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。本综述将交通数据集与仿真器作为支撑自动驾驶车辆发展的两大支柱进行系统性考察。与以往独立分析这些资源的研究不同，我们提出了一种贯穿整个自动驾驶流程——感知、定位、预测、规划与控制——的整合分析框架。在评估数据标注规范与质量指标的同时，我们深入探究了地理多样性与环境条件对系统可靠性的影响。分析内容包含按功能领域组织的数据集详细特征描述，以及对按研发专项贡献分类的交通仿真器的深度剖析。本文探讨了新兴技术趋势，包括应对关键边缘案例的新型架构框架、多模态人工智能融合及先进数据生成技术。通过揭示现实世界数据采集与仿真环境之间的内在联系，本综述为研究者提供了开发更鲁棒、更具适应性的自动驾驶系统的路线图，使其能够有效应对现实驾驶环境中遇到的各种复杂挑战。