Multisweep 3D Detection Network for Sparse Sensor Fusion
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing multi-sensor systems face challenges in handling diverse sensor configurations and sparse multi-sweep inputs, leading to reduced situational awareness and navigation difficulties due to the complexity of configuring and training networks for different sensor setups, particularly with LiDAR and radar.
Innovation Solution
An adaptable multi-task network that integrates recurrent models to handle different sensor inputs, including a vision backbone and sparse auxiliary networks, encoding and decoding features to densify sparse data from multi-sweep sensors, and using parallel processing pipelines to improve perception.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single network architecture is used for different sensor configurations, then device complexity is reduced, but the network cannot handle diverse sensor inputs effectively
Solution Approach 1:
The patent implements a universal network architecture that can handle multiple sensor modalities (camera, LiDAR, radar) through a single unified model. The network uses modality-specific encoders that can be selectively activated based on available sensors, allowing one network to serve multiple sensor configurations without requiring separate networks for each modality combination.
Solution Approach 2:
The network employs dynamic routing mechanisms that adaptively select which encoders and processing paths to activate based on the available sensor inputs. This dynamic architecture allows the system to flexibly configure processing paths at runtime, maintaining simplicity while handling diverse sensor configurations effectively.
2Reliability
If separate networks are used for different sensor configurations, then sensor input handling is optimized, but device complexity and training effort increase significantly
Solution Approach 1:
The patent segments the network into modality-specific encoders (vision encoder, LiDAR encoder, radar encoder) that can be independently designed and trained, then combines them through a shared processing architecture. This segmentation allows each encoder to be optimized for its specific sensor type while the shared components reduce overall system complexity compared to completely separate networks.
Solution Approach 2:
The patent merges multiple sensor processing paths into a unified architecture where different encoders feed into common processing layers. This combining approach shares computational resources and training parameters across modalities, reducing the total complexity and training effort required compared to maintaining completely separate networks for each sensor configuration.
3Area of stationary object
If multi-sweep sensors provide sparse inputs, then sensor coverage is improved, but information completeness is reduced
Solution Approach 1:
The patent applies preliminary encoding and feature extraction to sparse sensor inputs before they enter the main processing architecture. The modality-specific encoders perform preliminary organization and enrichment of sparse data, preparing it for more effective integration with other sensor modalities and improving the final output completeness.
Solution Approach 2:
The patent introduces intermediate processing layers and fusion mechanisms that act as mediators between sparse sensor inputs and the final perception output. These intermediate representations bridge the gap between sparse inputs and comprehensive environmental understanding, allowing the system to infer missing information through multi-modal fusion and contextual reasoning.
Data Source
AI summary
Systems, methods, and other embodiments described herein relate to a multi-task model that integrates recurrent models to improve handling of multi-sweep inputs. In one embodiment, a method includes acquiring sensor data from multiple modalities. The method includes separately encoding respective segments of the sensor data according to an associated one of the different modalities to form encoded features using separate encoders of a network. The method includes accumulating, in a detector, sparse features associated with sparse sensor inputs of the multiple modalities to densify the sparse features into dense features. The method includes providing observations according to the encoded features and the sparse features using the network.