HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalization (2311.16589v2)

Abstract: Lane detection is a vital task for vehicles to navigate and localize their position on the road. To ensure reliable driving, lane detection models must have robust generalization performance in various road environments. However, despite the advanced performance in the trained domain, their generalization performance still falls short of expectations due to the domain discrepancy. To bridge this gap, we propose a novel generative framework using HD Maps for Single-Source Domain Generalization (SSDG) in lane detection. We first generate numerous front-view images from lane markings of HD Maps. Next, we strategically select a core subset among the generated images using (i) lane structure and (ii) road surrounding criteria to maximize their diversity. In the end, utilizing this core set, we train lane detection models to boost their generalization performance. We validate that our generative framework from HD Maps outperforms the Domain Adaptation model MLDA with +3.01%p accuracy improvement, even though we do not access the target domain images.

• The paper proposes a single-source domain generalization framework that enhances lane detection diversity using HD maps and generative models.
• The framework decomposes lane detection into lane structures and surroundings, optimizing data diversity with a tailored selection algorithm.
• Experiments on CULane and TUSimple benchmarks show that the method outperforms existing techniques under diverse and challenging environmental conditions.

Improving Lane Detection Generalization: A Novel Framework using HD Maps for Boosting Diversity

The paper, "Improving Lane Detection Generalization: A Novel Framework using HD Maps for Boosting Diversity" by Daeun Lee, Minhyeok Heo, and Jiwon Kim, introduces an innovative framework designed to enhance the generalization capability of lane detection algorithms in autonomous driving systems. The authors focus on addressing the domain discrepancy that impacts lane detection performance when deployed in diverse and previously unseen road environments.

Summary

The core contribution of this work lies in the proposed framework for Single-Source Domain Generalization (SSDG) in lane detection. Traditional lane detection models suffer from performance degradation when tested on out-of-distribution data due to inherent differences in road environments like camera mounting positions, road widths, and varying lighting conditions. The framework presented leverages High-Definition (HD) maps and generative models to introduce a more diverse training dataset without relying on multiple source domains.

Key Contributions

1. Novel Training Framework: The authors propose a novel framework aimed at generalizing a lane detection model using only a single-source domain. This approach is crucial for real-world autonomous driving applications where obtaining multi-source domain data is impractical.
2. Decomposition into Lane Structures and Surroundings: The proposed method decomposes lane detection data into two primary components: lane structures and surrounding environments. Diversity within these components is enhanced separately using HD maps for lane data augmentation and generative models for surroundings augmentation.
3. Data Selection Optimization: Instead of expanding the entire dataset, the framework identifies a core subset of data that maximizes diversity, thus optimizing the training data without redundant samples.
4. Experimental Validation: Extensive experiments demonstrate the efficacy of the proposed framework, achieving superior generalization performance compared to existing domain adaptation techniques without using any prior target domain information.

Methodology

The framework meticulously addresses the problem of domain discrepancy by enhancing the diversity of lane structures and the visual surroundings. The method comprises several key phases:

1. Lane Mask Extraction from HD Maps:
   • Lane markings are extracted and projected onto RGB images using camera parameters from HD maps.
   • This stage ensures that a consistent and diversified set of lane label masks is available without extensive manual annotations.
2. Surrounding Diversity via Generative Models:
   • Generative models like OASIS and Pix2PixHD are employed to create realistic and varied surroundings for the lane label masks.
   • The synthetic images are selected based on their perceptual diversity using metrics like MS-SSIM to ensure the training data effectively captures diverse environmental conditions.
3. Data Selection Algorithm:
   • A data selection algorithm based on graph theory is employed to identify the subset of images with the minimum sum of similarities, thus emphasizing diversity.

Results

The paper presents strong numerical results, illustrating significant improvements in generalization performance on benchmark datasets CULane and TUSimple. Specifically:

• CULane Dataset: The framework achieved higher F1-scores compared to state-of-the-art domain adaptation methods, especially in challenging scenarios such as night scenes and dazzle light conditions.
• TUSimple Dataset: The proposed method significantly outperformed existing methods, highlighting its effectiveness even when limited to a single source domain.

Implications and Future Developments

The implications of this research are twofold:

1. Practical for Autonomous Driving:
   • The proposed SSDG approach is highly practical for autonomous driving, where pre-defining target domains is infeasible.
   • By enhancing the diversity of training data through methodical decomposition and augmentation, the framework provides a robust model capable of handling diverse road scenarios.
2. Scalability and Flexibility:
   • The framework's reliance on HD maps and image synthesis models makes it scalable across various geographic regions without extensive manual data collection.
   • Future research could explore the integration of this framework with other autonomous driving tasks such as object detection and traffic sign recognition, potentially leading to a more comprehensive solution for domain generalization in autonomous systems.

Conclusion

This paper presents a methodically crafted framework to tackle the enduring challenge of domain discrepancy in lane detection. By leveraging HD maps and generative models to enhance training data diversity, the authors demonstrate that significant improvements in generalization performance can be achieved. The proposed framework is a notable contribution to the field of autonomous driving, providing a scalable and practical solution to ensure consistent lane detection across varying road environments.