基于仿真的自主车辆验证游戏引擎的确定性研究
On Determinism of Game Engines used for Simulation-based Autonomous Vehicle Verification
摘要：
游戏引擎越来越多地被自动驾驶汽车（AV）社区用作仿真平台，以开发车辆控制系统和测试环境。基于仿真的开发和验证的一个关键要求是确定性，因为在给定相同的初始条件和事件历史的情况下，确定性过程将总是产生相同的输出。因此，在确定性的仿真环境中，测试是可重复的，并产生值得信赖的和直接调试的仿真结果。然而，游戏引擎很少是确定性的。本文回顾并确定了游戏引擎中的非确定性行为的潜在原因。使用CARLA的案例研究，一个开源的自动驾驶仿真环境提供动力的虚幻引擎，突出其固有的缺点，提供足够的精度在实验结果。探索软件和硬件的不同配置和利用，以确定仿真精度足够低的操作域，即对于开发和测试工作而言，重复执行之间的差异变得可以忽略不计。最后，提出了一种一般性的方法，可以用来找到任何给定的系统配置的游戏引擎模拟允许的方差域。
英文摘要：
Game engines are increasingly used as simulation platforms by the autonomous vehicle (AV) community to develop vehicle control systems and test environments. A key requirement for simulation-based development and verification is determinism, since a deterministic process will always produce the same output given the same initial conditions and event history. Thus, in a deterministic simulation environment, tests are rendered repeatable and yield simulation results that are trustworthy and straightforward to debug. However, game engines are seldom deterministic. This paper reviews and identifies the potential causes of non-deterministic behaviours in game engines. A case study using CARLA, an open-source autonomous driving simulation environment powered by Unreal Engine, is presented to highlight its inherent shortcomings in providing sufficient precision in experimental results. Different configurations and utilisations of the software and hardware are explored to determine an operational domain where the simulation precision is sufficiently low i.e.\ variance between repeated executions becomes negligible for development and testing work. Finally, a method of a general nature is proposed, that can be used to find the domains of permissible variance in game engine simulations for any given system configuration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
