Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests
Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for mu...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Meyer, Max-Arno [verfasserIn] Sauter, Lina [verfasserIn] Granrath, Christian [verfasserIn] Hadj-Amor, Hassen [verfasserIn] Andert, Jakob [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2021 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2021 |
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| Übergeordnetes Werk: |
Enthalten in: Automotive innovation - [Singapore] : Springer Singapore, 2018, 4(2021), 4 vom: 16. Okt., Seite 373-389 |
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| Übergeordnetes Werk: |
volume:4 ; year:2021 ; number:4 ; day:16 ; month:10 ; pages:373-389 |
| Links: |
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| DOI / URN: |
10.1007/s42154-021-00161-1 |
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| Katalog-ID: |
SPR04560651X |
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| 520 | |a Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. | ||
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| 700 | 1 | |a Andert, Jakob |e verfasserin |4 aut | |
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10.1007/s42154-021-00161-1 doi (DE-627)SPR04560651X (SPR)s42154-021-00161-1-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Meyer, Max-Arno verfasserin aut Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. Distributed co-simulation protocol (dpeaa)DE-He213 Co-simulation (dpeaa)DE-He213 Automated driving (dpeaa)DE-He213 Traffic simulation (dpeaa)DE-He213 Tool coupling (dpeaa)DE-He213 Sauter, Lina verfasserin aut Granrath, Christian verfasserin aut Hadj-Amor, Hassen verfasserin aut Andert, Jakob verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 4 vom: 16. Okt., Seite 373-389 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:4 day:16 month:10 pages:373-389 https://dx.doi.org/10.1007/s42154-021-00161-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 16 10 373-389 |
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10.1007/s42154-021-00161-1 doi (DE-627)SPR04560651X (SPR)s42154-021-00161-1-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Meyer, Max-Arno verfasserin aut Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. Distributed co-simulation protocol (dpeaa)DE-He213 Co-simulation (dpeaa)DE-He213 Automated driving (dpeaa)DE-He213 Traffic simulation (dpeaa)DE-He213 Tool coupling (dpeaa)DE-He213 Sauter, Lina verfasserin aut Granrath, Christian verfasserin aut Hadj-Amor, Hassen verfasserin aut Andert, Jakob verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 4 vom: 16. Okt., Seite 373-389 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:4 day:16 month:10 pages:373-389 https://dx.doi.org/10.1007/s42154-021-00161-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 16 10 373-389 |
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10.1007/s42154-021-00161-1 doi (DE-627)SPR04560651X (SPR)s42154-021-00161-1-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Meyer, Max-Arno verfasserin aut Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. Distributed co-simulation protocol (dpeaa)DE-He213 Co-simulation (dpeaa)DE-He213 Automated driving (dpeaa)DE-He213 Traffic simulation (dpeaa)DE-He213 Tool coupling (dpeaa)DE-He213 Sauter, Lina verfasserin aut Granrath, Christian verfasserin aut Hadj-Amor, Hassen verfasserin aut Andert, Jakob verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 4 vom: 16. Okt., Seite 373-389 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:4 day:16 month:10 pages:373-389 https://dx.doi.org/10.1007/s42154-021-00161-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 16 10 373-389 |
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10.1007/s42154-021-00161-1 doi (DE-627)SPR04560651X (SPR)s42154-021-00161-1-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Meyer, Max-Arno verfasserin aut Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. Distributed co-simulation protocol (dpeaa)DE-He213 Co-simulation (dpeaa)DE-He213 Automated driving (dpeaa)DE-He213 Traffic simulation (dpeaa)DE-He213 Tool coupling (dpeaa)DE-He213 Sauter, Lina verfasserin aut Granrath, Christian verfasserin aut Hadj-Amor, Hassen verfasserin aut Andert, Jakob verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 4 vom: 16. Okt., Seite 373-389 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:4 day:16 month:10 pages:373-389 https://dx.doi.org/10.1007/s42154-021-00161-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 16 10 373-389 |
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10.1007/s42154-021-00161-1 doi (DE-627)SPR04560651X (SPR)s42154-021-00161-1-e DE-627 ger DE-627 rakwb eng 620 ASE 620 ASE Meyer, Max-Arno verfasserin aut Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2021 Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. Distributed co-simulation protocol (dpeaa)DE-He213 Co-simulation (dpeaa)DE-He213 Automated driving (dpeaa)DE-He213 Traffic simulation (dpeaa)DE-He213 Tool coupling (dpeaa)DE-He213 Sauter, Lina verfasserin aut Granrath, Christian verfasserin aut Hadj-Amor, Hassen verfasserin aut Andert, Jakob verfasserin aut Enthalten in Automotive innovation [Singapore] : Springer Singapore, 2018 4(2021), 4 vom: 16. Okt., Seite 373-389 (DE-627)1019335262 (DE-600)2927503-9 2522-8765 nnns volume:4 year:2021 number:4 day:16 month:10 pages:373-389 https://dx.doi.org/10.1007/s42154-021-00161-1 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_266 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 4 2021 4 16 10 373-389 |
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Enthalten in Automotive innovation 4(2021), 4 vom: 16. Okt., Seite 373-389 volume:4 year:2021 number:4 day:16 month:10 pages:373-389 |
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Meyer, Max-Arno @@aut@@ Sauter, Lina @@aut@@ Granrath, Christian @@aut@@ Hadj-Amor, Hassen @@aut@@ Andert, Jakob @@aut@@ |
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Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Distributed co-simulation protocol</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Co-simulation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Automated driving</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Traffic simulation</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Tool coupling</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sauter, Lina</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Granrath, Christian</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hadj-Amor, Hassen</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Andert, Jakob</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Automotive innovation</subfield><subfield code="d">[Singapore] : Springer Singapore, 2018</subfield><subfield code="g">4(2021), 4 vom: 16. 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Meyer, Max-Arno |
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Meyer, Max-Arno ddc 620 misc Distributed co-simulation protocol misc Co-simulation misc Automated driving misc Traffic simulation misc Tool coupling Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests |
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620 ASE Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests Distributed co-simulation protocol (dpeaa)DE-He213 Co-simulation (dpeaa)DE-He213 Automated driving (dpeaa)DE-He213 Traffic simulation (dpeaa)DE-He213 Tool coupling (dpeaa)DE-He213 |
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ddc 620 misc Distributed co-simulation protocol misc Co-simulation misc Automated driving misc Traffic simulation misc Tool coupling |
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ddc 620 misc Distributed co-simulation protocol misc Co-simulation misc Automated driving misc Traffic simulation misc Tool coupling |
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Meyer, Max-Arno Sauter, Lina Granrath, Christian Hadj-Amor, Hassen Andert, Jakob |
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simulator coupled with distributed co-simulation protocol for automated driving tests |
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Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests |
| abstract |
Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. © The Author(s) 2021 |
| abstractGer |
Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. © The Author(s) 2021 |
| abstract_unstemmed |
Abstract To meet the challenges in software testing for automated vehicles, such as increasing system complexity and an infinite number of operating scenarios, new simulation methods must be developed. Closed-loop simulations for automated driving (AD) require highly complex simulation models for multiple controlled vehicles with their perception systems as well as their surrounding context. For the realization of such models, different simulation domains must be coupled with co-simulation. However, widely supported model integration standards such as functional mock-up interface (FMI) lack native support for distributed platforms, which is a key feature for AD due to the computational intensity and platform exclusivity of certain models. The newer FMI companion standard distributed co-simulation protocol (DCP) introduces platform coupling but must still be used in conjunction with AD co-simulations. As part of an assessment framework for AD, this paper presents a DCP compliant implementation of an interoperable interface between a 3D environment and vehicle simulator and a co-simulation platform. A universal Python wrapper is implemented and connected to the simulator to allow its control as a DCP slave. A C-code-based interface enables the co-simulation platform to act as a DCP master and to realize cross-platform data exchange and time synchronization of the environment simulation with other integrated models. A model-in-the-loop use case is performed with the traffic simulator CARLA running on a Linux machine connected to the co-simulation master xMOD on a Windows computer via DCP. Several virtual vehicles are successfully controlled by cooperative adaptive cruise controllers executed outside of CARLA. The standard compliance of the implementation is verified by exemplary connection to prototypic DCP solutions from 3rd party vendors. This exemplary application demonstrates the benefits of DCP compliant tool coupling for AD simulation with increased tool interoperability, reuse potential, and performance. © The Author(s) 2021 |
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| container_issue |
4 |
| title_short |
Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests |
| url |
https://dx.doi.org/10.1007/s42154-021-00161-1 |
| remote_bool |
true |
| author2 |
Sauter, Lina Granrath, Christian Hadj-Amor, Hassen Andert, Jakob |
| author2Str |
Sauter, Lina Granrath, Christian Hadj-Amor, Hassen Andert, Jakob |
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1019335262 |
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c |
| isOA_txt |
true |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s42154-021-00161-1 |
| up_date |
2024-07-03T17:07:09.628Z |
| _version_ |
1803578433493532673 |
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| score |
7.3990755 |