Automotive Sensor Fusion Module for Weighted Object Detection
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Solution Overview
Problem
Synchronizing object detection across various sensors in vehicles is challenging due to different sensing cycles and output data formats, leading to difficulties in identifying objects accurately, especially in adverse environmental conditions.
Innovation Solution
An automotive sensor integration module that includes a signal processor to select and apply weights to detection data from multiple sensors based on external environment data and recognition data, synchronizing the output to improve object identification performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If multiple sensors are disposed at different positions to detect objects, then the detection coverage is improved, but the synchronization of detection data becomes difficult
Solution Approach 1:
The patent applies preliminary action by pre-establishing a unified coordinate system and pre-defining spatial relationships between sensors mounted at different positions. The processing unit uses pre-calibrated transformation matrices to align detection data from various sensors, eliminating the need for real-time synchronization adjustments and enabling seamless data integration despite positional differences.
2Adaptability or versatility
If sensors with different sensing cycles and output data formats are used, then the adaptability to various environmental conditions is improved, but the data integration and object identification accuracy deteriorate
Solution Approach 1:
The processing unit dynamically adjusts detection parameters by applying time-dependent weighting factors to data from sensors with different sensing cycles. The system transforms heterogeneous data formats into a unified representation by changing temporal sampling parameters and applying adaptive fusion algorithms that account for each sensor's specific characteristics, thereby maintaining high identification accuracy despite parameter differences.
Solution Approach 2:
The processing unit acts as an intermediary that mediates between sensors with different output data formats and the object identification algorithm. It implements a data fusion layer that standardizes heterogeneous sensor outputs into a common format, applying transformation rules and calibration factors to bridge format differences while preserving the unique advantages of each sensor type for various environmental conditions.
3Speed
If unfiltered detection data from multiple sensors are used directly, then the data processing speed is improved, but the object identification accuracy deteriorates
Solution Approach 1:
The processing unit applies partial filtering by selectively processing only the most relevant detection data from each sensor based on current environmental conditions and object probability maps. Instead of filtering all data equally, the system applies adaptive filtering thresholds that maintain high processing speed while removing only the most problematic noise and redundant information, preserving identification accuracy without excessive computational overhead.
Data Source
AI summary
An automotive sensor integration module including a plurality of sensors differing from each other in at least one of a sensing cycle or an output data format, and a signal processor for selecting a plurality of weights for each of the plurality of sensors on the basis of external environment data, and applying the plurality of weights to a plurality of detection data output from the plurality of sensors respectively to generate a plurality of weighted data.