Predictive Visual Anchors for Autonomous Vehicle Control
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Solution Overview
Problem
Autonomous vehicles require complex sensor arrays for effective control, but existing systems face challenges in accurately determining control operations based on visual anchors, leading to potential inaccuracies and inefficiencies in navigation and operation.
Innovation Solution
The system utilizes cameras to capture video data, identifies visual anchors, determines differentials between actual and predicted anchors, and adjusts control operations to minimize these differentials, employing redundant processing and power fabrics for reliability and real-time processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If a complex array of sensors is used to determine control decisions, then the autonomous vehicle can perform autonomous functions, but the system complexity and cost increase significantly
Solution Approach 1:
The patent extracts and focuses on a specific subset of visual information (visual anchors) from the complex sensor data, isolating the most critical features for control decisions. This reduces the effective complexity by concentrating on key elements rather than processing all sensor inputs equally.
Solution Approach 2:
The system performs preliminary identification and tracking of visual anchors before making control decisions. By pre-processing the visual data to extract anchor points and their differentials, the system simplifies subsequent control calculations and reduces real-time processing complexity.
2Device complexity
If visual anchors are used to determine control operations, then the system can simplify sensor requirements, but measurement precision may be insufficient for accurate navigation
Solution Approach 1:
The patent implements a feedback mechanism by continuously tracking visual anchors across video frames and calculating differentials between consecutive positions. This feedback loop allows the system to refine measurements over time and compensate for individual measurement errors through temporal averaging and trend analysis.
Solution Approach 2:
The system performs preliminary tracking and prediction of visual anchor positions before making control decisions. By predicting where anchors should be based on previous positions and comparing with actual positions, the system enhances measurement precision through predictive modeling and error correction.
3Speed
If real-time processing of video data is performed to identify visual anchors, then control decisions can be made promptly, but processing time and computational load increase
Solution Approach 1:
The patent extracts only the most relevant visual features (anchors) from the complete video data, ignoring unnecessary details. This selective extraction dramatically reduces processing time while maintaining the speed needed for real-time control decisions.
Solution Approach 2:
The system performs partial processing by focusing computational resources only on identifying and tracking visual anchors rather than analyzing the entire video frame in detail. This partial action approach provides sufficient information for control decisions without the full computational burden of complete image analysis.
Data Source
AI summary
Using predictive visual anchors to control an autonomous vehicle, including: determining, based on a plurality of frames of video data from a camera of an autonomous vehicle, one or more predicted visual anchors, wherein the one or more predicted visual anchors comprise a predicted location of one or more visual anchors at a future time relative to when the plurality of frames were captured; identifying, in another frame of video data corresponding to the future time, the one or more visual anchors; determining one or more differentials between the one or more visual anchors and the one or more predicted visual anchors; determining, based on the one or more differentials, one or more control operations for the autonomous vehicle; and applying the one or more control operations.