  Increasing the implemented SAE level of autonomy in road vehicles requires
extensive simulations and verifications in a realistic simulation environment
before proving ground and public road testing. The level of detail in the
simulation environment helps ensure the safety of a real-world implementation
and reduces algorithm development cost by allowing developers to complete most
of the validation in the simulation environment. Considering sensors like
camera, LIDAR, radar, and V2X used in autonomous vehicles, it is essential to
create a simulation environment that can provide these sensor simulations as
realistically as possible. While sensor simulations are of crucial importance
for perception algorithm development, the simulation environment will be
incomplete for the simulation of holistic AV operation without being
complemented by a realistic vehicle dynamic model and traffic cosimulation.
Therefore, this paper investigates existing simulation environments, identifies
use case scenarios, and creates a cosimulation environment to satisfy the
simulation requirements for autonomous driving function development using the
Carla simulator based on the Unreal game engine for the environment, Sumo or
Vissim for traffic co-simulation, Carsim or Matlab, Simulink for vehicle
dynamics co-simulation and Autoware or the author or user routines for
autonomous driving algorithm co-simulation. As a result of this work, a
model-based vehicle dynamics simulation with realistic sensor simulation and
traffic simulation is presented. A sensor fusion methodology is implemented in
the created simulation environment as a use case scenario. The results of this
work will be a valuable resource for researchers who need a comprehensive
co-simulation environment to develop connected and autonomous driving
algorithms.
