Reproduction of Real-World Scenarios in CARLA: An Extension of CARLA Functionality

Reproducing driving scenarios involving near-collisions and collisions in a simulator can be useful in the development and testing of autonomous vehicles, as it provides a safe environment to explore detailed vehicular behavior during these critical events. CARLA, an open-source driving simulator, has been widely used for driving scenario reproduction. However, current versions of CARLA are limited to setting the start and destination points for vehicles, and are unable to replay precise waypoint paths that are collected from real-world collision and near-collision scenarios, due to the fact that the collision-free pathfinding modules built into the system. This paper presents a significant extension to CARLA’s source code, enabling the replay of exact vehicle paths, irrespective of safety implications encountered during scenario layouts. With this functionality, researchers can now simulate real-world scenarios with higher fidelity, so that the vehicular automation can avoid unsafe driving conditions and allow for root-cause analysis of near-collisions inherent in a given driving automation. It can bridge the gap between simulation and real-life testing and also support validation of autonomous driving system under controlled and repeatable conditions. The extension will improve CARLA’s utility in autonomous vehicle research, particularly in areas such as safety validation and machine learning model training using safety margins produced by near-collision scenarios.