Hardware-in-the-Loop, Traffic-in-the-Loop and Software-in-the-Loop Autonomous Vehicle Simulation for Mobility Studies 2020-01-0704
We are interested in finding and analyzing the relevant parameters affecting traffic flow when introducing Autonomous Vehicles for ride hailing applications and Autonomous Shuttles for circulator applications in geo-fenced urban areas. Different scenarios have been created in traffic simulation software that model the different levels of autonomy, traffic density, routes, and other traffic elements. Similarly, software that specializes in vehicle dynamics, physical limitations, and vehicle control has been used to closely simulate such scenarios. On the other hand, software for autonomous entities is also continuously improved. However, benchmarks for such software usually run in isolation from other factors such as the ones mentioned above. Yet, in order to effectively study the effects of the introduction of autonomous agents into city streets, all these factors must be considered. For these reasons, different simulation tools are needed to converge into a single simulation environment. We create a realistic simulator with Hardware-in-the-Loop (HiL), Traffic-in-the-Loop (TiL), and Software in-the-Loop (SiL) simulation capabilities. Our work merges the traffic simulation software Vissim to create realistic traffic, the vehicle dynamic simulation software CarMaker along with soft-sensors such as 3D Lidar and Camera, and the dedicated Nvidia Drive PX2 hardware platform for autonomous vehicles for data processing and decision-making in order to bring together simulation environments into a single simulation platform. We model geo-fenced areas in Columbus to accomplish a realistic simulation containing an autonomous ego-vehicle along with its dynamics, sensors, decision-making and data-processing as well as the traffic and subsequent autonomous agents. The ego-vehicle’s control is tuned to act as an autonomous shuttle and submerged in a mixed traffic environment. This traffic environment contains other AVs as well as ride hailing vehicles in order to study the AV penetration rate and the effect of ride sharing. Through the different scenarios; we change the routes, sensor parameters, AV traffic ratios, AV controllers for decision-making, signal phase and timing of traffic lights, and type of vehicles used. Furthermore, we demonstrate the flexibility of our simulator by extending it with V2X capabilities from an external Python library. We also discuss the limitations of the current state-of-the art software: creating realistic maps, building surrounding vegetation, and sensor limitation.
Karina Meneses Cime, Mustafa Ridvan Cantas, Garrett Dowd, Levent Guvenc, Bilin Aksun Guvenc, Archak Mittal, Adit Joshi, James Fishelson
Ohio State University, Ford Motor Company