Infrastructure Sensor Detection Optimization for Intelligent Intersections
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Solution Overview
Problem
Existing object detection systems at intersections, such as cameras and radars, are compromised by adverse ambient conditions like snow, rain, and fog, leading to reduced accuracy and increased risk of accidents involving vulnerable road users and vehicles.
Innovation Solution
A system integrating cameras and radars with ground-level markers that dynamically optimize detection performance based on ambient conditions, weighting camera data when clear and radar data when conditions impede camera visibility, ensuring accurate detection and warning signals for potential collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If camera is used for detection, then detection accuracy is improved under ideal conditions, but detection reliability deteriorates under adverse ambient conditions
Solution Approach 1:
The system merges camera and radar detection devices into a unified detection system. The camera provides high-accuracy detection under ideal conditions while radar provides reliable detection under adverse conditions. The system combines the capabilities of both devices to achieve both high accuracy and high reliability across all conditions.
Solution Approach 2:
The system dynamically switches between camera and radar based on ambient conditions. When conditions are favorable, the camera is used for detection. When conditions deteriorate (snow, rain, fog), the system dynamically transitions to radar detection, ensuring continuous reliable operation.
2Reliability
If radar is used for detection, then detection reliability is improved under adverse conditions, but detection precision deteriorates compared to camera
Solution Approach 1:
The system merges camera and radar detection devices into a unified detection system. The camera provides high-accuracy detection under ideal conditions while radar provides reliable detection under adverse conditions. The system combines the capabilities of both devices to achieve both high accuracy and high reliability across all conditions.
Solution Approach 2:
The system dynamically switches between camera and radar based on ambient conditions. When conditions are favorable, the camera is used for detection. When conditions deteriorate (snow, rain, fog), the system dynamically transitions to radar detection, ensuring continuous reliable operation.
3Device complexity
If single detection device is used, then device complexity is reduced, but adaptability to varying ambient conditions deteriorates
Solution Approach 1:
The detection system achieves multi-functionality by integrating both camera and radar devices. The system can adapt to different ambient conditions by selecting the appropriate detection device, providing universal detection capability across all weather conditions while maintaining reasonable system complexity through coordinated operation.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides enhanced accuracy in detecting vulnerable road users and vehicles across varying conditions, improving safety by ensuring timely warnings to both drivers and pedestrians, thereby reducing the risk of accidents.
Implementation Method 1
a radar, both of which are able to provide detection data regarding at least one of a location, a speed, and a direction of an object
Implementation Method 2
a camera, both of which are able to provide detection data regarding at least one of a location, a speed, and a direction of an object
Data Source
AI summary
An infrastructure-based warning system, where the system is used as part of an intelligent intersection or intelligent road, and is used to detect various objects, such as vulnerable road users (VRUs) and vehicles. The system includes both cameras and radar, the operation of which is optimized based on which of the camera or radar is least affected by current conditions. This optimization includes the use of a marker, attached to infrastructure in the field of view of the detection device, such as a camera, to be optimized. If the camera accurately and consistently detects the marker, then camera detections of objects are weighted with more significance than the detection of objects using radar. If on the other hand, if the camera is unable to accurately detect the marker because of ambient conditions, then the greatest amount of confidence is placed on radar detection.