Vehicle control device and vehicle control program

Abstract

To provide a vehicle control device and a vehicle control program capable of shifting a driver to a safe driving state even when the driver is in an excited mood.SOLUTION: A vehicle control device includes a processor and the processor detects a driving state of a vehicle, determines whether or not the detected driving state includes predetermined dangerous driving, determines whether or not a driver of the vehicle is in an excited mood when it is determined that dangerous driving is involved, and performs vehicle control so as to calm the driver's mood when it is determined that the driver is in the excited mode.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a vehicle control device and a vehicle control program. [Background technology]

[0002] Patent Document 1 discloses a technology for estimating the emotions of a driver or a passenger and controlling the running of a vehicle based on the estimation result. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2017-136922 Summary of the Invention [Problem to be solved by the invention]

[0004] In Patent Document 1, for example, a trained model can be created by machine learning, and the trained model can be used to control the vehicle. It is possible to automatically intervene in driving operations when the vehicle is being driven dangerously. However, if the driver is in a high mood and the driver is forced to intervene in driving operations, the driver's reaction to the intervention may be strong, which could actually lead to danger. Therefore, there has been a demand for technology that can transition the driver to a safe driving state even when the driver is in a high mood.

[0005] The present disclosure has been made in consideration of the above, and aims to provide a vehicle control device and a vehicle control program that can transition to a safe driving state even when the driver is in an excited mood. [Means for solving the problem]

[0006] The vehicle control device according to the present disclosure includes a processor that detects the driving state of the vehicle, determines whether the detected driving state includes a predetermined dangerous driving, and if it determines that the dangerous driving is included, determines whether the driver of the vehicle is in an excited mood, and if it determines that the driver is in an excited mood, performs vehicle control to calm the driver.

[0007] The vehicle control program according to the present disclosure causes a processor to detect the driving state of a vehicle, determine whether the detected driving state includes a predetermined dangerous driving state, and if it is determined that the dangerous driving state includes the dangerous driving state, determine whether the driver of the vehicle is in an excited mood, and if it is determined that the driver is in an excited mood, implement vehicle control to calm the driver. [Effects of the Invention]

[0008] According to the present disclosure, when a driver is in an excited mood, vehicle control is performed to calm the driver, thereby enabling the driver to transition to a safe driving state. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing a schematic configuration of a vehicle control device according to an embodiment. [Figure 2] FIG. 2 is a diagram showing the flow of information in each unit of the vehicle control device according to the embodiment. [Figure 3] FIG. 3 is a flowchart showing an example of a processing procedure of a vehicle control method executed by the vehicle control device according to the embodiment. [Figure 4] FIG. 4 is a flowchart showing an example of a processing procedure of a vehicle control method executed by the vehicle control device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] A vehicle control device and a vehicle control program according to an embodiment of the present disclosure will be described with reference to the drawings. Note that the components in the following embodiments include those that are easily replaceable by a person skilled in the art, or those that are substantially the same.

[0011] (Vehicle control device) The vehicle control device 1 is for controlling a vehicle. The vehicle control device 1 may be mounted on the vehicle, or may be realized by a server device or the like separate from the vehicle. In this embodiment, the description will be given on the assumption that the vehicle control device 1 is mounted on the vehicle.

[0012] When the vehicle control device 1 is realized by a server device, the vehicle and the server device are connected by a network such as the Internet network, a mobile phone network, etc. Then, the server device, which is the vehicle control device 1, remotely controls the vehicle by communicating via a communication unit (Data Communication Module: DCM) of the vehicle.

[0013] 1, the vehicle control device 1 includes a control unit 10, a storage unit 20, and a sensor group 30. The control unit 10 includes a processor and a memory (main storage unit). Specifically, the processor includes a central processing unit (CPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a graphics processing unit (GPU), etc. The memory includes a random access memory (RAM), a read-only memory (ROM), etc.

[0014] The control unit 10 loads a program stored in the storage unit 20 into a working area of ​​the main storage unit, executes it, and controls each component through the execution of the program, thereby realizing functions that meet a predetermined purpose. Through the execution of the program stored in the storage unit 20, the control unit 10 functions as a vehicle control unit 11, a driving state detection unit 12, and an emotion estimation unit 13.

[0015] The vehicle control unit 11 performs a vehicle control (first vehicle control) to calm the excited driver, and a vehicle control (second vehicle control) to avoid dangerous driving of the vehicle. Specific details of these controls will be described below.

[0016] (First vehicle control) For example, when the driving state detection unit 12 determines that predetermined dangerous driving is included and the emotion estimation unit 13 determines that the driver of the vehicle is in an excited mood, the vehicle control unit 11 performs vehicle control to calm the driver. In this case, the vehicle control unit 11 performs vehicle control to calm the driver's mood without performing (prohibiting) vehicle control to intervene in the driver's vehicle operation. In this way, by calming the driver's mood before intervening in the driver's vehicle operation, it is possible to prevent the driver from having an excessive reaction to intervention in vehicle operation.

[0017] Examples of predetermined dangerous driving include driving too close to other vehicles, driving too fast, accelerating too much, and tailgating. Examples of vehicle control that calms the driver's mood include controlling the vehicle's air conditioning to adjust the temperature to a comfortable level, controlling a fragrance generator to emit a comfortable fragrance, and so on. Examples of vehicle control that calms the driver's mood include controlling an audio device to play the news, controlling an audio device to play soothing proverbs or tips, and controlling an audio device to play comfortable music. When performing these controls, information about the driver's preferred temperature, proverbs, music, fragrance, and so on may be collected in advance.

[0018] Furthermore, the vehicle control unit 11 may further determine whether or not a change in the acceleration of the vehicle deviates from a predetermined change amount based on the driving state detected by the driving state detection unit 12, even if the driving state does not include a predetermined dangerous driving as determined by the driving state detection unit 12. If it is determined that the change in the acceleration of the vehicle deviates from the predetermined change amount, the emotion estimation unit 13 may determine whether or not the driver's mood is elevated, and if it is determined that the driver's mood is elevated, vehicle control may be implemented to calm the driver. In this way, even in a situation where dangerous driving is not detected, if the acceleration is gradually becoming larger and rougher than usual, the occurrence of dangerous driving can be prevented by calming the driver's mood in advance.

[0019] Furthermore, for example, when the driving state detection unit 12 determines that predetermined dangerous driving is not included and the emotion estimation unit 13 determines that the driver of the vehicle is in an excited mood, the vehicle control unit 11 may perform vehicle control to calm the driver. In this way, even in a situation where dangerous driving is not detected, if the driver is in an excited mood, the occurrence of dangerous driving can be prevented by calming the driver.

[0020] (Second vehicle control) For example, when vehicle control to calm the driver is implemented, if the driving state detection unit 12 determines that dangerous driving has continued for a predetermined time or worsened, the vehicle control unit 11 implements vehicle control to intervene in the driver's vehicle operation. In this case, the vehicle control unit 11 suspends (does not implement) the vehicle control to calm the driver's mood (determination of elevated mood to vehicle control) and forcibly executes vehicle control to intervene in the driver's vehicle operation.

[0021] Vehicle control that intervenes in the driver's vehicle operation refers to, for example, control that does not directly respond to the driver's vehicle operation. Examples of vehicle control that intervenes in the driver's vehicle operation include control that does not accelerate even when the accelerator pedal is depressed, and control that automatically reduces the vehicle speed to increase the distance from the vehicle ahead. In this way, if the driver is unable to calm down and continues to drive recklessly, the occurrence of an accident or the like can be prevented by forcibly intervening in the driver's vehicle operation.

[0022] Furthermore, for example, when the vehicle control unit 11 performs vehicle control to calm the driver's mood, if the emotion estimation unit 13 determines that the driver's excitement has not subsided, the vehicle control unit 11 may perform vehicle control to intervene in the driver's vehicle operation. In this way, when the driver's mood cannot be calmed down, the occurrence of an accident or the like can be suppressed by forcibly intervening in the driver's vehicle operation.

[0023] Furthermore, the vehicle control unit 11 may perform vehicle control to calm the driver, for example, multiple times at predetermined intervals. Then, when the vehicle control unit 11 has performed vehicle control to calm the driver a predetermined number of times and the emotion estimation unit 13 determines that the driver's excitement has not subsided, the vehicle control unit 11 may perform vehicle control to intervene in the driver's vehicle operation. In this way, when the driver's mood cannot be calmed down even after multiple attempts, the occurrence of an accident or the like can be suppressed by forcibly intervening in the driver's vehicle operation.

[0024] The vehicle control unit 11 can perform the above-described vehicle control (second vehicle control) based on a trained model trained in advance by machine learning or on predetermined rules. When a trained model is used, the input data is, for example, a detection result (determination result of dangerous driving) by the driving state detection unit 12 and an emotion estimation result (determination result of elevated mood) by the emotion estimation unit 13. The output data is, for example, a control amount of acceleration or vehicle speed.

[0025] The method for constructing the trained model used in the vehicle control unit 11 is not particularly limited, and various machine learning methods such as deep learning using neural networks, support vector machines, decision trees, naive Bayes, and k-nearest neighbor methods can be used.

[0026] The driving state detection unit 12 detects the driving state of the vehicle. As shown in Fig. 2, the driving state detection unit 12 acquires image data of the surroundings of the vehicle from, for example, a camera in the sensor group 30. Alternatively, the driving state detection unit 12 acquires sensor data of the surroundings of the vehicle from, for example, an on-board sensor in the sensor group 30. Then, the driving state detection unit 12 detects the driving state of the vehicle based on the acquired image data or sensor data.

[0027] Next, the driving state detection unit 12 determines whether or not the detected driving state includes a predetermined dangerous driving, and outputs the determination result to the vehicle control unit 11. In this case, the driving state detection unit 12 determines the dangerous driving as follows, for example:

[0028] (1) The distance between the vehicle and the preceding vehicle is smaller than a predetermined threshold (the distance between the vehicles is too close). (2) The vehicle speed is greater than the specified threshold (driving too fast) (3) The acceleration is greater than the specified threshold (too much acceleration) (4) Tailgating surrounding vehicles (tailgating)

[0029] In addition, when the vehicle control unit 11 implements vehicle control to calm the driver, the driving state detection unit 12 may determine whether the above-mentioned dangerous driving has continued for a predetermined time or has worsened, and output the determination result to the vehicle control unit 11.

[0030] In addition, if the driving condition detection unit 12 determines that the above-mentioned dangerous driving is not included, it may determine whether the change in the vehicle acceleration deviates from a predetermined change, and output the determination result to the vehicle control unit 11.

[0031] The driving state detection unit 12 can determine the risky driving described above in (1) to (4) based on a trained model trained in advance by machine learning or on predetermined rules. When a trained model is used, the input data is, for example, image data of the vehicle's surroundings and sensor data of the vehicle's surroundings. The output data is, for example, the presence or absence of risky driving described above in (1) to (4).

[0032] The method for constructing the trained model used in the driving state detection unit 12 is not particularly limited, and various machine learning methods such as deep learning using neural networks, support vector machines, decision trees, naive Bayes, and k-nearest neighbor methods can be used.

[0033] The emotion estimation unit 13 estimates the emotion of the driver. The emotion estimation unit 13 estimates the emotion of the driver based on data (image data, biological data) detected by the sensor group 30. The emotion of the driver estimated by the emotion estimation unit 13 specifically indicates whether the driver is in an elevated mood. Furthermore, the estimation of the emotion of the driver by the emotion estimation unit 13 is repeatedly performed at predetermined control cycles.

[0034] The determination by the emotion estimation unit 13 of whether the driver is in an elevated mood includes acquiring sensor data from the sensor group 30 that observes the state of the driver, and estimating the driver's mood from the acquired sensor data using a trained machine learning model. The determination by the emotion estimation unit 13 of whether the driver is in an elevated mood also includes determining whether the driver is in an elevated mood based on the result of the estimation.

[0035] When the driving state detection unit 12 determines that dangerous driving is included, the feeling estimation unit 13 acquires image data of the driver, for example, from a camera in the sensor group 30, as shown in Fig. 2. Then, the feeling estimation unit 13 estimates the driver's feeling based on the acquired image data. In this case, the feeling estimation unit 13 determines whether the driver is in an elevated mood based on, for example, the driver's facial expression included in the image data, and outputs the determination result to the vehicle control unit 11.

[0036] 2 , when the driving state detection unit 12 determines that dangerous driving is included, the feeling estimation unit 13 acquires biometric data of the driver, for example, from a biometric sensor in the sensor group 30. Then, the feeling estimation unit 13 estimates the driver's feeling based on the acquired biometric data. In this case, the feeling estimation unit 13 determines whether the driver is in an elevated mood based on, for example, the driver's body temperature, heart rate, pulse rate, blood pressure, brain waves, etc. included in the biometric data, and outputs the determination result to the vehicle control unit 11.

[0037] In addition, when the vehicle control unit 11 performs vehicle control to calm the driver's mood, the emotion estimation unit 13 may determine whether the driver's mood has calmed down and output the determination result to the vehicle control unit 11.

[0038] Furthermore, when the driving state detection unit 12 determines that dangerous driving is not included, the feeling estimation unit 13 may determine whether the driver is in an elevated mood and output the determination result to the vehicle control unit 11.

[0039] The emotion estimation unit 13 can perform the emotion estimation based on a trained model that has been trained in advance by machine learning. When using a trained model, input data is, for example, image data and biometric data of the driver. Then, output data is, for example, whether the driver is in a high mood.

[0040] The method for constructing the trained model used in the emotion estimation unit 13 is not particularly limited, and various machine learning methods such as deep learning using a neural network, a support vector machine, a decision tree, a naive Bayes method, and a k-nearest neighbor method can be used.

[0041] The storage unit 20 is realized by a recording medium such as an EPROM (Erasable Programmable ROM), a hard disk drive (HDD), or a removable medium. Examples of the removable medium include a disk recording medium such as a USB (Universal Serial Bus) memory, a CD (Compact Disc), a DVD (Digital Versatile Disc), or a BD (Blu-ray (registered trademark) Disc).

[0042] The storage unit 20 can store an operating system (OS), various programs, various tables, various databases, etc. The storage unit 20 may also store, for example, a detection result from the driving state detection unit 12 and an estimation result from the emotion estimation unit 13. The storage unit 20 may also store a machine-learned model (trained model) used by the vehicle control unit 11, the driving state detection unit 12, and the emotion estimation unit 13.

[0043] The sensor group 30 acquires data for detecting the driver and the situation around the vehicle. Examples of the sensor group 30 include a camera for capturing images of the driver and the surroundings of the vehicle, a temperature sensor and an infrared sensor for detecting the driver's situation, a biosensor for acquiring biometric data of the driver, and an on-board sensor for detecting the situation around the vehicle. Examples of the biosensor include a temperature sensor, a heart rate sensor, a pulse sensor, a blood pressure sensor, and an electroencephalogram sensor. Examples of the on-board sensor include a millimeter wave sensor, an infrared sensor, a laser sensor, a 3D-LiDAR, a GPS sensor, a vehicle speed sensor, and an acceleration sensor.

[0044] (Vehicle control method) An example of the processing procedure of the vehicle control method executed by the vehicle control device according to the embodiment will be described with reference to FIGS.

[0045] First, the driving state detection unit 12 detects the driving state of the vehicle based on image data or sensor data around the vehicle (step S1), as shown in Fig. 3. Then, the driving state detection unit 12 determines whether the detected driving state includes a predetermined dangerous driving (step S2).

[0046] If it is determined in step S2 that dangerous driving is included (Yes in step S2), the driving state detection unit 12 determines whether the dangerous driving is continuing (step S3). If it is determined in step S3 that dangerous driving is not continuing (No in step S3), the emotion estimation unit 13 estimates the emotion of the driver based on the image data or biometric data of the driver (step S4). Subsequently, the emotion estimation unit 13 determines whether the driver is in an elevated mood (step S5).

[0047] In step S5, if it is determined that the driver is feeling excited (Yes in step S5), the vehicle control unit 11 performs vehicle control to calm the driver's mood (step S6). Subsequently, the vehicle control unit 11 determines whether or not to repeat steps S1 to S6 (step S7), and if it determines to repeat (Yes in step S7), it returns to step S1, and if it determines not to repeat (No in step S7), it ends this process.

[0048] In step S3, if it is determined that dangerous driving is continuing (Yes in step S3), the vehicle control unit 11 implements vehicle control to suppress dangerous driving (step S8), and proceeds to step S7. In addition, in step S5, if it is determined that the driver is not feeling excited (No in step S5), the vehicle control unit 11 implements vehicle control to suppress dangerous driving (step S8), and proceeds to step S7.

[0049] In step S2, if it is determined that dangerous driving is not included (No in step S2), the vehicle control unit 11 determines whether the amount of change in the acceleration of the vehicle deviates from a predetermined amount of change, as shown in FIG. 4 (step S9).

[0050] In step S9, if it is determined that the change amount of the vehicle acceleration deviates from the predetermined change amount (Yes in step S9), the feeling estimation unit 13 estimates the feeling of the driver based on the image data or biometric data of the driver (step S10). Subsequently, the feeling estimation unit 13 determines whether the driver is in an elevated mood (step S11).

[0051] In step S11, if it is determined that the driver is in an elevated mood (Yes in step S11), the vehicle control unit 11 performs vehicle control to calm the driver (step S12), and proceeds to step S7.

[0052] In step S9, if it is determined that the amount of change in the acceleration of the vehicle does not deviate from the predetermined amount of change (No in step S9), the vehicle control unit 11 proceeds to step S7. In addition, in step S11, if it is determined that the driver is not feeling excited (No in step S11), the vehicle control unit 11 proceeds to step S7.

[0053] According to the vehicle control device and vehicle control program of the embodiment described above, when the driver is in an excited mood, vehicle control is performed to calm the driver, thereby transitioning the driver to a safe driving state.

[0054] Further advantages and modifications will readily occur to those skilled in the art. Thus, the invention in its broader aspects is not limited to the specific details and representative embodiments shown and described above. Accordingly, various modifications may be made without departing from the spirit or scope of the general inventive concept as defined by the appended claims and their equivalents. [Explanation of symbols]

[0055] 1 Vehicle control device 10 Control Unit 11 Vehicle control unit 12. Operating condition detection unit 13 Emotion estimation part 20 Memory section 30 sensors

Claims

1. a processor; The processor: Detect the driving state of the vehicle, determining whether the detected driving conditions include a predetermined dangerous driving condition; If it is determined that the dangerous driving is included, it is determined whether the driver of the vehicle is in an excited mood; When it is determined that the driver's mood is elevated, a vehicle control is implemented to calm the driver's mood; determining whether the driver of the vehicle is in an excited mood when the vehicle control for calming the driver is performed; When it is determined that the driver is in an elevated mood, a vehicle control is performed to intervene in the vehicle operation of the driver. Vehicle control device.

2. Determining whether the driver is in an elevated mood includes: acquiring sensor data from a sensor that observes the state of the driver; using a trained machine learning model to estimate the driver's mood from the acquired sensor data; and and determining whether the driver is in an elevated mood according to the result of the estimation. The vehicle control device according to claim 1 .

3. The processor: If it is determined that the dangerous driving is not included, it is further determined whether or not a change amount of the acceleration of the vehicle deviates from a predetermined change amount based on the detected driving state; If it is determined that the amount of change in the acceleration of the vehicle deviates from a predetermined amount of change, it is determined whether the driver of the vehicle is in an excited mood; When it is determined that the driver's mood is elevated, a vehicle control is carried out to calm the driver's mood. The vehicle control device according to claim 1 or 2.

4. The processor If it is determined that the dangerous driving is not included, it is determined whether the driver of the vehicle is in an excited mood; When it is determined that the driver's mood is elevated, a vehicle control is carried out to calm the driver's mood. The vehicle control device according to claim 1 .

5. The processor Detect the driving state of the vehicle, determining whether the detected driving state includes a predetermined dangerous driving; If it is determined that the dangerous driving is included, it is determined whether the driver of the vehicle is in an excited mood; When it is determined that the driver's mood is elevated, a vehicle control is implemented to calm the driver's mood; determining whether the driver of the vehicle is in an excited mood when the vehicle control for calming the driver is performed; When it is determined that the driver is in an elevated mood, a vehicle control is performed to intervene in the vehicle operation of the driver. A vehicle control program that executes the above.

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