Vehicle-to-Vehicle Image Signatures for Early Obstacle Detection
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Solution Overview
Problem
Autonomous driving systems face challenges in detecting obstacles, such as potholes, that are not adequately represented in roadmaps and may not be detected by sensors in time for evasive action, especially under conditions of limited visibility, high speed, or driver alertness.
Innovation Solution
A method involving a vehicle receiving image information from another vehicle, extracting object data from the image, and performing driving operations based on this information, using a signature generation process that includes dimension expansion and merge operations to enhance obstacle detection and avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are used to detect obstacles in autonomous driving, then obstacle detection capability is improved, but detection reliability deteriorates when visibility is limited, speed is high, or driver alertness is low
Solution Approach 1:
The patent combines image data from multiple vehicles (first vehicle's own sensors and second vehicle's transmitted images) to create a composite view. This merging of data sources compensates for individual sensor limitations under adverse conditions, improving both detection capability and reliability simultaneously.
Solution Approach 2:
The second vehicle captures and transmits images of obstacles before the first vehicle reaches the obstacle location. This preliminary action allows the first vehicle to receive advance warning of obstacles, enabling earlier detection and more reliable avoidance maneuvers even when the first vehicle's own sensors have limited detection range or are affected by environmental conditions.
2Reliability
If vehicle-to-vehicle communication is implemented to share image data, then obstacle detection reliability is improved, but system complexity increases
Solution Approach 1:
The patent extracts only the essential elements needed for obstacle detection: image data containing obstacle information and location coordinates. By transmitting only this extracted relevant information rather than complete sensor datasets, the system achieves improved detection reliability while minimizing the complexity increase from communication infrastructure.
3Measurement precision
If comprehensive image processing is performed to extract object information, then detection accuracy is improved, but computational power consumption increases
Solution Approach 1:
The system extracts only the essential object information (presence, location, and basic shape) from the received images rather than performing comprehensive analysis of all image features. This selective extraction maintains sufficient detection accuracy for safety-critical obstacle identification while significantly reducing the computational power consumption compared to full image processing.
Data Source
AI summary
A method for driving a first vehicle based on information received from a second vehicle, the method may include receiving, by the first vehicle, acquired image information regarding (a) a signature of an acquired image that was acquired by the second vehicle, (b) a location of acquisition of the acquired image; extracting, from the acquired image information, information about objects within the acquired image; and preforming a driving related operation of the first vehicle based on the information about objects within the acquired image.