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Lateral support systems in vehicles have a high potential for reduction of lane departure crashes. To profit from their full potential, such systems should function properly in adverse conditions. Literature indicates that their accuracy varies between day and night-time. However, detailed quantifications of the systems’ performance in these conditions are rare. The aim of this study is to investigate the differences in detection quality and view range of Mobileye 630 in dry daytime and night-time conditions. On-road tests on four rural road sections in Croatia were conducted. Wilcoxon signed-rank test was used to test the difference between the number of quality rankings while absolute average, average difference and standard deviation were used to analyse the view range. Also, a paired samples t-test was used to test the difference between conditions for each line on each road. The overall results confirm that a significant difference in lane detection quality view range exists between tested conditions. “Medium” and “high” detection confidence (quality level 3 and 2), increased by 5% and 8% during night-time compared to daytime while level 0 (“nothing detected”) decreased by 12%. The view range increased (almost 16% for middle line) during daytime compared to night-time. The findings of this study expand the existing knowledge and are valuable for research and development of machine-vision systems but also for road authorities to optimize the markings’ quality performance.
Darko Babić; Dario Babić; Mario Fiolić; Arno Eichberger; Zoltan Magosi. A Comparison of Lane Marking Detection Quality and View Range between Daytime and Night-Time Conditions by Machine Vision. Energies 2021, 14, 4666 .
AMA StyleDarko Babić, Dario Babić, Mario Fiolić, Arno Eichberger, Zoltan Magosi. A Comparison of Lane Marking Detection Quality and View Range between Daytime and Night-Time Conditions by Machine Vision. Energies. 2021; 14 (15):4666.
Chicago/Turabian StyleDarko Babić; Dario Babić; Mario Fiolić; Arno Eichberger; Zoltan Magosi. 2021. "A Comparison of Lane Marking Detection Quality and View Range between Daytime and Night-Time Conditions by Machine Vision." Energies 14, no. 15: 4666.
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.
Demin Nalic; Aleksa Pandurevic; Arno Eichberger; Martin Fellendorf; Branko Rogic. Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim. Energies 2021, 14, 3135 .
AMA StyleDemin Nalic, Aleksa Pandurevic, Arno Eichberger, Martin Fellendorf, Branko Rogic. Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim. Energies. 2021; 14 (11):3135.
Chicago/Turabian StyleDemin Nalic; Aleksa Pandurevic; Arno Eichberger; Martin Fellendorf; Branko Rogic. 2021. "Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim." Energies 14, no. 11: 3135.
A spectacular measurement campaign was carried out on a real-world motorway stretch of Hungary with the participation of international industrial and academic partners. The measurement resulted in vehicle based and infrastructure based sensor data that will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles—equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization—carried out special test scenarios while collecting detailed data using different sensors. All of the test runs were recorded by both vehicles and infrastructure. The paper also showcases application examples to demonstrate the viability of the collected data having access to the ground truth labeling. This data set may support a large variety of solutions, for the test and validation of different kinds of approaches and techniques. As a complementary task, the available 5G network was monitored and tested under different radio conditions to investigate the latency results for different measurement scenarios. A part of the measured data has been shared openly, such that interested automotive and academic parties may use it for their own purposes.
Viktor Tihanyi; Tamás Tettamanti; Mihály Csonthó; Arno Eichberger; Dániel Ficzere; Kálmán Gangel; Leander Hörmann; Maria Klaffenböck; Christoph Knauder; Patrick Luley; Zoltán Magosi; Gábor Magyar; Huba Németh; Jakob Reckenzaun; Viktor Remeli; András Rövid; Matthias Ruether; Selim Solmaz; Zoltán Somogyi; Gábor Soós; Dávid Szántay; Tamás Tomaschek; Pál Varga; Zsolt Vincze; Christoph Wellershaus; Zsolt Szalay. Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies. Sensors 2021, 21, 2169 .
AMA StyleViktor Tihanyi, Tamás Tettamanti, Mihály Csonthó, Arno Eichberger, Dániel Ficzere, Kálmán Gangel, Leander Hörmann, Maria Klaffenböck, Christoph Knauder, Patrick Luley, Zoltán Magosi, Gábor Magyar, Huba Németh, Jakob Reckenzaun, Viktor Remeli, András Rövid, Matthias Ruether, Selim Solmaz, Zoltán Somogyi, Gábor Soós, Dávid Szántay, Tamás Tomaschek, Pál Varga, Zsolt Vincze, Christoph Wellershaus, Zsolt Szalay. Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies. Sensors. 2021; 21 (6):2169.
Chicago/Turabian StyleViktor Tihanyi; Tamás Tettamanti; Mihály Csonthó; Arno Eichberger; Dániel Ficzere; Kálmán Gangel; Leander Hörmann; Maria Klaffenböck; Christoph Knauder; Patrick Luley; Zoltán Magosi; Gábor Magyar; Huba Németh; Jakob Reckenzaun; Viktor Remeli; András Rövid; Matthias Ruether; Selim Solmaz; Zoltán Somogyi; Gábor Soós; Dávid Szántay; Tamás Tomaschek; Pál Varga; Zsolt Vincze; Christoph Wellershaus; Zsolt Szalay. 2021. "Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies." Sensors 21, no. 6: 2169.
The paper presents the measurement campaign carried out on a real-world motorway stretch of Hungary with the participation of both industrial and academic partners from Austria and Hungary. The measurement included vehicle based as well as infrastructure based sensor data. The obtained results will be extremely useful for future automotive R&D activities due to the available ground truth for static and dynamic content. The aim of the measurement campaign was twofold. On the one hand, road geometry was mapped with high precision in order to build Ultra High Definition (UHD) map of the test road. On the other hand, the vehicles - equipped with differential Global Navigation Satellite Systems (GNSS) for ground truth localization - carried out special test scenarios while collecting detailed data using different sensors. All test runs were recorded by both vehicles and infrastructure. As a complementary task, the available 5G network was monitored and tested. The paper also showcases application examples based on the measurement campaign data, in which the added value of having access to the ground truth labeling and the created UHD map of the motorway section becomes apparent. In order to present our work transparently, a part of the measured data have been shared openly such that interested automotive as well as academic parties may use it for their own purposes.
Viktor Tihanyi; Tettamanti Tamás; Mihály Csonthó; Arno Eichberger; Dániel Ficzere; Kálmán Gangel; Leander B. Hörmann; Maria A. Klaffenböck; Christoph Knauder; Patrick Patrick Luley; Zoltan Ferenc Magosi; Gábor Magyar; Huba Németh; Gábor Soós; Jakob Reckenzaun; Viktor Remeli; András Rövid; Matthias Ruether; Selim Solmaz; Zoltán Somogyi; Dávid Szántay; Tamás Attila Tomaschek; Pál Varga; Zsolt Vincze; Christoph Wellershaus; Zsolt Szalay. Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies. 2021, 1 .
AMA StyleViktor Tihanyi, Tettamanti Tamás, Mihály Csonthó, Arno Eichberger, Dániel Ficzere, Kálmán Gangel, Leander B. Hörmann, Maria A. Klaffenböck, Christoph Knauder, Patrick Patrick Luley, Zoltan Ferenc Magosi, Gábor Magyar, Huba Németh, Gábor Soós, Jakob Reckenzaun, Viktor Remeli, András Rövid, Matthias Ruether, Selim Solmaz, Zoltán Somogyi, Dávid Szántay, Tamás Attila Tomaschek, Pál Varga, Zsolt Vincze, Christoph Wellershaus, Zsolt Szalay. Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies. . 2021; ():1.
Chicago/Turabian StyleViktor Tihanyi; Tettamanti Tamás; Mihály Csonthó; Arno Eichberger; Dániel Ficzere; Kálmán Gangel; Leander B. Hörmann; Maria A. Klaffenböck; Christoph Knauder; Patrick Patrick Luley; Zoltan Ferenc Magosi; Gábor Magyar; Huba Németh; Gábor Soós; Jakob Reckenzaun; Viktor Remeli; András Rövid; Matthias Ruether; Selim Solmaz; Zoltán Somogyi; Dávid Szántay; Tamás Attila Tomaschek; Pál Varga; Zsolt Vincze; Christoph Wellershaus; Zsolt Szalay. 2021. "Motorway Measurement Campaign to Support R&D Activities in the Field of Automated Driving Technologies." , no. : 1.
The increasingly used approach of combining different simulation softwares in testing of automated driving systems (ADSs) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSPs) provide the possibility to cover the high demand for testing kilometers for ADSs by combining vehicle simulation software (VSS) with traffic flow simulation software (TFSS) environments. The emphasis on the demand for testing kilometers is not enough to choose a suitable CSP. The complexity levels of the vehicle, object, sensors, and environment models used are essential for valid and representative simulation results. Choosing a suitable CSP raises the question of how the test procedures should be defined and constructed and what the relevant test scenarios are. Parameters of the ADS, environments, objects, and sensors in the VSS, as well as traffic parameters in the TFSS, can be used to define and generate test scenarios. In order to generate a large number of scenarios in a systematic and automated way, suitable and appropriate software designs are required. In this paper, we present a software design for a CSP based on the Model–View–Controller (MVC) design pattern as well as an implementation of a complex CSP for virtual testing of ADSs. Based on this design, an implementation of a CSP is presented using the VSS from IPG Automotive (CarMaker) and the TFSS from the PTV Group (Vissim). The results showed that the presented CSP design and the implementation of the co-simulation can be used to generate relevant scenarios for testing of ADSs.
Demin Nalic; Aleksa Pandurevic; Arno Eichberger; Branko Rogic. Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems. Sustainability 2020, 12, 10476 .
AMA StyleDemin Nalic, Aleksa Pandurevic, Arno Eichberger, Branko Rogic. Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems. Sustainability. 2020; 12 (24):10476.
Chicago/Turabian StyleDemin Nalic; Aleksa Pandurevic; Arno Eichberger; Branko Rogic. 2020. "Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems." Sustainability 12, no. 24: 10476.
One research area within the development of automated vehicles deals with the impact analysis on traffic flow by numerical simulation. This study investigates human drivers’ acceptance while interacting with different levels of automated vehicles on highways including on- and off-ramps. Reactions between conventional, human driven vehicles (CV) and automated vehicles (AV) were tested using a driving simulator. Gaps and headways between vehicles were recorded and analyzed. The analysis indicates similar behavior between CVs and aggressive AVs (short headways) while prudent AVs were perceived less favorable by the test drivers. Additionally, long headways showed more disturbance in traffic flow than shorter headway setups of the automatic distance control (ACC).
Georg Hanzl; Michael Haberl; Arno Eichberger; Martin Fellendorf. Human Driver’s Acceptance of Automated Driving Systems Based on a Driving Simulator Study. Advanced Microsystems for Automotive Applications 2016 2020, 186 -195.
AMA StyleGeorg Hanzl, Michael Haberl, Arno Eichberger, Martin Fellendorf. Human Driver’s Acceptance of Automated Driving Systems Based on a Driving Simulator Study. Advanced Microsystems for Automotive Applications 2016. 2020; ():186-195.
Chicago/Turabian StyleGeorg Hanzl; Michael Haberl; Arno Eichberger; Martin Fellendorf. 2020. "Human Driver’s Acceptance of Automated Driving Systems Based on a Driving Simulator Study." Advanced Microsystems for Automotive Applications 2016 , no. : 186-195.
Classical approaches for testing of automated driving systems (ADS) of SAE levels 1 and 2 were based on defined scenarios with specific maneuvers, depending on the function under test. For ADS of SAE level 3+, the scenario space is infinite and calling for virtual testing and verification. The biggest challenge for virtual testing methods lies in the realistic representation of the virtual environment where the ADS is tested. Such an environment shall provide the possibility to model and develop vehicles, objects, control algorithms, traffic participants and environment elements in order to generate valid and representative test data. An important and crucial aspect of such environments is the testing of vehicles in a complex traffic environment with a stochastic and realistic traffic representation. For this research we used a microscopic traffic flow simulation software (TFSS) PTV Vissim and the vehicle simulation software IPG CarMaker to test ADS. Although the TFSS provides realistic and stochastic behavior of traffic participants, the occurrence of safety-critical scenarios (SCS) is not guaranteed. To generate and increase such scenarios, a novel stress testing method (STM) is introduced. With this method, traffic participants are manipulated in the vicinity of the vehicle under test in order to provoke SCS derived from statistical accident data on motorways in Austria. Using the co-simulation between IPG CarMaker, PTV Vissim and external driver models in Vissim are used to imitate human driving errors, resulting in an increase of SCS.
Demin Nalic; Hexuan Li; Arno Eichberger; Christoph Wellershaus; Aleksa Pandurevic; Branko Rogic. Stress Testing Method for Scenario-Based Testing of Automated Driving Systems. IEEE Access 2020, 8, 224974 -224984.
AMA StyleDemin Nalic, Hexuan Li, Arno Eichberger, Christoph Wellershaus, Aleksa Pandurevic, Branko Rogic. Stress Testing Method for Scenario-Based Testing of Automated Driving Systems. IEEE Access. 2020; 8 (99):224974-224984.
Chicago/Turabian StyleDemin Nalic; Hexuan Li; Arno Eichberger; Christoph Wellershaus; Aleksa Pandurevic; Branko Rogic. 2020. "Stress Testing Method for Scenario-Based Testing of Automated Driving Systems." IEEE Access 8, no. 99: 224974-224984.
The increasingly used approach of combining different simulation software for testing of automated driving systems (ADS) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSP) provide the possibility to cover the high demand on testing kilometres for ADS by combining vehicle simulation with traffic flow simulation software (TFSS) environments. Having chosen a suitable CSP rises up the question how the test procedures should be defined and constructed and what are the relevant test scenarios. Parameters of the ADS in vehicle simulation, traffic parameter in TFSS and combination of all these can be used for the definition of test scenarios. Thus the automation of a process, consisting of vehicle and traffic parameters and a suitable CSP, a test procedure for ADS should be well designed and implemented. This paper presents the design and implementation of a complex co-simulation framework for virtual ADS testing combining IPG CarMaker and PTV Vissim.
Demin Nalic; Aleksa Pandurevic; Arno Eichberger; Branko Rogic. Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems. 2020, 1 .
AMA StyleDemin Nalic, Aleksa Pandurevic, Arno Eichberger, Branko Rogic. Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems. . 2020; ():1.
Chicago/Turabian StyleDemin Nalic; Aleksa Pandurevic; Arno Eichberger; Branko Rogic. 2020. "Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems." , no. : 1.
The development of automated driving is an ongoing process; nonetheless, certain problems remain unresolved. One of them is the question when the automated vehicle control system should hand over the control to a human driver and whether this ...
Clemens Kaufmann; Matthias Frühwirth; Dietmar Messerschmidt; Maximilian Moser; Arno Eichberger; Sadegh Arefnezhad. Driving and tiredness: Results of the behaviour observation of a simulator study with special focus on automated driving. Transactions on Transport Sciences 2020, 11, 51 -63.
AMA StyleClemens Kaufmann, Matthias Frühwirth, Dietmar Messerschmidt, Maximilian Moser, Arno Eichberger, Sadegh Arefnezhad. Driving and tiredness: Results of the behaviour observation of a simulator study with special focus on automated driving. Transactions on Transport Sciences. 2020; 11 (2):51-63.
Chicago/Turabian StyleClemens Kaufmann; Matthias Frühwirth; Dietmar Messerschmidt; Maximilian Moser; Arno Eichberger; Sadegh Arefnezhad. 2020. "Driving and tiredness: Results of the behaviour observation of a simulator study with special focus on automated driving." Transactions on Transport Sciences 11, no. 2: 51-63.
Drowsy driving is one of the main causes of road accidents. Accurate and reliable detection of drivers' drowsiness is significantly important to prevent drowsiness-related accidents. In the context of automated vehicle driving, it is important for intelligent systems to know the current state of the driver to prepare handover maneuvers. Previous studies are mostly based on manually extracted features from either driving performance or driver physiological data. This methodology of a priori defined features can lead to losing valuable information of input signals that are significant to classify drowsiness levels in individual drivers because generally, it is not known which features are suitable for drowsiness prediction before classification. By using deep neural networks, features can be extracted automatically from preprocessed data. This paper presents a new non-obtrusive drowsiness detection system based on deep neural networks using vehicle-based measures. The proposed method is based on a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN). Five vehicle-based measures, including lateral deviation from road centerline, lateral acceleration, yaw rate, steering wheel angle, and steering wheel velocity, are exploited as network inputs. The level of drowsiness is classified into three different classes. Long-short term memory (LSTM) and gated recurrent unit (GRU) layers are used as RNN in the structure of the designed deep network. The performance of the proposed method is evaluated on experimental data that were collected from 44 sessions in a fixed-base driving simulator simulating monotonous night-time highway drives. Results show that the classification accuracy of the designed deep networks outperforms traditional classifiers like support vector machine and k-nearest neighbors. The highest accuracy of 96.0% has been achieved with a combination of CNN and LSTM (CNN-LSTM). Further research should include more signal sources, including unobtrusively taken physiological signals, and test the system in real-world conditions.
Sadegh Arefnezhad; Sajjad Samiee; Arno Eichberger; Matthias Frühwirth; Clemens Kaufmann; Emma Klotz. Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures. Expert Systems with Applications 2020, 162, 113778 .
AMA StyleSadegh Arefnezhad, Sajjad Samiee, Arno Eichberger, Matthias Frühwirth, Clemens Kaufmann, Emma Klotz. Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures. Expert Systems with Applications. 2020; 162 ():113778.
Chicago/Turabian StyleSadegh Arefnezhad; Sajjad Samiee; Arno Eichberger; Matthias Frühwirth; Clemens Kaufmann; Emma Klotz. 2020. "Applying deep neural networks for multi-level classification of driver drowsiness using Vehicle-based measures." Expert Systems with Applications 162, no. : 113778.
The tire-road friction coefficient (μmax) is an important input for vehicle dynamics control system and automated driving modules. However, reliable and accurate measurement of this parameter is difficult and costly in mass-produced vehicles and thus estimation is necessary. In this research, an innovative optimization based framework to estimate μmax is proposed. The observation problem is formulated as a non-convex optimization. A novelty of the framework is that the μmax can be accurately estimated in real time together with side slip angle as a by-product without requiring a good initial guess for the non-convex optimization. A key observation is that the time derivative of μmax and side slip angle can be assumed as zero and computed based on measurement, respectively. This allows the observed variables to be updated at a relatively low frequency w.r.t. the solution of the optimization problem. During the interval between each two neighbouring updating time, the observer estimates the μmax and side slip angle by integrating sensor information based on the last update. To find the global optima approximately, a grid search method is implemented for solving non-convex optimization. The estimation results from the proposed observer and a linearization based observer (lbo) are finally compared under various tire-road conditions with simulations and experiments. The results showed that 1) the proposed observer can always guarantee stability in a wide range of vehicle operations while lbo cannot. 2) w.r.t. root mean square of estimation error, the proposed observer performs overall better than lbo in μmax estimation.
Liang Shao; Chi Jin; Arno Eichberger; Cornelia Lex. Grid Search Based Tire-Road Friction Estimation. IEEE Access 2020, 8, 81506 -81525.
AMA StyleLiang Shao, Chi Jin, Arno Eichberger, Cornelia Lex. Grid Search Based Tire-Road Friction Estimation. IEEE Access. 2020; 8 (99):81506-81525.
Chicago/Turabian StyleLiang Shao; Chi Jin; Arno Eichberger; Cornelia Lex. 2020. "Grid Search Based Tire-Road Friction Estimation." IEEE Access 8, no. 99: 81506-81525.
For market introduction of advanced driver assistant (ADAS) and automated driving (AD) systems on full vehicle level, testing and validation is one of the biggest challenges. The present study describes a novel approach that integrates a driving simulator in a virtual development process aiming to reduce time and effort for system development. The approach is demonstrated on a specific automated lane change assist (LCA) system. To this end, the LCA function and the corresponding human machine interface (HMI) are developed and implemented in the driving simulator. The core of the approach is a driving simulator-based testing method which proposes a novel two stage testing concept and involves multiple test drivers. The method provides better insight into the overall system performance and, moreover, detects potentials for improvements dedicated for the ADAS functionalities as well as for the design of the HMI system. Using this method, a driving simulator study with 20 volunteer drivers is conducted to evaluate the LCA system with respect to driver acceptance and user friendliness. The results of the study will be used for the parametrization and fine tuning of the LCA function as well as for the HMI improvement.
Branko Rogic; Demin Nalic; Arno Eichberger; Stefan Bernsteiner. A Novel Approach to Integrate Human-in-the-Loop Testing in the Development Chain of Automated Driving: The Example of Automated Lane Change. IFAC-PapersOnLine 2020, 53, 10188 -10195.
AMA StyleBranko Rogic, Demin Nalic, Arno Eichberger, Stefan Bernsteiner. A Novel Approach to Integrate Human-in-the-Loop Testing in the Development Chain of Automated Driving: The Example of Automated Lane Change. IFAC-PapersOnLine. 2020; 53 (2):10188-10195.
Chicago/Turabian StyleBranko Rogic; Demin Nalic; Arno Eichberger; Stefan Bernsteiner. 2020. "A Novel Approach to Integrate Human-in-the-Loop Testing in the Development Chain of Automated Driving: The Example of Automated Lane Change." IFAC-PapersOnLine 53, no. 2: 10188-10195.
This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.
Sadegh Arefnezhad; Sajjad Samiee; Arno Eichberger; Ali Nahvi. Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection. Sensors 2019, 19, 943 .
AMA StyleSadegh Arefnezhad, Sajjad Samiee, Arno Eichberger, Ali Nahvi. Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection. Sensors. 2019; 19 (4):943.
Chicago/Turabian StyleSadegh Arefnezhad; Sajjad Samiee; Arno Eichberger; Ali Nahvi. 2019. "Driver Drowsiness Detection Based on Steering Wheel Data Applying Adaptive Neuro-Fuzzy Feature Selection." Sensors 19, no. 4: 943.
Automated vehicles require information on the current road condition, i.e. the tyre–road friction coefficient for trajectory planning, braking or steering interventions. In this work, we propose a framework to estimate the road friction coefficient with stability and robustness guarantee using total aligning torque in vehicle front axle during steering. We first adopt a novel strategy to estimate the front axle lateral force which performs better than the classical unknown input observer. Then, combined with an indirect measurement based on estimated total aligning torque and front axle lateral force, a non-linear adaptive observer is designed to estimate road friction coefficient with stability guarantee. To increase the robustness of the estimation result, criteria are proposed to decide when to update the estimated road conditions. Simulations and experiments under various road conditions validate the proposed framework and demonstrate its advantage in stability by comparing it with the method utilising the wide-spread Extended Kalman Filter.
Liang Shao; Chi Jin; Cornelia Lex; Arno Eichberger. Robust road friction estimation during vehicle steering. Vehicle System Dynamics 2018, 57, 493 -519.
AMA StyleLiang Shao, Chi Jin, Cornelia Lex, Arno Eichberger. Robust road friction estimation during vehicle steering. Vehicle System Dynamics. 2018; 57 (4):493-519.
Chicago/Turabian StyleLiang Shao; Chi Jin; Cornelia Lex; Arno Eichberger. 2018. "Robust road friction estimation during vehicle steering." Vehicle System Dynamics 57, no. 4: 493-519.
Sinkende Grenzwerte für CO2-Emissionen und die steigende Anzahl an Fahrzeugvarianten erhöhen den Aufwand, den Flottenverbrauch zu optimieren. Maßnahmen zur Kraftstoffverbrauchsreduktion müssen für jede Fahrzeugkonfiguration separat betrachtet werden. In Zusammenarbeit zwischen der TU Graz und Magna Steyr wurde eine flexible, effiziente und aussagekräftige Simulationsumgebung hierfür entwickelt. Unter zusätzlicher Berücksichtigung von Kosten und Wechselwirkungen der einzelnen Maßnahmen kann das Kostenoptimum zur Zielerreichung der CO2-Emissionen eines Fahrzeugs oder einer kompletten Fahrzeugflotte bestimmt werden.
Michael Martin; Robert Premstaller; Arno Eichberger. Virtuelle Flottenverbrauchsoptimierung unter Kostenbetrachtung. Proceedings 2018, 271 -278.
AMA StyleMichael Martin, Robert Premstaller, Arno Eichberger. Virtuelle Flottenverbrauchsoptimierung unter Kostenbetrachtung. Proceedings. 2018; ():271-278.
Chicago/Turabian StyleMichael Martin; Robert Premstaller; Arno Eichberger. 2018. "Virtuelle Flottenverbrauchsoptimierung unter Kostenbetrachtung." Proceedings , no. : 271-278.
Objective: This study investigated drivers' evaluation of a conventional autonomous emergency braking (AEB) system on high and reduced tire–road friction and compared these results to those of an AEB system adaptive to the reduced tire–road friction by earlier braking. Current automated systems such as the AEB do not adapt the vehicle control strategy to the road friction; for example, on snowy roads. Because winter precipitation is associated with a 19% increase in traffic crashes and a 13% increase in injuries compared to dry conditions, the potential of conventional AEB to prevent collisions could be significantly improved by including friction in the control algorithm. Whereas adaption is not legally required for a conventional AEB system, higher automated functions will have to adapt to the current tire–road friction because human drivers will not be required to monitor the driving environment at all times. For automated driving functions to be used, high levels of perceived safety and trust of occupants have to be reached with new systems. The application case of an AEB is used to investigate drivers' evaluation depending on the road condition in order to gain knowledge for the design of future driving functions. Methods: In a driving simulator, the conventional, nonadaptive AEB was evaluated on dry roads with high friction (μ = 1) and on snowy roads with reduced friction (μ = 0.3). In addition, an AEB system adapted to road friction was designed for this study and compared with the conventional AEB on snowy roads with reduced friction. Ninety-six drivers (48 males, 48 females) assigned to 5 age groups (20–29, 30–39, 40–49, 50–59, and 60–75 years) drove with AEB in the simulator. The drivers observed and evaluated the AEB's braking actions in response to an imminent rear-end collision at an intersection. Results: The results show that drivers' safety and trust in the conventional AEB were significantly lower on snowy roads, and the nonadaptive autonomous braking strategy was considered less appropriate on snowy roads compared to dry roads. As expected, the adaptive AEB braking strategy was considered more appropriate for snowy roads than the nonadaptive strategy. In conditions of reduced friction, drivers' subjective safety and trust were significantly improved when driving with the adaptive AEB compared to the conventional AEB. Women felt less safe than men when AEB was braking. Differences between age groups were not of statistical significance. Conclusions: Drivers notice the adaptation of the autonomous braking strategy on snowy roads with reduced friction. On snowy roads, they feel safer and trust the adaptive system more than the nonadaptive automation.
Ioana Koglbauer; Jürgen Holzinger; Arno Eichberger; Cornelia Lex. Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust. Traffic Injury Prevention 2018, 19, 332 -337.
AMA StyleIoana Koglbauer, Jürgen Holzinger, Arno Eichberger, Cornelia Lex. Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust. Traffic Injury Prevention. 2018; 19 (3):332-337.
Chicago/Turabian StyleIoana Koglbauer; Jürgen Holzinger; Arno Eichberger; Cornelia Lex. 2018. "Autonomous emergency braking systems adapted to snowy road conditions improve drivers' perceived safety and trust." Traffic Injury Prevention 19, no. 3: 332-337.
Automated driving requires a reliable digital representation of the environment, which is achieved by various vehicle sensors. Wireless devices for communication between vehicles and infrastructure (Car2X communication) provide additional data beyond the vehicle’s sensor range. In order to reduce the amount of on-road testing, there has been an increased use of numerical simulation in the development of automated driving functions, which demands accurate simulation models for the sensors involved. The present research deals with the development of Car2X sensor models for conceptual, automated driving investigations based on relatively simple yet computationally efficient mathematical models featuring parameters derived from on-road hardware testing. For analysis purposes, variations in range and reliability in different driving situations were measured and depicted in Google Earth. For the sensor model, a combination of geometric and stochastic models was chosen. The modeling is based on a link budget calculation that considers system and path losses, where wave propagation is described using Nakagami probability density functions. For intersections, an additional term is added to account for the path loss with geometric parameters of the intersection. After model parametrization, an evaluation was conducted. In addition, as a sample case, Car2X was added to an adaptive cruise control, and the improved functionality was demonstrated using vehicle dynamics simulation. This extended adaptive cruise control used information from the indicator of surrounding vehicles to react faster to lane changes by these vehicles.
Arno Eichberger; Gerald Markovic; Zoltan Magosi; Branko Rogic; Cornelia Lex; Sajjad Samiee. A Car2X sensor model for virtual development of automated driving. International Journal of Advanced Robotic Systems 2017, 14, 1 .
AMA StyleArno Eichberger, Gerald Markovic, Zoltan Magosi, Branko Rogic, Cornelia Lex, Sajjad Samiee. A Car2X sensor model for virtual development of automated driving. International Journal of Advanced Robotic Systems. 2017; 14 (5):1.
Chicago/Turabian StyleArno Eichberger; Gerald Markovic; Zoltan Magosi; Branko Rogic; Cornelia Lex; Sajjad Samiee. 2017. "A Car2X sensor model for virtual development of automated driving." International Journal of Advanced Robotic Systems 14, no. 5: 1.
Chi Jin; Liang Shao; Cornelia Lex; Arno Eichberger. Vehicle Side Slip Angle Observation with Road Friction Adaptation. IFAC-PapersOnLine 2017, 50, 3406 -3411.
AMA StyleChi Jin, Liang Shao, Cornelia Lex, Arno Eichberger. Vehicle Side Slip Angle Observation with Road Friction Adaptation. IFAC-PapersOnLine. 2017; 50 (1):3406-3411.
Chicago/Turabian StyleChi Jin; Liang Shao; Cornelia Lex; Arno Eichberger. 2017. "Vehicle Side Slip Angle Observation with Road Friction Adaptation." IFAC-PapersOnLine 50, no. 1: 3406-3411.
This study investigates drivers’ interaction with Adaptive Cruise Control (ACC) in different road conditions and identifies areas of improvement. Ninety-six drivers drove with the ACC in a driving simulator showing either a summer scenery and a dry road with high grip potential or a winter scenery with a snowy road and reduced grip potential. The results show that on snowy roads the drivers set in average a lower ACC speed and preferred a larger ACC time gap. Drivers’ workload and effort were higher when using the ACC on snowy as compared to dry roads. Generally, the use of a shorter ACC gap resulted in lower ratings of comfort, safety, and trust and higher ratings of mental workload and effort in both dry and snowy road conditions. The drivers judged that ACC was braking too late and maintained a too short gap to the forward vehicle, especially when the ACC was set to 1 second as compared to a 1.8-second time gap. A future adaptation of ACC’s control strategy to reduced tire-road grip potential would not only improve comfort and user acceptance of the human driver but also increase the potential to react in emergency situations with braking or evasive steering.
Ioana Koglbauer; Jürgen Holzinger; Arno Eichberger; Cornelia Lex. Drivers’ Interaction with Adaptive Cruise Control on Dry and Snowy Roads with Various Tire-Road Grip Potentials. Journal of Advanced Transportation 2017, 2017, 1 -10.
AMA StyleIoana Koglbauer, Jürgen Holzinger, Arno Eichberger, Cornelia Lex. Drivers’ Interaction with Adaptive Cruise Control on Dry and Snowy Roads with Various Tire-Road Grip Potentials. Journal of Advanced Transportation. 2017; 2017 ():1-10.
Chicago/Turabian StyleIoana Koglbauer; Jürgen Holzinger; Arno Eichberger; Cornelia Lex. 2017. "Drivers’ Interaction with Adaptive Cruise Control on Dry and Snowy Roads with Various Tire-Road Grip Potentials." Journal of Advanced Transportation 2017, no. : 1-10.
L. Shao; C. Lex; A. Hackl; A. Eichberger. Estimation of tire road friction during vehicle steering. Advanced Vehicle Control AVEC’16 2016, 559 -566.
AMA StyleL. Shao, C. Lex, A. Hackl, A. Eichberger. Estimation of tire road friction during vehicle steering. Advanced Vehicle Control AVEC’16. 2016; ():559-566.
Chicago/Turabian StyleL. Shao; C. Lex; A. Hackl; A. Eichberger. 2016. "Estimation of tire road friction during vehicle steering." Advanced Vehicle Control AVEC’16 , no. : 559-566.