Episodic Memory Recall for Autonomous Vehicle Situational Awareness
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently processing and managing vast amounts of environmental and object information for safe navigation and control, requiring a system that reduces computational complexity without losing crucial details for improved decision-making.
Innovation Solution
A method and apparatus that convert environmental and object information into episodic event structures, which are saved, indexed, and recalled to generate control signals for autonomous vehicle control, using a combination of sensors, network interfaces, and processors to create a combined coordinate system and predict outcomes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all available environmental and object information is processed for autonomous driving decisions, then decision accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the continuous stream of environmental and object information into discrete episodic events with specific structures. Each episode captures a temporal sequence of relevant observations, transforming overwhelming raw data into manageable, structured units that can be processed efficiently while retaining crucial decision-making information.
Solution Approach 2:
The system extracts only the most relevant features and events from the complete environmental information stream. By identifying and isolating critical episodic patterns (such as specific object behaviors, environmental changes, or hazard conditions), the system discards redundant data while preserving the essential information needed for accurate autonomous driving decisions.
2Speed
If environmental information is processed in real-time for safe navigation, then response time is improved, but computational load increases
Solution Approach 1:
The system performs preliminary organization of environmental information into structured episodes as data is received, rather than processing everything simultaneously. Episodes are pre-structured with temporal ordering and key features identified in advance, enabling faster real-time query and decision-making without the need for intensive post-processing of raw data streams.
3Measurement precision
If detailed environmental information is maintained for situation awareness, then navigation accuracy is improved, but data management complexity increases
Solution Approach 1:
The patent implements a nested data structure where episodes contain sequences of observations, which in turn contain individual sensor measurements and object states. This hierarchical nesting organizes detailed environmental information within structured containers, allowing the system to maintain comprehensive navigation data while simplifying access and management through the nested organization paradigm.
Data Source
AI summary
A system and method is taught for data processing in an autonomous vehicle control system. Using information is acquired from the vehicle, network interface, and sensors mounted on the vehicle, the system can perceive situations around it with much less complexity in computation without losing crucial details, and then make navigation and control decisions. The system and method are operative to generate situation aware events, store them, and recall to predict situations for autonomous driving.