Vehicle AI Decision Probing Under Incomplete Observations

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Solution Overview

Problem

Autonomous vehicles face challenges in decision-making due to incomplete information, making the decision-making process opaque and inefficient.

Innovation Solution

A framework is developed using a Partially Observable Markov Decision Process (POMDP) model to quantify the impact of individual features on an AI agent's behavior with complete and incomplete observations, allowing for selective updates to the decision-making process.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the AI agent uses complete observations for decision-making, then the decision accuracy is improved, but the information processing complexity increases

Engineering Contradiction:

Improvedecision accuracyVSAvoidinformation processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The patent segments the decision-making process into multiple stages: initial decision-making with available observations, identification of missing critical information, selective probing for additional information, and updated decision-making. This segmentation allows the system to process information in manageable chunks rather than requiring all information upfront, reducing overall processing complexity while maintaining accuracy.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements partial action by making decisions based on available observations when they are sufficient, and only probes for additional information when the impact analysis indicates it is necessary. This avoids the excessive action of always gathering complete information before deciding, reducing processing complexity while maintaining decision accuracy when possible.

Inventive Principle:

Principle #16Partial or excessive action

2Productivity

If the AI agent probes for additional information sequentially, then the decision-making efficiency is improved, but the time consumption increases

Engineering Contradiction:

Improvedecision-making efficiencyVSAvoidtime consumption

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The patent applies preliminary action by calculating the impact of missing information on the decision outcome before actually probing for it. The system uses the world model to simulate how additional information would affect the decision, allowing it to pre-determine whether information gathering is worthwhile before committing time to the probing process.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts the decision-making process based on the calculated impact of missing information. When the impact is low, the system proceeds without additional probing, minimizing time consumption. When the impact is high, the system engages in sequential probing, accepting increased time investment for improved decision accuracy. This dynamic adaptation optimizes the balance between efficiency and time consumption.

Inventive Principle:

Principle #15Dynamics

3Speed

If the AI agent uses incomplete observations, then the information processing speed is improved, but the decision reliability deteriorates

Engineering Contradiction:

Improveinformation processing speedVSAvoiddecision reliability

Core Design Contradiction:

SpeedVSReliability

Solution Approach 1:

The patent implements feedback by using the world model to simulate and evaluate how additional information would change the decision outcome. This feedback mechanism allows the system to assess the reliability impact of incomplete observations and determine whether sequential probing is necessary to achieve acceptable decision reliability before finalizing the decision.

Inventive Principle:

Principle #23Feedback

4Loss of information

If the system calculates the impact of individual features, then the understanding of decision-making is improved, but the computational complexity increases

Engineering Contradiction:

Improveunderstanding of decision-makingVSAvoidcomputational complexity

Core Design Contradiction:

Loss of informationVSDevice complexity

Solution Approach 1:

The patent extracts and analyzes individual features separately by calculating the impact of each missing feature on the decision outcome independently. This extraction approach allows the system to understand the specific contribution of each feature to decision-making without requiring complex joint analysis of all features simultaneously, managing computational complexity while improving understanding.

Inventive Principle:

Principle #2Taking out (Extraction)

Data Source

PatentUS12559113B2Vehicle decision making using sequential information probing

Publication Date: 2026.02.24 NISSAN MOTOR CO LTD

AI summary

Vehicle decision-making is analyzed and can be used to modify a decision-making process. For subsets of features comprising a vehicle operational scenario, a first value is generated that quantifies behavior of an artificial intelligence (AI) agent as the AI agent performs a sequence of actions within a first world model based on a complete set of observations for the subset of features. A first world model is a copy of a second world model for sequential decision making. A second value is generated that quantifies behavior of an AI agent as the AI agent performs a sequence of actions in the second world model based on an incomplete set of observations for the subset of features. A difference between the first and second values determines the impact of individual features on the AI agent within the second world model. A decision-making process of the AI agent can be updated.