Autonomous driving has rapidly evolved through synergistic developments in hardware and artificial intelligence. This comprehensive review investigates traffic datasets and simulators as dual pillars supporting autonomous vehicle (AV) development. Unlike prior surveys that examine these resources independently, we present an integrated analysis spanning the entire AV pipeline-perception, localization, prediction, planning, and control. We evaluate annotation practices and quality metrics while examining how geographic diversity and environmental conditions affect system reliability. Our analysis includes detailed characterizations of datasets organized by functional domains and an in-depth examination of traffic simulators categorized by their specialized contributions to research and development. The paper explores emerging trends, including novel architecture frameworks, multimodal AI integration, and advanced data generation techniques that address critical edge cases. By highlighting the interconnections between real-world data collection and simulation environments, this review offers researchers a roadmap for developing more robust and resilient autonomous systems equipped to handle the diverse challenges encountered in real-world driving environments.

翻译：自动驾驶技术通过硬件与人工智能的协同发展迅速演进。本文对作为自动驾驶车辆发展双支柱的交通数据集与仿真器进行了全面综述。与以往独立考察这些资源的综述不同，我们提出了一个涵盖完整自动驾驶流程——感知、定位、预测、规划与控制——的整合性分析。我们评估了标注实践与质量指标，同时考察了地理多样性与环境条件如何影响系统可靠性。我们的分析包括按功能领域组织的详细数据集特征描述，以及对按其对研发的特殊贡献分类的交通仿真器的深入考察。本文探讨了新兴趋势，包括新颖的架构框架、多模态人工智能集成以及解决关键边缘案例的先进数据生成技术。通过强调真实世界数据采集与仿真环境之间的相互联系，本综述为研究人员提供了开发更鲁棒、更具适应性的自动驾驶系统的路线图，以应对现实驾驶环境中遇到的各种挑战。