Intersection Smart Node for Autonomous Vehicle Navigation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous vehicles face challenges in navigating intersections due to complex traffic patterns and occlusions from static and dynamic objects, which existing navigation systems fail to address effectively.
Innovation Solution
A system comprising a node with multiple cameras and a processor that captures and processes images of an intersection from different fields of view, detects objects of interest, determines their motion, and generates augmented perception data, which is transmitted to remote servers for vehicle navigation, using a combination of narrow and wide field of view cameras and advanced image processing algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single camera is used to capture intersection images, then the device complexity is low, but the measurement precision and coverage area are insufficient to detect all moving objects in complex traffic patterns
Solution Approach 1:
The camera system is segmented into multiple specialized cameras: wide field of view cameras for overall intersection monitoring, narrow field of view cameras for detailed object detection in specific directions, and fisheye cameras for 360-degree coverage. Each camera type is optimized for specific detection tasks, improving overall measurement precision while distributing system complexity across modular components.
Solution Approach 2:
The system transitions from two-dimensional image capture to three-dimensional spatial awareness by combining multiple camera viewpoints and using stereoscopic vision algorithms. This dimensional enhancement allows accurate depth perception and object tracking in complex intersection environments without requiring an excessive number of cameras.
2Area of stationary object
If multiple cameras with different fields of view are deployed to cover all intersection areas, then the coverage area and measurement precision improve, but the device complexity and data processing requirements increase
Solution Approach 1:
The camera system is designed with multi-functionality where wide field of view cameras serve both as overview sensors and as trigger sensors for activating narrow field of view cameras. The same camera network handles multiple tasks including object detection, tracking, classification, and intersection monitoring, reducing the need for separate specialized systems for each function.
Solution Approach 2:
Wide field of view cameras perform preliminary scanning of the intersection area to detect potential objects of interest. When motion or objects are detected in wide FOV images, this triggers the activation of narrow field of view cameras for detailed observation. This preliminary action approach ensures comprehensive coverage while activating high-resolution sensors only when needed, reducing overall system complexity.
3Reliability
If comprehensive image processing is performed to detect and track all moving objects, then the reliability of navigation data improves, but the loss of time for processing and the productivity of the system decrease
Solution Approach 1:
The image processing system applies local quality by focusing computational resources on specific regions of interest within the intersection. Instead of processing all images uniformly, the system identifies areas with detected objects or potential hazards and concentrates processing power there, maintaining high navigation data reliability while reducing overall processing time through selective attention to critical zones.
Solution Approach 2:
The system performs partial processing by initially analyzing only key features and motion vectors in wide field of view images, then conducting more intensive processing only on regions where objects of interest are detected. This selective processing approach maintains sufficient navigation reliability by focusing computational effort on critical areas rather than exhaustively processing all image data.
4Measurement precision
If advanced image processing algorithms are used to detect motion and generate augmented perception data, then the measurement precision and reliability improve, but the use of energy and computational resources increase
Solution Approach 1:
The image processing system operates periodically rather than continuously, activating advanced motion detection algorithms only at intervals when changes are detected in the intersection environment. Wide field of view cameras continuously monitor for changes, and intensive processing is triggered periodically when motion or new objects are detected, reducing energy consumption while maintaining high measurement precision for navigation-critical data.
Data Source
AI summary
A node is provided for capturing information about moving objects at an intersection. The node includes a plurality of first cameras that are positioned to capture first digital images of an intersection from different fields of view and a second camera positioned to capture second digital images in a field of view that is wider than that of each first camera. The node includes a processor that detects in the first and second digital images a set of objects of interest of the intersection, determines motion of each detected object of interest in the set from consecutive images of the first digital images or the second digital images. The node generates, for each object of interest of the set, augmented perception data that includes location data in the global coordinate system and the determined motion of each object of interest in the set.