Autonomous Vehicle Image Processing Using Lane Segmentation
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Solution Overview
Problem
Autonomous vehicles face challenges in efficiently and accurately detecting objects within their lane of travel due to the computational expense and noise in processing image data from cameras, which are prone to differentiating between metallic and non-metallic objects.
Innovation Solution
The method involves receiving image data from sensors, determining the lane of travel using environmental information, and focusing on the relevant portion of the image data to process, thereby ignoring irrelevant areas and enhancing object detection within the lane.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image data from sensors is processed to detect objects in the environment, then object detection capability is improved, but processing complexity and computational expense increase
Solution Approach 1:
The patent divides the image data into multiple regions corresponding to different lanes of travel. The processor segments the image data based on lane information to identify objects only in relevant regions, reducing the overall processing complexity while maintaining detection accuracy for objects in the vehicle's lane.
Solution Approach 2:
The patent applies different processing priorities to different regions of the image data. Regions corresponding to lanes of travel receive focused processing attention, while other regions are processed with lower priority or excluded. This local differentiation reduces computational expense while preserving object detection capability where it matters most.
2Reliability
If all image data is processed to ensure comprehensive object detection, then detection coverage is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary processing to identify regions of interest (lanes of travel) before conducting full object detection. By pre-segmenting the image data into relevant and irrelevant regions based on lane information, the system avoids unnecessary processing of irrelevant areas, reducing processing time while maintaining reliable detection coverage in critical regions.
3Measurement precision
If environmental information is integrated with image data processing, then object detection accuracy in lane is improved, but system complexity increases
Solution Approach 1:
The patent merges environmental information (lane data) with image data processing by integrating lane segment information with the image processing pipeline. This combination allows the system to leverage existing environmental knowledge to guide object detection, improving accuracy for objects in the lane without requiring a completely separate complex system.
Data Source
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AI summary
The invention relates to an autonomous vehicle configured to use environmental information for image processing. The vehicle (200) is configured to operate in an autonomous mode in an environment and may be operating substantially in a lane of travel of the environment. The vehicle includes a sensor (406) configured to receive image data indicative of the environment and a computer system (112) configured to compare environmental information indicative of the lane of travel to the image data so as to determine a portion of the image data that corresponds to the lane of travel of the environment. Based on the portion of the image data that corresponds to the lane of travel of the environment and by disregarding a remaining portion of the image data, the vehicle may determine whether an object is present in the lane, and based on the determination, provide instructions to control the vehicle in the autonomous mode in the environment.