End-to-End Autonomous Driving in CARLA: A Survey

Abstract

Autonomous Driving (AD) has evolved significantly since its beginnings in the 1980s, with continuous advancements driven by both industry and academia. Traditional AD systems break down the driving task into smaller modules—such as perception, localization, planning, and control– and optimizes them independently. In contrast, end-to-end models use neural networks to map sensory inputs directly to vehicle controls, optimizing the entire driving process as a single task. Recent advancements in deep learning have driven increased interest in end-to-end models, which is the central focus of this review. In this survey, we discuss how CARLA-based state-of-the-art implementations address various issues encountered in end-to-end autonomous driving through various model inputs, outputs, architectures, and training paradigms. To provide a comprehensive overview, we additionally include a concise summary of these methods in a single large table. Finally, we present evaluations and discussions of the methods, and suggest future avenues to tackle current challenges faced by end-to-end models.