HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps (2106.14880v1)

Abstract: High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous driving system, including motion forecasting and planning. However, there are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack to generalize onto new unseen scenarios. To address this issue, we introduce a new challenging task to generate HD maps. In this work, we explore several autoregressive models using different data representations, including sequence, plain graph, and hierarchical graph. We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse dataset and an in-house dataset show that HDMapGen significantly outperforms baseline methods. Additionally, we demonstrate that HDMapGen achieves high scalability and efficiency.

• The paper introduces a hierarchical graph generative model that integrates global topology and local geometry for enhanced HD map generation.
• The paper employs an autoregressive global graph generator with a directed spline based local decoder to manage complex map dependencies efficiently.
• The paper outperforms baselines using metrics like Fréchet Distance, demonstrating superior generation of realistic road map structures.

Overview of "HDMapGen: A Hierarchical Graph Generative Model of HD Maps"

The paper "HDMapGen: A Hierarchical Graph Generative Model of HD Maps" introduces an innovative approach for the generation of high-definition (HD) maps using a hierarchical graph generative model. The authors present a novel and valuable task within the field of automated map generation, addressing the challenge of creating diverse and complex HD maps without the reliance on pre-existing aerial images. This approach is particularly essential for applications requiring large quantities of custom HD maps, such as training models for motion forecasting and planning in autonomous vehicles.

Key Technical Contributions

The paper emphasizes the importance of a hierarchical representation in map generation. The model includes two primary components: the global graph generator and the local graph generator. The methodology utilizes a directed spline to represent local graph segments between control points, ensuring an efficient sequence representation without revisiting issues common in map generation. Unlike other models such as GRAN or PlainGen, HDMapGen integrates both topology and geometry during the generation process, significantly improving performance metrics.

An interesting aspect of the model is its autoregressive nature, which involves the global graph generator using previously generated graph steps as inputs, facilitating efficient hierarchical graph formation. This characteristic, combined with a local graph decoder, sets HDMapGen apart as it effectively manages complex dependencies within maps.

Evaluation and Performance

The evaluation framework of HDMapGen is rigorous, with assessments against both qualitative and quantitative metrics. The paper details the superiority of HDMapGen over baselines like SketchRNN and PlainGen in generating diverse road map patterns. Specifically, HDMapGen achieves improved transportation convenience metrics, calculated as the Fréchet Distance between urban planning features of real and generated maps. The model performs particularly well in metrics where alternative methods falter, such as the fidelity of connections and node degrees, demonstrating its capability in generating maps that more accurately reflect real-world structures.

The authors also acknowledge the limitations and potential future improvements of the evaluation metrics. They discuss the relationship between generation fidelity and the model's sampling temperature, indicating that while higher temperatures foster diversity, they may reduce fidelity. This balance forms an interesting area for future exploration.

Implications and Future Directions

The implications of this research are significant for the development and deployment of autonomous systems, where realistic and high-resolution HD maps are crucial. While existing methods typically require predefined images or exhibit limitations in map diversity and accuracy, HDMapGen opens up opportunities for generating an expansive variety of maps tailored for specific environments without such dependencies.

The paper outlines potential extensions, such as incorporating elements to account for varying city styles by training on multiple datasets with city-specific identifiers. This route would further the model's applicability across different urban settings, making it more versatile and robust.

Concluding Remarks

"HDMapGen" offers substantial contributions to automated HD map generation. By addressing the intricacies of topology and geometry simultaneously, it demonstrates enhanced performance and highlights future paths for research, notably in expanding its adaptability to different urban contexts. The integration of HDMapGen into autonomous system pipelines could significantly optimize the design and deployment of advanced navigational models. This foundational work sets the stage for continued exploration and refinement in HD map generative models.