## 3 Related Work
### Autonomous Driving Simulators--An Overview  
Considering the complexity of a dynamic urban environment, the system must be evaluated in a wide range of settings and situations to validate the entire autonomous driving architecture which would enormously increase the cost and development time using the physical method. Given this, leveraging virtual development and validation testing and designing acceptable driving scenarios are currently the cornerstones to building reliable and safe autonomous vehicles. These simulators have developed over time, moving beyond simply simulating vehicle dynamics to simulating more intricate functionalities. Therefore, autonomous vehicle technology testing simulators must meet specifications that go beyond, simulating actual vehicle models to include several sensor models, decision-making, path planning, control, and more. In what follows next, we briefly discuss the state-of-art simulators used to test and validate autonomous driving solutions. Furthermore, the comparison of some additional commercial and open-source simulators is also presented in Table 2.  
* _MATLAB/Simulink_: launched its Automated Driving Toolbox which offers various tools and algorithms to facilitate the design, simulation, and testing of Advanced Driver Assistance Systems (ADAS) and automated driving. It enables the users to test its main functionalities such as environment perception, path planning, and vehicle control. It provides the feature to import HERE HD live map data and OpenDRIVE(r) road networks into MATLAB and can be used for various design and testing applications. Last but not least, the toolbox allows the development of C/C++ code for faster prototyping and Hardware-in-the-Loop (HIL) testing, providing support for sensor fusion, tracking, path planning, and vehicle controller algorithms. * _PreScan:_ an open-source physics-based simulation platform that aims to design ADAS and autonomous vehicles. It introduces PreScan's automated traffic generator, which offers manufacturers a variety of realistic environments and traffic conditions to test their autonomous navigation solutions. It can also be used to design and evaluate V2V and vehicle-to-infrastructure (V2I) communication applications. Among other features, it also provides support for HIL simulation, real-time data, and Global Positioning System (GPS) vehicle data recording, which can then be replayed later. Furthermore, PreScan provides a special function known as the Vehicle Hardware-In-the-Loop (VeHIL) laboratory. The test/ego vehicle is set up on a rolling bench, and other vehicles are represented by wheeled robots that resemble vehicles allowing users to establish a hybrid real-virtual system. Real sensors are installed in the test vehicle. Therefore, the VeHIL is capable of offering thorough simulations for ADAS by utilizing this setup of ego vehicles and mobile robots. * _LGSVL:_ an open-source multi-robot autonomous driving simulator developed by LG Electronics America R&D Center. It is built on the Unity game Engine and offers various bridges to pass the message between the autonomous driving stack and the simulator backbone. The simulation engine provides different functions to simulate the environment (e.g., traffic simulation and physical environment simulation), sensor simulation, and vehicle dynamics. Additionally, a PythonAPI is available to control various environmental variables, such as the position of the adversaries, the weather, etc. Furthermore, it also provides a Functional Mockup Interface (FMI) to integrate the vehicle dynamics model platform with the external third-party dynamics models. Lastly, exporting high-definition (HD) maps from 3D settings is one of the key capabilities of LGSVL* _Gazebo_: a multi-robot, open-source, scalable, and flexible 3D simulator that enables the simulation of both indoor and outdoor environments. The _world_ and _model_ are the two fundamental elements that make up the 3D scene.