Multi-Source Object Detection for Real-Time Vehicle Image Processing
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Solution Overview
Problem
Autonomous vehicles face challenges in determining safe trajectories and control strategies due to limitations in accurately detecting and processing objects within their environment, particularly in real-time, using existing image processing systems.
Innovation Solution
The method involves receiving images from image-capture devices and additional information from sources like RADAR, LIDAR, and maps to determine object characteristics, process images, and develop control strategies for the vehicle, including handling occlusions and partial occlusions, to provide instructions for safe navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If autonomous vehicles use image-capture devices to capture images of the environment, then the vehicle can detect objects around it, but the system struggles with accurately detecting and processing objects in real-time
Solution Approach 1:
The patent segments the image processing task by identifying and focusing only on relevant portions of images that contain detected objects. Instead of processing entire images, the system extracts and processes only the object-containing regions, reducing computational load while maintaining detection accuracy in real-time
Solution Approach 2:
The system performs preliminary object detection using image-capture devices before detailed image processing. By first detecting objects and then processing only their relevant image portions, the system prepares data in advance to enable real-time decision-making without sacrificing accuracy
2Reliability
If the vehicle integrates multi-source data from RADAR, LIDAR, and maps, then the navigation safety improves, but the system complexity increases
Solution Approach 1:
The patent merges data from multiple sources including image-capture devices, RADAR, LIDAR, and maps into a unified processing framework. By integrating these diverse data streams and correlating them with detected objects, the system enhances navigation safety through multi-source verification while managing complexity through systematic data fusion
3Loss of information
If the system processes entire images to determine control strategies, then comprehensive environmental understanding is achieved, but processing time increases
Solution Approach 1:
The patent extracts and processes only the portions of images that contain detected objects, rather than processing entire images. This extraction approach maintains comprehensive environmental understanding by focusing on object-containing regions while significantly reducing processing time through selective computation
Data Source
AI summary
Methods and systems for the use of detected objects for image processing are described. A computing device autonomously controlling a vehicle may receive images of the environment surrounding the vehicle from an image-capture device coupled to the vehicle. In order to process the images, the computing device may receive information indicating characteristics of objects in the images from one or more sources coupled to the vehicle. Examples of sources may include RADAR, LIDAR, a map, sensors, a global positioning system (GPS), or other cameras. The computing device may use the information indicating characteristics of the objects to process received images, including determining the approximate locations of objects within the images. Further, while processing the image, the computing device may use information from sources to determine portions of the image to focus upon that may allow the computing device to determine a control strategy based on portions of the image.