JP-7856013-B2 - Driving data classification device

Inventors

• 牛山 慧介
• 竹内 伸一
• 則竹 真吾
• 村上 亮
• 柳川 涼

Assignees

• トヨタ自動車株式会社

Dates

Publication Date

20260511

Application Date

20230110

Claims (3)

1. This is a driving data classification device that, while driving a vehicle equipped with sensors, classifies driving data, including the output torque of the vehicle's prime mover, by applying it to multiple driving route names, which are pre- classified into sections on a test course . The aforementioned names of multiple roads include straight sections and uphill sections. A trained model that estimates the name of the road corresponding to a point where the vehicle was traveling when the driving data was being collected, using input data which is time-series data of explanatory variables including multiple types of moving average values of the output torque of the vehicle's engine, wherein the trained model is stored in a storage device that has been supervised and trained using training data which includes the input data and ground truth data indicating the name of the road corresponding to a point where the vehicle was traveling when the driving data was being collected. A driving data classification device comprising: an estimation process that outputs an estimated value corresponding to the driving route name from the input data using the trained model stored in the memory device; and a classification process that classifies the driving data by applying it to the driving route name based on the estimated value.
2. The driving data classification device according to claim 1, wherein the trained model is a long- and short-term memory neural network.
3. The aforementioned road names may also include sharp curves. The driving data classification device according to claim 1, wherein the explanatory variable includes information on the steering angle.

Description

This invention relates to a driving data classification device that classifies driving data collected while a vehicle is in motion. Data is sometimes collected using sensors mounted on a vehicle while it is in motion. Patent Document 1 discloses a test vehicle that travels on a test course. Furthermore, Patent Document 1 discloses a management system that displays an image representing each test vehicle on a scaled-down diagram of a test course where multiple test vehicles are traveling. Japanese Patent Publication No. 2004-093356 Figure 1 is a schematic diagram showing the configuration of a driving data classification device.Figure 2 is a graph illustrating one example of how training data is acquired.Figure 3 is an explanatory diagram illustrating the concept of long-term and short-term memory neural networks.Figure 4 is a flowchart showing the learning process flow.Figure 5 is a flowchart showing the processing flow for estimation and classification.Figure 6 is a graph showing the estimated values. Below, one embodiment of the driving data classification device will be described with reference to Figures 1 to 6. <Configuration of the driving data classification device 10> Figure 1 shows the configuration of the driving data classification device 10. The driving data classification device 10 is a computer. The driving data classification device 10 comprises a processor 11 which is a processing circuit, a storage device 12 which is a storage device, and a memory 13 which is an auxiliary storage device. The driving data classification device 10 also comprises an input device 14, a display device 15, an input/output interface 16, and a communication device 17. The input device 14 is, for example, a keyboard. The input device 14 may also be a touch panel. Each of these components—the processor 11, storage device 12, memory 13, input device 14, display device 15, input/output interface 16, and communication device 17—is connected via a bus 18. The storage device 12 stores the program. The processor 11 executes the program stored in the storage device 12 and performs various processes. The driving data classification device 10 classifies the driving data. The driving data is data collected by sensors while a vehicle equipped with sensors is in motion. Specifically, the driving data includes time-series data of output torque detected by a torque sensor mounted on the vehicle. The driving data classification device 10 classifies this driving data by matching it to a plurality of pre-set driving route names. In short, the driving data classification device 10 estimates which driving route the driving data was acquired on. Then, according to the estimation result, it classifies the driving data by matching it to the driving route name. <Output Torque> The torque sensor is connected to the vehicle's drive shaft. The drive shaft is the output shaft of the vehicle's prime mover. For example, the prime mover is an engine. The prime mover may also be an electric motor. Furthermore, the prime mover may be a hybrid system combining an engine and an electric motor. The drive shaft is connected to the drive wheels. The torque sensor detects the torque of the drive shaft. This torque, which is output from the engine, is referred to here as output torque. Even if the engine output is constant, the output torque changes depending on the road conditions while the vehicle is in motion. For example, in vehicle development, driving data including this output torque is sometimes collected and time-series data is analyzed. <Classification of driving data> When analyzing driving data, the collected data may be classified by the type of road. For example, if driving data is collected on a test course that includes multiple sections such as sharp curves, straightaways, and uphill roads, the data may be classified by section. Similarly, if driving data is collected over long distances including mountain roads, highways, and suburban roads, the data may be classified by road type, such as "mountain roads,""unpavedroads,""highways," and "general roads." The name of the road may also include the name of the region where the data was collected. Driving data may also be classified by the region where it was collected. For example, "Jibuzaka, Nagano Prefecture,""Hakone, Shizuoka Prefecture,""Mt. Rokko, Hyogo Prefecture," and "Mt. Ibuki, Shiga Prefecture." Traditionally, the classification of such driving data has been performed manually by visually checking the data displayed on a display device in the form of a graph or other format. Figure 2 shows a graph of time-series output torque data. For example, when classifying manually, the operator would determine from the shape of the graph and the magnitude of the values that the driving data up to time t_1 represents driving on a straight road. The operator would then classify the driving data up to time t_1 under classification number "1," indicating the road type "straight." The operator wou