A Comprehensive Survey on Autonomous Driving Datasets: Evaluating Impact, Annotation Quality, and Future Trends

Core Concepts

This comprehensive survey presents an exhaustive analysis of 265 autonomous driving datasets, evaluating their impact, annotation quality, and future development trends.

Abstract

This survey provides an in-depth analysis of autonomous driving datasets from multiple perspectives: Dataset Collection and Evaluation: The authors systematically collected 265 publicly available autonomous driving datasets through extensive searches. They introduced a novel "impact score" metric to evaluate the significance and influence of each dataset, considering factors like citation count, data dimensions, and environmental diversity. Sensor Modalities and Sensing Domains: The survey discusses the various sensor types used in autonomous driving, including cameras, LiDARs, radars, event-based cameras, and thermal cameras, along with their characteristics and applications. It also categorizes the sensing domains into onboard, Vehicle-to-Everything (V2X), drone-based, and others, highlighting the advantages and limitations of each. Autonomous Driving Tasks: The survey covers the key tasks in autonomous driving, such as perception and localization, prediction, planning, and control, outlining their objectives, required data, and evaluation metrics. Analysis of High-Impact Datasets: The authors provide in-depth discussions of several high-impact datasets, including KITTI, Cityscapes, nuScenes, and Waymo, examining their strengths, limitations, and suitability for different tasks. Annotation Quality and Process: The survey investigates the annotation processes and tools used for autonomous driving datasets, emphasizing the importance of establishing standard annotation pipelines to ensure high-quality labels. Data Distribution Analysis: The authors present detailed statistical analyses of the data distribution across various datasets, highlighting their inherent biases and suitable use cases. Future Trends and Challenges: The survey discusses emerging trends and potential research directions in autonomous driving datasets, such as integrating language, generating data using Vision-Language Models, and promoting open data ecosystems. This comprehensive survey serves as a valuable resource for researchers and practitioners in the autonomous driving domain, facilitating informed dataset selection and guiding the development of future datasets.

Stats

The Waymo Open Dataset provides an extensive size of multimodal sensory data with high-quality annotations, covering a comprehensive range of driving conditions and geographies. The BDD100K dataset is renowned for its large size and diversity, contributing to the robustness and generalizability of autonomous driving algorithms. The nuScenes dataset addresses the diversity in urban scenes and environmental conditions, with a multimodal sensor setup including LiDAR, radars, and cameras.

Quotes

"High-quality datasets are fundamental for developing reliable autonomous driving algorithms." "We present an exhaustive study of 265 autonomous driving datasets from multiple perspectives, including sensor modalities, data size, tasks, and contextual conditions." "We introduce a novel metric to evaluate the impact of datasets, which can also be a guide for creating new datasets."

Key Insights Distilled From

A Survey on Autonomous Driving Datasets: Statistics, Annotation Quality, and a Future Outlook

by Mingyu Liu,E... at arxiv.org 04-24-2024

https://arxiv.org/pdf/2401.01454.pdf

Deeper Inquiries

How can autonomous driving datasets be further diversified to address rare or extreme driving scenarios, such as accidents or natural disasters?

To address rare or extreme driving scenarios in autonomous driving datasets, several strategies can be implemented to enhance diversity and robustness: Data Augmentation: By augmenting existing datasets with simulated or synthetic data representing rare scenarios, such as accidents or natural disasters, the dataset's diversity can be increased. This can involve introducing variations in weather conditions, road obstacles, or unexpected events to better prepare autonomous systems for such situations. Collaborative Data Sharing: Encouraging collaboration among different stakeholders in the autonomous driving industry to share data from rare or extreme scenarios can help create more comprehensive datasets. This can involve pooling resources and data from various sources to build a more extensive and diverse dataset. Crowdsourcing: Engaging the broader community through crowdsourcing initiatives can help collect data from real-world incidents or rare driving scenarios. This approach can involve incentivizing individuals to contribute data from their driving experiences, especially in challenging conditions. Scenario-Specific Data Collection: Proactively designing data collection efforts to focus on specific rare or extreme scenarios can ensure that the dataset includes relevant and critical information. This targeted approach can help capture the nuances and complexities of these scenarios effectively. Simulation Environments: Utilizing advanced simulation environments to generate data for rare scenarios can be a cost-effective and efficient way to diversify datasets. Simulations can replicate challenging driving conditions, allowing autonomous systems to be trained on a wide range of scenarios. By implementing these strategies, autonomous driving datasets can be enriched with data from rare or extreme driving scenarios, enabling more robust and reliable performance of autonomous systems in challenging situations.

How can the potential ethical and privacy concerns associated with the collection and use of large-scale autonomous driving datasets be addressed?

Addressing the ethical and privacy concerns related to large-scale autonomous driving datasets is crucial to ensure responsible and ethical deployment of autonomous systems. Here are some key strategies to mitigate these concerns: Data Anonymization: Implementing robust data anonymization techniques to protect the privacy of individuals whose data is included in the datasets. This involves removing personally identifiable information and ensuring that data cannot be traced back to specific individuals. Informed Consent: Obtaining informed consent from individuals whose data is being used in the datasets is essential. Clear communication about data collection, storage, and usage practices can help build trust and ensure that individuals are aware of how their data is being utilized. Data Security Measures: Implementing stringent data security measures to safeguard the datasets from unauthorized access, breaches, or misuse. This includes encryption, access controls, and regular security audits to protect the integrity and confidentiality of the data. Ethical Guidelines and Governance: Establishing clear ethical guidelines and governance frameworks for the collection and use of autonomous driving datasets. This can involve setting standards for data usage, defining acceptable practices, and ensuring compliance with ethical principles. Transparency and Accountability: Promoting transparency in data collection and usage processes by providing clear information about how the data is being used. Additionally, establishing mechanisms for accountability and oversight can help address any ethical concerns that may arise. By incorporating these measures, stakeholders in the autonomous driving industry can address ethical and privacy concerns associated with large-scale datasets, fostering trust and responsible data practices.

How can the integration of language-based information, such as traffic signs or driver instructions, enhance the performance and decision-making capabilities of autonomous driving systems?

Integrating language-based information, such as traffic signs or driver instructions, can significantly enhance the performance and decision-making capabilities of autonomous driving systems in the following ways: Improved Contextual Understanding: Language-based information can provide additional context and semantic understanding to autonomous systems. By interpreting text-based instructions or traffic signs, autonomous vehicles can better comprehend their surroundings and make more informed decisions. Enhanced Communication with Users: Integrating language capabilities allows autonomous vehicles to communicate with pedestrians, cyclists, and other drivers more effectively. This can improve safety and facilitate smoother interactions on the road. Advanced Navigation and Route Planning: Language-based information can assist in navigation and route planning by incorporating textual cues, road signs, and verbal instructions into the decision-making process. This can help autonomous systems navigate complex road networks and challenging scenarios. Real-Time Updates and Alerts: Language integration enables autonomous vehicles to receive real-time updates and alerts, such as road closures, detours, or emergency notifications. This information can be crucial for adapting to dynamic driving conditions and ensuring passenger safety. Multimodal Perception: By combining language-based information with sensor data, autonomous systems can achieve multimodal perception, enhancing their ability to interpret and respond to diverse stimuli. This holistic approach can lead to more robust and reliable decision-making. Overall, the integration of language-based information empowers autonomous driving systems with a deeper understanding of their environment, improved communication capabilities, and enhanced decision-making skills, ultimately contributing to safer and more efficient autonomous transportation.