Probability Tree Reduction for Autonomous Vehicle Memory Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Large and complex probability trees in AI algorithms for autonomous vehicles are costly in terms of memory and computational resources, making it necessary to reduce their size while preserving desirable logic and information.
Innovation Solution
A method for reducing probability trees by selectively removing nodes and their subtrees based on defined parameters and criteria, such as removal criteria and tradeoff scores, to minimize the change in entropy and size, resulting in a significantly reduced tree structure that is stored and processed more efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the probability tree structure is maintained in its original form, then the completeness of decision-making information is preserved, but the memory requirements and computational resources increase significantly
Solution Approach 1:
The patent extracts and removes redundant nodes and subtrees from the probability tree structure based on entropy calculations and removal criteria. By identifying and eliminating nodes that contribute minimally to the overall decision-making information (such as nodes with low visit counts or single-child nodes), the system reduces memory requirements while preserving the essential decision logic.
Solution Approach 2:
The patent changes the structural parameters of the probability tree by dynamically adjusting which nodes are retained or removed based on calculated entropy values and visit counts. This parameter-based approach allows the system to optimize the tree structure by modifying node retention criteria, thereby reducing memory usage while maintaining decision-making quality.
2Measurement precision
If the probability tree structure is maintained in its original form, then the accuracy of decision-making is preserved, but the processing power and time required increase
Solution Approach 1:
The patent extracts and removes computationally expensive redundant nodes while preserving nodes that contribute most to decision accuracy. By eliminating subtrees with minimal impact on the final decision (identified through entropy and visit count metrics), the system reduces processing power requirements without significantly compromising decision-making accuracy.
Solution Approach 2:
The patent applies partial reduction by selectively removing only the most redundant nodes rather than aggressively pruning the entire tree. This partial action approach maintains sufficient decision-making accuracy by retaining nodes with higher visit counts and greater entropy contributions, while still achieving meaningful processing power savings.
3Device complexity
If nodes are removed from the probability tree, then the size of the tree is reduced, but the entropy and information content may be compromised
Solution Approach 1:
The patent uses entropy calculations as a key parameter to determine which nodes should be removed. By computing entropy values for different subtrees and comparing them against removal criteria, the system identifies nodes whose removal will minimize information loss. This parameter-driven approach ensures that tree size is reduced while entropy and information content are preserved to the greatest extent possible.
Solution Approach 2:
The patent implements feedback mechanisms by calculating updated entropy values after each node removal and using these updated values to guide subsequent removal decisions. This iterative feedback process allows the system to monitor information loss in real-time and adjust the reduction strategy accordingly, preventing excessive entropy loss while achieving size reduction goals.
4Quantity of substance
If aggressive node removal is performed, then memory and processing requirements are reduced, but the visit count and reliability of decisions may decrease
Solution Approach 1:
The patent establishes removal criteria based on visit count thresholds and entropy values to balance memory usage and decision reliability. By setting dynamic thresholds that consider both memory constraints and reliability requirements, the system determines which nodes can be safely removed without compromising the reliability of autonomous vehicle decisions. This parameter-based control prevents aggressive removal of critical nodes.
Data Source
AI summary
A method for probability tree reduction includes receiving a probability tree structure with a plurality of nodes. At least one node structural value is associated with each of the plurality of nodes and quantifies an entropy of a subtree extending from a corresponding one of the plurality of nodes. The method further includes receiving at least one parameter for removing one or more nodes of the probability tree structure, removing at least one node of the plurality of nodes from the probability tree structure according to the parameter, calculating an updated entropy for each of the plurality of nodes upstream from the removed node, and outputting a reduced probability tree structure without the removed node and with the updated entropy for each of the plurality of nodes upstream from the removed node. Other example methods and systems for probability tree reduction are also disclosed.