Closed-loop platoon simulation with cooperative intelligent transportation systems based on vehicle-to-X communication

Meyer, Max-Arno; Granrath, Christian; Feyerl, Günter; Richenhagen, Johannes; Kaths, Jakob; Andert, Jakob

doi:10.1016/j.simpat.2020.102173

Cited by 18 publications

(7 citation statements)

References 24 publications

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“…CACC regulates the velocity of a vehicle to keep the safety distance to the preceding vehicle based on sensor and vehicle-to-vehicle communication data. The safety distance is defined as the time interval needed to close the gap to the preceding vehicle at the current velocity considering the reaction time [3].…”

Section: Demonstrator Scenariomentioning

confidence: 99%

“…The two following vehicles are controlled by the CACC models. The integrated CACC prototype FMU has been developed and tested in the CrESt research project and was previously described and analyzed in depth [3], [34]. Thus, this evaluation focuses on the established DCP tool coupling solution between xMOD and CARLA and its applicability for tool integration in ADS co-simulations, while simulation results are not analyzed for functional correctness.…”

Section: Demonstrator Scenariomentioning

confidence: 99%

“…Virtual testing by the means of simulation is essential for the verification and validation of automated vehicles as solely physical testing is time and resource consuming to cope with large test scopes [1][2][3][4]. The safety of the intended functionality (SOTIF) certification of automated driving systems (ADS) recommends a mixture of simulation-based tests, tests in controlled environments, and real-world driving to achieve a meaningful coverage of possible driving scenarios [5].…”

Section: Introductionmentioning

confidence: 99%

“…High complexity, perceptual range, and degree of environmental interaction of HADS require complex cyber-physical plant models for closed-loop testing in simulation. A virtual test frame is composed of the controlled vehicle with its sensors and motion control as well as the controlled vehicle's context including the perceived environment and all actors interacting with the SUT such as the driver and other traffic participants [3]. Sophisticated context models include behavior and uncertainty simulation, further increasing model complexity [9].…”

Section: Introductionmentioning

confidence: 99%

“…Sophisticated context models include behavior and uncertainty simulation, further increasing model complexity [9]. For collaborative embedded systems (CES), multiple controlled vehicles and data exchange via networks between vehicles and roadside units have to be considered additionally in the test frame to analyze the SUT behavior in collaborative system groups [3].…”

Section: Introductionmentioning

confidence: 99%

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Simulator Coupled with Distributed Co-Simulation Protocol for Automated Driving Tests

et al. 2021

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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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Section: Demonstrator Scenariomentioning

confidence: 99%

Section: Demonstrator Scenariomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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