Pushing ROS towards the Dark Side: A ROS-based Co-Simulation Architecture for Mixed-Reality Test Systems for Autonomous Vehicles

Zofka, Marc René; Töttel, Lars; Zipfl, Maximilian; Heinrich, Marc; Fleck, Tobias; Schulz, Patrick; Zöllner, J. Marius

doi:10.1109/mfi49285.2020.9235238

Cited by 16 publications

(7 citation statements)

References 26 publications

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“…Cantas and Guvenc created a co-simulation environment that integrates a simulation environment with a traffic simulator, a vehicle dynamics simulator, and an autonomous driving algorithm ( 25 ). Zofka et al proposed a co-simulation architecture that would include pedestrian behavior in the simulation ( 26 ).…”

Section: Literature Reviewmentioning

confidence: 99%

Extensible Co-Simulation Framework for Supporting Cooperative Driving Automation Research

Cui

Shen

Yang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Autonomous vehicles (AVs) and cooperative automated vehicles (CAVs) are expected to largely reshape our mobility systems. The limited deployment of AVs and CAVs on roads makes it difficult to fully assess their impact and interactions with other road users. Advanced simulations are often sought for conducting accelerated tests of AVs and CAVs in a virtual environment. However, existing off-the-shelf simulators are typically focused on conventional traffic simulation and human-driving simulation. Advanced simulators that enable core functionalities (e.g., sensing and communication) of AVs and CAVs have been underexploited. In this paper, the authors aim to develop a realistic co-simulation framework for testing autonomous driving and cooperative driving automation (CDA). The proposed co-simulation framework utilizes the open-source concept to support the AV and CAV community in developing and deploying AV and CAV technologies. This framework integrates multiple open-source platforms, including Eclipse MOSAIC™ simulation framework, Eclipse Simulation of Urban Mobility (SUMO™) traffic simulator, and CARLA AV driving simulator. The framework enables AV and CAV simulation in mixed traffic environments. The developed co-simulation models have been tested with different scales of networks and traffic flow. The assessment of the co-simulation framework shows that it can support faster-than-real-time simulation for use in accelerated tests with more realistic scenarios. In addition, the developed co-simulation framework is proven to be extensible with the inclusion of other network simulators for supporting vehicle-to-everything (V2X) communication among vehicles.

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Section: Literature Reviewmentioning

confidence: 99%

Extensible Co-Simulation Framework for Supporting Cooperative Driving Automation Research

Cui

Shen

Yang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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“…ROS is optimized for application to virtual environments developed as simulators, and autonomous driving research using ROS is actively conducted. As mentioned above, when developing an autonomous driving module, a module developed based on a simulator has the advantage of being immediately applicable to an actual vehicle [11]. Considerable research on simulators based on hardwarein-the-loop simulation (HILS) is underway, especially for designing a vehicle-mounted electronic control unit (ECU) module [7,12].…”

Section: Robot Operating System (Rosmentioning

confidence: 99%

Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

2021

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Currently, the existing vehicle-centric semi-autonomous driving modules do not consider the driver's situation and emotions. In an autonomous driving environment, when changing to manual driving, human–machine interface and advanced driver assistance systems (ADAS) are essential to assist vehicle driving. This study proposes a human–machine interface that considers the driver's situation and emotions to enhance the ADAS. A 1D convolutional neural network model based on multimodal bio-signals is used and applied to control semi-autonomous vehicles. The possibility of semi-autonomous driving is confirmed by classifying four driving scenarios and controlling the speed of the vehicle. In the experiment, by using a driving simulator and hardware-in-the-loop simulation equipment, we confirm that the response speed of the driving assistance system is 351.75 ms and the system recognizes four scenarios and eight emotions through bio-signal data.

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“…For example, a vehicle can occlude the driver's field of view, which might not have been obvious from an abstract, top-down perspective of the scene. Regarding this aspect, we make use of our existing co-simulation approach [43] which is based on the Robot Operating System (ROS) [29].…”

Section: Analysis Frameworkmentioning

confidence: 99%

“…The 3D world is then integrated into CARLA, and traffic participants are now inserted and moved by exchanging ROS messages. Here, we make use of our ROS-based co-simulation architecture [43]: CARLA is integrated as a visualization model, and an additional module is used to integrate traffic participants from the given dataset into the 3D world at any timestamp. Fig.…”

Section: Subjective Analysis Modulementioning

confidence: 99%

See 1 more Smart Citation

Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality

Töttel,

Zipfl,

Bogdoll

et al. 2021

Preprint

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Pushing ROS towards the Dark Side: A ROS-based Co-Simulation Architecture for Mixed-Reality Test Systems for Autonomous Vehicles

Cited by 16 publications

References 26 publications

Extensible Co-Simulation Framework for Supporting Cooperative Driving Automation Research

Extensible Co-Simulation Framework for Supporting Cooperative Driving Automation Research

Human–Machine Interaction in Driving Assistant Systems for Semi-Autonomous Driving Vehicles

Reliving the Dataset: Combining the Visualization of Road Users' Interactions with Scenario Reconstruction in Virtual Reality

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