Browse Publications Technical Papers 2020-01-0704

Hardware-in-the-Loop, Traffic-in-the-Loop and Software-in-the-Loop Autonomous Vehicle Simulation for Mobility Studies 2020-01-0704

This paper focuses on finding and analyzing the relevant parameters affecting traffic flow when autonomous vehicles are introduced for ride hailing applications and autonomous shuttles are introduced for circulator applications in geo-fenced urban areas. For this purpose, 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 realistic autonomous vehicle behavior under such scenarios. Different simulation tools for realistic autonomous vehicle simulation and traffic simulation have been merged together in this paper, creating 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, Ohio 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 autonomous vehicles as well as ride hailing vehicles in order to study the autonomous vehicle penetration rate and the effect of ride hailing. Through the different scenarios; we change the routes, sensor parameters, percentage of autonomous vehicles in traffic, autonomous vehicle 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 in creating realistic maps, building surrounding vegetation, and sensor limitation.


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