On Determinism of Game Engines used for Simulation-based Autonomous Vehicle Verification
This addresses the need for repeatable and trustworthy simulation results in autonomous vehicle verification, though it is incremental as it builds on existing simulation platforms like CARLA.
The paper tackles the problem of non-determinism in game engines used for autonomous vehicle simulation, identifying causes and proposing a method to find configurations where variance is negligible, achieving negligible variance in repeated executions for reliable testing.
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.