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Scalable Simulation Environment for Adaptive Cruise Controller Development
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
In the development of an Adaptive Cruise Control (ACC) system, a model-based design process uses a simulation environment with models for sensor data, sensor fusion, ACC, and vehicle dynamics. Previous work has sought to control the dynamics between two vehicles both in simulation and empirical testing environments. This paper outlines a new modular simulation framework for full model- based design integration to iteratively design ACC systems. The simulation framework uses physics-based vehicle models to test ACC systems in three ways. The first two are Model-in-the-Loop (MIL) testing, using scripted scenarios or Driver-in-the-Loop (DIL) control of a target vehicle. The third testing method uses collected test data replayed as inputs to the simulation to additionally test sensor fusion algorithms. The simulation framework uses 3D visualization of the vehicles and implements mathematical driver comfortability models to better understand the perspectives of the driver or passenger. The addition of a high-fidelity vehicle plant model provides energy consumption and emissions predictions for autonomous conventional or hybrid electric vehicles (HEV) in realistic driving scenarios. Finally, the simulations are run for different test cases and the results are presented and compared.
CitationBarnes, D., Folden, J., Yoon, H., and Puzinauskas, P., "Scalable Simulation Environment for Adaptive Cruise Controller Development," SAE Technical Paper 2020-01-1359, 2020, https://doi.org/10.4271/2020-01-1359.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
- SAE International Surface Vehicle Recommended Practice , “Taxonomy and Definitions for Terms Related to Driving Automation Systems for On-Road Motor Vehicles,” SAE Standard J3016, Rev. June 2018.
- The International Organization for Standardization (ISO) , “Intelligent Transport Systems-Adaptive Cruise Control Systems-Performance Requirements and Test Procedures,” ISO 15622, September 2018.
- Aarenstrup, R. , Managing Model-Based Design (The MathWorks, Inc.: Natick, MA, 2015).
- Guvenc, B. and Kural, E. “Adaptive Cruise Control Simulator: A Low-Cost, Multiple-Driver-in-the-Loop Simulator,” IEEE, June 2006, 10.1109/MCS.2006.1636309.
- The MathWorks, Inc. , “Vehicle Body 3DOF,” https://www.mathworks.com/help/vdynblks/ref/vehiclebody3dof.html?s_tid=srchtitle, accessed November 1, 2019.
- Onori, S., Lorenzo, S., and Rizzoni, G. , Hybrid Electric Vehicles: Energy Management Strategies (London: Springer, 2016).
- The MathWorks, Inc. , “Adaptive Cruise Control System,” https://www.mathworks.com/help/mpc/ug/adaptive-cruise-control-using-model-predictive-controller.html, accessed November 1, 2019.
- Wu, Z., Liu, Y., and Pan, G. , “A Smart Car Control Model for Brake Comfort Based on Car Following,” IEEE Transaction on Intelligent Transport Systems 10(9), March 2009, doi:10.1109/TITS.2008.2006777.
- Shalev-Shwartz, S., Shammah, S., and Amnon, S. , “On a Formal Model of Safe and Scalable Self-Driving Cars,” arXiv, 2017.
- National Association of City Transportation Officials , “Vehicle Stopping Distance and Time,” https://nacto.org/docs/usdg/vehicle_stopping_distance_and_time_upenn.pdf, accessed October 15, 2019.
- Bae, I., Moon, J., and Seo, J. , “Toward a Comfortable Driving Experience for a Self-Driving Shuttle Bus,” Electronics 8(9), August 27, 20.
- Sultan, B. and McDonald, M. , “Assessing The Safety Benefit of Automatic Collision Avoidance Systems (during Emergency Braking Situations),” National Highway Traffic Safety Administration, https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/18esv-000381.pdf, accessed October 15, 2019.