Probabilistic Machine Learning Network for Autonomous Vehicle Behavior Enforcement
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Solution Overview
Problem
Current autonomous vehicle systems face challenges in real-time prediction of actions by drivers and pedestrians, especially when they break traffic rules, due to inefficient modeling of probabilistic dependencies and lack of automatic data collection for improving vehicle operations.
Innovation Solution
Implementing a probabilistic machine learning network that models temporal, causal, and statistical dependencies between automated system components, using a Bayesian network or neural network with learned structure, to encode situational, behavioral, and operational constraint questions, and performing inference algorithms to adjust vehicle behavior based on inferred probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional deterministic models are used to predict actions of drivers and pedestrians, then the system structure is simple, but the prediction accuracy deteriorates when traffic rules are broken
Solution Approach 1:
The patent replaces traditional deterministic mechanical models with a probabilistic machine learning network that uses Bayesian inference to predict actions of drivers and pedestrians. This substitution allows the system to handle uncertainty and rule-breaking behaviors by modeling probabilistic dependencies between environmental factors and human actions, significantly improving prediction accuracy in complex traffic scenarios.
Solution Approach 2:
The patent transforms the modeling approach by changing from fixed deterministic parameters to dynamic probabilistic parameters. The machine learning network learns and updates probability distributions for various traffic participants' actions based on observed data, allowing the system to adapt to changing traffic patterns and unpredictable human behaviors while maintaining manageable computational complexity through efficient inference algorithms.
2Measurement precision
If real-time probabilistic prediction is implemented, then the prediction accuracy improves, but the computational time increases
Solution Approach 1:
The patent performs preliminary actions by pre-compiling the probabilistic machine learning network structure and pre-calculating probability distributions for common traffic scenarios. The Bayesian network is constructed offline with predefined relationships between environmental factors and traffic participant actions, allowing real-time inference to proceed efficiently by simply updating probabilities based on current sensor data rather than performing full probabilistic calculations from scratch.
Solution Approach 2:
The system implements self-service through automatic model updating and refinement. The probabilistic network continuously learns from incoming sensor data and observed traffic patterns, automatically adjusting its probability distributions and structural relationships without requiring manual intervention. This self-updating capability allows the system to maintain high prediction accuracy while optimizing computational efficiency based on learned patterns from historical data.
3Measurement precision
If comprehensive sensor data collection is performed, then the model accuracy improves, but the data processing complexity increases
Solution Approach 1:
The patent extracts and focuses on the most critical probabilistic relationships from comprehensive sensor data. The Bayesian network structure is designed to selectively model only the essential dependencies between environmental factors and traffic participant actions, filtering out redundant information. This extraction approach allows the system to maintain high model accuracy by capturing key predictive relationships while avoiding the computational burden of processing all possible sensor data combinations.
Solution Approach 2:
The patent segments the complex data processing task by dividing the probabilistic model into modular components representing different traffic participants and environmental factors. Each node in the Bayesian network represents a specific variable or concept, and the relationships between nodes are independently modeled. This segmentation allows the system to process comprehensive sensor data efficiently by updating probabilities in discrete, manageable units rather than attempting to process the entire dataset as a single complex computation.
Data Source
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AI summary
Systems and methods comprising: obtaining a probabilistic machine learning model encoded with at least one of following categories of questions for an automated system - a situational question, a behavioral question and an operational constraint relevant question; receiving behavior information specifying a manner in which the automated system was to theoretically behave or actually behaved in response to detected environmental circumstances, and/or perception information indicating errors in a perception of a surrounding environment made by the automated system; performing an inference algorithm using the probabilistic machine learning model to obtain at least one inferred probability that a certain outcome will result based on at least one of the behavior information and the perception information; and causing the automated system to perform a given behavior to satisfy a pre-defined behavioral policy, in response to the at least one inferred probability being a threshold probability.