Infrastructure Sensor Computer for Real-Time Traffic Risk Control
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Solution Overview
Problem
Current systems for controlling road traffic and responding to infrastructure issues or events lack effective real-time data processing to identify risk conditions and send instructions to vehicles based on vehicle and object proximity, speed, and classification.
Innovation Solution
A system comprising stationary support structures with mounted sensors and computers that detect vehicles and objects, classify risk conditions, and send instructions to vehicles based on data from cameras and lidar sensors, enabling real-time risk assessment and control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sensor data processing is implemented to identify risk conditions, then traffic safety and response time are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system segments the complex task of risk identification into distinct functional modules: sensor data acquisition, vehicle detection, object detection, risk condition identification, and instruction generation. Each module processes specific aspects of the data independently, reducing overall system complexity while maintaining real-time safety monitoring capabilities.
Solution Approach 2:
The patent introduces an intermediary computing system that acts as a mediator between the sensors and the traffic control infrastructure. This intermediary processes sensor data, identifies risk conditions, and generates instructions, thereby isolating the complexity of real-time analysis from the core traffic control system while improving safety response.
2Measurement precision
If multiple sensors (camera and lidar) are deployed for comprehensive detection, then detection accuracy and risk identification capability are improved, but device complexity and cost increase
Solution Approach 1:
The system merges camera and lidar sensors into a unified detection framework where both sensor types operate simultaneously but are processed through a single risk identification algorithm. The camera provides visual classification information while lidar provides precise distance and speed data, and their combined output feeds into one integrated risk condition identification process, improving detection accuracy without proportionally increasing complexity.
Solution Approach 2:
The risk identification system is designed as a universal processor that handles data from multiple sensor types (camera, lidar, and potentially other sensors) through a single algorithmic framework. This multi-functional approach allows the same system to process diverse sensor inputs and identify various risk conditions, reducing the need for separate processing systems for each sensor type.
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
Enhances traffic control and safety by enabling real-time identification of risk scenarios and sending appropriate instructions to vehicles, improving response times and reducing accidents.
Implementation Method 1
detect a vehicle and an object proximate to the support structure from data from the sensor, wherein the sensor includes a camera
Implementation Method 2
the sensor and the second sensor include a camera and a lidar
Data Source
AI summary
In a computer mounted to a stationary support structure a vehicle and an object proximate to the support structure can be detected from data from a sensor mounted to the support structure. A risk condition can be identified based on the detected vehicle and object.