Smart Road Sensor with Local Machine Learning Processing
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Solution Overview
Problem
Current traffic monitoring systems rely on expensive and difficult-to-install sensors like cameras and pressure/magnetic sensors, which are not cost-effective or ubiquitous enough to provide real-time, dense traffic insights and predictions.
Innovation Solution
Ubiquitous smart road sensors that use machine learning to process and filter raw data locally, resembling low-profile road reflectors, equipped with various sensors and a power source, allowing them to operate in both local and connected modes to provide real-time traffic and environmental insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive sensors like cameras and pressure/magnetic sensors are used for traffic monitoring, then measurement precision is improved, but device complexity and installation difficulty increase
Solution Approach 1:
The patent divides the traffic monitoring function into multiple simple sensor nodes embedded in road segments. Each node contains basic sensors (accelerometers, barometers, temperature sensors) that independently monitor local conditions. These segmented nodes collectively provide comprehensive traffic monitoring without requiring complex centralized sensor systems.
Solution Approach 2:
The patent employs low-cost, simple sensor nodes that can be easily deployed and replaced. Each node uses inexpensive components like accelerometers and barometers rather than expensive cameras or magnetic sensors. The nodes are designed to be disposable or easily replaceable, reducing overall system complexity and installation burden while maintaining adequate monitoring precision through distributed deployment.
2Productivity
If traffic monitoring systems are deployed densely along roadways, then productivity and real-time insight are improved, but installation cost and difficulty increase
Solution Approach 1:
The monitoring system is segmented into numerous small, independent sensor nodes that can be individually deployed along roadways. Each node is a simple, self-contained unit that can be installed independently, enabling dense deployment without requiring complex coordinated installation procedures. This segmentation allows high productivity through parallel deployment of multiple nodes.
Solution Approach 2:
The patent uses inexpensive sensor nodes that reduce deployment costs. Each node contains basic sensors and minimal processing capabilities, making them cheap to manufacture and install. The low cost per node enables dense deployment along roadways to achieve comprehensive real-time traffic insights without prohibitive installation expenses.
3Loss of information
If raw sensor data is processed locally using machine learning, then loss of information is reduced, but device complexity increases
Solution Approach 1:
The sensor nodes are pre-configured with machine learning models and processing algorithms during manufacturing. This preliminary action allows the nodes to perform local data processing without requiring complex runtime configuration or updates. The pre-loaded models enable immediate local processing of sensor data, reducing information loss while keeping the operational device complexity manageable.
Solution Approach 2:
Each sensor node autonomously processes its own raw sensor data using embedded machine learning algorithms. The nodes self-manage data filtering, feature extraction, and local decision-making without requiring external processing infrastructure. This self-service capability reduces information loss by processing data at the source while maintaining relatively simple device architecture through autonomous operation.
Data Source
AI summary
A road sensor has a housing, at least one sensor inside the housing, a processor inside the housing, the processor configured to execute instructions to cause the processor to: receive data from the at least one sensor; use machine learning to recognize conditions local to the sensor from the sensor data; and provide an output signal of the conditions. A method of providing road conditions includes receiving, at a road sensor, input detectable by at least one sensor, using a processor in the road sensor to execute code that will cause the processor to: receive sensor data from the at least one sensor; apply machine learning to the sensor data to recognize at least one road condition associated with the sensor data; and transmit an output signal identifying the road conditions.