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Traffic simulation is an efficient and cost-effective way to test AutonomousVehicles (AVs) in a complex and dynamic environment. Numerous studies have beenconducted for AV evaluation using traffic simulation over the past decades.However, the current simulation environments fall behind on two fronts -- thebackground vehicles (BVs) fail to simulate naturalistic driving behavior andthe existing environments do not test the entire pipeline in a modular fashion.This study aims to propose a simulation framework that creates a complex andnaturalistic traffic environment. Specifically, we combine a modified versionof the Simulation of Urban MObility (SUMO) simulator with the Cars Learning toAct (CARLA) simulator to generate a simulation environment that could emulatethe complexities of the external environment while providing realistic sensoroutputs to the AV pipeline. In a past research work, we created an open-sourcePython package called SUMO-Gym which generates a realistic road network andnaturalistic traffic through SUMO and combines that with OpenAI Gym to provideease of use for the end user. We propose to extend our developed software byadding CARLA, which in turn will enrich the perception of the ego vehicle byproviding realistic sensors outputs of the AVs surrounding environment. Usingthe proposed framework, AVs perception, planning, and control could be testedin a complex and realistic driving environment. The performance of the proposedframework in constructing output generation and AV evaluations are demonstratedusing several case studies.

A Novel Traffic Simulation Framework for Testing Autonomous Vehicles Using SUMO and CARLA