TUGraz OPEN Library

Selinger, Julian

Stock, Sebastian

Schlemmer, Werner

Hobisch, Mathias

Kostoglou, Nikolaos

Abbas, Qamar

Paris, Oskar

Mitterer, Christian

Hummel, Michael

Spirk, Stefan

English

Nanoporous Carbon Electrodes Derived from Coffee Side Streams for Supercapacitors in Aqueous Electrolytes

Tengg, Allan

Stolz, Michael

Hillebrand, Joachim

A Feasibility Study of a Traffic Supervision System Based on 5G Communication

Settgast, Volker

Kostarakos, Konstantinos

Eggeling, Eva

Hartbauer, Manfred

Ullrich, Torsten

Product Tests in Virtual Reality: Lessons Learned during Collision Avoidance Development for Drones

Hirz, Mario

Nguyen, Thu Trang

Life-Cycle CO2-Equivalent Emissions of Cars Driven by Conventional and Electric Propulsion Systems

Belei, Carlos

Joeresse, Jana

Amancio-Filho, Sergio

Fused-Filament Fabrication of Short Carbon Fiber-Reinforced Polyamide: Parameter Optimization for Improved Performance under Uniaxial Tensile Loading

Eichberger, Arno

Szalay, Zsolt

Fellendorf,  Martin

Liu, Henry

Citation: Eichberger, A.; Szalay, Z.;
Fellendorf, M.; Liu, H. Advances in
Automated Driving Systems. Energies
2022, 15, 3476. https://doi.org/
10.3390/en15103476
Received: 20 April 2022
Accepted: 6 May 2022
Published: 10 May 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
energies
Editorial
Advances in Automated Driving Systems
Arno Eichberger 1,* , Zsolt Szalay 2 , Martin Fellendorf 3 and Henry Liu 4
1 Institute of Automotive Engineering, Graz University of Technology, 8010 Graz, Austria
2 Department of Automotive Technologies, Faculty of Transportation Engineering and Vehicle Engineering,
Budapest University of Technology and Economic, 1111 Budapest, Hungary; szalay.zsolt@kjk.bme.hu
3 Institute of Transport Planning and Traffic Engineering, Graz University of Technology, 8010 Graz, Austria;
martin.fellendorf@tugraz.at
4 Civil and Environmental Engineering, University of Michigan, Ann Arbor, MI 48109, USA;
henryliu@umich.edu
* Correspondence: arno.eichberger@tugraz.at; Tel.: +43-316-873-35210
1. Introduction
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 world-
wide, many challenges remain to bring automated vehicles on the market for private and
commercial use.
The main challenges related to automated vehicle control are:
1. Reliable machine perception; accepted standards for vehicle approval and homologation;
2. verification and validation of the functional safety especially at SAE level 3+ systems;
3. legal and ethical implications;
4. acceptance of vehicle automation by occupants and society;
5. interaction between automated- and human-controlled vehicles in mixed traffic;
6. human–machine interaction and usability;
7. manipulation, misuse and cyber-security;
8. but also the system costs for hard- and software and development effort.
These challenges mainly relate to the complex interaction between the human occu-
pants, the automated vehicle and the environment the vehicle is operated in (see Figure 1).
The main system components and the related challenges are elaborated in the following:
Energies 2022, 15, 3476. https://doi.org/10.3390/en15103476 https://www.mdpi.com/journal/energies
Energies 2022, 15, 3476 2 of 5 , , x FOR PEER REVIEW 2 of  
 
 
 
Figure 1. Traffic system in automated driving. The figure shows the complex interaction of the main 
system components in automated driving systems and how the articles of the Special Issue are the-
matically classified. 
1.1. Environment 
The environment, comprised of the static and dynamic content, as well as the ambi-
ent conditions, roadside infrastructure and traffic control systems are essential aspects in 
driving automation. It provides information to the driver with lane markings and traffic 
signs that were initially developed for human perception. However, requirements of the 
future road infrastructure need to consider opportunities and limitations of perception 
sensors. 
1.2. Perception 
Machine perception, highlighted in orange in Figure 1, is traditionally vehicle-based. 
Here, many challenges arise because of the increasingly complex algorithms to process 
raw sensor data into a reliable digital environment that allows planning of the vehicle 
trajectory. In addition, environmental conditions such as rain, fog or lighting conditions 
can deteriorate the machine perception, leading to enhance machine perception with data 
fused from different sensors.  
Due to the high cost for the components and their integration in the vehicle, increas-
ingly sensors located in the roadside environment communicate with the automated ve-
hicle. They shall provide additional information about the static contents such as the road 
network, roadside infrastructure and buildings, as well as the dynamic content such as 
moving objects. For the vehicle-to-X communication (V2X) we see different technologies 
Figure 1. Traffic system in automated driving. The figure shows the complex interaction of the
main system components in automated driving systems and how the articles of the Special Issue are
thematically classified.
i
t, c prised of the static and dynamic ontent, as well as the ambient
conditions, roadside nfrast uc ure and traffic contr l system are ess ntial aspect in dr v-
ing automation. It provides information o the driver with lane markings a d traffic signs
that were initially developed for human perception. However, r quirements of the futur
road infr structure need to consider pportunities and l mitations of perception sensors.
1.2. Perception
Machine perception, highlighted in orange in Figure 1, is traditionally vehicle-based.
Here, many challenges arise because of the increasingly complex algorithms to process
raw sensor data into a reliable digital environment that allows planning of the vehicle
trajectory. In addition, environmental conditions such as rain, fog or lighting conditions
can deteriorate the machine perception, leading to enhance machine perception with data
fused from different sensors.
Due to the high cost for the components and their integration in the vehicle, increas-
ingly sensors located in the roadside environment communicate with the automated vehicle.
They shall provide additional information about the static contents such as the road net-
work, roadside infrastructure and buildings, as well as the dynamic content such as moving
objects. For the vehicle-to-X communication (V2X) we see different technologies such
as dedicated short range communication and mobile communication to allow for data
Energies 2022, 15, 3476 3 of 5
exchange of a huge amount of data at minimum delay, while maintaining data security
and privacy.
1.3. Vehicle Guidance
Visualized in grey, the driving robot (right side of Figure 1) increasingly performs tasks
of the human driver (left side), consisting of mission and trajectory planning and control.
The mission planning is something that is an intrinsic human task but widely supported
by machine navigation systems. Here, external traffic control systems located in the road
infrastructure or cloud-based services can additionally support to optimize transportation
tasks by re-routing. However, the most difficult task is the trajectory planning using the
horizon offered by the field of view of the human or machine perception. Instead of
the traditional approach, namely to deterministically program driving tasks such as for
adaptive cruise control, modern methods of artificial intelligence (AI) offer a data driven
approach to handle complex and maybe even situations not being experienced before.
Nevertheless, the safety validation of AI base trajectory planning is a not solved issue.
Vehicle control, usually handled by traditional methods of automation and control, aims
to minimize the error in planed and driven trajectories. Here, they need to cooperate
with vehicle dynamics control (VDC) systems. Implementing intelligence in the road
infrastructure allows for advanced traffic control that maybe even perform trajectory
planning as the most delicate step in vehicle control.
1.4. Base Vehicle
The vehicle, depicted in green in Figure 1, is based on a traditional vehicle but en-
hanced with actuators, which will evolve from classical steering, power train and braking
systems to advanced X-by-wire systems offering new levels of vehicle control.
1.5. Human Machine Interface
The human–machine interface (HMI) is a delicate component that needs to be designed
carefully in order to improve the already high level of reliability in human vehicle control.
Literature reports that billions of kilometers need to be driven with an automated vehicle in
order to prove statistical significance of a superior behavior of a driving robot. As long as
we have the human driver as an operator that needs to perform tasks in vehicle guidance,
such as observation of the environment and fallback in case of system failure, the HMI is
essential to avoid distraction or inappropriate behavior of the human driver.
1.6. Evaluation
Formerly the evaluation of driving behavior focused on the driver, and included
criteria related to controllability, disturbance behavior, observability and parameter insen-
sitivity. These criteria could be evaluated in a manageable amount of testing on proving
grounds, often with open-loop maneuvers to exclude human vehicle guidance. In addition,
human impression of the driving behavior and comfort was rated with different subjective
and objective methods, leading into an evaluation of drivability of the vehicle. In driv-
ing automation, the driver increasingly transforms into a passenger, so we need to take
into account the human as a co-driver or even a passenger, so rating becomes more of a
co-drivability feature. Additional focus has to be put on other aspects such as perceived
trust, safety and acceptance of the human occupant, which happens on a psychological
level. However, the physiological aspect also has to be taken into account; for example,
motion sickness as experienced often from passengers.
However, the sheer infinite amount of possible driving scenarios call for innovative
methods for evaluating not only the safe behavior of an automated vehicle, but also a high
rating of co-drivability.
Energies 2022, 15, 3476 4 of 5
2. Articles of the Special Issue
This Special Issue deals with recent advances related to the technological aspects of
the aforementioned challenges:
• 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.
The collection includes 15 articles that deal with the aforementioned challenges. In
Figure 1, the thematic classification of the different articles related to the different system
components is illustrated. Not surprisingly, it illustrates that many studies are focused on
reliable human perception.
Article [1] deals with a methodology to quantify the performance of sensor models in
virtual validation and verification (V & V) of automated driving functions, an important
step towards reduction of on-road testing. The effect of automation on traffic flow during
the transition phase in mixed traffic was investigated by [2]. Article [3] deals with the
quality of ground truth annotation data to improve the transfer of on-road testing results
into simulation. The evaluation of perceived trust was examined in [4], demonstrated in
a driving simulator study. The topic of drowsiness classification in the context of driving
automation was investigated in [5]. In simulation of Automated Driving (AD) functions,
modelling of camera sensors is often carried out with physical modelling; however, research
in [6] presented an alternative with phenomenological modeling. Article [7] introduced a
conflicted management framework, especially focusing on aiming at managing urban and
peri-urban traffic. The potential of implementing AI into vehicle guidance, demonstrated
on the safety of ACC, was investigated in [8]. Article [9] deals with insufficiencies during
the decomposition of testing of ADAS functions from the system to lower levels, and
defining rules for testing of modules to dispense with system tests. Improvement of vehicle
control for wheel loaders was investigated in [10] using a deep learning-based prediction
model of the throttle valve. The difficulties in reliable detection of pedestrians is addressed
in [11], based on convolutional neural network algorithms applied on images manipulated
with inverse gamma correction. Vehicle control at handling limits was investigated in [12],
introducing a model-predictive controller that is able to initiate and maintain steady-
state drifting. Article [13] deals with a functional prototype of a cooperative perception
system aiming at future cloud-based services of automated driving functions, focusing on
motorway use. A field study dealing with the capability of a market-introduced traffic
sign recognition system was conducted in [14], revealing deficits by misreading of signs. A
method to introduce automated driving functions in traffic flow simulation for virtual V &
V was introduced by [15], based on a co-simulation framework between multi-body and
traffic flow simulation.
As the Special Issue is dedicated to this topic, future research will continue in the de-
velopment of the individual system components and their complex interaction, constantly
rising the level of autonomy while providing an acceptable behavior for the individual and
the society, superior compared to human vehicle guidance.
Author Contributions: Conceptualization, A.E., Z.S., M.F. and H.L.; writing—original draft prepa-
ration, A.E.; writing—review and editing, A.E., Z.S., M.F. and H.L.; visualization, A.E.; project
administration, A.E. All authors have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Energies 2022, 15, 3476 5 of 5
Data Availability Statement: Not applicable.
Acknowledgments: The editors express their thanks to the excellent and elaborative work of the
international reviewers in evaluating the articles of this Special Issue.
Conflicts of Interest: The authors declare no conflict of interest.
References
1. Magosi, Z.F.; Wellershaus, C.; Tihanyi, V.R.; Luley, P.; Eichberger, A. Evaluation Methodology for Physical Radar Perception
Sensor Models Based on On-Road Measurements for the Testing and Validation of Automated Driving. Energies 2022, 15, 2545.
[CrossRef]
2. Fang, X.; Li, H.; Tettamanti, T.; Eichberger, A.; Fellendorf, M. Effects of Automated Vehicle Models at the Mixed Traffic Situation
on a Motorway Scenario. Energies 2022, 15, 2008. [CrossRef]
3. Holder, M.; Elster, L.; Winner, H. Digitalize the Twin: A Method for Calibration of Reference Data for Transfer Real-World Test
Drives into Simulation. Energies 2022, 15, 989. [CrossRef]
4. Clement, P.; Veledar, O.; Könczöl, C.; Danzinger, H.; Posch, M.; Eichberger, A.; Macher, G. Enhancing Acceptance and Trust in
Automated Driving trough Virtual Experience on a Driving Simulator. Energies 2022, 15, 781. [CrossRef]
5. Arefnezhad, S.; Eichberger, A.; Frühwirth, M.; Kaufmann, C.; Moser, M.; Koglbauer, I.V. Driver Monitoring of Automated Vehicles
by Classification of Driver Drowsiness Using a Deep Convolutional Neural Network Trained by Scalograms of ECG Signals.
Energies 2022, 15, 480. [CrossRef]
6. Li, H.; Tarik, K.; Arefnezhad, S.; Magosi, Z.F.; Wellershaus, C.; Babic, D.; Babic, D.; Tihanyi, V.; Eichberger, A.; Baunach, M.C.
Phenomenological Modelling of Camera Performance for Road Marking Detection. Energies 2022, 15, 194. [CrossRef]
7. Sziroczák, D.; Rohács, D. Automated Conflict Management Framework Development for Autonomous Aerial and Ground
Vehicles. Energies 2021, 14, 8344. [CrossRef]
8. Jurj, S.L.; Grundt, D.; Werner, T.; Borchers, P.; Rothemann, K.; Möhlmann, E. Increasing the Safety of Adaptive Cruise Control
Using Physics-Guided Reinforcement Learning. Energies 2021, 14, 7572. [CrossRef]
9. Klamann, B.; Winner, H. Comparing Different Levels of Technical Systems for a Modular Safety Approval—Why the State of the
Art Does Not Dispense with System Tests Yet. Energies 2021, 14, 7516. [CrossRef]
10. Huang, J.; Cheng, X.; Shen, Y.; Kong, D.; Wang, J. Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders.
Energies 2021, 14, 7202. [CrossRef]
11. Junaid, M.; Szalay, Z.; Török, Á. Evaluation of Non-Classical Decision-Making Methods in Self Driving Cars: Pedestrian Detection
Testing on Cluster of Images with Different Luminance Conditions. Energies 2021, 14, 7172. [CrossRef]
12. Czibere, S.; Domina, Á.; Bárdos, Á.; Szalay, Z. Model Predictive Controller Design for Vehicle Motion Control at Handling Limits
in Multiple Equilibria on Varying Road Surfaces. Energies 2021, 14, 6667. [CrossRef]
13. Tihanyi, V.; Rövid, A.; Remeli, V.; Vincze, Z.; Csonthó, M.; Pethő, Z.; Szalai, M.; Varga, B.; Khalil, A.; Szalay, Z. Towards
Cooperative Perception Services for ITS: Digital Twin in the Automotive Edge Cloud. Energies 2021, 14, 5930. [CrossRef]
14. Babić, D.; Babić, D.; Fiolić, M.; Šarić, Ž. Analysis of Market-Ready Traffic Sign Recognition Systems in Cars: A Test Field Study.
Energies 2021, 14, 3697. [CrossRef]
15. Nalic, D.; Pandurevic, A.; Eichberger, A.; Fellendorf, M.; Rogic, B. Software Framework for Testing of Automated Driving Systems
in the Traffic Environment of Vissim. Energies 2021, 14, 3135. [CrossRef]


Advances in Automated Driving Systems

Zanetti, Matteo

Luchetti, Alessandro

Maheshwari, Sharad

Kalkofen, Denis

Labrador-Ortega, Manuel

De Cecco, Mariolino

Object Pose Detection to Enable 3D Interaction from 2D Equirectangular Images in Mixed Reality Educational Settings

Painer, Florian

Baldermann, Andre

Gallien, Florian

Eichinger, Stefanie

Steindl, Florian

Dohrmann, Reiner

Dietzel, Martin

Synthesis of Zeolites from Fine-Grained Perlite and Their Application as Sorbents

Wied, Peter

Carraro, Francesco

Bolivar, Juan Manuel

Doonan, Christian

Falcaro, Paolo

Nidetzky, Bernd

Combining a Genetically Engineered Oxidase with Hydrogen-Bonded Organic Frameworks (HOFs) for Highly Efficient Biocomposites

Drescher, Adrian

Kienberger, Marlene

A Systematic Review on Waste as Sustainable Feedstock for Bioactive Molecules—Extraction as Isolation Technology

Graz University of Technology

Not a member yet