Autonomous vehicle (AV) algorithms need to be tested extensively in order to make sure the vehicle and the passengers will be safe while using it after the implementation. Testing these algorithms in real world create another important safety critical point. Real world testing is also subjected to limitations such as logistic limitations to carry or drive the vehicle to a certain location. For this purpose, hardware in the loop (HIL) simulations as well as virtual environments such as CARLA and LG SVL are used widely. This paper discusses a method that combines the real vehicle with the virtual world, called vehicle in virtual environment (VVE). This method projects the vehicle location and heading into a virtual world for desired testing, and transfers back the information from sensors in the virtual world to the vehicle. As a result, while vehicle is moving in the real world, it simultaneously moves in the virtual world and obtains the situational awareness via multiple virtual sensors. This would allow testing in a safe environment with the real vehicle while providing some additional benefits on vehicle dynamics fidelity, logistics limitations and passenger experience testing. The paper also demonstrates an example case study where path following and the virtual sensors are utilized to test a radar based stopping algorithm.
翻译:自动车辆算法需要广泛测试,以确保车辆和乘客在执行后使用时安全。在现实世界中测试这些算法创造了另一个重要的安全临界点。现实世界测试还受到后勤限制的限制,如将车辆运到或开车到某个特定地点的后勤限制。为此,将广泛使用循环(HIL)模拟硬件以及诸如CARLA和LG SVL等虚拟环境。本文讨论了一种将实际车辆与虚拟世界(虚拟环境中称为车辆)相结合的方法。这种方法预测车辆的位置,并进入虚拟世界进行预期测试,并将虚拟世界的传感器信息转移回到车辆。因此,在虚拟世界中,车辆同时在虚拟世界中移动,通过多个虚拟传感器了解情况。这样就可以在安全的环境中与实际车辆进行测试,同时在车辆动态、后勤限制和乘客经验测试方面提供一些额外的好处。文件还展示了一种实例案例研究,即跟踪路径和虚拟传感器用于测试基于雷达的停止算法。