Vehicle-in-Virtual-Environment Method for ADAS and Connected and Automated Driving Function Development/Demonstration/Evaluation
<|reference_start|>Vehicle-in-Virtual-Environment Method for ADAS and Connected and Automated Driving Function Development/Demonstration/Evaluation: The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called VehicleInVirtualEnvironment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed. The virtual environment can be easily configured and modified to construct different testing scenarios on demand. This paper focuses on the VVE approach and introduces the coordinate transformations needed to sync pose (location and orientation) in the virtual and physical worlds and handling of localization and perception sensor data using the highly realistic 3D simulation model of a recent autonomous shuttle deployment site in Columbus, Ohio as the virtual world. As a further example that uses multiple actors, the use of VVE for VehicleToVRU communication based Vulnerable Road User (VRU) safety is presented in the paper using VVE experiments and real pedestrian(s) in a safe and repeatable manner. VVE experiments are used to demonstrate the efficacy of the method.<|reference_end|>
@article{cao2024vehicle-in-virtual-environment,
    title={Vehicle-in-Virtual-Environment Method for ADAS and Connected and
  Automated Driving Function Development/Demonstration/Evaluation},
    author={Xincheng Cao, Haochong Chen, Bilin Aksun-Guvenc, Levent Guvenc},
    journal={arXiv preprint arXiv:2410.04313},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04313},
    primaryClass={cs.RO cs.SY eess.SY}
}