Embedding-Space Anomaly Detection for Autonomous Vehicle Navigation
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the sheer volume of data from sensors and traditional mapping technologies, which can limit navigation accuracy and efficiency.
Innovation Solution
A system for autonomous vehicles that uses a camera to generate representations in embedding space, identifies anomalous conditions, and determines navigational actions based on these representations, utilizing a processing unit and memory to execute navigational maneuvers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mapping technology is used for navigation, then navigation coverage is improved, but data storage requirements and processing complexity increase significantly
Solution Approach 1:
The patent extracts and removes the need for traditional extensive mapping data by using anomaly detection on embedding representations. Instead of relying on stored map data, the system processes captured images through embedding generation and identifies anomalies directly from the visual data, eliminating the burden of storing and processing large volumes of mapping information while maintaining navigation reliability.
2Loss of information
If vast volumes of sensor data are collected and analyzed, then navigation information completeness is improved, but processing time and computational resources increase
Solution Approach 1:
The patent replaces traditional mechanical data processing methods with embedding-based anomaly detection. Instead of analyzing vast volumes of raw sensor data through complex algorithms, the system transforms image data into compact embedding representations and detects anomalies through spatial region analysis in the embedding space, significantly reducing processing time while maintaining information completeness.
3Device complexity
If traditional image processing methods are used, then system simplicity is maintained, but anomaly detection accuracy decreases
Solution Approach 1:
The patent changes the parameter space by transforming images into embedding representations rather than processing raw pixel data. This parameter transformation enables more accurate anomaly detection through embedding space region analysis while keeping the overall system architecture relatively simple and modular, avoiding the need for complex traditional image processing pipelines.
Data Source
AI summary
A system for navigating a host vehicle relative to a road segment includes: at least one processor comprising circuitry and a memory, wherein the memory includes instructions that when executed by the circuitry cause the at least one processor to: receive a captured image acquired by a camera onboard the host vehicle; generate a representation in embedding space of at least a portion of the captured image; determine whether the representation in embedding space of the at least a portion of the captured image falls outside of a predetermined embedding space region, wherein the predetermined embedding space region is defined as a non-anomalous embedding space region; determine a navigational action for the host vehicle based on a determination that the representation in embedding space of the at least a portion of the captured image falls outside of the predetermined embedding space region; and cause at least one system associated with the host vehicle to implement the navigational action.