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A Geographically Distributed Simulation Framework for the Analysis of Mixed Traffic Scenarios Involving Conventional and Autonomous Vehicles
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 29, 2022 by SAE International in United States
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In this paper we present a project that interfaced the National Advanced Driving Simulator (NADS) with SynChrono, a module of the Project Chrono open source simulation platform, to enable real-time, physics-based simulation of multiple autonomous vehicles (AVs) interacting with manned vehicles. In this setup, a driver at NADS, at the University of Iowa, participates in a traffic scenario that involves AVs that run at the University of Wisconsin-Madison on a cluster supercomputer. The NADS simulator is a driving simulator giving the “most realistic driving simulation experience in the country” . Thanks to its actuators, it can move across its 64-foot by 64-foot bay, rotate and tilt, to emulate vehicle movement and vibrations. In addition, the human driver drives in a full-size cab, surrounded by LED monitors, resulting in an immersive, high fidelity driving simulation experience. SynChrono draws on the Chrono::Vehicle module for detailed vehicle dynamics simulation, providing simulation of vehicle subsystems and tire models, together with a template-based API to model these components. The Chrono::Sensor module provides simulation of a variety of sensor (IMU, GPS, Camera, Lidar); the exteroceptive sensors simulation is based on ray-tracing. SynChrono was demonstrated to run more than 120 vehicles in real time, using the Message Passing Interface (MPI) standard in which one CPU core runs the simulation of one vehicle. The simulation is both time coherent and space coherent, for the AVs and conventional vehicles to interact in a realistic fashion. The proposed simulation infrastructure is expected to enable the study of mixed-traffic scenarios with no risk and reduced costs, while fidelity of the physics and sensor simulation, together with the realism of the driving simulation, are expected to reduce the simulation to reality gap.
CitationBenatti, S., Schwarz, C., Young, A., Elmquist, A. et al., "A Geographically Distributed Simulation Framework for the Analysis of Mixed Traffic Scenarios Involving Conventional and Autonomous Vehicles," SAE Technical Paper 2022-01-0839, 2022, https://doi.org/10.4271/2022-01-0839.
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