Blind Spot Incursion Prediction From Rear Camera Kinematics
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Solution Overview
Problem
Autonomous vehicles face challenges in navigating due to the vast amounts of data they need to process and store, including visual information, GPS data, and sensor data, which can limit their navigation capabilities and require significant storage and updating of maps.
Innovation Solution
The use of cameras and processing units to analyze visual information and create sparse maps for navigation, allowing autonomous vehicles to identify road features and obstacles while reducing data storage needs through polynomial representations and landmark data, enabling efficient navigation with minimal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mapping technology is used to navigate, then navigation accuracy is improved, but data storage requirements and system complexity increase significantly
Solution Approach 1:
The patent extracts only the essential navigational features from complete map data, creating sparse maps that contain only critical information (road geometry, landmarks, traffic signals) needed for navigation decisions. This extraction approach maintains navigation accuracy while dramatically reducing data storage requirements by eliminating redundant information.
Solution Approach 2:
The patent segments the environment into discrete navigational features (road segments, landmarks, traffic signals, intersections) that can be independently processed and stored. This segmentation allows the system to store only relevant features rather than complete map data, reducing overall data volume while preserving essential navigational information.
2Reliability
If complete map data is stored and updated frequently, then navigation reliability is improved, but data transfer requirements and processing time increase
Solution Approach 1:
The system extracts only essential navigational elements from complete map data, creating compact sparse maps that maintain reliability for navigation decisions. By storing only critical features rather than complete data sets, the system reduces processing time while preserving the reliability needed for safe autonomous operation.
3Measurement precision
If vast volumes of sensor data are processed, then navigation accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts essential navigational information from vast sensor data volumes, identifying and processing only the critical features needed for navigation (road boundaries, landmarks, obstacles). This extraction approach maintains navigation accuracy by preserving essential information while reducing computational complexity through selective processing.
Solution Approach 2:
The system segments sensor data into distinct feature categories (road geometry, landmarks, traffic signals, obstacles), processing each segment with appropriate algorithms. This segmentation reduces overall computational complexity by breaking down the complex task of processing vast sensor volumes into manageable, specialized processing streams.
Data Source
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AI summary
Systems and methods are provided for predicting blind spot incursions for a host vehicle. In one implementation, a navigation system for a host vehicle may comprise a processor. The processor may be programmed to receive, from an image capture device located on a rear of the host vehicle, at least one image representative of an environment of the host vehicle. The processor may be programmed to analyze the at least one image to identify an object in the environment of the host vehicle and to determine kinematic information associated with the object. The processor may further be programmed to predict, based on the kinematic information, that the object will travel in a region outside of a field of view of the image capture device and perform a control action based on the prediction.