The autonomous driving community has witnessed a rapid growth in approaches that embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle motion plans, instead of concentrating on individual tasks such as detection and motion prediction. End-to-end systems, in comparison to modular pipelines, benefit from joint feature optimization for perception and planning. This field has flourished due to the availability of large-scale datasets, closed-loop evaluation, and the increasing need for autonomous driving algorithms to perform effectively in challenging scenarios. In this survey, we provide a comprehensive analysis of more than 250 papers, covering the motivation, roadmap, methodology, challenges, and future trends in end-to-end autonomous driving. We delve into several critical challenges, including multi-modality, interpretability, causal confusion, robustness, and world models, amongst others. Additionally, we discuss current advancements in foundation models and visual pre-training, as well as how to incorporate these techniques within the end-to-end driving framework. To facilitate future research, we maintain an active repository that contains up-to-date links to relevant literature and open-source projects at https://github.com/OpenDriveLab/End-to-end-Autonomous-Driving.

翻译：自动驾驶领域正快速兴起基于端到端算法框架的方法，该方法直接利用原始传感器输入生成车辆运动规划，而非专注于检测和运动预测等独立任务。与模块化流水线相比，端到端系统得益于感知与规划的联合特征优化。由于大规模数据集的可用性、闭环评估的发展，以及对自动驾驶算法在复杂场景中有效执行的需求日益增长，该领域已蓬勃发展。本篇综述对超过250篇论文进行了全面分析，涵盖端到端自动驾驶的动机、路线图、方法论、挑战及未来趋势。我们深入探讨了若干关键挑战，包括多模态、可解释性、因果混淆、鲁棒性和世界模型等。此外，我们还讨论了基础模型和视觉预训练的当前进展，以及如何将这些技术融入端到端驾驶框架中。为促进未来研究，我们维护了一个活跃的代码库，其中包含相关文献和开源项目的最新链接，网址为 https://github.com/OpenDriveLab/End-to-end-Autonomous-Driving。