Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems

Nalic, Demin; Pandurevic, Aleksa; Eichberger, Arno; Rogic, Branko

doi:10.20944/preprints202011.0252.v1

2020

DOI: 10.20944/preprints202011.0252.v1

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Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems

Demin Nalic

Aleksa Pandurevic

Arno Eichberger

et al.

Abstract: 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 … Show more

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Cited by 4 publications

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Virtual Validation of an Automated Lane-Keeping System with an Extended Operational Design Domain

et al. 2021

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Virtual testing using simulation will play a significant role in future safety validation procedures for automated driving systems, as it provides the needed scalability for executing a scenario-based assessment approach. This article combines multiple essential aspects that are necessary for the virtual validation of such systems. First, a general framework that contains the vital subsystems needed for virtual validation is introduced. Secondly, the interfaces between the subsystems are explored. Additionally, the concept of model fidelities is presented and extended towards all relevant subsystems. For an automated lane-keeping system with two different definitions of an operational design domain, all relevant subsystems are defined and integrated into an overall simulation framework. The resulting difference between both operational design domains is the occurrence of lateral manoeuvres, leading to greater demands of the fidelity of the vehicle dynamics model. The simulation results support the initial assumption that by extending the operation domain, the requirements for all subsystems are subject to adaption. As an essential aspect of harmonising virtual validation frameworks, the article identifies four separate layers and their corresponding parameters. In particular, the tool-specific co-simulation capability layer is critical, as it enables model exchange through consistently defined interfaces and reduces the integration effort. The introduction of this layered architecture for virtual validation frameworks enables further cross-domain collaboration.

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Virtual Validation of an Automated Lane-Keeping System with an Extended Operational Design Domain

et al. 2021

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Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim

et al. 2021

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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.

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An NN-Based Double Parallel Longitudinal and Lateral Driving Strategy for Self-Driving Transport Vehicles in Structured Road Scenarios

Xiong

Liu

et al. 2021

Sustainability

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Studies on self-driving transport vehicles have focused on longitudinal and lateral driving strategies in automated structured road scenarios. In this study, a double parallel network (DP-Net) combined with longitudinal and lateral strategy networks is constructed for self-driving transport vehicles in structured road scenarios, which is based on a convolutional neural network (CNN) and a long short-term memory network (LSTM). First, in feature extraction and perception, a preprocessing module is introduced that can ensure the effective extraction of visual information under complex illumination. Then, a parallel CNN sub-network is designed that is based on multifeature fusion to ensure better autonomous driving strategies. Meanwhile, a parallel LSTM sub-network is designed, which uses vehicle kinematic features as physical constraints to improve the prediction accuracy for steering angle and speed. The Udacity Challenge II dataset is used as the training set with the proposed DP-Net input requirements. Finally, for the proposed DP-Net, the root mean square error (RMSE) is used as the loss function, the mean absolute error (MAE) is used as the metric, and Adam is used as the optimization method. Compared with competing models such as PilotNet, CgNet, and E2E multimodal multitask network, the proposed DP-Net is more robust in handling complex illumination. The RMSE and MAE values for predicting the steering angle of the E2E multimodal multitask network are 0.0584 and 0.0163 rad, respectively; for the proposed DP-Net, those values are 0.0107 and 0.0054 rad, i.e., 81.7% and 66.9% lower, respectively. In addition, the proposed DP-Net also has higher accuracy in speed prediction. Upon testing the collected SYSU Campus dataset, good predictions are also obtained. These results should provide significant guidance for using a DP-Net to deploy multi-axle transport vehicles.

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Design and Implementation of a Co-Simulation Framework for Testing of Automated Driving Systems

Cited by 4 publications

References 10 publications

Virtual Validation of an Automated Lane-Keeping System with an Extended Operational Design Domain

Virtual Validation of an Automated Lane-Keeping System with an Extended Operational Design Domain

Software Framework for Testing of Automated Driving Systems in the Traffic Environment of Vissim

An NN-Based Double Parallel Longitudinal and Lateral Driving Strategy for Self-Driving Transport Vehicles in Structured Road Scenarios

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