Selim Solmaz | TU Graz (original) (raw)

Videos by Selim Solmaz

This is a video presentation for the perception algorithms for automated driving developed by Vir... more This is a video presentation for the perception algorithms for automated driving developed by Virtual Vehicle Research GmbH in the scope of the EU-ECSEL Project PRYSTINE. The presentation was made at the EFECS2020 conference as an online event.

13 views

Presentation video of our paper for the 31st IEEE Intelligent Vehicles Symposium (IV) 2020 about ... more Presentation video of our paper for the 31st IEEE Intelligent Vehicles Symposium (IV) 2020 about the Hybrid Testing methodology developed by Virtual Vehicle Research GmbH in the scope of the EU-H2020 Project Inframix.

6 views

Papers by Selim Solmaz

Transportation Research Procedia, Dec 31, 2022

In this paper, a generic run-time risk evaluation methodology utilizing sensor status and data qu... more In this paper, a generic run-time risk evaluation methodology utilizing sensor status and data quality metrics is proposed. The suggested risk quantification method is then utilized as a basis for a run-time monitoring device (MonDev) concept for automated vehicles. The MonDev concept utilizes an aggregation function of a set of risk factors associated with each sensor. A data-driven SVM method is used to generate weighting factors in the aggregation function. The implementation of the MonDev concept and the corresponding results are demonstrated using two example use cases in a simulation framework.

Transportation Research Procedia, Dec 31, 2022

The perception supply chain (SC1) of the ArchitectECA2030 project investigates failure modes, fau... more The perception supply chain (SC1) of the ArchitectECA2030 project investigates failure modes, fault detection, and residual risk in perception systems of electrified, connected, and automated (ECA) vehicles. This accounts for the needs of a reliable understanding of the surrounding environment. The three demonstrators of SC1, described in this paper, address steps of a typical ECA usage cycle: charge-drive-restart charging. The foreign object detection (FOD) demonstrator improves safety within a wireless charging system. The robust physical sensors demonstrator creates a more robust perception by detecting failures within fused and single sensor data. The position enhancement demonstrator improves vehicle localization in areas with reduced GNSS signal coverage. All demonstrators are linked to the challenges that occur during the ECA vehicle usage cycle.

Transportation Research Procedia, Dec 31, 2022

Higher levels of Automated driving (AD) vehicles require new allocations of functions among drive... more Higher levels of Automated driving (AD) vehicles require new allocations of functions among drivers, vehicles, and road infrastructure. The European Horizon 2020 project HADRIAN investigates how such reallocations could be practically achieved as part of Collaborative Connected and Automated Mobility (CCAM) to meet the benefit expectations of drivers while increasing safety. In a field demonstration it is shown how road infrastructure can be used to expand the prediction horizon of AD vehicles and how multimodal, driver-state dependent human machine interactions (HMI) could help address European mobility needs with AD vehicles and increase operational acceptance and safety. Whereas performance results of the various innovations are reported elsewhere, in this paper the evaluation of the feasibility of the HADRIAN innovation in an open road field-demonstration is described.

We present a comparative analysis of EGNSS-based path tracking with and without open service navi... more We present a comparative analysis of EGNSS-based path tracking with and without open service navigation message authentication (OSNMA), which was recently made available in mass market EGNSS (Galileo) receivers. The EGNSS receivers provide dual-band GPS L1/L2 and Galileo E1/E5a RTK positioning for cm-level GNSS localization. The path following task utilizes mainly the accurate RTK-assisted EGNSS position and heading information to track a reference path. The lateral error from the reference path is used as the correction signal for the tracking controller. We compare the performance of the tracking controller in an open-sky and urban setting within the Graz University of Technology Inffeldgasse campus using an automated driving demonstrator vehicle. The positioning utilized two different OSNMA schemes, namely "strict" OSNMA solution that utilize only authenticated Galileo satellites or not using authentication, implying utilisation of all the available GNSS satellites.

IEEE open journal of instrumentation and measurement, 2024

IEEE Intelligent Vehicle Symposium 2024 (IV 2024), 2024

Advanced Driver Assistance Systems (ADAS) play a pivotal role in modern road vehicles, enhancing ... more Advanced Driver Assistance Systems (ADAS) play a pivotal role in modern road vehicles, enhancing safety. However, persistent challenges in managing ADAS systems and automated vehicles during dynamic traffic scenarios hinder the widespread adoption of ADAS and Automated Driving (AD). Recognizing the susceptibility of perception sensors to weather and road hazards, along with their typical operational limitations, V2X communication becomes critically important to achieve higher levels of autonomy and robustness. In the EU-funded project ESRIUM, safety improvement is attained by developing a digital map capable of accurately identifying road damage and offering real-time recommendations for connected vehicles. In this paper, we report the implementation and road testing results for the infrastructure-assisted AD system developed within the ESRIUM project. These tests were conducted on the public Austrian highway A2 under typical driving conditions, showcasing the effectiveness of infrastructureassisted AD vehicles in diverse traffic scenarios. The findings represent a significant advancement in validating automated vehicles on operational highways, emphasizing the vital role of infrastructure and V2X communication in enhancing ADAS/AD road safety and efficiency.

Proceedings of the IEEE Intelligent Vehicle Symposium 2024 (IV24), 2024

This paper explores the intersection of recent AI advancements and Intelligent Transportation Sys... more This paper explores the intersection of recent AI advancements and Intelligent Transportation Systems (ITS), specifically focusing on enhancing the capabilities of Connected and Automated Vehicles (CAVs) in dynamic traffic scenarios. While combinations of vehicular sensors and AI offer promising prospects for advanced environmental perception, challenges still persist in accurately identifying dangers during the transition to automated traffic. The ESRIUM project, funded by the EU Horizon 2020 Programme, aims to address these challenges by developing digital maps representing road deterioration and employing Vehicle-to-Everything (V2X) communication to generate infrastructure-assisted routing recommendations for CAVs. While the solutions for sending standardized safety messages and controlling enabled CAVs were demonstrated in the ESRIUM project, the solution for the automatic generation of Cooperative Intelligent Transport Systems (C-ITS) safety messages was not studied. In this paper, we propose a pipeline named "ConnectGPT", which connects Large Language Models (LLMs) with CAVs, utilizing GPT-4, to observe traffic conditions, identify conditions that can endanger the flow of traffic, and automate the generation of the corresponding standardized C-ITS messages, such as Decentralized Environmental Notification Message (DENM) about the actual safety problem. Practical experiments with ongoing development show potential for real-world applications, which can significantly improve traffic management efficiency and enhance the security of all traffic participants, marking a crucial advancement in the integration of AI tools in ITS.

TRA2024, 2024

High-accuracy GNSS (Global Navigation Satellite System) positioning requires the receiver to use ... more High-accuracy GNSS (Global Navigation Satellite System) positioning requires the receiver to use correction data. This data is typically delivered via 4G mobile internet. In this paper, we present a novel method to deliver the data via C-ITS (Cooperative Intelligent Transport Systems and Service). We compare its performance against using 4G and analyze the impact on the accuracy during data gaps, all using data collected in test drives from a real deployment in a small segment of a motorway. The results show that with C-ITS, a comparable performance can be achieved. The observed 2D position errors in our tests were below 3.1 cm for 95 % and below 10 cm for more than 99 % of the time.

Automated Vehicles (AVs) are designed to enhance road safety by utilizing Automated Driving Syste... more Automated Vehicles (AVs) are designed to enhance road safety by utilizing Automated Driving Systems (ADS) that leverage behavioral competencies within the targeted Operational Design Domain (ODD). However, operation within the current ODD always carries a residual risk that must be kept within acceptable limits to ensure safe and robust operation. This paper proposes a system-level residual risk management strategy for ACC/LKA behavioral competencies, which comprises a receivemonitor-transmit concept for hierarchical monitoring functionalities, a system-level residual risk management strategy, and fault injection campaigns to challenge the implemented multi-layer monitoring functionalities. The proposed strategy is implemented ACC/LKA-driven benchmark example, which demonstrates the efficient and effective handling of residual risks at the system level. The study concludes that targeted ODD and/or related behavioral competence reductions are a promising approach to maintaining the residual risk within acceptable limits.

2023 IEEE International Automated Vehicle Validation Conference (IAVVC 2023), Oct 15, 2023

Despite recent advances in algorithms and technology, self-driving vehicles are still susceptible... more Despite recent advances in algorithms and technology, self-driving vehicles are still susceptible to errors that can have severe consequences. As a result, effective risk monitoring and mitigation measures for autonomous driving systems are in high demand. To overcome this issue, several specifications and standards have been developed. However, a theoretical framework for dealing with autonomous vehicle hazards has rarely been presented. This study suggests a risk modeling method inspired by ideas from control theory and introduces a Model Predictive Control (MPC) Framework to deal with risks in general. Two application examples are presented. The first example shows how MPC parameters may affect the aggressiveness of the response. In the second example, our proposed risk monitoring and mitigation module is integrated into a visionbased Adaptive Cruise Control (ACC) system. Simulation results indicate a significant improvement in collision avoidance rate (from 0% to 47% in edge scenarios) during the Euro NCAP ACC Car-to-Car tests with a stationary target, which demonstrates the utility of our approach for addressing various types of hazards faced by autonomous vehicles.

IEEE Open Journal of Instrumentation and Measurement, 2024

We present an assessment of simulated lidar point clouds based on different phenomenological rang... more We present an assessment of simulated lidar point clouds based on different phenomenological rangereflectivity models. In sensor model development, the validation of individual model features is favorable. For lidar sensors, range limits depend on surface reflectivities. Two phenomenological feature models are derived from the lidar range equation, for clear and adverse weather conditions. The underlying parameters are the maximum ranges for best environment conditions, based on sensor datasheets, and a maximum range measurement for attenuation conditions. Furthermore, an assessment of different feature models is needed, similar to unit tests. Therefore, resulting point clouds are compared with respect to the total number of corresponding points and the number of points with no correspondences for pair-wise cloud comparison. Applications are presented using a point cloud lidar model. Results of the point cloud comparison are demonstrated for a single scene or time step and an entire scenario of 40 time steps. When a reference point cloud is provided by the sensor manufacturer, feature validation becomes possible.

Sensors, 2024

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistanc... more In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments
becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar’s perception, particularly the radar cross-section (RCS),
proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar’s perception for various vehicles and aspect angles. ABayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model’s effectiveness is demonstrated through accurate reproduction of the RCS
behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more
extensive validation is proposed to refine accuracy and broaden the model’s applicability.

Engineering Proceedings, 2023

To realize the societal need for greener, safer, and smarter mobility, ambitious technical challe... more To realize the societal need for greener, safer, and smarter mobility, ambitious technical challenges need to be addressed. With this aim, the H2020-EUSPA project ESRIUM investigates various aspects of highly accurate, reliable, and assured EGNSS localization information for road
vehicles with a particular focus on automated vehicles. To analyze the achievable accuracy, reliability, and availability of multi-frequency and multi-GNSS mass-market receivers, we have conducted test drives under different GNSS reception conditions. In the tests, special focus was placed on using the Galileo Open Service Navigation Message Authentication (OSNMA) service, offering an additional feature for assured PVT (position, velocity, and time) information with respect to spoofing. We analyzed the performance of three Septentrio Mosaic-X5 receivers operated with different OSNMA settings. It could be shown that strict use of OSNMA provides very good positioning accuracy as long as sufficient suitable satellites are available. However, the overall performance suffers from a
reduced satellite number and is therefore limited. The performance of a receiver using authenticated Galileo with GPS signals (final status of Galileo OSNMA) is very good for a mass-market receiver: 92.55% of the solutions had a 2D position error below 20 cm during 8.5 h of driving through different environments.

Proceedings of the ... American Control Conference, Jul 1, 2007

Journal of Dynamic Systems Measurement and Control-transactions of The Asme, Mar 10, 2016

In this paper, a novel algorithm for indirect tire failure indication is described. The estimatio... more In this paper, a novel algorithm for indirect tire failure indication is described. The estimation method is based on measuring changes in the lateral dynamics behavior resulting from certain types of tire failure modes including excessive deflation or significant thread loss in a combination of tires. Given the fact that both failures will notably affect the lateral dynamics behavior, quantifying these changes constitutes the basis of the estimation method. In achieving this, multiple models and switching method are utilized based on lateral dynamics models of the vehicle that are parametrized to account for the uncertainty in tire pressure levels. The results are demonstrated using representative numerical simulations.

This is a video presentation for the perception algorithms for automated driving developed by Vir... more This is a video presentation for the perception algorithms for automated driving developed by Virtual Vehicle Research GmbH in the scope of the EU-ECSEL Project PRYSTINE. The presentation was made at the EFECS2020 conference as an online event.

13 views

Presentation video of our paper for the 31st IEEE Intelligent Vehicles Symposium (IV) 2020 about ... more Presentation video of our paper for the 31st IEEE Intelligent Vehicles Symposium (IV) 2020 about the Hybrid Testing methodology developed by Virtual Vehicle Research GmbH in the scope of the EU-H2020 Project Inframix.

6 views

Transportation Research Procedia, Dec 31, 2022

In this paper, a generic run-time risk evaluation methodology utilizing sensor status and data qu... more In this paper, a generic run-time risk evaluation methodology utilizing sensor status and data quality metrics is proposed. The suggested risk quantification method is then utilized as a basis for a run-time monitoring device (MonDev) concept for automated vehicles. The MonDev concept utilizes an aggregation function of a set of risk factors associated with each sensor. A data-driven SVM method is used to generate weighting factors in the aggregation function. The implementation of the MonDev concept and the corresponding results are demonstrated using two example use cases in a simulation framework.

Transportation Research Procedia, Dec 31, 2022

The perception supply chain (SC1) of the ArchitectECA2030 project investigates failure modes, fau... more The perception supply chain (SC1) of the ArchitectECA2030 project investigates failure modes, fault detection, and residual risk in perception systems of electrified, connected, and automated (ECA) vehicles. This accounts for the needs of a reliable understanding of the surrounding environment. The three demonstrators of SC1, described in this paper, address steps of a typical ECA usage cycle: charge-drive-restart charging. The foreign object detection (FOD) demonstrator improves safety within a wireless charging system. The robust physical sensors demonstrator creates a more robust perception by detecting failures within fused and single sensor data. The position enhancement demonstrator improves vehicle localization in areas with reduced GNSS signal coverage. All demonstrators are linked to the challenges that occur during the ECA vehicle usage cycle.

Transportation Research Procedia, Dec 31, 2022

Higher levels of Automated driving (AD) vehicles require new allocations of functions among drive... more Higher levels of Automated driving (AD) vehicles require new allocations of functions among drivers, vehicles, and road infrastructure. The European Horizon 2020 project HADRIAN investigates how such reallocations could be practically achieved as part of Collaborative Connected and Automated Mobility (CCAM) to meet the benefit expectations of drivers while increasing safety. In a field demonstration it is shown how road infrastructure can be used to expand the prediction horizon of AD vehicles and how multimodal, driver-state dependent human machine interactions (HMI) could help address European mobility needs with AD vehicles and increase operational acceptance and safety. Whereas performance results of the various innovations are reported elsewhere, in this paper the evaluation of the feasibility of the HADRIAN innovation in an open road field-demonstration is described.

We present a comparative analysis of EGNSS-based path tracking with and without open service navi... more We present a comparative analysis of EGNSS-based path tracking with and without open service navigation message authentication (OSNMA), which was recently made available in mass market EGNSS (Galileo) receivers. The EGNSS receivers provide dual-band GPS L1/L2 and Galileo E1/E5a RTK positioning for cm-level GNSS localization. The path following task utilizes mainly the accurate RTK-assisted EGNSS position and heading information to track a reference path. The lateral error from the reference path is used as the correction signal for the tracking controller. We compare the performance of the tracking controller in an open-sky and urban setting within the Graz University of Technology Inffeldgasse campus using an automated driving demonstrator vehicle. The positioning utilized two different OSNMA schemes, namely "strict" OSNMA solution that utilize only authenticated Galileo satellites or not using authentication, implying utilisation of all the available GNSS satellites.

IEEE open journal of instrumentation and measurement, 2024

IEEE Intelligent Vehicle Symposium 2024 (IV 2024), 2024

Advanced Driver Assistance Systems (ADAS) play a pivotal role in modern road vehicles, enhancing ... more Advanced Driver Assistance Systems (ADAS) play a pivotal role in modern road vehicles, enhancing safety. However, persistent challenges in managing ADAS systems and automated vehicles during dynamic traffic scenarios hinder the widespread adoption of ADAS and Automated Driving (AD). Recognizing the susceptibility of perception sensors to weather and road hazards, along with their typical operational limitations, V2X communication becomes critically important to achieve higher levels of autonomy and robustness. In the EU-funded project ESRIUM, safety improvement is attained by developing a digital map capable of accurately identifying road damage and offering real-time recommendations for connected vehicles. In this paper, we report the implementation and road testing results for the infrastructure-assisted AD system developed within the ESRIUM project. These tests were conducted on the public Austrian highway A2 under typical driving conditions, showcasing the effectiveness of infrastructureassisted AD vehicles in diverse traffic scenarios. The findings represent a significant advancement in validating automated vehicles on operational highways, emphasizing the vital role of infrastructure and V2X communication in enhancing ADAS/AD road safety and efficiency.

Proceedings of the IEEE Intelligent Vehicle Symposium 2024 (IV24), 2024

This paper explores the intersection of recent AI advancements and Intelligent Transportation Sys... more This paper explores the intersection of recent AI advancements and Intelligent Transportation Systems (ITS), specifically focusing on enhancing the capabilities of Connected and Automated Vehicles (CAVs) in dynamic traffic scenarios. While combinations of vehicular sensors and AI offer promising prospects for advanced environmental perception, challenges still persist in accurately identifying dangers during the transition to automated traffic. The ESRIUM project, funded by the EU Horizon 2020 Programme, aims to address these challenges by developing digital maps representing road deterioration and employing Vehicle-to-Everything (V2X) communication to generate infrastructure-assisted routing recommendations for CAVs. While the solutions for sending standardized safety messages and controlling enabled CAVs were demonstrated in the ESRIUM project, the solution for the automatic generation of Cooperative Intelligent Transport Systems (C-ITS) safety messages was not studied. In this paper, we propose a pipeline named "ConnectGPT", which connects Large Language Models (LLMs) with CAVs, utilizing GPT-4, to observe traffic conditions, identify conditions that can endanger the flow of traffic, and automate the generation of the corresponding standardized C-ITS messages, such as Decentralized Environmental Notification Message (DENM) about the actual safety problem. Practical experiments with ongoing development show potential for real-world applications, which can significantly improve traffic management efficiency and enhance the security of all traffic participants, marking a crucial advancement in the integration of AI tools in ITS.

TRA2024, 2024

High-accuracy GNSS (Global Navigation Satellite System) positioning requires the receiver to use ... more High-accuracy GNSS (Global Navigation Satellite System) positioning requires the receiver to use correction data. This data is typically delivered via 4G mobile internet. In this paper, we present a novel method to deliver the data via C-ITS (Cooperative Intelligent Transport Systems and Service). We compare its performance against using 4G and analyze the impact on the accuracy during data gaps, all using data collected in test drives from a real deployment in a small segment of a motorway. The results show that with C-ITS, a comparable performance can be achieved. The observed 2D position errors in our tests were below 3.1 cm for 95 % and below 10 cm for more than 99 % of the time.

Automated Vehicles (AVs) are designed to enhance road safety by utilizing Automated Driving Syste... more Automated Vehicles (AVs) are designed to enhance road safety by utilizing Automated Driving Systems (ADS) that leverage behavioral competencies within the targeted Operational Design Domain (ODD). However, operation within the current ODD always carries a residual risk that must be kept within acceptable limits to ensure safe and robust operation. This paper proposes a system-level residual risk management strategy for ACC/LKA behavioral competencies, which comprises a receivemonitor-transmit concept for hierarchical monitoring functionalities, a system-level residual risk management strategy, and fault injection campaigns to challenge the implemented multi-layer monitoring functionalities. The proposed strategy is implemented ACC/LKA-driven benchmark example, which demonstrates the efficient and effective handling of residual risks at the system level. The study concludes that targeted ODD and/or related behavioral competence reductions are a promising approach to maintaining the residual risk within acceptable limits.

2023 IEEE International Automated Vehicle Validation Conference (IAVVC 2023), Oct 15, 2023

Despite recent advances in algorithms and technology, self-driving vehicles are still susceptible... more Despite recent advances in algorithms and technology, self-driving vehicles are still susceptible to errors that can have severe consequences. As a result, effective risk monitoring and mitigation measures for autonomous driving systems are in high demand. To overcome this issue, several specifications and standards have been developed. However, a theoretical framework for dealing with autonomous vehicle hazards has rarely been presented. This study suggests a risk modeling method inspired by ideas from control theory and introduces a Model Predictive Control (MPC) Framework to deal with risks in general. Two application examples are presented. The first example shows how MPC parameters may affect the aggressiveness of the response. In the second example, our proposed risk monitoring and mitigation module is integrated into a visionbased Adaptive Cruise Control (ACC) system. Simulation results indicate a significant improvement in collision avoidance rate (from 0% to 47% in edge scenarios) during the Euro NCAP ACC Car-to-Car tests with a stationary target, which demonstrates the utility of our approach for addressing various types of hazards faced by autonomous vehicles.

IEEE Open Journal of Instrumentation and Measurement, 2024

We present an assessment of simulated lidar point clouds based on different phenomenological rang... more We present an assessment of simulated lidar point clouds based on different phenomenological rangereflectivity models. In sensor model development, the validation of individual model features is favorable. For lidar sensors, range limits depend on surface reflectivities. Two phenomenological feature models are derived from the lidar range equation, for clear and adverse weather conditions. The underlying parameters are the maximum ranges for best environment conditions, based on sensor datasheets, and a maximum range measurement for attenuation conditions. Furthermore, an assessment of different feature models is needed, similar to unit tests. Therefore, resulting point clouds are compared with respect to the total number of corresponding points and the number of points with no correspondences for pair-wise cloud comparison. Applications are presented using a point cloud lidar model. Results of the point cloud comparison are demonstrated for a single scene or time step and an entire scenario of 40 time steps. When a reference point cloud is provided by the sensor manufacturer, feature validation becomes possible.

Sensors, 2024

In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistanc... more In the realm of road safety and the evolution toward automated driving, Advanced Driver Assistance and Automated Driving (ADAS/AD) systems play a pivotal role. As the complexity of these systems grows, comprehensive testing becomes imperative, with virtual test environments
becoming crucial, especially for handling diverse and challenging scenarios. Radar sensors are integral to ADAS/AD units and are known for their robust performance even in adverse conditions. However, accurately modeling the radar’s perception, particularly the radar cross-section (RCS),
proves challenging. This paper adopts a data-driven approach, using Gaussian mixture models (GMMs) to model the radar’s perception for various vehicles and aspect angles. ABayesian variational approach automatically infers model complexity. The model is expanded into a comprehensive radar sensor model based on object lists, incorporating occlusion effects and RCS-based detectability decisions. The model’s effectiveness is demonstrated through accurate reproduction of the RCS
behavior and scatter point distribution. The full capabilities of the sensor model are demonstrated in different scenarios. The flexible and modular framework has proven apt for modeling specific aspects and allows for an easy model extension. Simultaneously, alongside model extension, more
extensive validation is proposed to refine accuracy and broaden the model’s applicability.

Engineering Proceedings, 2023

To realize the societal need for greener, safer, and smarter mobility, ambitious technical challe... more To realize the societal need for greener, safer, and smarter mobility, ambitious technical challenges need to be addressed. With this aim, the H2020-EUSPA project ESRIUM investigates various aspects of highly accurate, reliable, and assured EGNSS localization information for road
vehicles with a particular focus on automated vehicles. To analyze the achievable accuracy, reliability, and availability of multi-frequency and multi-GNSS mass-market receivers, we have conducted test drives under different GNSS reception conditions. In the tests, special focus was placed on using the Galileo Open Service Navigation Message Authentication (OSNMA) service, offering an additional feature for assured PVT (position, velocity, and time) information with respect to spoofing. We analyzed the performance of three Septentrio Mosaic-X5 receivers operated with different OSNMA settings. It could be shown that strict use of OSNMA provides very good positioning accuracy as long as sufficient suitable satellites are available. However, the overall performance suffers from a
reduced satellite number and is therefore limited. The performance of a receiver using authenticated Galileo with GPS signals (final status of Galileo OSNMA) is very good for a mass-market receiver: 92.55% of the solutions had a 2D position error below 20 cm during 8.5 h of driving through different environments.

Proceedings of the ... American Control Conference, Jul 1, 2007

Journal of Dynamic Systems Measurement and Control-transactions of The Asme, Mar 10, 2016

In this paper, a novel algorithm for indirect tire failure indication is described. The estimatio... more In this paper, a novel algorithm for indirect tire failure indication is described. The estimation method is based on measuring changes in the lateral dynamics behavior resulting from certain types of tire failure modes including excessive deflation or significant thread loss in a combination of tires. Given the fact that both failures will notably affect the lateral dynamics behavior, quantifying these changes constitutes the basis of the estimation method. In achieving this, multiple models and switching method are utilized based on lateral dynamics models of the vehicle that are parametrized to account for the uncertainty in tire pressure levels. The results are demonstrated using representative numerical simulations.

International Journal of Vehicle Autonomous Systems, 2010