Dynamic Sensor Model Fusion for Vehicle Environment Modeling
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Solution Overview
Problem
Existing sensor systems for vehicles, particularly in semi-autonomous or autonomous vehicles, face challenges in accurately merging sensor data from various sources like cameras, radars, and lidars due to uncertainties in object detection, position, type, and false alarms, leading to suboptimal environment modeling.
Innovation Solution
A method and sensor system that dynamically and situation-adaptively merge sensor data by generating and combining first and second sensor models, using analysis units and a fusion unit, with uncertainty data sets to improve the accuracy of object detection and reduce false alarms, employing machine learning and Bayesian fusion methods for enhanced data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If static sensor models are used for merging sensor data, then the merging process is simple and fast, but the accuracy of uncertainty description and fusion result is insufficient
Solution Approach 1:
The patent applies dynamics by transforming static sensor models into dynamic sensor models that are continuously adapted based on current sensor data. The sensor models are updated in real-time to reflect current detection conditions, object types, and environmental factors, thereby improving uncertainty description accuracy while managing complexity through systematic adaptation mechanisms.
Solution Approach 2:
The patent implements parameter changes by modifying sensor model parameters dynamically based on detected objects, environmental conditions, and sensor performance characteristics. Different object types, distances, and detection scenarios trigger adjustments in model parameters such as uncertainty weights, detection thresholds, and fusion coefficients, enabling accurate adaptation without complete model reconstruction.
2Reliability
If dynamic and situation-adaptive sensor models are used, then the accuracy and reliability of the fusion result is improved, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies preliminary action by pre-defining sensor model structures, uncertainty calculation methods, and adaptation rules before actual sensor data merging occurs. Training data is pre-processed to establish baseline models and detection patterns, allowing the system to perform rapid real-time adaptation without computing complex models from scratch during critical fusion operations.
Solution Approach 2:
The patent implements feedback mechanisms where detection results and uncertainty measurements are continuously fed back to adjust sensor models in real-time. The system monitors detection performance, identifies discrepancies between predicted and actual sensor behavior, and automatically recalibrates model parameters, thereby improving reliability through iterative refinement while maintaining computational efficiency through targeted adjustments.
3Measurement precision
If multiple sensor data sources are merged with detailed uncertainty analysis, then the environment model accuracy improves, but the processing time and computational resources increase
Solution Approach 1:
The patent applies segmentation by dividing the sensor data merging process into distinct stages: individual sensor data processing, uncertainty calculation for each sensor, selective fusion based on object type and detection confidence, and final environment model generation. This segmented approach allows parallel processing of independent sensor streams and selective application of computational resources to critical fusion operations, maintaining accuracy while improving processing speed.
Data Source
AI summary
The present disclosure relates to a method for merging sensor data. A sensor data set including first sensor data is provided. Furthermore, the first sensor data is analyzed, and a first sensor result is generated, the first sensor result being based on the analysis of the first sensor data. Moreover, a first sensor model is generated, the first sensor model being associated with the first sensor result and being dependent on a first uncertainty data set. The first uncertainty data set is a subset of the sensor data set. A second sensor result and a second sensor model are also generated, the second sensor model being associated with the second sensor result. Lastly, the first sensor result and the second sensor result are merged to form a fusion result, wherein the merging is performed on the basis of the first sensor model and the second sensor model.