Vectorized HD Map Prediction With Anchor-Guided Transformer Queries
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Solution Overview
Problem
Existing transformer-based HD map prediction systems struggle with capturing long-range dependencies in sensor data, leading to inaccurate and computationally expensive map element detection, especially for elongated objects like lane boundaries, due to quadratic complexity and inefficient attention mechanisms.
Innovation Solution
The system uses a transformer encoder-decoder structure with a separate segmentation unit to identify anchor regions, providing focused initial object queries and refining them using a transformer decoder, thereby improving attention scope and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a transformer model is used for HD map prediction with full attention scope, then comprehensive map element detection is achieved, but computational complexity increases quadratically
Solution Approach 1:
The patent segments the feature map into multiple regions of interest (ROIs) based on detected objects or map elements. Instead of applying attention mechanisms to the entire feature map, the transformer model processes only these segmented regions, reducing the attention scope from O(N²) to O(kN) where k is the number of ROIs. This segmentation maintains detection accuracy for relevant elements while dramatically reducing computational complexity.
Solution Approach 2:
The patent applies different processing quality to different regions of the feature map. High-attention regions are identified where map elements are likely to exist (based on object detections or semantic information), and full transformer attention is applied only to these local areas. Other regions receive reduced or no attention processing, creating a local quality distribution that optimizes the trade-off between accuracy and computational cost.
2Productivity
If attention scope is reduced to improve efficiency, then computational complexity decreases, but detection accuracy for elongated objects deteriorates
Solution Approach 1:
The patent introduces an additional dimension to the attention mechanism by incorporating semantic map information or object detection results as a guiding layer. This extra dimension allows the system to identify and focus attention on specific regions without losing contextual information about elongated objects, as the semantic layer provides continuity information that bridges gaps in the reduced attention scope.
Solution Approach 2:
The patent performs preliminary actions by first detecting objects or generating semantic map representations before applying the transformer attention mechanism. This preliminary detection identifies potential regions containing map elements, allowing the subsequent attention mechanism to focus efficiently on these pre-identified areas. For elongated objects, this preliminary step ensures that all relevant segments are captured before detailed processing begins.
3Device complexity
If generic queries are used for transformer model initialization, then model simplicity is maintained, but convergence speed decreases
Solution Approach 1:
The patent performs preliminary initialization of transformer queries using semantic map information or object detection results before the main detection process. Instead of using random or generic query initialization, the system pre-computes query representations based on detected objects or semantic segments. This preliminary action provides better initial conditions for the transformer model, significantly accelerating convergence while maintaining relatively simple model architecture.
Solution Approach 2:
The patent introduces an intermediary component that generates initial query representations for the transformer model. This intermediary layer processes semantic information or object detections and transforms them into appropriate query embeddings for the transformer. This intermediary mechanism bridges the gap between simple input data and the complex transformer processing, enabling faster convergence without substantially increasing overall system complexity.
Data Source
AI summary
A method for generating predictions for vectorized High Definition (HD) map elements includes obtaining sensor data generated by sensors of a vehicle; extracting feature maps from the sensor data; identifying anchor regions based on the feature maps, wherein the anchor regions represent potential locations for vectorized HD map elements, and wherein the vectorized HD map represents an environment surrounding the vehicle; generating initial object queries in the anchor regions, wherein the initial object queries are associated with a specific vectorized HD map element; refining, by a transformer decoder, the initial object queries based on the feature maps to generate refined object queries; and generating predictions for the vectorized HD map elements based on the refined object queries.