EvoDrive: an evolutionary testing framework for the Carla Simulator

Adarash Kumar, Qinggang Meng, Baihua Li

Abstract: The rapid development of Autonomous Driving Systems (ADSs) calls for advanced testing frameworks to ensure their safety, reliability, and robustness across diverse traffic scenarios. Traditional software testing techniques are insufficient for ADS validation due to the complexity and unpredictability of real-world environments. Although numerous approaches have been proposed in both academia and industry, a comprehensive and flexible benchmark for ADS testing is still lacking. To address this gap, we present EvoDrive, an automated scenario-based testing framework that introduces a novel search space representation, enabling search algorithms to more effectively explore and exploit the scenario space to generate failure-inducing test cases. EvoDrive is built on top of the open-source CARLA simulator, enabling high-fidelity, scalable testing in virtual environments. Through extensive experiments, we demonstrate the effectiveness of EvoDrive, using a Genetic Algorithm (GA) to identify diverse and challenging scenarios that expose ADS failures. Our framework finds more meaningful infractions than general testing with the Carla Leaderboard Challenge framework and other state-of-the-art frameworks. Through testing, we also highlight EvoDrive’s capability to uncover critical infractions when comparing three different controllers.

External IDs:dblp:conf/case/KumarML25