Self-learning gate paddles for safe operation
AssigneeCubic Corporation
InventorsGavin R. Smith, Steffen Reymann, Jonathan Packham
Life of this patent
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Abstract
A system and method for self-learning operation of gate paddles is disclosed. Opening and closing of the gate paddles requires timing and other settings to avoid injury and fare evasion. Self-learning allows a machine learning model to adapt to new data dynamically. The new data captured at a fare gate improves the machine learning model, which can be shared the other similar fare gates within a transit system so that learning disseminates.
Claims20
A self-learning gate system that automatically adjusts for transit users, the self-learning system comprising:
- a plurality of sensors configured to capture sensor data for the transit users, wherein input data is formed using the sensor data for the transit users;
- a processor configured to execute a machine learning model that generates control data based on the input data;
- a barrier, wherein the control data includes instructions for a desired movement of the barrier;
- and
- a barrier actuator coupled to the barrier and configured to cause movement of the barrier based on the control data bounded by predetermined operating guidelines for the barrier,
- wherein the machine learning model is trained using:
- the input data for different type of transit users, and
- a comparison between the desired movement of the barrier and an actual movement of the barrier for different transit users.
A method for implementing a self-learning fare gate system, the method comprising:
- capturing first sensor data using one or more sensors for a first transit user;
- forming input data comprising the first sensor data;
- generating first control data using a machine learning model based on the first input data, wherein the first control data includes instructions for a desired movement of a barrier of a fare gate;
- sending the first control data to a barrier actuator to cause a desired movement of the barrier, wherein the barrier actuator is coupled to the barrier;
- determining actual movement of the barrier did not match the desired movement;
- forming training data based on a comparison between the desired movement of the barrier and the actual movement of the barrier;
- training the machine learning model using the training data;
- capturing second sensor data using the one or more sensors for a second transit user;
- forming second input data comprising the second sensor data;
- and
- generating second control data using the machine learning model based on the first input data, wherein the second control data is different from the first control data.
A self-learning gate system that automatically adjusts for transit users, comprising one or more processors and one or more memories with computer code for:
- capturing first sensor data using one or more sensors for a first transit user;
- forming input data comprising the first sensor data;
- generating first control data using a machine learning model based on the first input data, wherein the first control data includes instructions for a desired movement of a barrier of a fare gate;
- sending the first control data to a barrier actuator to cause a desired movement of the barrier, wherein the barrier actuator is coupled to the barrier;
- determining actual movement of the barrier did not match the desired movement;
- forming training data based on a comparison between the desired movement of the barrier and the actual movement of the barrier;
- training the machine learning model using the training data;
- capturing second sensor data using the one or more sensors for a second transit user;
- forming second input data comprising the second sensor data;
- and
- generating second control data using the machine learning model based on the first input data, wherein the second control data is different from the first control data.
Dependencies are inferred from the claim text.