Traffic Light Status Prediction via Visual Sensor Analytics
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Solution Overview
Problem
Current traffic control systems face challenges such as limited access to traffic light switching cycle data, high data delivery latency, and lack of compatibility between different systems, which hinders real-time applications like vehicle powertrain optimization and driver safety, and often lack support for Signal Phase and Timing (SPaT) and Road Topology (MAP) data formats, making it difficult to integrate and transport traffic light information across regions.
Innovation Solution
A computer-implemented method using machine learning algorithms to predict future traffic light signal statuses based on current and historical data from visual sensors, integrated with a monitoring system that receives data from multiple traffic lights, and sends messages to vehicles to influence their operation, including lane-specific status data for SPaT and MAP applications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traffic light information is accessed through multiple infrastructure systems from different technology providers, then access to traffic light status data is possible, but system complexity and integration difficulty increase
Solution Approach 1:
The patent introduces an intermediary component that acts as a mediator between vehicles and multiple traffic light infrastructure systems. This intermediary receives, standardizes, and relays traffic light status information from various providers in a unified manner, eliminating the need for vehicles to directly integrate with multiple complex systems while ensuring comprehensive data access.
2Reliability
If traffic light switching cycle data is retrieved in real-time, then vehicle powertrain optimization and driver safety can be improved, but data delivery latency remains high
Solution Approach 1:
The patent implements preliminary action by predicting future traffic light status changes before they actually occur. The system analyzes current traffic light patterns and proactively calculates expected future states, allowing vehicles to receive advance notice of upcoming signal changes. This enables powertrain optimization and safety preparations to be made in advance rather than reacting to delayed real-time data.
3Measurement precision
If machine learning algorithms are used to predict future traffic light status, then prediction accuracy improves, but computational requirements and processing time increase
Solution Approach 1:
The patent applies partial action by implementing a hierarchical prediction approach where simple pattern recognition is used for immediate future predictions and more complex machine learning models are only engaged when patterns are ambiguous or when longer-term predictions are needed. This selective application of computational resources maintains high prediction accuracy while avoiding unnecessary processing power consumption for routine predictions.
Data Source
AI summary
A traffic control system can includes a plurality of traffic lights. A monitoring system can include an inbound interface component, an analytics component, and an outbound status provisioning component. The inbound interface component can be configured to receive a stream of sensor data from visual sensor(s). Each visual sensor can be configured to capture light signals of traffic lights. Each traffic light can be sensed by the visual sensor(s). The received stream of sensor data can represent a current signal status of each traffic light. The analytics component can be configured to predict at least one future signal status for each of the traffic lights based on the use of a machine learning algorithm. The outbound status provisioning component can be configured to send a message(s) to a vehicle. The sent message can influence the operation of the vehicle.