IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments (1811.10200v1)

Abstract: While several datasets for autonomous navigation have become available in recent years, they tend to focus on structured driving environments. This usually corresponds to well-delineated infrastructure such as lanes, a small number of well-defined categories for traffic participants, low variation in object or background appearance and strict adherence to traffic rules. We propose IDD, a novel dataset for road scene understanding in unstructured environments where the above assumptions are largely not satisfied. It consists of 10,004 images, finely annotated with 34 classes collected from 182 drive sequences on Indian roads. The label set is expanded in comparison to popular benchmarks such as Cityscapes, to account for new classes. It also reflects label distributions of road scenes significantly different from existing datasets, with most classes displaying greater within-class diversity. Consistent with real driving behaviours, it also identifies new classes such as drivable areas besides the road. We propose a new four-level label hierarchy, which allows varying degrees of complexity and opens up possibilities for new training methods. Our empirical study provides an in-depth analysis of the label characteristics. State-of-the-art methods for semantic segmentation achieve much lower accuracies on our dataset, demonstrating its distinction compared to Cityscapes. Finally, we propose that our dataset is an ideal opportunity for new problems such as domain adaptation, few-shot learning and behaviour prediction in road scenes.

• The paper presents the IDD dataset with 10,004 annotated images and 34 classes, offering a detailed resource for unstructured autonomous navigation research.
• It details a novel four-level label hierarchy that supports varied semantic complexities and the development of advanced computer vision models.
• Empirical results show that state-of-the-art segmentation models perform poorly on IDD, emphasizing the need for robust algorithms in unstructured environments.

IDD: A Dataset for Exploring Problems of Autonomous Navigation in Unconstrained Environments

The paper presents the IDD (Indian Driving Dataset) designed for enhancing autonomous navigation capabilities in unstructured environments prevalent in many parts of the world. Unlike existing datasets, such as Cityscapes and KITTI, which focus on more organized driving conditions typical of North America and Europe, IDD offers a more complex scenario with diverse classes and conditions, better reflecting the challenges faced in Indian roadways.

Core Contributions

1. Dataset Composition: IDD comprises 10,004 images meticulously annotated with 34 classes collected from 182 driving sequences on Indian roads. This dataset is notable for its expanded label set compared to Cityscapes, showcasing a broader and more diverse array of classes, such as autorickshaws and animals, indicative of the heterogeneous traffic ecosystem.
2. Four-Level Label Hierarchy: The authors introduce a novel four-level label hierarchy which facilitates different levels of semantic complexity. This structure potentially allows for the development and application of varied training methodologies and advanced computer vision algorithms.
3. Empirical Analysis and Results: Experiments conducted with state-of-the-art semantic segmentation models demonstrate significantly lower accuracies on IDD when compared to structured datasets. This divergence highlights the unique complexities and challenges posed by the IDD dataset, underscoring its role in fostering the development of algorithms robust to unstructured environments.

Implications and Future Research Directions

The creation of IDD opens multiple pathways for both theoretical inquiry and practical applications. Its label diversity and increased complexity provide an ideal foundation for exploring problems such as domain adaptation, few-shot learning, and behavior prediction within the unstructured contexts encountered globally in developing regions.

• Practical Implications: IDD's detailed and complex label structure can enhance the reliability of autonomous vehicles by equipping them to navigate dynamic environments more effectively. This is particularly crucial for vehicles intended to operate in geographies with less regulated traffic conditions, such as those in Asia, South America, and Africa.
• Theoretical Value: From a research perspective, IDD presents an opportunity to refine machine learning models to better generalize across diverse operational environments. This includes developing algorithms capable of recognizing novel and overlapping classes, and coping with the significant variability in ambient conditions like lighting and weather.
• Future Research Directions: The authors suggest that IDD provides fertile ground for future work in semantic segmentation and adjacent fields. The dataset's variety and the distinct challenges it presents can drive innovation in scene understanding and path planning mechanisms, essential components for advancing autonomous navigation solutions. Researchers are encouraged to explore domain adaptation techniques between structured and unstructured settings and tackle few-shot learning with the statistically rare entities present in IDD.

In summary, the IDD dataset constitutes a significant step forward in addressing the pressing need for comprehensive datasets to train and evaluate autonomous navigation systems in unstructured environments. Its introduction aligns with the broader goals of expanding the applicability and reliability of autonomous technologies across diverse global contexts.