JP7718166B2 - Traffic flow simulation system and traffic flow simulation method - Google Patents

Description

This article relates to a traffic flow simulation system and a traffic flow simulation method, and in particular to technology for improving the accuracy of traffic flow simulations.

Patent Document 1 discloses a system that simulates traffic flow by taking into account the driver's reaction to a driving assistance device. The system disclosed in Patent Document 1 is equipped with a driver reaction setting device to consider how the driver reacts to the driving assistance device. The driver reaction setting device stores a distribution of the driver's operational behavior in response to the activation of the information provision function and operation assistance function of the driving assistance device, measured in advance using a driving simulator or the like, and uses this distribution to perform a Monte Carlo simulation.

JP 2009-169912 A

Patent Document 1 describes simulating the driver's reaction after issuing an alarm. However, it does not describe the circumstances under which the alarm should be issued. Even if the vehicle's driving conditions, such as the environment in which the vehicle is driving and the vehicle's speed, are the same, the timing of issuing the alarm should differ depending on the driver's condition.

In order for an actual driving assistance device installed in a vehicle to vary the timing of warnings depending on the driver's state, it is necessary to have a driver state detection device that detects the driver's state and use the driver state detection device to detect the driver's state sequentially. For this reason and other reasons, in order to accurately simulate traffic flow, it is preferable to also simulate the driver state detection device. The system disclosed in Patent Document 1 does not simulate the driver state detection device, and therefore does not simulate traffic flow accurately enough.

This disclosure was made based on this situation, and its purpose is to provide a traffic flow simulation system and traffic flow simulation method that can accurately simulate traffic flow.

The above object is achieved by the combination of features recited in the independent claims, and the subclaims define further advantageous embodiments. The reference numerals in parentheses in the claims indicate a correspondence with specific embodiments described below as one aspect, and do not limit the scope of the disclosed technology.

One disclosure of a traffic flow simulation system for achieving the above object is:
a traffic environment setting unit (10) that sets a simulated traffic environment in a virtual space;
a traffic flow simulation unit (30) that moves a simulated moving object, which includes at least a simulated vehicle, in a simulated traffic environment;
a driver state simulation unit (31) for successively determining the state of a simulated driver who drives a simulated vehicle;
a driver state detection device simulation unit (21) that simulates in a virtual space a driver state detection device that detects the state of a driver;
a vehicle device simulation unit (20) that sequentially determines the operation of a simulated vehicle device mounted on the simulated vehicle ,
the driver state detection device simulation unit detects the state of the simulated driver determined by the driver state simulation unit based on the detection characteristics of the driver state detection device;
The vehicle device simulation unit is a traffic flow simulation system that sequentially determines the operation of the simulated vehicle device based on the detected driver state, which is the state of the simulated driver detected by the driver state detection device simulation unit .

This traffic flow simulation system includes a driver state detection device simulation unit that simulates a driver state detection device. It also includes a driver state simulation unit that sequentially determines the state of the simulated driver so that the driver state detection device simulation unit can simulate the driver state detection device. The driver state detection device simulation unit can sequentially determine the state of the simulated driver detected by the driver state detection device (i.e., the detected driver state) by using the actual driver state, which is the state of the simulated driver determined by the driver state simulation unit, and the detection characteristics of the driver state detection device.

Because the detected driver state can be determined, the vehicle equipment simulation unit can sequentially determine the operation of the simulated vehicle equipment installed in the simulated vehicle based on the detected driver state. In this way, because this traffic flow simulation system can determine the operation of the simulated vehicle equipment based on the detected driver state, it can accurately simulate the movement of the simulated vehicle equipped with the simulated vehicle equipment, and the traffic flow including the simulated vehicle.

One disclosure relating to a traffic flow simulation method for achieving the above object is a traffic flow simulation method executed by the above traffic flow simulation system. That is, the traffic flow simulation method includes:
A simulated traffic environment is set up in a virtual space,
In the simulated traffic environment, a simulated moving object at least included in the simulated vehicle is moved (S7);
The state of the simulated driver driving the simulated vehicle is determined sequentially (S1, S5);
A driver condition detection device that detects the driver's condition is simulated in a virtual space.
A traffic flow simulation method comprising: sequentially determining an operation of a simulated vehicle device mounted on the simulated vehicle (S4, S6),
The sequentially determined states of the simulated driver are detected by a driver detection device simulated in a virtual space based on the detection characteristics of the driver state detection device;
The traffic flow simulation method sequentially determines the operation of a simulated vehicle device based on a detected driver state, which is the detected state of a simulated driver .

1 is a diagram showing the configuration of a traffic flow simulation system 1 according to an embodiment. FIG. 2 is a diagram showing a detailed configuration of a vehicle device simulation unit 20. FIG. 10 is a diagram showing detection characteristic parameters relative to the driver's face direction. FIG. 10 is a diagram showing detection characteristic parameters relative to drowsiness levels. FIG. 10 is a diagram illustrating an example of occurrence probability distribution of face directions. 10A and 10B are diagrams illustrating examples of a recognition time distribution, a judgment time distribution, and an operation time distribution. FIG. 10 is a diagram illustrating an example of an avoidance behavior reaction time distribution DB 64. FIG. 10 is a diagram illustrating an example of an avoidance behavior selection rate distribution DB 65. FIG. 2 is a diagram illustrating an example of an outline of processing executed by the traffic flow simulation system 1. 10A and 10B are diagrams illustrating examples of changes over time in the facial direction of a simulated driver. FIG. 4 is a diagram illustrating an example of a change in the drowsiness level of a simulated driver over time.

Embodiments will be described below with reference to the drawings. Figure 1 shows the configuration of a traffic flow simulation system 1 according to an embodiment. Traffic flow simulation system 1 is a system that sets various conditions and simulates the flow of moving objects located on roads in the real world. Traffic flow simulation system 1 can be realized by a configuration including one or more computers.

<Overall structure>
The traffic flow simulation system 1 includes a traffic environment setting unit 10, a vehicle device simulation unit 20, a traffic flow simulation unit 30, a traffic condition recording unit 40, a support effect calculation unit 50, and a driver response setting unit 60. Each of these units is realized by a computer executing a simulation program stored in a predetermined storage medium.
The traffic environment setting unit 10 sets a traffic environment in a virtual space. Hereinafter, the traffic environment set in the virtual space is referred to as a simulated traffic environment. The traffic environment includes roads and their surrounding environments. Specific examples of the traffic environment include road structure, road surface conditions, off-road structures, traffic volume, ambient illuminance, rain, humidity, wind, lighting, and noise. The traffic environment setting unit 10 sets the simulated traffic environment in accordance with input operations performed by an operator on an input device. The traffic environment setting unit 10 may also acquire data from another device for part or all of the settings of the simulated traffic environment.

The simulated traffic environment may include the environment inside a vehicle (hereinafter referred to as the simulated vehicle) traveling on a road included in the simulated traffic environment. The environment inside the simulated vehicle may also include the level of sound inside the vehicle cabin, such as from conversation. The environment inside the simulated vehicle may also include the level of visual stimuli inside the vehicle cabin that the driver can sense. Visual stimuli inside the vehicle cabin that the driver can sense are caused, for example, by changes in the posture of the passenger's arms or torso. Some real-world traffic environments change dynamically. Therefore, the traffic environment setting unit 10 may dynamically change at least a portion of the simulated traffic environment.

The vehicle device simulation unit 20 simulates vehicle devices installed in real-world vehicles and sequentially determines the operation of the simulated vehicle devices installed in the simulated vehicle. The simulated vehicle devices include mechanical and electrical configurations.

<Detailed configuration of vehicle device simulation unit 20>
2 shows a detailed configuration of the vehicle device simulation unit 20. The vehicle device simulation unit 20 includes a driver state detection device simulation unit 21, an HMI simulation unit 22, a vehicle control device simulation unit 23, and an outside vehicle notification device simulation unit 24.

The driver state detection device simulation unit 21 simulates a driver state detection device. A driver state detection device is a device that detects the state of a driver in the real world. The driver state detection device can include one or more of the following: a camera that photographs the driver, a sensor that observes the driver using radio waves, and a sensor that contacts the driver's body surface to detect the driver's biometric information.

An example of a driver condition detection device will be described in more detail. The driver condition detection device includes a camera imaging unit that captures an image of the area around the headrest of the driver's seat and an illumination unit that illuminates the area around the headrest of the driver's seat. While the driver is seated in the driver's seat, the driver condition detection device uses the camera imaging unit to capture an image of the area around the driver's face illuminated with light, and analyzes the facial image of the driver contained in the captured image to monitor the driver's condition. The driver condition detection device determines whether the driver is looking away, closing the eyes, drowsiness, fatigue, attention level, irritation, etc. based on the facial image, such as the degree of eyelid drooping, pupil dilation, gaze direction, gaze movement speed, facial orientation, three-dimensional facial position, and degree of change in facial expression. Based on the determination results, the device determines whether the driver is in an appropriate state for driving, or an inappropriate state for driving. Furthermore, the device detects drowsiness, poor posture, leaving the driver's seat, etc., and determines whether the driver is in an incapable state where they cannot continue driving, based on the determination results.

The driver condition detection device can also be configured to detect the driver's condition, such as the above-mentioned appropriate driving state, unsuitable driving state, or incapable driving state, using a ToF (Time of Flight) sensor, millimeter wave sensor, ultrasonic sensor, etc. The driver condition detection device can also be configured to use a sensor that detects the driver's biological information using millimeter waves installed at a location away from the driver, or a sensor that detects the driver's biological information by contacting the driver's body surface, to determine drowsiness, fatigue level, stress level, tachyarrhythmia, bradyarrhythmia, heart failure, etc. based on the degree of change in heart rate, R-R interval, pulse rate, respiratory rate, etc., and to determine whether the driver is in an appropriate driving state (suitable for driving), an unsuitable driving state (unsuitable for driving), or an incapable driving state (unable to continue driving) based on the results of this determination.

The driver condition detection device can also include sensors that detect vehicle signals. This is because the degree to which the driver is aware of the conditions around the vehicle can be estimated from the vehicle behavior that can be inferred from the vehicle signals. Examples of sensors that detect vehicle signals included in the driver condition detection device include a steering sensor, steering touch sensor, and steering torque sensor. The signals detected by these sensors are vehicle signals related to the driver's steering operation. Other examples of sensors that detect vehicle signals included in the driver condition detection device include an accelerator pedal sensor and a brake pedal sensor. The signals detected by these sensors are vehicle signals related to pedal operation.

The driver state detection device simulation unit 21 sequentially determines the driver state to be detected by the driver state detection device when the driver's state is the simulated driver's state determined by the driver state simulation unit 31. Hereinafter, the driver's state determined by the driver state simulation unit 31 will be referred to as the actual driver state, and the driver's state sequentially determined by the driver state detection device simulation unit 21 will be referred to as the detected driver state. Even in the real world, the driver state detection device may not always be able to correctly detect the driver's state. Therefore, the actual driver state and the detected driver state may differ.

The actual driver state can be set to various inappropriate and incapable driving states, which will be explained in detail later. The driver state detection device simulation unit 21 simulates whether the driver state detection device can detect these various inappropriate and incapable driving states.

The driver state detection device simulation unit 21 simulates the driver state detection device using parameters that indicate the detection characteristics of the driver state detection device (hereinafter referred to as detection characteristic parameters). One driver state is the driver's facial direction. Figure 3 shows the detection characteristic parameters for the driver's facial direction. In Figure 3, the correct detection rate is determined for each 15-degree or 30-degree range of the driver's facial direction.

The detection characteristic parameters vary depending on the settings of the driver detection device. Therefore, it is preferable that detection characteristic parameters are prepared for each different setting of the driver detection device. Specific examples of driver detection device settings will be explained below. For example, if the driver detection device includes a camera and image analysis software that analyzes images captured by the camera, these parameters may include the camera's installation position, angle of view, resolution, and software specifications.

As mentioned above, the actual driver state and the detected driver state can differ. Therefore, there may be situations where the actual driver state is inattentive, but the detected driver state is not.

Driving conditions include drowsiness levels. Figure 4 shows the detection characteristic parameters for drowsiness levels. In Figure 4, drowsiness levels are divided into six levels: D1 to D5 and S. A correct detection rate is assigned to each drowsiness level. D1 represents "not at all drowsy," D2 represents "slightly drowsy," D3 represents "sleepy," D4 represents "quite drowsy," D5 represents "very drowsy," and S represents "seems dozing." Note that drowsiness levels are not limited to six levels. For example, the drowsiness levels shown in Figure 4 may be further subdivided, such as into drowsiness levels 0.1 and 0.2. Drowsiness levels can be estimated from the driver's facial expression. As with inattentive driving, situations may arise where the actual driver state is drowsy, but the detected driver state is not drowsy.

Whether a driver condition detection device can correctly detect the driver's facial direction may depend on other driver conditions besides facial direction. A driver condition detection device detects driver conditions that change over time, such as facial direction and drowsiness level, in other words, the dynamic driver condition. The driver condition detection device's detection performance in detecting dynamic driver conditions is affected by the driver's static state.

An example of a static driver state is whether or not the driver is wearing facial coverings. The presence or absence of facial coverings may affect the performance of detecting the driver's facial direction and the performance of detecting drowsiness levels estimated from facial expressions. Therefore, detection characteristic parameters can be set for each static driver state.

For example, the detection characteristic parameters shown in Figures 3 and 4 may be set separately for when a face covering is worn and when no face covering is worn. Face coverings include masks, glasses, sunglasses, hats, etc. Furthermore, even when no face covering is worn, the state of light shining on the driver's face may also affect the results of the driver state detection device's detection of the driver's state. Therefore, the detection characteristic parameters shown in Figures 3 and 4 may be set separately for the state of light shining on the driver's face. The state of light shining on the driver's face can be expressed as uneven direct sunlight, night, tunnel, etc.

The detection characteristic parameters shown in Figures 3 and 4 have the correct detection rate on the vertical axis. However, the detection characteristic parameters may be parameters other than those that determine the correct detection rate. For example, they may determine the time until detection.

In the real world, if a driver is looking away or falling asleep, warning the driver early reduces the likelihood of the driver panicking and making driving maneuvers. Whether or not a driver can be warned early depends on the performance of the driver condition detection device.

The traffic flow simulation system 1 can determine the detected driver state by taking into account the performance of the driver detection device, so it can also simulate differences in the behavior of the simulated driver due to differences in the timing of warning the simulated driver.

The driver state detection device simulation unit 21 uses the actual driver state determined sequentially by the driver state simulation unit 31 as input values and sequentially (i.e., chronologically) determines the detected driver state. The detected driver state can include an unsuitable driving state, an incapable driving state, an appropriate driving state, etc. The detected driver state can also include specific driver states, such as a drowsy state, a drowsy state, and an inattentive state. A drowsy state (in other words, a state of reduced alertness), an inattentive state, a closed eye state, and a poorly-adjusted posture state are examples of an unsuitable driving state. A drowsy state is an example of an incapable driving state. An incapable driving state can also include a deadman state. A deadman state is a state in which the driver is unconscious or dead.

The HMI simulation unit 22 simulates the operation of the HMI (Human Machine Interface) installed in the simulated vehicle. The HMI is an information transmission device. The HMI can include either or both a device that transmits information from the driver to the vehicle and a device that transmits information from the vehicle to the driver. The device that transmits information from the driver to the vehicle can include either or both a switch and a microphone.

Devices that transmit information from the vehicle to the driver may include one or more of a display, a speaker, and a device that transmits vibrations to the driver through the seat and steering wheel, etc. There may be two or more displays and speakers.

The HMI includes a driver warning device that issues a warning to the driver. The driver warning device is a device that determines whether to issue a warning to the driver based on the driver's condition. The driver warning device issues a warning to the driver when the driver's condition becomes unsuitable for driving. The warning means providing a stimulus to the driver. The stimulus is one or a combination of sound, visual, tactile, and somatosensory stimuli. When a combination of multiple types of stimuli is provided to the driver, the HMI includes multiple driver warning devices that act on different sensory organs of the driver. The HMI simulation unit 22 simulates these multiple driver warning devices.

Audible stimuli are generated by outputting an alarm sound from a speaker. Visual stimuli are generated by displaying an alarm on a display. If a vehicle has multiple displays, the alarm can be displayed on one or more of the displays. Haptic stimuli are generated by using the steering wheel, seat, or seatbelt to provide a stimulus that the driver perceives through their sense of touch. Somatosensory stimuli are vehicle behavior controls intended to alert the driver, such as gradual deceleration or gradual lateral movement toward the center of the lane. Somatosensory stimuli are distinct from tactile stimuli, which are generated by parts that come into contact with the driver.

The intensity of the stimuli provided to the driver by the driver warning device can be changed to multiple levels of intensity. In the case of sound stimulation, the intensity of the stimulation can be adjusted by volume, pitch, etc. In the case of visual stimulation, the intensity of the stimulation can be adjusted by brightness, size of the display area, color, etc. In the case of tactile stimulation, the intensity of the stimulation can be adjusted by vibration magnitude, vibration period, etc. In the case of somatosensory stimulation, the intensity of the stimulation can be adjusted by the acceleration caused in the vehicle, the direction of vehicle movement, etc. The intensity of the stimulation can also be referred to as warning intensity. The HMI simulation unit 22 simulates a driver warning device that can issue warnings at multiple levels of warning intensity.

The driver warning device may be configured to warn the driver if the vehicle is about to come into contact with an obstacle. In other words, the driver warning device may warn the driver even without a driver condition detection device. However, the vehicle often comes close to coming into contact with an obstacle if the driver continues to look away, etc. Therefore, if a driver condition detection device is present, the driver warning device will be able to warn the driver earlier.

The driver warning device may also include a lane departure warning device. The lane departure warning device can also provide a warning to the driver without a driver condition detection device. However, the driver condition detection device may be configured to simulate an early warning operation when the driver's posture is inappropriate for driving.

When simulating multiple driver warning devices and deciding to warn the simulated driver, the HMI simulation unit 22 selects which of the multiple driver warning devices to simulate based on the detected driver state. The operator can set which driver warning device to activate for each specific detected driver state. For example, a setting that provides a visual stimulus to the simulated driver when the detected driver state is drowsiness level D3 is compared with a setting that provides an auditory stimulus to the simulated driver when the detected driver state is also drowsiness level D3. In this way, it is possible to confirm the difference in the simulated driver's response depending on the type of stimulus.

When simulating a driver warning device capable of issuing a warning at multiple warning intensities, the HMI simulation unit 22 determines the warning intensity to be simulated based on the detected driver state when it decides to warn the simulated driver. The operator can set the specific warning intensity for each detected driver state. By setting and simulating multiple different warning intensities for the same detected driver state, it is possible to confirm the difference in the simulated driver's reaction due to differences in warning intensity. Note that the HMI simulation unit 22 may also simulate multiple driver warning devices, each capable of issuing a warning at multiple warning intensities.

The vehicle control device simulation unit 23 simulates a vehicle control device installed in a vehicle. A vehicle control device refers to a device that controls the behavior of a vehicle. The vehicle control device simulation unit 23 sequentially determines the operation of the vehicle control device when the vehicle control device is installed in a simulated vehicle. The vehicle control device may include a device that accelerates or decelerates the vehicle, and a device that controls the steering of the vehicle. Furthermore, the vehicle control device may include a driving assistance device and an automatic driving device. Examples of driving assistance devices include a collision damage mitigation braking device, a vehicle distance control device, and a lane departure prevention device. Furthermore, the driving assistance device may include an emergency stop control device that brings the vehicle to an emergency stop on behalf of the driver if the driver becomes unable to drive.

The vehicle control device simulation unit 23 can include a function for simulating this driving assistance device. A signal indicating the detected driver state, which is sequentially determined by the driver state detection device simulation unit 21, is input to the part that simulates the driving assistance device.

The vehicle control device simulation unit 23 determines the content of driving assistance control based on the detected driver state. For example, even if the actual driver state is an unsuitable driving state or an incapable driving state, if the detected driver state is neither an unsuitable driving state nor an incapable driving state, the vehicle control device simulation unit 23 does not execute driving assistance control.

When the detected driver condition is an unsuitable driving condition, the vehicle control device simulation unit 23 simulates the operation that the vehicle control device would perform in the real world if the driver condition detection device detected that the driver is in an unsuitable driving condition. For example, the vehicle control device simulation unit 23 can simulate a state in which the collision damage mitigation brake device is activated early, or a state in which a brake pressure command is output early.

If the drowsiness level of the detected driver state is lower than the drowsiness level of the actual driver state (i.e., closer to wakefulness), insufficient driving assistance control will be implemented when the actual driver state is used as the standard. One example of driving assistance control is warning the driver. Even if the actual driver state is at a drowsiness level that warrants a warning, if the detected driver state continues to be at a drowsiness level that does not warrant a warning, the warning to the driver will be delayed.

The outside-vehicle notification device simulation unit 24 simulates an outside-vehicle notification device installed in a vehicle. The outside-vehicle notification device is a device that provides notifications to moving objects, particularly surrounding vehicles, that are present in the vicinity of the vehicle on which the vehicle device is installed. The outside-vehicle notification device simulation unit 24 sequentially determines the operation of the outside-vehicle notification device when it is installed in a simulated vehicle. The outside-vehicle notification device includes a light and a horn. Therefore, the outside-vehicle notification device simulation unit 24 is equipped with a light simulation unit 25 and a horn simulation unit 26. In addition to the light and the horn, the outside-vehicle notification device may also include a wireless communication device. The wireless communication device is, for example, a vehicle-to-vehicle communication device or a communication device that communicates with a center.

Specific examples of lighting devices include headlights, turn signals, sidelights, taillights, back-up lights, and brake lights. The lighting device simulation unit 25 simulates the operation of one or more of the specific lighting devices described above when they are installed on a simulated vehicle. A horn can also be called a warning device. The horn simulation unit 26 simulates the operation of a horn when it is installed on a simulated vehicle.

The vehicle exterior notification device is activated when the vehicle makes an emergency stop. In the real world, when a vehicle makes an emergency stop, the lighting device flashes. Therefore, the vehicle exterior notification device simulation unit 24 simulates the activation of the vehicle exterior notification device installed in the simulated vehicle when the simulated vehicle makes an emergency stop.

In addition, the vehicle exterior notification device simulation unit 24 simulates a situation in which the vehicle exterior notification device mounted on the simulated vehicle is activated when the simulated traffic conditions match a situation in which a vehicle exterior notification device would be used in the real world. A situation in which a vehicle exterior notification device would be used in the real world is when a vehicle enters an intersection where visibility to the left and right is poor. In the real world, when a vehicle enters an intersection where visibility to the left and right is poor, the driver may honk the horn.

Referring back to Figure 1 for the explanation, the traffic flow simulation unit 30 sets up simulated moving objects that move through a simulated traffic environment. The simulated moving objects include at least simulated vehicles that simulate real-world vehicles, and may also include simulated moving objects that simulate moving objects other than vehicles, such as simulated pedestrians. Various movement-related characteristics can be set for the simulated moving objects. The traffic flow simulation unit 30 moves the simulated moving objects set up in the simulated traffic environment based on the characteristics set for each simulated moving object.

In addition, a simulated driver who drives the simulated vehicle can be set for at least a portion of the simulated vehicle. The driver state simulation unit 31 is a part that simulates the driver's state. The driver state simulation unit 31 sequentially determines the state of the simulated driver who drives the simulated vehicle. The driver state simulation unit 31 sequentially determines the state of the simulated driver using parameters (hereinafter referred to as driver state specifying parameters) that affect the driver's state detected by the driver state detection device and identify the driver's state. The actual driver state is defined by the driver state specifying parameters.

Examples of driver state specification parameters include the three-dimensional position of the simulated driver's face, facial orientation, degree of eye opening/closing, gaze direction, degree of mouth opening/closing, three-dimensional position of the torso, orientation of the torso, three-dimensional position of the shoulders, three-dimensional position of the elbows, and three-dimensional position of the hands. The driver state simulation unit 31 uses one or more types of driver state specification parameters to simulate the state of the simulated driver.

The driver state simulation unit 31 can define the posture of the simulated driver using driver state specification parameters. Various postures that are inappropriate for driving may be defined as the posture of the simulated driver. Postures that are inappropriate for driving are examples of unsuitable driving conditions. Examples of postures that are inappropriate for driving include reclining the seat back too far, sitting cross-legged, grabbing something in the back seat, or holding a smartphone, food, drink, cigarette, etc. in one's hand.

Driver state identification parameters can include not only parameters that identify the external appearance of the simulated driver, but also parameters that identify the internal state of the simulated driver. One example of a parameter that identifies the internal state of the simulated driver is drowsiness level. Furthermore, driver state identification parameters can also include the simulated driver's alcohol consumption level and drug dependency level.

The driver state simulation unit 31 has characteristic distributions for determining at least some of the driver state specification parameters. Figure 5 shows an example of the occurrence probability distribution of face direction. Figure 5 also shows an example of the range of forward gaze. In Figure 5, the range of face direction angle from -30 degrees to 30 degrees is defined as forward gaze. If the proportion of time the face direction angle is outside this range during the inattentiveness determination time (for example, 2 seconds) is equal to or greater than a certain proportion (for example, 70%), it is determined that the driver is looking inattentively.

The driver state simulation unit 31 uses this occurrence probability distribution to perform a Monte Carlo simulation to determine the facial orientation of the simulated driver. The driver state simulation unit 31 can similarly determine the orientation and position of other body parts of the simulated driver. Of course, for multiple body parts whose positions are interrelated, such as elbows and hands, restrictions on the positions that other body parts can take relative to one of the interrelated body parts are also set.

The driver state simulation unit 31 sequentially determines the actual driver state, using the various databases set by the driver response setting unit 60. The driver response setting unit 60 sets various setting values that determine how the driver will react to the state they perceive. This is because the driver's state changes depending on the driver's reaction.

For a simulated vehicle driven by a simulated driver, the traffic flow simulation unit 30 determines the behavior of the simulated vehicle based on the driving operations of the simulated driver. The traffic flow simulation unit 30 has a database that stores vehicle behavior distributions under various conditions, such as collision time with a preceding vehicle, inter-vehicle time, vehicle speed, and braking initiation time distribution when the preceding vehicle's brake lights are on. The traffic flow simulation unit 30 also has a database that stores behavior distributions under various traffic environments for simulated moving objects other than simulated vehicles. The traffic flow simulation unit 30 sequentially determines the behavior of the simulated moving objects using Monte Carlo simulation based on the various distributions. The traffic flow simulation unit 30 determines the behavior of multiple simulated moving objects over time. The change in the behavior of multiple simulated moving objects over time is traffic flow. Traffic flow can also be referred to as traffic conditions.

The traffic condition recording unit 40 stores some or all of the traffic flow determined by the traffic flow simulation unit 30 in a specified memory unit. The traffic flow simulation unit 30 can reproduce the traffic flow using the files stored by the traffic condition recording unit 40.

The assistance effect calculation unit 50 compares the presence of some or all of the driving assistance devices provided by the vehicle control device simulation unit 23 with the absence of those driving assistance devices, and calculates the effect of driving assistance provided by the driving assistance devices.

The driver response setting unit 60 is a unit that sets various setting values that determine how the driver will react to the conditions they perceive. The driver response setting unit 60 is equipped with a perception time distribution DB 61, a judgment time distribution DB 62, and an operation time distribution DB 63. Note that DB is an abbreviation for database. The driver response setting unit 60 also has an avoidance action reaction time distribution DB 64, an avoidance action selection rate distribution DB 65, and annoyance level distribution DB 66.

The recognition time distribution DB61 is a database that stores the distribution of the time required for a driver to recognize a situation in which a driving operation is required, from the moment the situation arises in which the driver needs to perform the driving operation. The judgment time distribution DB62 is a database that stores the distribution of the time required for a driver to recognize a situation in which the driver needs to perform the driving operation, from the moment the driver decides to perform the driving operation. The operation time distribution DB63 is a database that stores the distribution of the time required for a driver to actually perform the driving operation, from the moment the driver decides to perform the driving operation. The recognition time, judgment time, and operation time vary depending on the driver's state. Therefore, the recognition time distribution DB61, judgment time distribution DB62, and operation time distribution DB63 each have multiple time distributions corresponding to the driver's state determined by the driver state detection device simulation unit 21.

Figure 6 shows examples of the recognition time distribution, judgment time distribution, and operation time distribution. Figure 6 shows the recognition time distribution, judgment time distribution, and operation time distribution corresponding to three detected driver states: normal, inattentive, and drowsy. The detected driver state "normal" is a state in which the driver is driving with the correct posture while checking the surroundings, including the road ahead.

In the example shown in Figure 6, the recognition time distribution, judgment time distribution, and operation time distribution are all normal distributions. However, the distributions are not limited to normal distributions and can be set appropriately based on experiments, etc.

The recognition time distribution, judgment time distribution, and operation time distribution shift to later times when the detected driver state is looking away than when it is normal. Furthermore, the recognition time distribution, judgment time distribution, and operation time distribution shift to later times when the detected driver state is drowsy than when it is looking away. In the example shown in Figure 6, the three recognition time distributions, three judgment time distributions, and three operation time distributions have the same shape. However, each time distribution can be set individually based on experiments, etc.

The avoidance behavior reaction time distribution DB64 is a database that shows the probability of the reaction time it takes for a driver to take evasive action for each of multiple reaction time periods T when the driving assistance device issues an alert to the driver. Figure 7 shows an example of the avoidance behavior reaction time distribution DB64. The avoidance behavior reaction time distribution DB64 has output items and setting items.

The output items are set to multiple reaction time periods T, and a probability for another output item, "missed". "Missed" means that the reaction time is longer than a predetermined time, which is too late to take evasive action. "Missed" is a state in which the driver does not react to the warning, i.e., does not take any action. "Missed" includes cases in which the driver is unable to recognize the warning and cases in which the driver is unable to react to the warning.

Setting items are items that affect the output items and can be set by the operator. Setting items can include characteristics of the driving assistance device. The driving assistance device here is a driver warning device. The characteristics that can be set for the driving assistance device can be audio stimuli, visual stimuli, tactile stimuli, somatosensory stimuli, and combinations thereof. The level of these stimuli may also be set. For audio stimuli, the level of the stimulus is the volume. The loudness of the warning sound perceived by the driver is affected by the loudness of sounds other than the warning sound. The louder the noise, the quieter the warning sound perceived by the driver. Therefore, the loudness of the audio stimuli may be set relative to the ambient noise. Alternatively, the loudness of the audio stimuli may be an absolute value, allowing the loudness of the noise to be set separately from the loudness of the audio stimuli. Noise includes noise inside the vehicle and noise outside the vehicle. Interior noise includes conversations between passengers and music output by the in-vehicle audio system. For visual stimuli, the position of the display showing the warning may be set.

The setting items can also include driver characteristics. Driver characteristics can include either the driver's age or gender, or both. When setting the age, the age itself can be set, or a generation can be set. The setting items can also include the actual driver state and the detected driver state. By including the actual driver state and the detected driver state in the setting items, it is possible to simulate the reaction time period T when the drowsiness level of the detected driver state is lower than the drowsiness level of the actual driver state.

The avoidance behavior selection rate distribution DB65 is a database that indicates, in terms of probability, the behavior that the driver will select to avoid contact with an obstacle. Figure 8 shows an example of the avoidance behavior selection rate distribution DB65. The avoidance behavior selection rate distribution DB65 has output items and setting items. The output items are set with the probability of selection for each type of avoidance behavior that the driver may select and execute (i.e., output).

As with the avoidance action reaction time distribution DB64, the setting items can include the characteristics of the driving assistance device, the actual driver state, and the detected driver state. If the actual driver state and the detected driver state are at the same drowsiness level, a warning can be given to the driver according to the actual driver state. However, if the drowsiness level of the detected driver state is lower than the drowsiness level of the actual driver state (i.e., closer to wakefulness), the level of stimulation given to the driver will be insufficient. For this reason and other reasons, two setting items can be set: the actual driver state and the detected driver state.

The closer the detected driver state is to a normal driver state compared to the actual driver state, the lower the level of the warning and the later the timing of the warning may be. Therefore, the closer the detected driver state is to a normal driver state compared to the actual driver state, the greater the urgency at which the driver may take evasive action. Therefore, the output items increase the proportion of evasive actions with a higher urgency the closer the detected driver state is to a normal driver state compared to the actual driver state.

The setting items shown in Figure 8 are an example. The discrepancy between the actual driver state and the detected driver state is affected by the detection performance of the driver state detection device. Therefore, instead of the actual driver state and the detected driver state, the characteristics of the driver state detection device may be set. Examples of the characteristics of the driver state detection device include high detection performance, low detection performance, and no driver state detection device. Of course, the detection performance of the driver state detection device may also be expressed numerically. The characteristics of the driver state detection device may also be expressed by the presence or absence of a specific detection device, or the detection performance of a specific detection device.

The annoyance level distribution DB 66 stores the probability of each level of annoyance felt by the driver due to an alarm issued by the simulated driving assistance device, for each condition of the driving assistance device characteristics and driver characteristics simulated by the vehicle device simulation unit 20. In addition to the characteristics of the driving assistance device in the annoyance level distribution DB 66, there is also the level of the frequency of alarm occurrence per unit time.

<Traffic flow simulation method>
An example of a traffic flow simulation method is for the traffic flow simulation system 1 to execute the process shown in Fig. 9. Fig. 9 is an example of an outline of the process executed by the traffic flow simulation system 1. Before executing the process shown in Fig. 9, the traffic environment setting unit 10 sets a simulated traffic environment in accordance with an operation by an operator. The simulated traffic environment can also be set to change dynamically. The traffic environment setting unit 10 may also set the presence or absence of face attachments and the type of face attachments for one or more simulated drivers.

In S1, the driver state simulation unit 31 simulates the state of the simulated driver. Simulating the state of the simulated driver means determining the actual driver state. The actual driver state is determined using driver state identification parameters. The simulated driver states simulated by the processing in S1 include, for example, movement fluctuations, poor posture, looking aside, changes in facial direction and line of sight to check for safety, and drowsiness. Poor posture can occur when the driver suddenly experiences a physical abnormality. Looking aside includes looking aside at the side of the vehicle and looking aside at the interior of the vehicle (for example, at the center display). These simulated driver states are defined by time-series changes in facial position, facial direction, degree of eye opening and closing, and gaze direction, which are identified by the driver state identification parameters.

In S2, the driver state detection device simulation unit 21 simulates the process by which the driver state detection device detects the state of the simulated driver simulated in S1 (i.e., the actual driver state), and determines the detected driver state. The detected driver state is determined using detection characteristic parameters that indicate the detection characteristics of the driver state detection device.

By processing S2, it is possible to determine whether the detected driver state is an unsuitable driving state, an incapable driving state, or an appropriate driving state. If the actual driver state is a large facial orientation that exceeds the detection limit of the driver state detection device, the detected driver state can include a facial orientation detection inability. Furthermore, if the simulated driver is wearing a face covering, or depending on the state of light shining on the simulated driver's face, the detected driver state may be determined to be one in which the driver state detection device is unable to detect the simulated driver's facial orientation and/or facial expression. Because the detection characteristic parameters have a probability distribution, there are multiple types of detected driver states that may be determined for the same actual driver state.

In S3, the HMI simulation unit 22 simulates the operation of the HMI. For example, if the detected driver state determined in S2 is inattentiveness or drowsiness, the operation of a driver warning device, which is one of the HMIs, to output a warning to the simulated driver can be simulated.

In S4, the vehicle control device simulation unit 23 simulates the operation of the vehicle control device. This processing in S4 successively updates the position and behavior of the simulated vehicle in the virtual space. For example, if the detected driver state determined in S2 is an unsuitable driving state, the operation of the emergency stop control device is simulated, and the simulated vehicle is brought to an emergency stop.

In S5, the driver state simulation unit 31 simulates how a simulated driver in the detected driver state determined in S2 will react to the HMI operation and vehicle control device operation simulated in S3 and S4.

For example, if the detected driver state determined in S2 is that the simulated driver's posture is inappropriate for driving, and the driver warning device issues a warning to the simulated driver in S4, the system simulates how the simulated driver's posture will change. The change in the simulated driver's posture is also determined by including a probability calculation. If the simulated driver's posture is inappropriate for driving because the simulated driver is holding an object in his or her hand, different posture changes may be probability calculated depending on the type of object being held. For example, the probability calculation of posture changes may be performed by distinguishing between objects that can be thrown, such as a smartphone, and objects that cannot be thrown, such as cigarettes and drinks.

In S5, the driver state simulation unit 31 also uses various databases set by the driver response setting unit 60. For example, as shown in Figure 6, if the detected driver state is looking away or dozing, recognition, judgment, and reaction times will be delayed compared to when the detected driver state is normal.

In addition, using the evasive action reaction time distribution DB64 shown in Figure 7, it is possible to determine whether the simulated driver will respond or ignore the warning when it is given to the simulated driver, and the reaction time period T if the simulated driver responds. For example, if the actual driver state is an inattentive state where the driver is looking at a sign ahead to the right, and in S3 the driver warning device determines to operate by displaying a warning on the center display of the simulated vehicle, the gaze direction of the detected driver state is not the direction in which the warning is being displayed. Therefore, the evasive action of the simulated driver determined using the evasive action reaction time distribution DB64 is likely to result in an oversight. If there is a lot of noise, such as loud conversations in the vehicle cabin, the evasive action of the simulated driver determined using the evasive action reaction time distribution DB64 is also likely to result in an oversight.

Furthermore, the avoidance action selection rate distribution DB65 shown in Figure 8 can be used to determine which avoidance action the simulated driver will select when choosing an avoidance action.

In S6, the vehicle exterior notification device simulation unit 24 simulates the operation of the vehicle exterior notification device. For example, if the detected driver state determined in S2 is an incapacitated state, the lighting device simulation unit 25 simulates the operation of flashing the lighting device. Also, in the real world, when a vehicle enters an intersection where visibility to the left and right is poor, it may be necessary to honk the horn. Therefore, when the simulated vehicle enters an intersection where visibility to the left and right is poor, the horn simulation unit 26 may simulate the operation of honking the horn.

In S7, the traffic flow simulation unit 30 simulates traffic flow based on the position and behavior of the simulated vehicle determined in S4 and the activation of the external notification device of the simulated vehicle simulated in S6. The position and behavior of a simulated vehicle affect the position and behavior of simulated moving objects around that simulated vehicle. Also, if the external notification device of a simulated vehicle is activated, the simulated moving objects around that simulated vehicle may be affected by the activation of the external notification device. Once the processing of S7 is completed, the process returns to S1.

<Example of operation when looking away>
An example of operation when warning against inattentive driving will be described with reference to Fig. 10. In Fig. 10, the upper graph shows the change over time in the face direction angle, which is the actual driver state. The middle graph shows the change over time in the face direction angle, which is the detected driver state. The lower graph shows the detected driver state in the middle graph superimposed with the change over time in the detected driver state when a warning is given to the simulated driver.

In the example of Figure 10, as shown in the upper graph, at time t1, the face direction angle, which is the actual driver state, falls below -30 degrees. As shown in Figure 5, when the face direction angle falls below -30 degrees, the driver is not looking forward. In the actual driver state, the face direction angle falls below -30 degrees at time t1, and remains below -30 degrees from time t1 to time t5. The time from time t1 to time t5 is assumed to be, for example, 2.5 seconds. When gazing at the display of a navigation device, for example, it is conceivable that this level of change in face direction angle will continue for this length of time.

Unlike the actual driver state, the detected driver state has not yet fallen below -30 degrees at time t1. In the example of Figure 5, the actual driver state and the detected driver state differ as shown here. Because the detected driver state is simulated using the detection characteristic parameters shown in Figure 3, the detected driver state changes for each trial, even if the actual driver state is the same. In the example of Figure 5, the face direction angle, which is the detected driver state, falls below -30 degrees at time t2.

Time t3 is the time when the inattentiveness determination time has elapsed since time t2, and the HMI simulation unit 22 issues a warning to the simulated driver. As shown in the lower graph, the simulated driver's face turns toward 0 degrees in response to the warning, and returns to -30 degrees at time t4. If the driver is not warned, the simulated driver's face turns back to -30 degrees at time t5, so the effect of the warning is simulated.

Unlike the example in Figure 10, a simulation is also possible in which the simulated driver's face is turned more than -60 degrees and the warning is displayed on a display directly in front of the driver. In this case, even if a warning is issued, the simulated driver will not notice the warning, and it will take a long time for the simulated driver to return their face to looking forward.

<Example of dozing off>
Next, an example of operation when determining that a simulated driver is dozing will be described with reference to FIG. 11 . In the example of FIG. 11 , the actual driver state and the detected driver state always match. A real-world driver state detection device determines the drowsiness level from facial images during the drowsiness determination time. If the drowsiness determination time is 5 seconds and there are 30 frames per second, 150 frames of facial images are used. If, among the facial images used, a facial image determined to have a drowsiness level equal to or higher than the warning reference level (e.g., drowsiness level D4) is equal to or higher than the drowsiness determination ratio threshold (e.g., 70%), it is determined that the driver is dozing and an alarm is issued.

This traffic flow simulation system 1 simulates the drowsiness level determined by a real-world driver state detection device through probability calculations using the actual driver state related to the face and detection characteristic parameters. The actual driver state related to the face is sequentially identified by the driver state simulation unit 31, and includes the facial direction, the degree to which the eyes are open or closed, the degree to which the mouth is open or closed, etc. The detection characteristic parameters are determined by the driver state detection device simulation unit 21.

From time t10, the simulated driver's drowsiness level rises, and at time t11, the drowsiness level exceeds drowsiness level 3, the standard for issuing an alert. However, at time t12, the drowsiness level again falls below 3. If the time from time t11 to time t12 is shorter than the drowsiness judgment time, then even at time t12, the HMI simulation unit 22 does not determine that an alert should be issued to the simulated driver.

At time t13, the drowsiness level again becomes 3 or higher. Time t14 is the time when the drowsiness judgment time has elapsed since time t13. The drowsiness level remains at 3 or higher from time t13 onwards. Therefore, at time t14, the HMI simulation unit 22 determines that the simulated driver is dozing and issues a warning to the simulated driver.

<Summary of the embodiment>
The traffic flow simulation system 1 of this embodiment described above includes a driver state detection device simulation unit 21 that simulates a driver state detection device. It also includes a driver state simulation unit 31 that sequentially determines the state of a simulated driver so that the driver state detection device simulation unit 21 can simulate the driver state detection device. The driver state detection device simulation unit 21 can sequentially determine the detected driver state by using the actual driver state, which is the state of the simulated driver determined by the driver state simulation unit 31, and the detection characteristics of the driver state detection device.

Because the detected driver state can be determined, the vehicle device simulation unit 20 can sequentially determine the operation of the simulated vehicle device installed in the simulated vehicle based on the detected driver state. In this way, because the traffic flow simulation system 1 can determine the operation of the simulated vehicle device based on the detected driver state, it can accurately simulate the movement of the simulated vehicle equipped with the simulated vehicle device, and the traffic flow including the simulated vehicle.

The vehicle device simulation unit 20 includes an HMI simulation unit 22 that simulates the operation of the HMI installed in the simulated vehicle. The HMI simulation unit 22 simulates the operation of the HMI based on the detected driver state, so it can simulate not only the operation of the HMI when the detected driver state is the same as the actual driver state, but also the operation of the HMI when the detected driver state differs from the actual driver state.

The HMI simulation unit 22 determines whether to issue a warning to the simulated driver based on the detected driver state. Therefore, it is possible to simulate a situation where the detected driver state is closer to normal than the actual driver state, resulting in an insufficient level of warning.

The HMI simulation unit 22 is also capable of simulating multiple driver warning devices that stimulate different sensory organs in the driver. When it is decided to warn the simulated driver, it can select the driver warning device to simulate from among the multiple driver warning devices based on the detected driver state. This makes it possible to confirm differences in the simulated driver's reaction depending on the type of stimulus.

The HMI simulation unit 22 is also capable of simulating a driver warning device that warns the driver at multiple warning intensities. When it is decided to warn the simulated driver, the simulated warning intensity can be determined based on the detected driver state. This makes it possible to confirm how the simulated driver reacts to different warning intensities.

The driver states detected by the driver state detection device simulation unit 21 include appropriate driving, unsuitable driving, and incapable driving. Therefore, the behavior of the simulated vehicle can be simulated for each of these three types of detected driver states.

The vehicle device simulation unit 20 includes a vehicle control device simulation unit 23 that simulates the vehicle control device. The vehicle control device simulation unit 23 simulates the operation of the vehicle control device based on the detected driver state, and can therefore also simulate the operation of the vehicle control device when the detected driver state differs from the actual driver state.

The vehicle control device simulation unit 23 simulates the operation of executing emergency driving assistance control when the detected driver state becomes an incapacitated state. Even if the actual driver state is an incapacitated state, the vehicle control device simulation unit 23 does not execute emergency driving assistance control unless the detected driver state is an incapacitated state. Therefore, it is possible to simulate traffic flow when the driver detection device is unable to detect an incapacitated state.

In addition to inoperable states, the vehicle control device simulation unit 23 may also simulate the operation of executing emergency driving assistance control when the detected driver state becomes unsuitable for driving.

The vehicle device simulation unit 20 is equipped with an external notification device simulation unit 24 that simulates an external notification device, so it can not only simulate the traffic flow when a simulated vehicle controls its own behavior, but also how surrounding simulated moving objects move in response to notifications from the simulated vehicle.

The outside-vehicle notification device simulation unit 24 can simulate a state in which the outside-vehicle notification device notifies the outside of the vehicle of information indicating that emergency driving assistance control will be executed when the detected driver state becomes incapacitated. This makes it possible to accurately simulate how surrounding simulated moving objects will move when the simulated vehicle executes emergency driving assistance control.

Although the embodiments have been described above, the disclosed technology is not limited to the above-described embodiments and can be implemented with various modifications within the scope of the gist of the invention.

1: Traffic Flow Simulation System 10: Traffic Environment Setting Unit 20: Vehicle Device Simulation Unit 21: Driver State Detection Device Simulation Unit 22: HMI Simulation Unit 23: Vehicle Control Device Simulation Unit 24: Vehicle External Notification Device Simulation Unit 25: Lighting Device Simulation Unit 26: Horn Simulation Unit 30: Traffic Flow Simulation Unit 31: Driver State Simulation Unit 40: Traffic Condition Recording Unit 50: Assistance Effect Calculation Unit 60: Driver Response Setting Unit 61: Perception Time Distribution DB 62: Decision Time Distribution DB 63: Operation Time Distribution DB 64: Evasive Action Reaction Time Distribution DB 65: Evasive Action Selection Rate Distribution DB 66: Level Distribution DB

Claims (11)

a traffic environment setting unit (10) that sets a simulated traffic environment in a virtual space;
a traffic flow simulation unit (30) that moves a simulated moving object at least included in a simulated vehicle in the simulated traffic environment;
a driver state simulation unit (31) for sequentially determining the state of a simulated driver who drives the simulated vehicle;
a driver state detection device simulation unit (21) that simulates in a virtual space a driver state detection device that detects the state of a driver;
a vehicle device simulation unit (20) that sequentially determines the operation of a simulated vehicle device mounted on the simulated vehicle ,
the driver state detection device simulation unit detects the state of the simulated driver determined by the driver state simulation unit based on the detection characteristics of the driver state detection device;
A traffic flow simulation system, wherein the vehicle device simulation unit sequentially determines the operation of the simulated vehicle device based on a detected driver state, which is the state of the simulated driver detected by the driver state detection device simulation unit . The traffic flow simulation system of claim 1, wherein the vehicle device simulation unit includes an HMI simulation unit (22) that simulates an information transmission device that transmits information between the driver and the vehicle. The traffic flow simulation system described in claim 2, wherein the HMI simulation unit includes a function for simulating a device that warns the driver by determining whether to warn the simulated driver based on the detected driver state. The traffic flow simulation system described in claim 3, wherein the HMI simulation unit is capable of simulating multiple driver warning devices that act on different sensory organs of the driver, and when it decides to warn the simulated driver, it selects the driver warning device to simulate from among the multiple driver warning devices based on the detected driver state. The traffic flow simulation system described in claim 3 or 4, wherein the HMI simulation unit is capable of simulating a driver warning device that warns the driver at multiple warning intensities, and when it decides to warn the simulated driver, determines the warning intensity to be simulated based on the detected driver state. The traffic flow simulation system described in any one of claims 1 to 5, wherein the detected driver state detected by the driver state detection device simulation unit includes an appropriate driving state, an unsuitable driving state, and an incapable driving state. The traffic flow simulation system according to any one of claims 1 to 6, wherein the vehicle device simulation unit includes a vehicle control device simulation unit (23) that simulates a vehicle control device that controls vehicle behavior. The traffic flow simulation system of claim 7, wherein the vehicle control device simulation unit includes a function for simulating emergency driving assistance control by executing emergency driving assistance control when the detected driver state becomes unsuitable for driving or unable to drive. The traffic flow simulation system described in any one of claims 1 to 8, wherein the vehicle device simulation unit includes an exterior notification device simulation unit (24) that simulates an exterior notification device installed in a vehicle. The vehicle device simulation unit includes a vehicle control device simulation unit (23) that simulates a vehicle control device that controls the behavior of the vehicle,
the vehicle control device simulation unit includes a function of simulating emergency driving assistance control by executing emergency driving assistance control when the detected driver state becomes an incapacitated state;
10. The traffic flow simulation system according to claim 9, wherein the outside-vehicle notification device simulation unit notifies outside the vehicle information indicating that emergency driving assistance control will be executed when the detected driver state becomes an incapacitated state. A simulated traffic environment is set up in a virtual space,
In the simulated traffic environment, a simulated moving object at least included in the simulated vehicle is moved (S7);
The state of the simulated driver who drives the simulated vehicle is determined sequentially (S1, S5);
A driver condition detection device that detects the driver's condition is simulated in a virtual space.
and sequentially determining the operation of a vehicle simulator device mounted on the vehicle simulator (S4, S6) ,
The sequentially determined states of the simulated driver are detected by the driver detection device simulated in a virtual space based on the detection characteristics of the driver state detection device;
A traffic flow simulation method, comprising sequentially determining operation of the simulated vehicle device based on a detected driver state, which is the detected state of the simulated driver .