  Traffic simulation is an efficient and cost-effective way to test Autonomous
Vehicles (AVs) in a complex and dynamic environment. Numerous studies have been
conducted for AV evaluation using traffic simulation over the past decades.
However, the current simulation environments fall behind on two fronts -- the
background vehicles (BVs) fail to simulate naturalistic driving behavior and
the existing environments do not test the entire pipeline in a modular fashion.
This study aims to propose a simulation framework that creates a complex and
naturalistic traffic environment. Specifically, we combine a modified version
of the Simulation of Urban MObility (SUMO) simulator with the Cars Learning to
Act (CARLA) simulator to generate a simulation environment that could emulate
the complexities of the external environment while providing realistic sensor
outputs to the AV pipeline. In a past research work, we created an open-source
Python package called SUMO-Gym which generates a realistic road network and
naturalistic traffic through SUMO and combines that with OpenAI Gym to provide
ease of use for the end user. We propose to extend our developed software by
adding CARLA, which in turn will enrich the perception of the ego vehicle by
providing realistic sensors outputs of the AVs surrounding environment. Using
the proposed framework, AVs perception, planning, and control could be tested
in a complex and realistic driving environment. The performance of the proposed
framework in constructing output generation and AV evaluations are demonstrated
using several case studies.
