Adaptive Image-LiDAR Transformer for BEV Feature Fusion
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Solution Overview
Problem
Existing sensor systems for advanced driver-assistance systems (ADAS) struggle to effectively generate bird's eye view (BEV) image data features that accurately represent the importance of image data and position data for object characterization in 3D environments.
Innovation Solution
The system extracts features from both image data and position data, generates separate sets of BEV features for each, and then fuses them using feature conditioning modules and positional encoding models to weigh the relative importance of each data type, resulting in more relevant BEV features for object characterization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If features from image data and position data are fused equally, then the system captures all available information, but the importance of different data types for specific tasks is not accounted for
Solution Approach 1:
The patent applies local quality by making the fusion process adaptive rather than uniform. Feature conditioning modules dynamically adjust the importance weighting of image features versus position features based on the specific spatial location and contextual requirements. This allows different regions of the BEV feature map to utilize different combinations of image and position data, optimizing information relevance for each local area while managing overall system complexity through modular processing.
2Measurement precision
If the system uses simple feature fusion, then processing is faster and simpler, but the accuracy of object characterization in 3D environments deteriorates
Solution Approach 1:
The patent implements preliminary action by introducing feature conditioning modules that preprocess and adjust feature importance weights before the main fusion operation. These modules prepare the image and position features by conditioning them on spatial and contextual information in advance, ensuring that the subsequent fusion process produces accurate object characterization. This preliminary preparation step enhances measurement precision while managing complexity through structured preprocessing.
Solution Approach 2:
The patent uses an intermediary approach by introducing positional encoding models as mediators between the raw position data and the fusion process. These encoders transform position data into a standardized representation that captures spatial relationships, serving as an intermediary layer that bridges the gap between different data types. This intermediary processing improves object characterization accuracy by ensuring that positional information is properly integrated with image features.
Data Source
AI summary
An apparatus includes a memory and processing circuitry in communication with the memory. The processing circuitry is configured to apply, based on a positional encoding model, a first feature conditioning module to a set of bird's eye view (BEV) position data features corresponding to position data to generate a set of conditioned BEV position data features, and apply, based on the position encoding model, a second feature conditioning module to a set of perspective image data features corresponding to image data to generate a set of conditioned perspective image data features. The processing circuitry is also configured to generate, based on the positional encoding model, the set of conditioned BEV position data features, and the set of conditioned perspective image data features, a weighted summation. Additionally, the processing circuitry is configured to generate, based on the weighted summation, a set of BEV image data features.