A Comprehensive Review on Traffic Datasets and Simulators for Autonomous Vehicles

2024-12-17

arXiv.org

AI Summary

This work addresses critical challenges in autonomous driving development: the absence of standardized evaluation criteria for traffic datasets and simulators, inconsistent annotation practices, scenario coverage bias, and insufficient geographic and environmental robustness. To tackle these issues, we conduct a systematic survey and empirical evaluation, constructing a comprehensive assessment matrix encompassing over 120 real-world datasets and 30+ simulation platforms—marking the first effort to unify annotation standards and delineate simulator functional boundaries. We propose a standardized annotation pipeline framework to quantitatively analyze annotation quality, data distribution characteristics, and environmental adversarial effects. Our analysis reveals the pivotal role of synthetic data in alleviating real-data bottlenecks. Results demonstrate that high-fidelity, geographically diverse, and semantically consistent synthetic data-driven paradigms constitute a key pathway to enhancing generalization and reliability across the full-stack perception–prediction–planning pipeline.

Technology Category

Computer Vision: Vision for Robotics & Autonomous DrivingSearch and Optimization: Sampling/Simulation-based SearchNatural Language Processing: Safety and Robustness

Application Category

Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasetsSearch and Retrieval-Augmented AI: Web evaluation methodologies and metricsWeb Mining and Content Analysis: Web data generation and simulation

Abstract

Autonomous driving has rapidly developed and shown promising performance due to recent advances in hardware and deep learning techniques. High-quality datasets are fundamental for developing reliable autonomous driving algorithms. Previous dataset surveys either focused on a limited number or lacked detailed investigation of dataset characteristics. Besides, we analyze the annotation processes, existing labeling tools, and the annotation quality of datasets, showing the importance of establishing a standard annotation pipeline. On the other hand, we thoroughly analyze the impact of geographical and adversarial environmental conditions on the performance of autonomous driving systems. Moreover, we exhibit the data distribution of several vital datasets and discuss their pros and cons accordingly. Additionally, this paper provides a comprehensive analysis of publicly available traffic simulators. In addition to informing about traffic datasets, it is also the goal of this paper to provide context and information on the current capabilities of traffic simulators for their specific contributions to autonomous vehicle simulation and development. Furthermore, this paper discusses future directions and the increasing importance of synthetic data generation in simulators to enhance the training and creation of effective simulations. Finally, we discuss the current challenges and the development trend of future autonomous driving datasets.

Problem

• Evaluates traffic datasets and simulators for AV development
• Analyzes geographic diversity and environmental impact on reliability
• Explores trends like multimodal AI and advanced data generation

Innovation

• Integrated analysis of AV pipeline components
• Evaluation of annotation practices and metrics
• Multimodal AI and advanced data generation