Scalable Simulation Environment for Adaptive Cruise Controller Development 2020-01-1359
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 in 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 vehicles 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.
David Barnes, Jared Folden, Hwan-Sik Yoon, Paulius Puzinauskas