TY - CHAP A1 - Vaculin, Ondrej T1 - Assessment of a pilot deployment of an automated shuttle bus T2 - Autosympo 2019 UR - http://www.cas-sae.cz/autosympo.php Y1 - 2019 UR - http://www.cas-sae.cz/autosympo.php ER - TY - JOUR A1 - Vaculín, Ondřej A1 - Gellrich, Michael A1 - Matawa, Robert A1 - Witschass, Steffen T1 - Testing of automated driving systems JF - MECCA : Journal of Middle European Construction and Design of Cars N2 - The automated driving requires new testing approaches, which are more complex than the current testing systems. The complexity and requirements for accuracy is important, because of interconnection of virtual with physical testing. This paper presents a generic approach to testing of automated driving functions and demonstrates its implementation on measurement of two scenarios. N2 - Automatizované rízení vyžaduje nové testovací prístupy, které jsou daleko komplexnejší než soucasné testovací systémy. Komplexnost a požadavky na presnost jsou duležité z pohledu na propojení fyzického a virtuálního testování. Tento clánek prezentuje obecný prístup k testování funkcí automatizovaného rízení a demonstruje jeho implementaci na mereních dvou scénáru. UR - https://doi.org/10.14311/mecdc.2020.01.02 KW - automated driving KW - testing KW - testing scenarios Y1 - 2020 UR - https://doi.org/10.14311/mecdc.2020.01.02 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-16888 SN - 1804-9338 VL - 17 IS - 1 SP - 7 EP - 13 PB - Czech Technical University CY - Prag ER - TY - CHAP A1 - Plaschkies, Franz A1 - Vaculín, Ondřej A1 - Pelisson, Angelo A. A1 - Schumacher, Axel T1 - Schnelle Abschätzung des Crashverhaltens von Insassen unter Berücksichtigung der Vielfalt des Menschen BT - Robustheit, Datenintensität und Vorhersagekraft von Metamodellen T2 - Fahrzeugsicherheit 2022: Auf dem Weg zur Fahrzeugsicherheit 2030 UR - https://doi.org/10.51202/9783181023877-313 Y1 - 2022 UR - https://doi.org/10.51202/9783181023877-313 SN - 978-3-18-092387-1 SN - 978-3-18-102387-7 SP - 313 EP - 326 PB - VDI Verlag CY - Düsseldorf ER - TY - JOUR A1 - Borba, Thiago De A1 - Vaculín, Ondřej A1 - Marzbani, Hormoz A1 - Jazar, Reza Nakhaie T1 - Increasing Safety of Automated Driving by Infrastructure-Based Sensors JF - IEEE Access N2 - This paper describes the development of an intelligent infrastructure, a test field, for the safety assurance of automated vehicles within the research project Ingolstadt Innovation Laboratory (IN2Lab). It includes a description of the test field architecture, the RoadSide Units (RSU) concept based on infrastructure-based sensors, the environment perception system, and the mission control system. The study also proposes a global object fusion method to fuse objects detected by different RSUs and investigate the overall measurement accuracy obtained from the usage of different infrastructure-based sensors. Furthermore, it presents four use cases: traffic monitoring, assisted perception, collaborative perception, and extended perception. The traffic monitoring, based on the perception information provided by each roadside unit, generates a global fused object list and monitors the state of the traffic participants. The assisted perception, using vehicle-to-infrastructure communication, broadcasts the state information of the traffic participants to the connected vehicles. The collaborative perception creates a global fused object list with the local detections of connected vehicles and the detections provided by the roadside units, making it available for all connected vehicles. Lastly, the extended environment perception monitors specific locations, recognizes critical scenarios involving vulnerable road users and automated vehicles, and generates a suitable avoidance maneuver to avoid or mitigate the occurrence of collisions. UR - https://doi.org/10.1109/ACCESS.2023.3311136 KW - Automated vehicles KW - infrastructure-based sensors KW - safety KW - test field Y1 - 2023 UR - https://doi.org/10.1109/ACCESS.2023.3311136 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-39623 SN - 2169-3536 VL - 11 SP - 94974 EP - 94991 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Plaschkies, Franz A1 - Possoli, Ketlen A1 - Vaculin, Ondřej A1 - Schumacher, Axel A1 - de Andrade Junior, Pedro T1 - Evaluation Approach for Machine Learning Concepts in Occupant Protection Based on Multi-Attribute Decision Making T2 - Proceedings of the 27th International Technical Conference on the Enhanced Safety of Vehicles Y1 - 2023 UR - https://www-nrd.nhtsa.dot.gov/departments/esv/27th/TOC.htm PB - NHTSA CY - Washington ER - TY - CHAP A1 - Vaculín, Ondřej T1 - Holistic Environment for Development and Testing of Cooperative, Connected and Automated Mobility Functions T2 - FISITA World Congress 2023 Y1 - 2023 UR - https://www.fisita.com/library/fwc2023-sca-025 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Plaschkies, Franz A1 - Vaculín, Ondřej A1 - Schumacher, Axel T1 - Assessment of the Influence of Human Body Diversity on Passive Safety Systems BT - A State-of-the-art Overview T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-pif-071 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - de Borba, Thiago A1 - Vaculin, Ondrej A1 - Patel, Parth T1 - Concept of a Vehicle Platform for Development and Testing of Low-Speed Automated Driving Functions T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-acm-118 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Negri de Azeredo, Rodrigo A1 - Vaculin, Ondrej A1 - da Costa Oliveira, Gustavo Henrique T1 - Automatic Car Reverse Braking System Based on a ToF Camera Sensor T2 - FISITA Web Congress 2020 Y1 - 2020 UR - https://www.fisita.com/library/f2020-pif-049 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Plaschkies, Franz A1 - Vaculin, Ondrej T1 - Estimation of the Impact of Human Body Variation on Its Crash Behavior Using Machine Learning Methods T2 - FISITA Web Congress 2020 Y1 - 2020 UR - https://www.fisita.com/library/f2020-pif-051 PB - Fisita CY - Bishops Stortford ER - TY - CHAP A1 - Slavík, Martin A1 - Vaculín, Ondřej T1 - Concept of Mission Control System for IN2Lab testing field for Automated Driving T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-acm-119 PB - FISITA CY - Bishops Stortford ER - TY - CHAP A1 - Afraj, Shahabaz A1 - Böhmländer, Dennis A1 - Vaculin, Ondrej A1 - Hynčík, Luděk T1 - Quantification methodology for crash behavior comparison between virtual crash simulations and real-time crash tests T2 - FISITA World Congress 2021 Y1 - 2021 UR - https://www.fisita.com/library/f2021-pif-072 PB - FISITA CY - Bishops Stortford ER - TY - JOUR A1 - Dönmez, Ömer A1 - Vaculín, Ondřej A1 - de Borba, Thiago T1 - A Cost Effective Solution to an Automated Valet Parking System JF - International Journal of Automotive Technology N2 - Automated Valet Parking Systems (AVPS) relieve the driver of the entire parking process. Many of the systems known today rely on a combination of automotive sensors with sensors of the infrastructure. For this purpose, parking facilities are equipped with comprehensive sensor technology to support the vehicles in environment sensing and route planning. This approach is comparatively expensive which is why many parking operators don’t provide that technology to their customers. This paper proposes a lean AVPS system architecture that requires minimal effort to adapt the infrastructure. At the same time, state-of-the-art vehicle technology is used to make AVPS more profitable overall. At the beginning, an overview will be given describing the state of the art of AVPS. Subsequently, requirements for the AVPS will be elaborated, whereby the system can be designed and implemented in the following. Finally, the presentation of simulation results shows that one doesn’t have to extend the infrastructure with sensors to develop a safe and reliable AVPS. UR - https://doi.org/10.1007/s12239-024-00031-9 KW - Automated driving KW - Automated valet parking KW - Trajectory planning KW - Smart parking KW - System architecture Y1 - 2024 UR - https://doi.org/10.1007/s12239-024-00031-9 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46333 SN - 1976-3832 SN - 1229-9138 VL - 25 IS - 2 SP - 369 EP - 380 PB - Springer CY - Heidelberg ER - TY - INPR A1 - Afraj, Shahabaz A1 - Vaculín, Ondřej A1 - Böhmländer, Dennis A1 - Hynčík, Luděk T1 - Vehicle Crash Simulation Models for Reinforcement Learning driven crash-detection algorithm calibration T2 - Research Square N2 - The development of finite element vehicle models for crash simulations is a highly complex task. The main aim of these models is to simulate a variety of crash scenarios and assess all the safety systems for their respective performances. These vehicle models possess a substantial amount of data pertaining to the vehicle's geometry, structure, materials, etc., and are used to estimate a large set of system and component level characteristics using crash simulations. It is understood that even the most well-developed simulation models are prone to deviations in estimation when compared to real-world physical test results. This is generally due to our inability to model the chaos and uncertainties introduced in the real world. Such unavoidable deviations render the use of virtual simulations ineffective for the calibration process of the algorithms that activate the restraint systems in the event of a crash (crash-detection algorithm). In the scope of this research, authors hypothesize the possibility of accounting for such variations introduced in the real world by creating a feedback loop between real-world crash tests and crash simulations. To accomplish this, a Reinforcement Learning (RL) compatible virtual surrogate model is used, which is adapted from crash simulation models. Hence, a conceptual methodology is illustrated in this paper for developing an RL-compatible model that can be trained using the results of crash simulations and crash tests. As the calibration of the crash-detection algorithm is fundamentally dependent upon the crash pulses, the scope of the expected output is limited to advancing the ability to estimate crash pulses. Furthermore, the real-time implementation of the methodology is illustrated using an actual vehicle model. UR - https://doi.org/10.21203/rs.3.rs-3004299/v1 KW - virtual vehicle models KW - crash tests KW - crash simulations KW - surrogate model KW - crash-detection algorithm KW - reinforcement Learning Y1 - 2023 UR - https://doi.org/10.21203/rs.3.rs-3004299/v1 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - CHAP A1 - Vaculín, Ondřej ED - Černý, David ED - Vaculín, Ondřej ED - Zámečník, Petr T1 - Senzory pro automatizované řízení T2 - Automatizované řízení vozidel a autonomní doprava: Technické a humanitní perspektivy Y1 - 2022 SN - 978-80-200-3358-1 SP - 153 EP - 178 PB - Academia CY - Prag ER - TY - CHAP A1 - Vaculín, Ondřej ED - Černý, David ED - Vaculín, Ondřej ED - Zámečník, Petr T1 - Automatizované řízení T2 - Automatizované řízení vozidel a autonomní doprava: Technické a humanitní perspektivy Y1 - 2022 SN - 978-80-200-3358-1 SP - 131 EP - 152 PB - Academia CY - Prag ER -