Intersection Object Detection via Multi-Source Data Comparison
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Solution Overview
Problem
Existing traffic management systems and autonomous vehicle navigation technologies face challenges in accurately navigating through congested traffic and intersections, especially in low light conditions, due to discrepancies between camera and sensor data, leading to potential safety issues and inefficiencies.
Innovation Solution
A smart traffic camera system that compares and cross-references data from multiple sources, including cameras and sensors, to identify discrepancies and initiate corrective actions such as recalibration or alerts, ensuring accurate object location and distance calculations, thereby improving navigation and traffic management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If camera and sensor data are used for autonomous vehicle navigation, then navigation capability is improved, but measurement precision deteriorates due to discrepancies between different data sources
Solution Approach 1:
The system continuously compares object locations detected by cameras and sensors, identifies discrepancies between the two data sources, and uses this feedback to trigger recalibration actions. This closed-loop feedback mechanism ensures that measurement precision is maintained despite using multiple data sources for enhanced navigation capability.
Solution Approach 2:
The system performs self-calibration by automatically detecting discrepancies between camera and sensor data and initiating recalibration procedures without external intervention. This self-service approach resolves measurement precision issues while maintaining the versatility of multi-source navigation.
2Productivity
If traffic control systems monitor multiple parameters at intersections, then traffic management effectiveness is improved, but device complexity increases
Solution Approach 1:
The traffic control system uses a multi-functional platform that can detect, track, and analyze multiple types of objects (vehicles, pedestrians, cyclists) using the same camera and sensor infrastructure. This universal system improves traffic management effectiveness without proportionally increasing device complexity, as a single system performs multiple functions.
Solution Approach 2:
The system combines camera data and sensor data into a unified traffic monitoring platform that processes multiple parameters simultaneously. By merging these data sources and functions into a single integrated system, the complexity is managed more efficiently than having separate systems for each monitoring function.
Data Source
AI summary
Methods and apparatus consistent with the present disclosure may receive different sets of image or sensor data that may be compared for inconsistencies. Each of these sets of received data may include images of a roadway or intersection that are processed to identify objects and object locations at or near the roadway/intersection. In certain instances data received from a camera at a traffic control system may be compared with data received from a computer at a vehicle. Alternatively or additionally, different sets of data acquired by a same or by different cameras may be compared for discrepancies. When one or more discrepancies are identified in different sets of received data, corrective actions may be initiated. In certain instances, such corrective actions may include recalibrating an image acquisition system or may include sending message to a vehicle computer that identifies the discrepancy.