Sensor Fusion Frontend Parameter Adjustment via Cross-Validation
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Solution Overview
Problem
In autonomous vehicle systems, data from different sensor types is typically combined in a fusion unit without consideration from other frontend devices, limiting the ability of sensors to evaluate their data streams based on information from other sensors, which can lead to inefficiencies and potential malfunctions due to unaccounted environmental factors.
Innovation Solution
A sensor data evaluation system where each frontend unit receives output from other frontend units, allowing it to modify its processing parameters based on contextual data from other sensors, such as adjusting sensor resolution or sensitivity, and employing machine learning to improve data quality and identify malfunctioning sensors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from different sensor types is combined in a fusion unit without consideration from other frontend devices, then the system structure is simple and processing is straightforward, but the ability of sensors to evaluate their data streams based on information from other sensors is limited, leading to potential inefficiencies and malfunctions
Solution Approach 1:
The patent merges multiple sensor frontend outputs into a unified data stream that is distributed back to all frontend units. This allows each frontend to access and evaluate data from other sensors, improving reliability through cross-validation while maintaining a relatively simple overall structure through the use of a central distribution mechanism.
Solution Approach 2:
The system implements feedback by routing the combined output from the fusion unit back to individual sensor frontends. This feedback loop enables each frontend to adjust its processing based on information from other sensors, improving evaluation reliability without requiring complex direct peer-to-peer connections between all sensor units.
2Reliability
If sensor frontends process data independently without considering other sensor data, then the processing is efficient and fast, but the system cannot account for environmental factors and sensor malfunctions that could be detected through cross-sensor evaluation
Solution Approach 1:
The system performs preliminary combining of all sensor data at the fusion unit before distribution back to frontends. This preliminary action allows potential issues like sensor malfunctions or environmental interference to be identified early in the processing chain, enabling faster corrective actions without requiring time-consuming iterative evaluations.
Solution Approach 2:
By merging all sensor outputs into a single combined data stream that is then distributed to each frontend, the system enables efficient cross-sensor evaluation. Each frontend receives pre-processed combined data, allowing rapid detection of anomalies without requiring multiple rounds of communication between sensors.
3Adaptability or versatility
If each sensor frontend accesses and processes data from all other sensors, then context-driven adjustments and improved reliability are achieved, but the data transmission complexity and processing overhead increase
Solution Approach 1:
The fusion unit serves multiple functions: it combines data from all sensors, processes the combined stream, and distributes it back to all frontends. This universal component enables context-driven adjustments at each frontend without requiring complex direct connections between all sensor pairs, simplifying the overall transmission architecture.
Solution Approach 2:
The fusion unit acts as an intermediary that mediates between individual sensor frontends. Instead of each frontend directly accessing and processing data from all other sensors (which would create complex peer-to-peer transmission), the fusion unit consolidates and redistributes data, reducing transmission complexity while enabling full contextual awareness.
Data Source
AI summary
Herein is disclosed a sensor data evaluation system comprising one or more first sensors, configured to deliver first sensor data to a first sensor frontend; the first sensor frontend, configured to generate a first sensor frontend output corresponding to the first sensor data and to deliver the first sensor frontend output to a second sensor frontend; and the second sensor frontend, configured to receive second sensor data from one or more second sensors; and modify a second sensor parameter based at least on the first sensor frontend output.