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.

翻译：提升道路交通车辆自动驾驶SAE等级的实施，需要在真实试验场和公共道路测试之前，在逼真的仿真环境中进行大量仿真与验证。仿真环境的细节水平有助于确保实际应用的安全性，并通过允许开发人员在仿真环境中完成大部分验证工作来降低算法开发成本。考虑到自动驾驶车辆中使用的摄像头、激光雷达、雷达及V2X等传感器，构建能够尽可能真实地提供这些传感器仿真的环境至关重要。尽管传感器仿真对于感知算法的开发至关重要，但若缺少真实的车辆动力学模型和交通协同仿真，仿真环境将无法完整模拟自动驾驶车辆的整体运行。因此，本文研究了现有仿真环境，识别了应用场景，并构建了一个协同仿真环境，以满足自动驾驶功能开发的仿真需求。该环境采用基于虚幻引擎的Carla模拟器作为环境模块，Sumo或Vissim进行交通协同仿真，Carsim或Matlab/Simulink进行车辆动力学协同仿真，并利用Autoware或作者/用户例程进行自动驾驶算法协同仿真。作为研究成果，本文提出了一种集成真实传感器仿真与交通仿真的基于模型的车辆动力学仿真方法。在所构建的仿真环境中，以传感器融合方法作为应用场景进行了实现。本研究成果将为需要综合协同仿真环境以开发网联自动驾驶算法的研究人员提供宝贵资源。