Polyline Association in Latent Space for Continuous HD Maps
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Solution Overview
Problem
Existing HD map generation methods struggle to accurately represent continuous and occluded objects like lane boundaries and pedestrian crossings due to their reliance on single-frame bounding boxes, leading to inefficiencies in localization and representation.
Innovation Solution
The use of autoencoders to transform polyline and polygon predictions into a latent space, cluster these points across multiple frames, and utilize centroids to track and denoise predictions, enhancing the accuracy and continuity of HD map representation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If single-frame bounding boxes are used for object representation, then the method is simple to implement, but the accuracy of representing continuous and occluded objects deteriorates
Solution Approach 1:
The system performs preliminary actions by transforming object representations into latent space before clustering, and by tracking objects across multiple frames in advance. This allows the system to prepare denoised and refined object representations before they are needed for final localization, improving accuracy without complicating the core implementation
Solution Approach 2:
The patent introduces latent space as an intermediary representation between the raw bounding box data and the final object representation. This latent space serves as a mediator that enables clustering and denoising operations, allowing the system to achieve higher localization accuracy while maintaining implementation simplicity through the use of this intermediate computational layer
2Reliability
If points are clustered across multiple frames, then the representation of continuous objects is improved, but the computational complexity increases
Solution Approach 1:
The patent applies dimensionality change by transforming spatial coordinates into latent space, where clustering operations become more efficient. This dimensional transformation allows the system to cluster points across multiple frames more effectively, improving continuity representation while reducing the computational burden of direct spatial clustering
Solution Approach 2:
The system creates copies of object representations in latent space for each frame, allowing clustering operations to be performed on these replicated representations. This copying approach enables the system to track continuity across frames without requiring complex real-time processing of the original high-dimensional data, thus managing computational complexity
3Measurement precision
If noise is reduced through clustering and denoising, then the accuracy of map features is improved, but the processing time increases
Solution Approach 1:
The system performs denoising and clustering operations in advance across multiple frames, preparing refined object representations before they are needed for map generation. This preliminary processing allows the system to achieve high map feature accuracy without requiring extensive processing time during critical map generation operations
Solution Approach 2:
The patent maintains continuous useful action by performing clustering and denoising operations continuously across multiple frames rather than as discrete batch operations. This continuous processing approach improves map feature accuracy through consistent noise reduction while optimizing processing time by maintaining steady-state computational operations
Data Source
AI summary
Systems and techniques are described herein for determining object-location information. For instance, a method for determining object-location information is provided. The method may include: generating, using an encoder machine-learning model, a latent-space representation of objects based on a representation of the objects in a scene; clustering points of the latent-space representation of the objects, to generate clusters of points; determining representative values of the clusters of points; and generating, using a decoder machine-learning model, a reconstructed representation of the objects in the scene based on the representative values of the clusters of points.