A Comprehensive Review on Traffic Datasets and Simulators for Autonomous Vehicles

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.