Vehicle Threat Detection Using Physics-Constrained ML
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Solution Overview
Problem
Conventional machine learning algorithms in vehicles often produce false positive threat identifications due to unrealistic inputs, leading to unnecessary computations and actuation of subsystems, as they fail to account for real-world physics in threat assessments.
Innovation Solution
Incorporating real-world physics models into the loss function of machine learning algorithms for threat assessment, using a virtual boundary model based on distance and approach speed, to reduce false positives and improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning algorithms are used for threat assessment, then the system can process and analyze sensor data, but false positive threat identifications increase due to unrealistic inputs
Solution Approach 1:
The patent modifies the loss function parameters by incorporating physics-based constraints (distance, speed, acceleration) to change how the machine learning model evaluates threats. This transforms the optimization criteria from purely data-driven to physics-constrained, reducing false positives while maintaining detection accuracy
Solution Approach 2:
The patent introduces physics models (distance calculations, speed assessments, acceleration measurements) as intermediary layers between raw sensor data and threat classification. These intermediaries filter unrealistic inputs before they reach the final decision layer, improving reliability
2Productivity
If conventional machine learning algorithms perform threat assessment without physics constraints, then computations can be performed quickly, but unnecessary computations and memory usage increase due to false positives
Solution Approach 1:
The patent performs preliminary physics-based filtering (checking distance, speed, acceleration constraints) before full threat assessment computation. By pre-screening targets with simple physics checks, the system avoids expensive computations on clearly unrealistic targets, reducing energy consumption while maintaining speed
3Reliability
If conventional machine learning algorithms are used, then the system can identify potential threats, but unnecessary actuation of subsystems occurs due to false positive identifications
Solution Approach 1:
The patent implements feedback loops where physics model outputs (distance feasibility, speed consistency, acceleration realism) continuously inform the threat assessment process. This feedback mechanism allows the system to correct unrealistic predictions and avoid triggering subsystems based on false threats
Data Source
AI summary
Image data are input to a machine learning program. The machine learning program is trained with a virtual boundary model based on a distance between a host vehicle and a target object and a loss function based on a real-world physical model. An identification of a threat object is output from the machine learning program. A subsystem of the host vehicle is actuated based on the identification of the threat object.