Multi-Channel Object Matching for Autonomous Vehicle Perception
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately matching and fusing data from different sensor types, such as LiDAR and camera systems, which affects the accuracy and speed of object perception and decision-making.
Innovation Solution
A method that involves obtaining sensor data from multiple types, applying region-based convolutional neural networks and Hungarian matching algorithms to detect and match objects, and using Kalman filtering and probabilistic fusion processes to determine the identity and motion parameters of objects, thereby improving the accuracy and adaptability of object perception systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensor types (LiDAR, camera, radar) are used for object detection, then the reliability and coverage of object perception is improved, but the complexity of matching and fusing data from different sensor channels increases
Solution Approach 1:
The patent introduces an object matching module as an intermediary that receives detection results from multiple sensor channels (LiDAR, camera, radar) and performs matching based on spatial relationships, motion parameters, and object features. This mediator component coordinates the data fusion process, matching objects across different sensor channels before fusing their detection results, thereby managing the complexity of multi-sensor data integration while improving perception reliability
Solution Approach 2:
The patent segments the object detection and fusion process into distinct stages: individual sensor detection, object matching across channels, and detection result fusion. By dividing the complex multi-sensor processing into sequential segments (detection → matching → fusion), the system manages complexity through structured decomposition while achieving reliable multi-channel object perception
2Measurement precision
If multi-channel sensor data is processed and fused, then the accuracy of object detection and tracking is improved, but the processing time and computational load increase
Solution Approach 1:
The patent performs preliminary object matching before final detection result fusion by pre-computing spatial relationships, motion parameter comparisons, and feature similarities between objects detected by different sensors. This preliminary matching organizes data in advance, creating structured associations that accelerate the subsequent fusion process and reduce real-time computational burden while maintaining high detection accuracy
Solution Approach 2:
The patent transforms multi-sensor detection data into standardized parameters including spatial coordinates, motion vectors, and confidence scores that can be efficiently compared and fused. By converting diverse sensor outputs into unified parameter representations, the system enables faster processing through standardized computations while preserving the accuracy benefits of multi-channel data fusion
Data Source
AI summary
A method may include obtaining first sensor data captured by a first sensor system and second sensor data captured by a second sensor system of a different type from the first sensor system. The method may include detecting a first object included in the first sensor data and a second object included in the second sensor data. The method may include assigning a first label to the first object and a second label to the second object after comparing the first and the second sensor data. The first and second labels may indicate degrees to which the first and the second objects match. Responsive to the first and second labels indicating that the first and the second objects match, the method may include designating a matched object representative of the first object and the second object and sending the matched object to a downstream computing system of an autonomous vehicle.