JP-7855939-B2 - Training device, estimation device, and trained model

Inventors

• 島村 拓志
• 岡山 藤治

Assignees

• 住友電気工業株式会社

Dates

Publication Date

20260511

Application Date

20220624

Claims (9)

1. Memory for storing untrained models, A training device comprising a processor that performs training on the aforementioned untrained model, The aforementioned untrained model, This model uses data included in signal control commands for remotely controlling a traffic signal controller as the input node, and the fluctuating parameters in the offset tracking performed by the traffic signal controller as the output node. The aforementioned processor, A training device that performs the training using a set of data, consisting of data included in the signal control command and data included in the signal control execution information representing the control content executed by the traffic signal controller, as training data.
2. The aforementioned input node includes: The training device according to claim 1, comprising at least one of the following: cycle length, split, offset change status, tracking direction specification, tracking width specification, offset tracking monitoring, offset reference time, and offset value.
3. The output node includes: The training apparatus according to claim 1 or claim 2, comprising cycle length and duration of a variable step, or duration of a plurality of steps including the variable step.
4. The aforementioned processor, The training device according to claim 1 or claim 2, wherein the training is performed on the condition that the traffic signal controller is operating remotely.
5. The aforementioned processor, The training device according to claim 1 or claim 2, wherein the training is performed on the condition that the traffic signal controller is in offset synchronization.
6. The aforementioned processor, The training device according to claim 1 or claim 2, wherein the training is performed on the condition that the operation of the traffic signal controller is normal.
7. A trained model for causing a processing unit to function to output variable parameters in offset tracking performed by a traffic signal controller based on data included in a signal control command for remotely controlling the traffic signal controller, An input layer containing multiple input nodes, A hidden layer containing multiple intermediate nodes, It consists of a neural network having an output layer containing multiple output nodes, The weight and bias values between each node in the aforementioned neural network are: A trained model is a set of data that has been trained using as training data the data included in the signal control command as input data and the data included in the signal control execution information representing the control content performed by the traffic signal controller as training data.
8. A memory in which the trained model described in claim 7 is stored , A processor for estimating the variable parameters in the offset tracking performed by the traffic signal controller, The aforementioned processor, An estimation device that inputs data included in a signal control command for remotely controlling the traffic signal controller into a trained model stored in the memory, and uses the output data obtained as the variable parameter.
9. Memory for storing data conversion tools, An estimation device comprising a processor for estimating fluctuation parameters in offset tracking performed by a traffic signal controller, The aforementioned data conversion tool is A transformation tool capable of performing data transformations equivalent to or approximating those of a pre-trained model. The aforementioned trained model is A model trained by the training device described in claim 1, The aforementioned processor, An estimation device that inputs data included in a signal control command for remotely controlling the traffic signal controller into the data conversion tool, and uses the output data obtained as the variable parameter.

Description

This disclosure relates to a training device, an estimation device, and a trained model. Patent Document 1 describes a roadside relay device that provides signal information to a vehicle. The roadside relay device described in Patent Document 1 generates signal information for vehicles based on signal control commands transmitted by a central device to remotely control traffic signal controllers and operating status information transmitted by traffic signal controllers to the central device. The roadside relay device also transmits the generated vehicle-oriented signal information to a communication device capable of wireless communication with vehicles. WO2021/152947 Figure 1 is a block diagram showing an example of an information provision system.Figure 2 shows an example of a signal control command format.Figure 3 shows an example of the format of signal control execution information.Figure 4 shows an example of the format for signal operation status information.Figure 5 shows an example of a timetable generated by the connection adapter.Figure 6A shows the data structure of signal information.Figure 6B is an explanatory diagram showing the data values and data contents stored in the header and data sections of the signal information.Figure 7 is a block diagram showing an example of the model's configuration.Figure 8 is a block diagram showing an example configuration of a training device for an untrained model.Figure 9 is a block diagram showing an example configuration of a device for estimating variable parameters. <Summary of Embodiments in This Disclosure> The embodiments of this disclosure are outlined below. (1) The training device according to this embodiment is a training device comprising a memory for storing an unlearned model and a processor for training the unlearned model, wherein the unlearned model is a model in which data included in a signal control command for remotely controlling a traffic signal controller is used as an input node and fluctuating parameters in offset tracking performed by the traffic signal controller are used as output nodes, and the processor performs the training using a set of data included in the signal control command and data included in signal control execution information representing the control content performed by the traffic signal controller as training data. According to the training device of this embodiment, the above training generates a trained model that outputs variable parameters from the data included in the signal control command. Therefore, by inputting the data included in the signal control command into the trained model, the variable parameters in offset tracking are output. Consequently, it is possible to estimate the variable parameters in offset tracking, which may differ depending on the manufacturer's calculation method. (2) In the training device of this embodiment, the input node may include, for example, at least one of the following: cycle length, split, offset change status, tracking direction specification, tracking width specification, offset tracking monitoring, offset reference time, and offset value. The reason is that at least one piece of the above information is necessary for the traffic signal controller to perform offset tracking, regardless of the algorithm the traffic signal controller employs. (3) In the training device of this embodiment, the output node may include, for example, the cycle length and the duration of a variable step, or the duration of a plurality of steps including the variable step. The reason for this is that the parameters that the traffic signal controller changes in stages during offset tracking (variable parameters) typically include the cycle length and the number of seconds for each variable step. (4) In the training device of this embodiment, the processor may perform the training on the condition that the traffic signal controller is operating remotely. This prevents training models based on signal control execution information from traffic signal controllers that are not being remotely controlled, and allows training to be performed based on normal training data. (5) In the training device of this embodiment, the processor may perform the training on the condition that the traffic signal controller is in offset synchronization. This prevents training models based on signal control execution information from traffic signal controllers that are not performing offset tracking, and allows training to be performed based on normal training data. (6) In the training device of this embodiment, the processor may perform the training on the condition that there is no abnormality in the operation of the traffic signal controller. In this way, it is possible to prevent training models based on signal control execution information from traffic signal controllers that are presumed to be malfunctioning, and instead perform training based on normal training data. (7) The model according to this embodimen