Perception Uncertainty Feedback for Automated Control Safety
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Solution Overview
Problem
Automated control systems face challenges in handling ambiguity in perception, leading to potential safety concerns and reduced productivity due to imperfect sensor data categorization, as they either ignore uncertainty or attempt to substitute object types, resulting in increased processing overhead and power consumption.
Innovation Solution
An augmented perception system that provides categorical probability distributions for environmental objects, allowing for uncertainty quantification and feedback, which enables intelligent planning and resource management by increasing sensor resolution and frequency in uncertain conditions, while suppressing irrelevant ambiguities to reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system attempts to substitute safe object types for suspected false categorizations, then safety concerns are addressed, but productivity is reduced and processing overhead increases
Solution Approach 1:
The system changes the parameter of uncertainty representation from binary (certain/uncertain) to continuous probability distributions. This allows the planning system to work with expected values and variances rather than conservative substitutions, maintaining safety while improving productivity by avoiding unnecessary conservative replacements of object types.
Solution Approach 2:
The patent introduces an intermediary layer between perception and planning that transforms uncertain categorical data into probabilistic distributions. This intermediary processing layer allows the planning system to handle uncertainty mathematically through expected values, avoiding the need for conservative object type substitutions while maintaining safety.
2Reliability
If the system attempts to substitute safe object types for suspected false categorizations, then safety concerns are addressed, but processing overhead increases
Solution Approach 1:
The system transforms the nature of uncertainty handling from discrete conservative substitutions to continuous probabilistic calculations. By working with expected values and variances, the system reduces processing overhead while maintaining safety, as mathematical operations on probability distributions are more efficient than repeated conservative object type replacements.
3Measurement precision
If sensor resolution and frequency are increased in uncertain conditions, then accuracy is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts sensor resolution and frequency based on the calculated uncertainty levels from probability distributions. When uncertainty is high for critical objects, the system increases sensor activity to improve accuracy. When uncertainty is low or objects are irrelevant, the system reduces sensor activity to minimize power consumption, creating a dynamic adaptation strategy.
Solution Approach 2:
The system applies different sensor resolutions and frequencies locally to different objects based on their individual uncertainty levels and relevance to current goals. Rather than uniformly increasing all sensor activity, the system selectively enhances sensing for specific uncertain or relevant objects, optimizing the balance between accuracy and power consumption.
4Use of energy by moving object
If the system suppresses irrelevant ambiguities, then power consumption is reduced, but measurement precision may be affected
Solution Approach 1:
The system applies different levels of uncertainty handling to different objects based on their relevance to current goals. Irrelevant ambiguities are suppressed with lower measurement precision to reduce power consumption, while relevant objects maintain higher precision. This local differentiation optimizes the balance between power efficiency and measurement accuracy.
Solution Approach 2:
The system applies partial action by selectively processing only the most relevant uncertainties with full precision, while suppressing less relevant ambiguities. This partial processing approach reduces overall power consumption while maintaining adequate measurement precision for critical decision-making.
Data Source
AI summary
Systems, apparatuses and methods may provide for technology that obtains categorization information and corresponding uncertainty information from a perception subsystem, wherein the categorization information and the corresponding uncertainty information are to be associated with an object in an environment. The technology may also determine whether the corresponding uncertainty information satisfies one or more relevance criteria, and automatically control the perception subsystem to increase an accuracy in one or more subsequent categorizations of the object if the corresponding uncertainty information satisfies the one or more relevance criteria. In one example, determining whether the corresponding uncertainty information satisfies the relevance criteria includes taking a plurality of samples from the categorization information and the corresponding uncertainty information, generating a plurality of actuation plans based on the plurality of samples, and determining a safety deviation across the plurality of actuation plans, wherein the relevance criteria are satisfied if the safety deviation exceeds a threshold.