Hidden Hazard Detection via Multi-Sensor Fusion
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Solution Overview
Problem
Current vehicle systems for situational awareness are primarily limited to line of sight detection and fail to effectively identify hidden hazards, such as occluded vehicles, due to reliance on direct detection methods.
Innovation Solution
A method and system that utilize a combination of proximity sensors including ambient light, vision, acoustic, and seismic sensors to collect and classify information, estimate hidden hazard presence probabilities using comparative and dynamic neural network processes, and perform data fusion to determine the presence of hidden hazards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If line of sight detection methods are used, then system complexity is reduced, but detection capability for hidden hazards deteriorates
Solution Approach 1:
The system segments the detection task across multiple sensor types (ambient light sensors, vision systems, acoustic sensors, seismic sensors), with each sensor detecting different aspects of the environment. This segmentation allows the system to detect hidden hazards through multiple independent channels, improving reliability without requiring a single complex detection system
Solution Approach 2:
The system merges data from multiple sensor types and detection methods (optical, acoustic, seismic) into a unified hazard assessment. By combining information from ambient light sensors detecting headlight patterns, vision systems capturing images, acoustic sensors recording sound waves, and seismic sensors detecting vibrations, the system achieves comprehensive hidden hazard detection that exceeds the capability of any single sensor type
2Measurement precision
If multiple sensor types are integrated, then detection accuracy for hidden hazards improves, but information processing complexity increases
Solution Approach 1:
The system introduces intermediary processing layers that translate raw sensor data into meaningful hazard indicators. Ambient light sensors detect headlight patterns and convert them into presence/absence information about occluded vehicles. Acoustic sensors convert sound waves into hazard probability scores. These intermediaries simplify the integration of multiple sensor types by providing standardized input formats for the fusion process
Solution Approach 2:
The system implements feedback loops where detection results from one sensor type inform the processing of other sensor data. For example, when ambient light sensors detect abnormal headlight patterns suggesting a hidden vehicle, this information triggers enhanced processing of acoustic and seismic sensor data in the same spatial region, improving detection accuracy while avoiding unnecessary processing of unrelated sensor inputs
3Measurement precision
If data fusion from multiple sources is performed, then hidden hazard identification accuracy improves, but computational requirements increase
Solution Approach 1:
The system applies partial data fusion by selectively combining sensor data based on detected conditions rather than continuously fusing all available data. When ambient light sensors detect normal lighting conditions without abnormal headlight patterns, the system reduces processing of acoustic and seismic data. This partial action approach maintains high hazard identification accuracy while reducing computational power consumption during normal driving conditions
Solution Approach 2:
The system performs preliminary analysis of sensor data to identify potential hazard conditions before initiating full data fusion. Ambient light sensors first scan for abnormal headlight patterns, and acoustic sensors perform initial sound wave analysis to detect potential engine noises. Only when these preliminary actions detect anomalies does the system activate the computationally intensive fusion process with vision and seismic sensors, thereby maintaining accuracy while reducing overall computational requirements
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enhances situational awareness by detecting hidden hazards beyond the line of sight, improving safety in various driving conditions by accurately identifying occluded vehicles and other obstacles.
Implementation Method 1
collecting ambient light and classifying regions of differing brightness
Implementation Method 2
collecting images of light beams from a vision system and classifying the images based upon a plurality of predetermined light beam characteristics
Implementation Method 3
collecting acoustic waveforms and classifying the waveforms based on a plurality of predetermined acoustic characteristics
Implementation Method 4
collecting seismic waveforms and classifying the waveforms based on a plurality of predetermined seismic characteristics
Data Source
AI summary
A system and method for determining the presence of a hidden hazard may include identification of an operational scene for a host vehicle, and identification of an operational situation for the host vehicle. Information from a plurality of proximity sensors is collected and classified. A plurality of hidden hazard presence probabilities corresponding to the information from each of the plurality of proximity sensors, the operational scene, the operational situation, and at least one of a comparative process and a dynamic neural network process are estimated. A fusion process may be performed upon the plurality of hidden hazard presence probabilities to determine the presence of a hidden hazard.