en14227516 - 11 Nov 2021
Cited by 2 | Viewed by 1625
Abstract
While systems in the automotive industry have become increasingly complex, the related processes require comprehensive testing to be carried out at lower levels of a system. Nevertheless, the final safety validation is still required to be carried out at the system level by [...] Read more.
While systems in the automotive industry have become increasingly complex, the related processes require comprehensive testing to be carried out at lower levels of a system. Nevertheless, the final safety validation is still required to be carried out at the system level by automotive standards like ISO 26262. Using its guidelines for the development of automated vehicles and applying them for field operation tests has been proven to be economically unfeasible. The concept of a modular safety approval provides the opportunity to reduce the testing effort after updates and for a broader set of vehicle variants. In this paper, we present insufficiencies that occur on lower levels of hierarchy compared to the system level. Using a completely new approach, we show that errors arise due to faulty decomposition processes wherein, e.g., functions, test scenarios, risks, or requirements of a system are decomposed to the module level. Thus, we identify three main categories of errors: insufficiently functional architectures, performing the wrong tests, and performing the right tests wrongly. We provide more detailed errors and present examples from the research project UNICARagil. Finally, these findings are taken to define rules for the development and testing of modules to dispense with system tests. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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16 pages, 2467 KiB  
Article
Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
by Jianfei Huang, Xinchun Cheng, Yuying Shen, Dewen Kong and Jixin Wang
Energies 2021, 14(21), 7202; https://doi.org/10.3390/en14217202 - 02 Nov 2021
Cited by 6 | Viewed by 2017
Abstract
Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between [...] Read more.
Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel loaders and the environment. In this paper, a deep-learning-based prediction model is developed to predict the throttle value and state for wheel loaders by learning from driving data. Multiple long–short-term memory (LSTM) networks are used to extract the temporal features of different stages during the operation of the wheel loader. Two backward-propagation neural networks (BPNNs), which use the temporal feature extracted by LSTM as the input, are designed to output the final prediction results of throttle value and state, respectively. The proposed prediction model is trained and tested using the data from two different conditions. The end-to-end LSTM prediction model and BPNNs are used as benchmark models. The results indicate that the proposed prediction model has good prediction accuracy and adaptability. Furthermore, the relationship between the prediction performance and signal sampling frequency is also studied. The proposed prediction method that combines driving data and deep learning can make the throttle action conform to the decisions of an experienced operator, providing technical support for the autonomous operation of construction machinery. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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16 pages, 1791 KiB  
Article
Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection Testing on Cluster of Images with Different Luminance Conditions
by Mohammad Junaid, Zsolt Szalay and Árpád Török
Energies 2021, 14(21), 7172; https://doi.org/10.3390/en14217172 - 01 Nov 2021
Cited by 4 | Viewed by 1941
Abstract
Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There [...] Read more.
Self-driving cars, i.e., fully automated cars, will spread in the upcoming two decades, according to the representatives of automotive industries; owing to technological breakthroughs in the fourth industrial revolution, as the introduction of deep learning has completely changed the concept of automation. There is considerable research being conducted regarding object detection systems, for instance, lane, pedestrian, or signal detection. This paper specifically focuses on pedestrian detection while the car is moving on the road, where speed and environmental conditions affect visibility. To explore the environmental conditions, a pedestrian custom dataset based on Common Object in Context (COCO) is used. The images are manipulated with the inverse gamma correction method, in which pixel values are changed to make a sequence of bright and dark images. The gamma correction method is directly related to luminance intensity. This paper presents a flexible, simple detection system called Mask R-CNN, which works on top of the Faster R-CNN (Region Based Convolutional Neural Network) model. Mask R-CNN uses one extra feature instance segmentation in addition to two available features in the Faster R-CNN, called object recognition. The performance of the Mask R-CNN models is checked by using different Convolutional Neural Network (CNN) models as a backbone. This approach might help future work, especially when dealing with different lighting conditions. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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17 pages, 5532 KiB  
Article
Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces
by Szilárd Czibere, Ádám Domina, Ádám Bárdos and Zsolt Szalay
Energies 2021, 14(20), 6667; https://doi.org/10.3390/en14206667 - 14 Oct 2021
Cited by 7 | Viewed by 2663
Abstract
Electronic vehicle dynamics systems are expected to evolve in the future as more and more automobile manufacturers mark fully automated vehicles as their main path of development. State-of-the-art electronic stability control programs aim to limit the vehicle motion within the stable region of [...] Read more.
Electronic vehicle dynamics systems are expected to evolve in the future as more and more automobile manufacturers mark fully automated vehicles as their main path of development. State-of-the-art electronic stability control programs aim to limit the vehicle motion within the stable region of the vehicle dynamics, thereby preventing drifting. On the contrary, in this paper, the authors suggest its use as an optimal cornering technique in emergency situations and on certain road conditions. Achieving the automated initiation and stabilization of vehicle drift motion (also known as powerslide) on varying road surfaces means a high level of controllability over the vehicle. This article proposes a novel approach to realize automated vehicle drifting in multiple operation points on different road surfaces. A three-state nonlinear vehicle and tire model was selected for control-oriented purposes. Model predictive control (MPC) was chosen with an online updating strategy to initiate and maintain the drift even in changing conditions. Parameter identification was conducted on a test vehicle. Equilibrium analysis was a key tool to identify steady-state drift states, and successive linearization was used as an updating strategy. The authors show that the proposed controller is capable of initiating and maintaining steady-state drifting. In the first test scenario, the reaching of a single drifting equilibrium point with −27.5° sideslip angle and 10 m/s longitudinal speed is presented, which resulted in −20° roadwheel angle. In the second demonstration, the setpoints were altered across three different operating points with sideslip angles ranging from −27.5° to −35°. In the third test case, a wet to dry road transition is presented with 0.8 and 0.95 road grip values, respectively. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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26 pages, 11079 KiB  
Article
Towards Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud
by Viktor Tihanyi, András Rövid, Viktor Remeli, Zsolt Vincze, Mihály Csonthó, Zsombor Pethő, Mátyás Szalai, Balázs Varga, Aws Khalil and Zsolt Szalay
Energies 2021, 14(18), 5930; https://doi.org/10.3390/en14185930 - 18 Sep 2021
Cited by 19 | Viewed by 3422
Abstract
We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage [...] Read more.
We demonstrate a working functional prototype of a cooperative perception system that maintains a real-time digital twin of the traffic environment, providing a more accurate and more reliable model than any of the participant subsystems—in this case, smart vehicles and infrastructure stations—would manage individually. The importance of such technology is that it can facilitate a spectrum of new derivative services, including cloud-assisted and cloud-controlled ADAS functions, dynamic map generation with analytics for traffic control and road infrastructure monitoring, a digital framework for operating vehicle testing grounds, logistics facilities, etc. In this paper, we constrain our discussion on the viability of the core concept and implement a system that provides a single service: the live visualization of our digital twin in a 3D simulation, which instantly and reliably matches the state of the real-world environment and showcases the advantages of real-time fusion of sensory data from various traffic participants. We envision this prototype system as part of a larger network of local information processing and integration nodes, i.e., the logically centralized digital twin is maintained in a physically distributed edge cloud. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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10 pages, 1445 KiB  
Article
Analysis of Market-Ready Traffic Sign Recognition Systems in Cars: A Test Field Study
by Darko Babić, Dario Babić, Mario Fiolić and Željko Šarić
Energies 2021, 14(12), 3697; https://doi.org/10.3390/en14123697 - 21 Jun 2021
Cited by 7 | Viewed by 2944
Abstract
Advanced Driver Assistance System (ADAS) represents a collection of vehicle-based intelligent safety systems. One in particular, Traffic Sign Recognition System (TSRS), is designed to detect and interpret roadside information in the form of signage. Even though TSRS has been on the market for [...] Read more.
Advanced Driver Assistance System (ADAS) represents a collection of vehicle-based intelligent safety systems. One in particular, Traffic Sign Recognition System (TSRS), is designed to detect and interpret roadside information in the form of signage. Even though TSRS has been on the market for more than a decade now, the available ones differ in hardware and software solutions they use, as well as in quantity and typology of signs they recognize. The aim of this study is to determine whether differences between detection and readability accuracy of market-ready TSRS exist and to what extent, as well as how different levels of “graphical changes” on the signs affect their accuracy. For this purpose, signs (“speed limit” and “prohibition of overtaking”) were placed on a test field and 17 vehicles from 14 different car brands underwent testing. Overall, the results showed that sign detection and readability by TSRS differ between car brands and that even small changes in the design of signs can drastically affect TSRS accuracy. Even in a controlled environment where no sign has been altered, there has been a 5% margin of misread signs. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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9 pages, 234 KiB  
Article
Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim
by Demin Nalic, Aleksa Pandurevic, Arno Eichberger, Martin Fellendorf and Branko Rogic
Energies 2021, 14(11), 3135; https://doi.org/10.3390/en14113135 - 27 May 2021
Cited by 11 | Viewed by 2710
Abstract
As the complexity of automated driving systemss (ADSs) with automation levels above level 3 is rising, virtual testing for such systems is inevitable and necessary. The complexity of testing these levels lies in the modeling and calculation demands for the virtual environment, which [...] Read more.
As the complexity of automated driving systemss (ADSs) with automation levels above level 3 is rising, virtual testing for such systems is inevitable and necessary. The complexity of testing these levels lies in the modeling and calculation demands for the virtual environment, which consists of roads, traffic, static and dynamic objects, as well as the modeling of the car itself. An essential part of the safety and performance analysis of ADSs is the modeling and consideration of dynamic road traffic participants. There are multiple forms of traffic flow simulation software (TFSS), which are used to reproduce realistic traffic behavior and are integrated directly or over interfaces with vehicle simulation software environments. In this paper we focus on the TFSS from PTV Vissim in a co-simulation framework which combines Vissim and CarMaker. As it is a commonly used software in industry and research, it also provides complex driver models and interfaces to manipulate and develop customized traffic participants. Using the driver model DLL interface (DMDI) from Vissim it is possible to manipulate traffic participants or adjust driver models in a defined manner. Based on the DMDI, we extended the code and developed a framework for the manipulation and testing of ADSs in the traffic environment of Vissim. The efficiency and performance of the developed software framework are evaluated using the co-simulation framework for the testing of ADSs, which is based on Vissim and CarMaker. Full article
(This article belongs to the Special Issue Advances in Automated Driving Systems)
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