CN116569236A - Vehicle support device and vehicle support method - Google Patents

Abstract

The pressure of the driver of the vehicle can be improved with higher accuracy by providing the important factor estimating unit (212) for estimating the important factor of the pressure of the driver of the vehicle, the recommended route determining unit (203) for determining the recommended route including a plurality of links, which corresponds to the important factor of the pressure estimated by the important factor estimating unit (212) and is estimated to be effective for alleviating the pressure, and the presentation control unit (204) for presenting the recommended route determined by the recommended route determining unit (203) to the driver.

Description

Vehicle support device and vehicle support method

Cross-reference to related applications

The present application is based on japanese patent application nos. 2020-204478 of the japanese application at 12/9/2020, and the content of the basic application is incorporated by reference in its entirety.

Technical Field

The present disclosure relates to a vehicle assistance device and a vehicle assistance method.

Background

A technique of checking a state of a driver of a vehicle is known. For example, patent document 1 discloses a technique of estimating emotion of a subject based on a recognition result of an expression of a face.

Patent document 1: japanese patent No. 6467965

There is a possibility that the mental state of the driver affects the running of the vehicle. Therefore, it is preferable to improve the mental state of the driver in the case where the mental state is deteriorated.

Disclosure of Invention

An object of the present disclosure is to provide a vehicle assistance device and a vehicle assistance method that can improve the mental state of a driver of a vehicle with higher accuracy in the event that the mental state is deteriorated.

The above object is achieved by a combination of features recited in the independent claims, further advantageous embodiments of the disclosure being specified in the dependent claims. Any reference numerals in parentheses in the claims indicate correspondence with specific units described in the embodiment described later as one embodiment, and do not limit the technical scope of the present disclosure.

In order to achieve the above object, a vehicle assist device according to the present disclosure includes: a mental state associated state estimating unit that estimates a mental state associated with at least one of a degree of deterioration of a mental state of a driver of the vehicle and an important factor of deterioration of the mental state of the driver; a recommended route determination unit that determines a recommended route including a plurality of links corresponding to the mental association state estimated by the mental association state estimation unit; and a presentation control unit that presents the recommended route determined by the recommended route determination unit to the driver.

In order to achieve the above object, a vehicle assistance method of the present disclosure includes executing, by at least one processor, the following steps: a mental state estimation step of estimating a mental state, which is at least one of the degree of deterioration of the mental state of the driver of the vehicle and an important factor of deterioration of the mental state of the driver; a recommended route determination step of determining a recommended route including a plurality of links corresponding to the mental association state estimated by the mental association state estimation step; and a presentation control step of presenting the recommended route determined in the recommended route determination step to the driver.

Accordingly, the recommended route corresponding to the mental association state, which is at least one of the degree of deterioration of the mental state of the driver of the vehicle and the important factor of deterioration of the mental state of the driver, can be presented to the driver. Thus, it is possible to present the driver with a route for improving the deterioration of the mental state corresponding to at least one of the degree of the deterioration of the mental state and the important factor of the deterioration of the mental state of the driver, which is more detailed than the presence or absence of the deterioration of the mental state alone. As a result, when the mental state of the driver of the vehicle is deteriorated, the mental state can be improved with higher accuracy.

Drawings

Fig. 1 is a diagram showing an example of a schematic configuration of a driving support system 1.

Fig. 2 is a diagram showing an example of a schematic configuration of the HCU 20.

Fig. 3 is a flowchart showing an example of the flow of the state improvement relating process in the HCU 20.

Fig. 4 is a diagram showing an example of a schematic configuration of the HCU20 a.

Fig. 5 is a flowchart showing an example of the flow of the state improvement relating process in the HCU20 a.

Fig. 6 is a diagram showing an example of a schematic configuration of the HCU20 b.

Detailed Description

Various embodiments for disclosure will be described with reference to the accompanying drawings. For convenience of explanation, the same reference numerals are given to portions having the same functions as those shown in the drawings used in the description so far, and the explanation thereof may be omitted. Parts to which the same reference numerals are attached can refer to the description in other embodiments.

(embodiment 1)

< outline Structure of Driving support System 1 >)

Hereinafter, this embodiment will be described with reference to the drawings. The driving support system 1 shown in fig. 1 can be used in an automobile (hereinafter, simply referred to as a vehicle). The driving support system 1 includes an HMI (Human Machine Interface: human-machine interface) system 2, a communication module 3, a locator 4, a map database (hereinafter, referred to as map DB) 5, a vehicle control ECU6, a periphery monitoring sensor 7, an automated driving ECU8, and a vehicle state sensor 9. The HMI system 2, the communication module 3, the locator 4, the map DB5, the vehicle control ECU6, the automated driving ECU8, and the vehicle state sensor 9 are connected to, for example, an in-vehicle LAN. Hereinafter, the vehicle using the driving support system 1 will be referred to as the host vehicle.

The communication module 3 includes a short-range communication unit 31 and a wide-range communication unit 32. The short-range communication unit 31 performs short-range wireless communication. Examples of the short-range wireless communication include short-range wireless communication according to a short-range wireless communication standard such as Bluetooth (registered trademark) Low Energy (Bluetooth Low Energy), UWB (Ultra Wide Band) communication, and the like. As the short-range wireless communication according to the short-range wireless communication standard, wi-Fi (registered trademark), zigBee (registered trademark), or the like can also be used. The short-range communication unit 31 is connected to the mobile terminal by short-range wireless communication. As the mobile terminal, a wearable device worn by a passenger of the host vehicle, a multifunctional mobile phone carried by the passenger of the host vehicle, and the like can be cited. The wide area communication unit 32 communicates with the center server via a public communication network. As the public communication network, a mobile telephone network, the internet, and the like can be cited. The wide area communication unit 32 may be configured to communicate with the center via a road side machine. The wide area communication part 32 acquires traffic information and the like from a center server and outputs to the in-vehicle LAN.

The locator 4 is provided with a GNSS (Global Navigation Satellite System: global navigation satellite system) receiver and an inertial sensor. The GNSS receiver receives positioning signals from a plurality of satellites. The inertial sensor includes, for example, a gyro sensor and an acceleration sensor. The locator 4 combines the positioning signal received by the GNSS receiver and the measurement result of the inertial sensor to sequentially position the vehicle of the host vehicle. Further, the positioning of the vehicle position may be configured to use a travel distance obtained from a signal sequentially output from a vehicle speed sensor mounted on the vehicle.

The map DB5 stores map data for guidance and high-precision map data. The map DB5 is, for example, a nonvolatile memory. The guidance map data and the high-precision map data may be stored in separate memories. The guidance map data and the high-precision map data may be acquired from a center server outside the host vehicle using the wide-area communication unit 32. In the case where the host vehicle is not an autonomous vehicle capable of autonomous driving, the map DB5 may not include high-precision map data.

The guidance map data is map data used for a navigation function for route guidance. As an example, a recommended route to the destination is searched for, and route guidance of the recommended route is performed. The guidance map data is data representing a road on which the vehicle is traveling, such as a node or a link. The nodes are points at which each road on the map crosses, branches, and merges. Road segments connect nodes. The road segment represents a road section. The link data is composed of each data that identifies the inherent number of the link, the link length indicating the length of the link, the link direction, the shape information of the link, the coordinates of the nodes at the start and end of the link, and the road attribute. The road attribute includes a road name, a road type, a road width, the number of lanes, a speed limit value, and the like. On the other hand, the node data is composed of data such as a node ID, a node coordinate, a node name, a node type, a link ID describing a link ID of a link connected to the node, and a junction type, each of which is given a unique number for each node on the map.

The high-precision map data is map data used for running control of the vehicle. The high-precision map data is more detailed map data than the guidance map data. The high-precision map data includes, for example, a three-dimensional map composed of a road shape and a point group of feature points of a structure.

The vehicle control ECU6 is an electronic control device that performs acceleration/deceleration control and/or steering control of the host vehicle. The vehicle control ECU6 includes a steering ECU, a power unit control ECU, and a brake ECU. The steering ECU performs steering control. The power unit control ECU performs acceleration and deceleration control. The brake ECU performs deceleration control. The vehicle control ECU6 acquires detection signals output from sensors such as an accelerator position sensor, a brake stroke sensor, a steering angle sensor, and a vehicle speed sensor mounted on the vehicle, and outputs control signals to travel control devices such as an electronically controlled throttle valve, a brake actuator, and an EPS (Electric Power Steering: electric power steering) motor.

The surrounding area monitoring sensor 7 detects objects around the host vehicle, such as moving objects such as pedestrians and other vehicles, and stationary objects such as falling objects on the road. In addition, road surface marks such as lane dividing lines around the host vehicle are detected. The periphery monitoring sensor 7 is, for example, a periphery monitoring camera that captures a predetermined range around the host vehicle, a millimeter wave radar that transmits a detection wave to the predetermined range around the host vehicle, a sonar, a LIDAR (Light Detection and Ranging/Laser Imaging Detection and Ranging: light detection ranging/laser imaging detection ranging), or the like. The periphery monitoring camera sequentially outputs the sequentially photographed images as sensing information to the automated driving ECU8. As the periphery monitoring camera, a plurality of cameras may be used to capture the entire periphery of the vehicle. The sensor that transmits the detection wave, such as the sonar or millimeter wave radar LIDAR, sequentially outputs, as the sensing information, the scanning result based on the reception signal obtained when the reflected wave reflected by the obstacle is received, to the automated driving ECU8. The surrounding area monitoring sensor 7 detects the surrounding environment state of the vehicle.

The automated driving ECU8 performs an automated driving function of a proxy for performing a driving operation of the driver by controlling the vehicle control ECU 6. As the degree of autopilot in the autopilot function (hereinafter, referred to as an automation level), there may be a plurality of levels, for example, as defined by SAE. The automation level is classified into levels 0 to 5 as follows in the definition of SAE, for example.

The level 0 is a level at which the system does not intervene and the driver performs all driving tasks. The driving task is steering, for example, acceleration and deceleration. Class 0 corresponds to so-called manual driving. Level 1 is a level of any one of steering assist and acceleration and deceleration by the system. Level 2 is a level of system assisted steering and acceleration and deceleration. The classes 1 to 2 correspond to so-called driving assistance.

The level 3 is a level at which all driving tasks can be performed by the system at a specific place such as an expressway, and a driver can perform driving operations in an emergency. In level 3, it is required that the driver can quickly cope with the request for driving replacement from the system. Grade 3 corresponds to so-called conditional automatic driving. The level 4 is a level at which all driving tasks can be performed by the system, except for specific conditions such as roads and extreme environments that cannot be handled. Class 4 corresponds to so-called highly automated driving. Level 5 is a level at which all driving tasks can be performed by the system in all circumstances. Class 5 corresponds to so-called fully automatic driving. Grades 3 to 5 correspond to so-called autopilot.

In embodiment 1, a case where the host vehicle is a vehicle capable of performing automatic driving of level 3 or more will be described as an example. It may also be possible to switch the automation level. In embodiment 1, the following description will be made by taking an example of a case where automatic driving at an automation level of 3 or more and manual driving at a level of 0 can be switched. The host vehicle may be a vehicle that cannot perform automatic driving of level 3 or more. In this case, instead of searching for a recommended route by the automated driving ECU8, a navigation device used in the host vehicle may search for a recommended route.

The automated driving ECU8 recognizes the traveling environment of the host vehicle based on the vehicle position of the host vehicle acquired from the locator 4, the high-precision map data acquired from the map DB5, and the detection result in the surroundings monitoring sensor 7. As an example, the shape and the moving state of the object around the host vehicle are recognized or the shape of the sign around the host vehicle is recognized based on the detection result in the surroundings monitoring sensor 7. Then, by combining the vehicle position of the host vehicle and the high-precision map data, a virtual space that reproduces the actual running environment in a three-dimensional manner is generated.

The automated driving ECU8 generates a travel plan for automatically traveling the host vehicle by the automated driving function based on the identified travel environment. As the travel plan, a medium-long travel plan and a short travel plan are generated. In the medium-long travel plan, a route for moving the vehicle to a set destination is generated. The path refers to a path including a plurality of road segments. The automated driving ECU8 may generate the route in the same manner as the route search of the navigation function. For example, the path search may be performed by cost calculation based on the Dijkstra method. In the cost calculation by the dickst method, the link cost of the link satisfying the search conditions such as the distance priority and the time priority is set to be small. Also, a path having a smaller value of the search section cost is searched as the recommended path. When the search conditions are sent from the HCU20 described later, the automated driving ECU8 searches for a recommended route that satisfies the search conditions sent from the HCU20 and returns the recommended route to the HCU20.

The automated driving ECU8 generates a predetermined travel route for realizing travel according to the medium-long travel plan, using the generated virtual space around the host vehicle in the short-term travel plan. Specifically, the execution of steering for lane change, acceleration and deceleration for speed adjustment, steering for obstacle avoidance, braking, and the like is determined. The automated driving ECU8 performs acceleration/deceleration control and/or steering control of the vehicle in cooperation with the vehicle control ECU6, based on the generated travel plan, thereby performing automated driving.

The vehicle state sensor 9 is a sensor group for detecting the state of the host vehicle, such as the running state and the operation state of the host vehicle. The vehicle state sensor 9 includes a vehicle speed sensor that detects the vehicle speed of the vehicle, a steering sensor that detects the steering angle of the steering wheel of the vehicle, an accelerator position sensor that detects the opening degree of the accelerator pedal of the vehicle, a brake stroke sensor that detects the depression amount of the brake pedal of the vehicle, and the like. The vehicle state sensor 9 outputs the detection result to the in-vehicle LAN. The detection result from the vehicle state sensor 9 may be output to the in-vehicle LAN via an ECU mounted on the vehicle.

The HMI system 2 includes an HCU (Human Machine Interface Control Unit: human-machine interface control unit) 20, an indoor camera 21, a biosensor 22, an audio output device 23, a display device 24, a microphone 25, an operation device 26, and a state improvement device 27. The HMI system 2 accepts an input operation from the driver. The HMI system 2 monitors the state of the passenger of the host vehicle including the driver. The HMI system 2 prompts the driver for information.

The indoor camera 21 captures a predetermined range in the cabin of the host vehicle. Further, a DSM (Driver Status Monitor: driver status monitor) for monitoring the driver of the host vehicle may be used as the indoor camera 21. Further, a camera that photographs a range including a passenger seat and a rear seat of the host vehicle may be used. The information of the image captured by the indoor camera 21 may be sequentially output to the HCU20.

The biometric sensor 22 measures biometric information of the driver, and sequentially outputs the measured biometric information to the HCU20. The biosensor 22 may be configured to be provided in a part of the host vehicle. The biosensor 22 may be configured as a wearable device that is provided to be worn by a passenger such as a driver. In the case where the biosensor 22 is provided in a wearable device worn by the driver, the HCU20 may be configured to acquire the measurement result of the biosensor 22 via the short-range communication unit 31.

The biosensor 22 may be a pulse wave sensor that measures pulse waves. As the pulse wave sensor, a photoelectric pulse wave sensor, an impedance pulse wave sensor, or the like is used. The pulse wave sensor may be a contact sensor or a non-contact sensor. As the biosensor 22, a body temperature sensor that measures a body temperature can be cited. As the body temperature sensor, an IR sensor or the like may be used. As the biosensor 22, an exhalation sensor that measures exhalation can be cited. As the exhalation sensor, a gas sensor or the like may be used. As the biosensor 22, a body composition sensor that measures a body composition can be cited. As the body composition sensor, a body composition meter is used which flows a microcurrent to the body and estimates the body fat rate, the muscle mass, and the body moisture amount from the resistance value thereof. The biosensor 22 that needs to be in contact with the body of the passenger to perform measurement may be provided in a steering wheel, a driver seat, or the like of the vehicle.

Further, a sensor that measures biological information other than pulse wave, body temperature, exhalation, and body composition may be used as the biological sensor 22. For example, sensors measuring respiration, brain waves, heart rhythm fluctuations, perspiration, blood pressure, skin conductance can be cited.

The voice output device 23 presents information by outputting voice. The sound output device 23 outputs a sound according to the instruction of the HCU20. The sound output device 23 may be a speaker or the like. The display device 24 presents information by displaying images and texts. The display device 24 displays images and texts according to the instruction of the HCU20. The display device 24 is provided with a display surface facing the cabin of the host vehicle. For example, the display device 24 is provided such that the display surface is located on the front face of the driver's seat of the host vehicle. As the display device 24, a CID (Center Information Display: center information display), a display of a navigation device, a head-up display (hereinafter referred to as HUD), or the like can be used.

The microphone 25 collects sounds generated by the passenger of the vehicle, converts the sounds into an electric sound signal, and outputs the electric sound signal to the HCU20. The microphone 25 may be configured to be provided on the upper surface of the steering column cover, a sunshade on the driver seat side, or the like, for example, so as to easily collect the sound of the driver. The microphone 25 may be provided for each seat so that sounds of passengers in the respective seats can be discriminated and collected. In this case, a zoom microphone with reduced directivity may be used as the microphone 25 provided for each seat. Microphone 25 may also accept input of voice-based instructions from a user.

The operation device 26 accepts an operation input from the driver. The operation device 26 may be a mechanical switch or a touch switch integrated with the display device 24. The mechanical switch may be a steering switch provided in a spoke portion of a steering wheel. The microphone 25 and the operation device 26 can also be referred to as an input means that accepts an input from the driver.

The state improvement means 27 is means for giving the driver a stimulus for improving the mental state from the deterioration of the mental state. The state improvement device 27 is provided in the host vehicle. Examples of the deterioration of mental state include stress, anxiety, sadness, and exclamation. Stress can also be referred to as stress. Anxiety may also be refined to anxiety and fear. The anxiety may be a mental state without anxiety of the subject. Fear may be a mental state of anxiety in a subject. In the present embodiment, a case where stress is the target of deterioration of mental state will be described as an example.

The state improvement device 27 gives the driver a stimulus for relieving the stress. For example, the state improving device 27 may be a device that ejects the odor component estimated to reduce the pressure. The device can be realized by combining an air conditioning device and an aromatic unit. The state improving device 27 may be a device for relieving stress by improving blood circulation and relieving stiffness by giving warm stimulation to the driver. In this case, the heater and the blower provided in the backrest of the driver seat may be used. In order to improve the blood circulation of the driver, it is preferable to repeatedly heat or cool the neck and shoulder of the driver. The state improvement device 27 may be a device that relieves pressure by guiding the driver's breath to a breath estimated to be able to relax. In this case, the deep breath may be guided by tightening the seat belt. In addition, the state improvement device 27 may be a device that relieves stress by emitting light that is estimated to be relaxed by the driver. The light emission may be performed by an LED or the like. The state improvement device 27 may be a device that relieves stress by playing a musical composition that the driver prefers. In this case, the sound output device 23 may be used as the state improvement device 27.

The state improving device 27 may be a device that generates a stimulus estimated to alleviate pressure, and may be other than the above. Even when the stress is not the subject, the state improvement device 27 may be configured to improve the mental state by giving a stimulus for relaxing the driver.

The HCU20 is mainly composed of a microcomputer including a processor, a memory, I/O, and a bus connecting the processor and the memory, and executes various processes such as a process related to improvement of deterioration of the driver from the mental state (hereinafter referred to as a state improvement related process) by executing a control program stored in the memory. The HCU20 corresponds to an auxiliary device for a vehicle. The memory referred to herein is a non-transitory physical storage medium (non-transitory tangible storage medium) capable of storing programs and data non-transitory for reading by a computer. In addition, the non-migration entity storage medium can be realized by a semiconductor memory, a magnetic disk, or the like.

< schematic Structure of HCU20 >

Next, a schematic structure of the HCU20 will be described with reference to fig. 2. The HCU20 includes, as functional blocks, an estimating unit 201, a search instructing unit 202, a recommended route determining unit 203, a presentation control unit 204, a voice recognition unit 205, a selecting unit 206, and a stimulation control unit 207 as shown in fig. 2 for the state improvement related process. Further, part or all of the functions performed by the HCU20 may be configured in hardware by one or more ICs or the like. In addition, some or all of the functional modules included in the HCU20 may be realized by a combination of hardware components and execution of software by a processor. Executing the processing of each functional module of the HCU20 by a computer corresponds to executing the vehicle assistance method.

The estimating unit 201 performs estimation associated with the state of the passenger of the vehicle. The estimating unit 201 includes a driver state estimating unit 211, an importance factor estimating unit 212, and a co-passenger state estimating unit 213 as sub-functional blocks.

The driver state estimating unit 211 estimates the degree of deterioration of the mental state of the driver of the host vehicle. In the example of the present embodiment, the driver state estimating unit 211 estimates the degree of pressure of the driver. The driver state estimation unit 211 corresponds to a mental association state estimation unit. The process in the driver state estimation unit 211 corresponds to a mental association state estimation process. The driver state estimating unit 211 may estimate the degree of the pressure in two stages, i.e., the "presence" and "absence", or may estimate the degree of the pressure in three or more stages.

The driver state estimating unit 211 may estimate the degree of pressure of the driver from the image of the driver before the passenger captured by the periphery monitoring camera in the periphery monitoring sensor 7. For example, the degree of pressure of the driver may be estimated from the posture and gait of the driver in the image.

The driver state estimating unit 211 may estimate the degree of the pressure of the driver from the image of the driver captured by the indoor camera 21. For example, the degree of pressure of the driver may be estimated from the expression of the driver in the image.

The driver state estimating unit 211 may estimate the degree of the pressure of the driver based on the pulse wave of the driver measured by the pulse wave sensor in the biosensor 22. In this case, the driver state estimating unit 211 may estimate the degree of sympathetic dominance in the autonomic nerve by frequency analysis of the pulse wave, and may estimate the degree of sympathetic dominance as the degree of stress.

The driver state estimating unit 211 may estimate the degree of the pressure of the driver based on the exhalation of the driver measured by the exhalation sensor in the biometric sensor 22. In this case, the amount of cortisol secretion as the stress judgment substance may be estimated from the exhalation, and the degree of the stress may be estimated from the amount of cortisol secretion.

The driver state estimating unit 211 may estimate the degree of pressure of the driver based on the operation state detected by the vehicle state sensor 9. In this case, the degree of pressure may be estimated to be higher as the frequency of sudden acceleration, sudden braking, sudden steering, etc. is higher. The term "sudden" as used herein may mean that the amount of change per unit time is above a threshold.

The driver state estimating unit 211 may estimate the degree of the driver's pressure from the sound of the driver collected by the microphone 25. In this case, the degree of stress may be estimated from the sound of the driver by using the frequency of the sound of the person at the time of stress to be high. In addition, the degree of pressure may also be estimated from the speed of sound production.

The important factor estimating unit 212 estimates an important factor of deterioration of the mental state of the driver of the host vehicle. In the example of the present embodiment, the important factor estimating unit 212 estimates an important factor of the pressure of the driver. The importance factor estimating unit 212 also corresponds to a mental association state estimating unit. The processing in the importance factor estimating section 212 corresponds to the mental association state estimating step. In order to suppress unnecessary processing, the important factor estimating unit 212 is preferably configured to estimate the important factor of the pressure only when the degree of the pressure estimated by the driver state estimating unit 211 is equal to or greater than a threshold value. The threshold value referred to herein may be a value that distinguishes the presence or absence of pressure.

The important factor estimating unit 212 may estimate the important factor of the pressure based on input information such as the surrounding environment of the vehicle, the front-rear schedule of the driver, the physical state of the driver, and the operation state of the driver. The important factor of the pressure may be estimated based on only a part of the input information, or may be estimated based on a combination of a plurality of kinds of input information. As an example, the important factor of the pressure may be estimated with reference to a correspondence relation in which the input information and the important factor of the pressure are previously associated. As an example of the correspondence relationship, a lookup table can be cited. Further, a learner performing machine learning, which takes the input information as an input and outputs an important factor of the pressure, may be used to estimate the important factor of the pressure from the input information.

The surrounding environment of the host vehicle may be detected by the surrounding monitoring sensor 7. As an example of the surrounding environment, the number of surrounding pedestrians, the road type, the road width, the degree of traffic congestion, and the like can be cited. Since a large number of pedestrians around the vehicle may cause a high load of driving operation, the number of pedestrians may become an important factor of the pressure. The case where the road type is an expressway may cause a load of a driving operation to be high during manual driving, and thus may become an important factor of the pressure. The narrower road width may cause the load of the driving operation to become high, and thus may become an important factor of the pressure. For backlighting, glare may be an important factor in pressure. The greater degree of traffic congestion may also be an important factor in stress.

The schedule of the driver may be acquired from the schedule application of the mobile terminal carried by a part of the driver via the short-range communication section 31. As an example of the front and rear schedules, the job contents before and after can be cited. When the work content of the previous work is a case work, the fatigue of eyes may be an important factor of stress. In the case of returning home after the previous work, the manifestation of mental fatigue due to the end of the work may become an important factor of stress.

The physical state of the driver may be detected by the surrounding monitoring sensor 7, the indoor camera 21, the biosensor 22, or the like. Examples of the physical state include physical fatigue and physical stiffness. In the case where the physical state is physical fatigue, it may become an important factor of stress. The physical fatigue may be estimated from the posture and gait of the driver before the passenger captured by the surrounding monitoring camera in the surrounding monitoring sensor 7. In addition, physical fatigue may be estimated from the expression of the driver photographed by the indoor camera 21. In the case where the physical state is stiffness of the body, it may also become an important factor in stress. For example, the stiffness of the body may be estimated from a decrease in the temperature of the shoulder, neck, or the like measured by the body temperature sensor.

The operation state of the driver may be detected by the vehicle state sensor 9. As an example of the operation state, frequent acceleration, frequent braking, frequent steering, and the like can be cited. As used herein, "frequent" may mean that the number of times of generation per unit time is above a threshold. In the case where the operation state is frequent acceleration, frequent braking, frequent steering, or the like, the driving load caused by these frequent driving operations may become an important factor of the pressure.

The passenger state estimating unit 213 estimates the state of a passenger other than the driver of the host vehicle (hereinafter referred to as a passenger). The co-passenger state estimating unit 213 can estimate states such as fatigue, sleep, and poor physical condition of the co-passenger. The co-passenger state estimating unit 213 can estimate fatigue, sleep, and physical condition failure of the co-passenger from the image of the co-passenger captured by the indoor camera 21. For example, fatigue and poor physical condition of the co-passenger can be estimated from the expression of the co-passenger in the image. For example, the sleep of the co-occupant may be estimated from the eye opening degree of the co-occupant in the image. The co-passenger state estimating unit 213 may estimate the physical condition failure of the co-passenger based on the biological information measured by the biological sensor 22 provided in the seat. The passenger state estimating unit 213 may be configured to estimate states other than fatigue, sleep, and physical condition failure. The co-passenger state estimating unit 213 may also estimate the type of the co-passenger. The co-passenger state estimating unit 213 may estimate the type of the co-passenger from the image of the co-passenger captured by the indoor camera 21. As the type of the fellow passenger, old people, children, and the like can be estimated separately. The boarding status estimating unit 213 may estimate the boarding status by differentiating the sex.

The search instruction unit 202 sends an instruction to the automated driving ECU8 to search for a recommended route corresponding to the important factor that is estimated to be worsening of the mental state by the important factor estimation unit 212. In the example of the present embodiment, the search instruction unit 202 determines a search condition for preferentially searching for a route estimated to be effective for alleviating the pressure, based on an important factor of the estimated pressure. Then, the search instruction unit 202 sends an instruction to search for a recommended route under the search condition to the automated driving ECU8. The determination of the search condition may be performed by referring to a correspondence relationship that is previously associated with the search condition for preferentially searching the route estimated to be effective for alleviating the stress, for each of the important factors of the stress. As an example of the correspondence relationship, a lookup table can be cited. The search condition can also be referred to as a condition of a link that reduces the link cost.

When the number of pedestrians around the search instruction unit 202 is estimated to be an important factor of the pressure, the search instruction unit may determine a search condition for preferentially searching for a route having a smaller number of pedestrians. For example, the search condition of the link of the priority vehicle-specific road may be determined. In addition, the search condition of the road section which is preferentially far from the facility or house may be determined. The search instruction unit 202 may determine a search condition for preferentially searching for a general road when it is estimated that the travel of the expressway is an important factor of the pressure. For example, a search condition for prioritizing links of a general road may be determined. When it is estimated that the road width is a significant factor of the pressure, the search instruction unit 202 may determine a search condition for preferentially searching for a route having a wider road width. For example, a search condition may be determined that is prioritized for a link having a wider road width. When it is estimated that the traffic congestion is an important factor of the pressure, the search instruction unit 202 may determine a search condition for preferentially searching for a route with less traffic congestion. For example, a search condition may be determined that is prioritized for a road section with a lower degree of traffic congestion.

When it is estimated that fatigue of eyes due to previous work of the case is an important factor of stress, the search instruction unit 202 may determine a search condition for preferentially searching a route estimated to be easy to rest eyes. For example, the search condition may be determined such that the more preferable the link is to be a place estimated to be a place where eye fatigue is likely to be cured via a forest, coast, or the like. In addition, the search condition may be determined to be more preferable as the road section estimated not to excessively use the eyes. As a link estimated not to excessively use eyes, a link having a low speed limit value and the like can be cited. When the mental fatigue due to the end of the work is significant as a stress, the search instruction unit 202 may determine a search condition for preferentially searching for a route through a place or landscape estimated to be relaxed. For example, the search condition may be determined to be more prioritized as the road section is estimated to be a site that can be relaxed via a forest, coast, or the like. In addition, the search condition may be determined such that the more preferable the road section is to be through the shop favored by the driver. The store that the driver prefers may be estimated from the search history of the mobile terminal carried by the driver acquired via the short-range communication unit 31.

When it is estimated that physical fatigue is an important factor of stress, the search instruction unit 202 may determine a search condition for preferentially searching for a route estimated to be easily reduced in physical fatigue. For example, the search condition may be determined to be more preferable as the road section capable of continuing the automatic driving of the rank 3 or more. In addition, a search condition that is more prioritized than a link passing through the service area may be determined. The search instruction unit 202 may determine a search condition that is prioritized as the road section passes through the public land, when the stiffness of the body is estimated to be an important factor of the stress.

The search instruction unit 202 may use a learner that performs machine learning of inputting an important factor of the pressure and outputting a search condition for preferentially searching a route estimated to be effective in alleviating the pressure, and may determine the search condition based on the estimated important factor of the pressure. In the machine learning, it is preferable to learn the phenomenon when the degree of the driver's pressure estimated by the driver state estimating unit 211 is actually reduced in order. For example, when the pressure is relaxed in the case of approaching a specific store, the learning may be performed so as to output search conditions for preferentially searching paths through the store and stores of a similar type to the store. In addition, if the pressure is relaxed when the specific scenery-visible location is passed, the learning may be performed so as to output search conditions for preferentially searching for a route passing through the location and the location where the same scenery as the location is visible. The machine learning may be further configured to accurately determine a search condition for preferentially searching a route estimated to be effective for pressure alleviation by adding another element such as a time period and an environment such as a traffic congestion level to the input. In this case, the search instruction unit 202 receives as input, in addition to the important factors of the estimated pressure, factors such as the time period and the environment, and determines the search condition by the learner.

The preferable search instruction unit 202 transmits an instruction to search for a recommended route corresponding to the state of the co-passenger estimated by the co-passenger state estimation unit 213 to the automated driving ECU8. As an example, the search instruction unit 202 determines the search condition based on the estimated state of the co-located person. Then, the search instruction unit 202 sends an instruction to search for a recommended route under the search condition to the automated driving ECU8. The search condition may be determined by referring to a correspondence relation in which a correspondence relation is established in advance with the search condition for each state of the co-located person. As an example of the correspondence relationship, a lookup table can be cited. For example, when the state of the fellow passenger is estimated to be poor, the search instruction unit 202 may determine a search condition for preferentially searching for a route having less curves. For example, a search condition of a road section of a priority straight line may be decided. Accordingly, a recommended route that prevents deterioration of the state of the fellow passenger can be proposed.

More preferably, the search instruction unit 202 transmits an instruction to search for a recommended route corresponding to the type and state of the co-passenger estimated by the co-passenger state estimation unit 213 to the automated driving ECU8. In this case, the search condition may be determined with reference to a correspondence relationship in which a correspondence relationship is established in advance with the search condition for each combination of the type and the state of the co-occupant. As an example of the correspondence relationship, a lookup table can be cited. For example, when it is estimated that the type of the fellow passenger is the old person and the state of the fellow passenger is tired, the search instruction unit 202 may determine a search condition for preferentially searching for an air-freshening path that can open the window of the host vehicle. For example, search conditions for road segments that preferentially pass through forests, parks, coasts, and the like may be determined. In the case of giving information of an air-freshening region to map data, a search condition of a link of the air-freshening region may be determined by using the information. The search instruction unit 202 may determine a search condition for preferentially searching for a route far from the child if the type of the co-passenger is estimated to be child and the state of the co-passenger is sleep. For example, a search condition for a road segment having a longer priority road segment may be determined. Accordingly, a desired route corresponding to the combination of the types and states of the riders can be proposed.

If the host vehicle does not have a co-passenger, but the co-passenger state estimating unit 213 cannot estimate the type and state of the co-passenger, the process of searching for the recommended route corresponding to the state of the co-passenger is not performed. The search instruction unit 202 is preferably configured to, when the degree of the driver's pressure estimated by the driver state estimation unit 211 is equal to or greater than a predetermined threshold value, not to instruct the driver to search for a recommended route corresponding to the state of the co-passenger, but to instruct the driver to search for a recommended route corresponding to an important factor of the driver's pressure. On the other hand, when the degree of the driver's pressure estimated by the driver state estimating unit 211 is smaller than the predetermined threshold, the search instructing unit 202 is preferably configured to instruct to search for a recommended route corresponding to the state of the co-passenger without instructing to search for a recommended route corresponding to an important factor of the driver's pressure. The predetermined threshold may be a value that distinguishes between the presence and absence of pressure. Accordingly, when the driver is under pressure, a route estimated to alleviate the pressure is proposed to suppress the influence on the driving, and when the driver is not under pressure and the influence on the driving is small, a desired route corresponding to the state of the fellow passenger is proposed to perform the traveling comfortable for the fellow passenger.

Further, the automatic driving ECU8 may be configured to search for a recommended route satisfying both of the recommended route corresponding to the important factor of the driver's pressure and the recommended route corresponding to the state of the occupant by performing both of the instruction to search for the recommended route. In this case, the search instruction unit 202 may instruct to make the priority of the search condition (hereinafter, referred to as the driver consideration search condition) for preferentially searching for the recommended route corresponding to the important factor of the driver's stress higher than the priority of the search condition (hereinafter, referred to as the co-passenger consideration search condition) for preferentially searching for the recommended route corresponding to the state of the co-passenger when the degree of the driver's stress estimated by the driver state estimating unit 211 is equal to or higher than the predetermined threshold. Specifically, an instruction to increase the magnitude of the decrease in the link cost of the driver considering the search condition as compared with the case where the same occupant considers the search condition may be made. The search instruction unit 202 may instruct the co-rider to consider the search condition higher in priority than the driver to consider the search condition when the degree of the driver's pressure estimated by the driver state estimation unit 211 is smaller than a predetermined threshold. Specifically, an instruction to increase the magnitude of the decrease in the road section cost of the passenger considering the search condition as compared with the driver considering the search condition may be made.

In the automated driving ECU8, a recommended route satisfying the search condition sent from the search instruction unit 202 is searched for and returned to the HCU20. In other words, the automated driving ECU8 searches for a recommended route corresponding to the important factor of the deterioration of the mental state estimated by the important factor estimating portion 212. The automated driving ECU8 also searches for a recommended route corresponding to at least one of the type and the state of the co-passenger estimated by the co-passenger state estimating unit 213. In this case, the automated driving ECU8 may search for a recommended route satisfying the search condition sent from the search instruction unit 202 in preference to time and distance. The automated driving ECU8 may search for a recommended route using the current vehicle position measured by the locator 4 as the departure point. The automated driving ECU8 may search for a recommended route with a place set by an input to the operation device 26 or the like as a destination. In the automatic driving ECU8, when an instruction to increase the magnitude of decrease in the link cost of one of the search conditions considered by the driver and the search conditions considered by the co-located person is received, the link cost of the one search condition may be set smaller. The automated driving ECU8 may search for a plurality of paths whose values of the link costs become smaller as recommended paths. The searched recommended path is then returned to the HCU20. The recommended route searched for by the automated driving ECU8 is a recommended route including a plurality of road segments.

The recommended route determination unit 203 determines the recommended route searched for by the automated driving ECU8 according to the instruction from the search instruction unit 202 as the recommended route. In other words, the recommended route determination unit 203 determines a recommended route corresponding to the important factor of the deterioration of the mental state estimated by the important factor estimation unit 212. The recommended route determination unit 203 also determines the recommended route based on at least one of the type and the state of the co-passenger estimated by the co-passenger state estimation unit 213. The process in the recommended route determination unit 203 corresponds to a recommended route determination step.

The search instruction unit 202 is configured to, when the degree of the driver's pressure estimated by the driver state estimation unit 211 is equal to or greater than a predetermined threshold value, not to instruct the driver to search for a recommended route corresponding to the state of the occupant, but to instruct the driver to search for a recommended route corresponding to an important factor of the driver's pressure, as follows. When the degree of the pressure of the driver estimated by the driver state estimating unit 211 is equal to or greater than a predetermined threshold, the recommended route determining unit 203 determines a recommended route corresponding to the importance factor of the pressure estimated by the importance factor estimating unit 212 in preference to the state of the co-passenger estimated by the co-passenger state estimating unit 213. On the other hand, when the degree of the pressure of the driver estimated by the driver state estimating unit 211 is smaller than the predetermined threshold, the recommended route determining unit 203 determines a recommended route corresponding to the state of the fellow passenger estimated by the fellow passenger state estimating unit 213, in preference to the important factor of the pressure estimated by the important factor estimating unit 212.

The presentation control unit 204 presents the recommended route determined by the recommended route determination unit 203 to the driver. The process in the presentation control unit 204 corresponds to a presentation control step. The presentation control unit 204 may present the recommended route to the driver by causing the display device 24 to display the recommended route. As one example, the recommended route may be displayed in a distinguishable manner on the map. When a plurality of recommended routes are determined, the plurality of recommended routes may be displayed. In addition, the presentation control unit 204 may present the recommended route by displaying text on the display device 24 via the destination of the recommended route for each recommended route.

When route guidance is being performed, the presentation control unit 204 may present a route in route guidance in addition to the recommended route. In this case, the presentation is preferably made so that the guidance can be distinguished into paths in the path guidance. The route in the route guidance referred to herein refers to a recommended route searched without taking into consideration neither the degree of deterioration of the mental state of the driver nor the important factors thereof. This path will be referred to hereinafter as a state-non-considered path. The presentation control unit 204 preferably presents whether or not guidance of the recommended route is required even when presenting the recommended route.

The presentation control unit 204 can also perform route guidance of the recommended route determined by the recommended route determination unit 203. Route guidance may be performed by causing the display device 24 to display guidance for traveling along the recommended route or by performing sound output from the sound output device 23.

The voice recognition unit 205 performs voice recognition on the voice collected by the microphone 25, and recognizes the content of the sound produced by the passenger. The selection unit 206 selects whether or not guidance of the recommended route determined by the recommended route determination unit 203 is required, based on an input received from the driver via the microphone 25 or the operation device 26. When receiving an input of the instruction to guide the recommended route through the microphone 25 or the operation device 26, the selection unit 206 selects the guidance to guide the recommended route. When a configuration of a plurality of recommended routes is presented, an input of the subject matter that guidance of one of the recommended routes is required is accepted, and guidance that requires the recommended route is selected. On the other hand, when the input of the instruction to avoid the need for guidance of the recommended route is received through the microphone 25 or the operation device 26, the selection unit 206 selects the guidance to avoid the need for guidance of the recommended route. The selection unit 206 may select guidance that does not require the recommended route if the input of the subject matter that guidance that requires the recommended route is not accepted within a constant time from the presentation of the recommended route or the presentation of the need or not for guidance that requires the recommended route. Whether or not an input relating to the necessity of guidance of the recommended route is accepted through the microphone 25 may be determined by the selection section 206 based on the result of voice recognition in the voice recognition section 205.

The stimulus control unit 207 controls the operation of the state improvement device 27. Preferably, the stimulus control unit 207 automatically starts the operation of the state improvement device 27 when the selection unit 206 selects guidance that does not require a recommended route. On the other hand, the stimulus control unit 207 may be configured not to automatically start the operation of the state improvement device 27 when the selection unit 206 selects the guidance requiring the recommended route. The stimulus control unit 207 may start the operation of the state improvement device 27 even when the selection unit 206 selects the guidance requiring the recommended route and the microphone 25 or the operation device 26 receives an input indicating the start of the operation of the state improvement device 27.

When the selection unit 206 selects guidance that does not require a recommended route, the presentation control unit 204 does not perform guidance of the recommended route. When the path guidance of the state non-considered path is being executed, the path guidance of the state non-considered path is continued. If the route guidance is not being performed, the state in which the route guidance is not being performed is continued. On the other hand, when the guidance requiring the recommended route is selected by the selection unit 206, the presentation control unit 204 performs guidance of the recommended route.

< State improvement association Process in HCU20 >)

Here, an example of the flow of the state improvement association processing in the HCU20 will be described with reference to the flowchart of fig. 3. For example, the flowchart of fig. 3 may be started when a switch for starting the internal combustion engine or the motor generator of the host vehicle (hereinafter referred to as a power switch) is turned on. In addition, in the case where a configuration is adopted in which the state of the driver is estimated from the image captured by the surrounding area monitoring camera, the flow chart of fig. 3 may be started when the driver approaches the vehicle in the parking state. The HCU20 can estimate the approach of the driver to the host vehicle based on the intensity of the electric wave exchanged between the electronic key carried by the driver and the host vehicle side, or the like.

First, in step S1, the estimating unit 201 performs estimation associated with the state of the passenger of the host vehicle. In S1, the driver state estimating unit 211 estimates the degree of pressure of the driver. In S1, the co-occupant state estimation unit 213 estimates the state of the co-occupant.

In step S2, when the degree of the driver' S pressure estimated in S1 is equal to or greater than a predetermined threshold (S2: yes), the routine proceeds to step S3. On the other hand, when the degree of the driver' S pressure estimated in S1 is smaller than the predetermined threshold (S2: no), the routine proceeds to step S5.

In step S3, the important factor estimating section 212 estimates an important factor of the pressure of the driver. In step S4, the search instruction unit 202 determines a search condition for preferentially searching for a route estimated to be effective for alleviating the pressure corresponding to the important factor of the pressure estimated in step S3, and moves to step S7.

In step S5, if the host vehicle has a fellow passenger (S5: yes), the process proceeds to step S6. On the other hand, if the host vehicle does not have a fellow passenger (S5: NO), the routine proceeds to step S14. The co-passenger state estimating unit 213 may determine whether or not there is a co-passenger of the host vehicle based on whether or not the type and state of the co-passenger can be estimated. In step S6, the search instruction unit 202 determines a search condition corresponding to at least one of the type and the state of the co-occupant estimated in step S1, and proceeds to step S7.

In step S7, the search instruction unit 202 sends an instruction to search for the recommended route under the search condition determined in S4 or S6 to the automated driving ECU8. In step S8, the recommended route determination unit 203 acquires the recommended route searched for by the automated driving ECU8 according to the search condition transmitted in S7. Then, the recommended route determination unit 203 determines the acquired recommended route as a recommended route. In step S9, the presentation control unit 204 presents the recommended route determined in S8 to the driver.

In step S10, when the selection unit 206 selects guidance requiring a recommended route (yes in S10), the process proceeds to step S11. On the other hand, when the selection unit 206 selects guidance that does not require the recommended route (S10: no), the process proceeds to step S12. In step S11, the presentation control unit 204 performs guidance in which the selection unit 206 selects the recommended route to be guided, and the process proceeds to step S13. In S11, the stimulus control unit 207 does not automatically start the operation of the state improvement device 27. On the other hand, in step S12, the stimulus control section 207 automatically starts the operation of the state improvement device 27, and the flow proceeds to step S13. In S12, the presentation control unit 204 does not perform guidance of the recommended route determined in S8.

In step S13, when a constant time has elapsed since the presentation of the recommended route in S9 (S13: yes), the process proceeds to step S14. On the other hand, if a constant time has not elapsed since the presentation of the recommended route was performed in S9 (S13: no), the process of S13 is repeated. The constant time can be arbitrarily set. As an example, the frequency of the presentation of the recommended route by the repetition of the flow may be set to a time estimated to be a frequency that is not annoying to the driver. In addition, the travel distance may be used instead of the time.

In step S14, when the end timing of the state improvement relating process is set (S14: yes), the state improvement relating process is ended. On the other hand, when the end timing of the state improvement relating process is not set (S14: NO), the process returns to S1 to repeat the process. As an example of the end timing of the state improvement relating process, a power switch off and the like can be cited.

Summary of embodiment 1

According to the configuration of embodiment 1, it is possible to present the driver with a route for improving the pressure corresponding to an important factor of the pressure more detailed than the presence or absence of the simple pressure. In detail, the following is described. Even in a state where the driver feels the pressure, there are cases where paths effective for relieving the pressure are different depending on important factors of the pressure. For example, in the case of pressure in which fatigue of eyes is an important factor in the work of a desk, although the pressure can be alleviated by traveling on a recommended route with less traffic congestion, traveling on a recommended route estimated to be easy to rest eyes is considered to be easier to alleviate. In this way, by providing a recommended route estimated to alleviate the pressure according to an important factor of the pressure and enabling guidance of the recommended route, the pressure can be eased with higher accuracy. As a result, when the mental state of the driver of the vehicle is deteriorated, the mental state can be improved with higher accuracy.

In addition, according to the configuration of embodiment 1, even when the driver does not want the guidance of the recommended route corresponding to the important factor of the pressure, the pressure can be relaxed by automatically starting the operation of the state improvement device 27.

In embodiment 1, the case where the deterioration of the mental state is stress has been described as an example, but the deterioration of the mental state other than stress may be improved from the deterioration of the mental state with higher accuracy by presenting a recommended route corresponding to an important factor of the deterioration of the mental state.

(embodiment 2)

In embodiment 1, the recommended route configuration corresponding to the content of the worsening of the mental state is shown, but the recommended route configuration is not necessarily limited to this. For example, a recommended route corresponding to three or more stages of deterioration of mental state may be proposed (embodiment 2 below). An example of embodiment 2 will be described below with reference to the drawings. The driving support system 1 of embodiment 2 is the same as the driving support system 1 of embodiment 1 except that the HCU20a is included instead of the HCU 20.

< schematic structure of HCU20a >

Here, a schematic structure of the HCU20a will be described with reference to fig. 4. The HCU20a includes, as functional blocks, an estimating unit 201a, a search instructing unit 202a, a recommended route determining unit 203, a presentation control unit 204, a voice recognition unit 205, a selecting unit 206, and a stimulation control unit 207 as shown in fig. 4 for the state improvement related process. The HCU20a is similar to the HCU20 of embodiment 1, except that the HCU includes an estimating unit 201a and a search instructing unit 202a instead of the estimating unit 201 and the search instructing unit 202. The HCU20a also corresponds to an auxiliary device for a vehicle. Further, executing the processing of each functional module of the HCU20a by a computer corresponds to executing the vehicle assistance method.

The estimating unit 201a includes a driver state estimating unit 211a and a passenger state estimating unit 213 as sub-functional blocks. The estimating unit 201a is similar to the estimating unit 201 of embodiment 1, except that the estimating unit 211a is provided instead of the driver state estimating unit 211, and the estimating unit 212 is not provided with an important factor estimating unit.

The driver state estimating unit 211a estimates the degree of deterioration of the mental state of the driver of the host vehicle in three or more stages. The driver state estimating unit 211a is the same as the driver state estimating unit 211 of embodiment 1, except that it is limited to a configuration in which the degree of deterioration of the mental state is three or more stages. In the example of the present embodiment, the degree to which the driver state estimating unit 211a estimates the pressure of the driver will be described as an example. The driver state estimation unit 211a also corresponds to a mental association state estimation unit. The process in the driver state estimation unit 211a corresponds to a mental association state estimation step.

The search instruction unit 202a transmits an instruction to the automated driving ECU8 to search for a recommended route corresponding to three or more stages of deterioration of the mental state estimated by the driver state estimation unit 211 a. In the example of the present embodiment, the search instruction unit 202a determines a search condition (hereinafter referred to as a "relaxation search condition") for preferentially searching for a route estimated to be effective for the relaxation of the pressure, based on the three or more stages of the estimated pressure. Then, the search instruction unit 202a sends an instruction to search for a recommended route under the search condition to the automated driving ECU8.

The search instruction unit 202a may determine a greater variety of mild search conditions based on the degree of deterioration of the mental state of the driver estimated by the driver state estimation unit 211 a. The following conditions can be cited as the conditions for alleviating countermeasures.

As one of the categories of the relaxed search condition, a search condition for preferentially searching for a route with less traffic congestion can be cited. The search condition may be, for example, a search condition that is more prioritized for a road section where the degree of traffic congestion is lower. This is because traffic congestion is less estimated to be more able to alleviate stress. As one of the types of the mild search conditions, a search condition for preferentially searching a route having a small number of lines can be cited. The search condition may be, for example, a search condition of a road section of a priority vehicle-specific road. This is because surrounding pedestrians are less likely to pay attention to the flying-out of pedestrians, and are estimated to be able to alleviate the pressure. One of the types of the mild search conditions is a search condition for a route having a low priority search speed limit value. The search condition may be, for example, a search condition of a road section having a lower priority speed limit value. This is because the travel can be performed at a low speed, and the pressure is estimated to be relaxed.

As one of the types of the mild search conditions, a search condition for preferentially searching a path through the service area can be cited. The search condition may be, for example, a search condition of a road section of the priority service area. This is because it is estimated that the stress can be relaxed by taking a rest in the service area. As one of the types of the mild search conditions, a search condition for preferentially searching for a route that can continue the automatic driving of level 3 or more can be cited. The search condition may be a search condition of a road section that can preferentially continue the automatic driving of level 3 or more. This is because it is estimated that the pressure can be relaxed by releasing from driving. As one of the types of the mild search conditions, a search condition for preferentially searching a route through a place or landscape estimated to be relaxed can be cited. The search condition may be, for example, a search condition that is prioritized as a road section of a place estimated to be able to relax via a forest, coast, or the like. As one of the types of the mild search conditions, a search condition for preferentially searching for a route through a store preferred by the driver can be cited. The search condition may be, for example, a search condition that is more prioritized as to the road section via the shop favored by the driver. The store that the driver prefers can be estimated from the search history of the mobile terminal carried by the driver acquired via the short-range communication unit 31, and the like.

The search instruction unit 202a may determine a greater variety of mild search conditions according to the estimated increase in the degree of pressure of the driver, and may send an instruction to search for a recommended route using the determined mild search conditions to the automated driving ECU 8. For example, the search conditions as a combination of the relaxed search conditions may be increased by one category or the like according to the degree of stress becoming larger by one stage. The combination of the mild search conditions according to the degree of stress may be a fixed combination set in advance or a combination learned by machine learning. For learning the combination of the mild search conditions, for example, the combination of the mild search conditions may be changed at random in accordance with the degree of stress, and at the same time, a combination having a high effect of relieving the degree of stress estimated by the driver state estimating unit 211a may be learned.

The search instruction unit 202a may determine a combination of search conditions based on the estimated degree of stress by using a learner that performs machine learning that takes the degree of stress as an input and takes as an output a search condition that preferentially searches for a route estimated to be effective in alleviating stress. In the machine learning, as in embodiment 1, it is preferable to sequentially learn the phenomenon when the degree of the driver's pressure estimated by the driver state estimating unit 211a is actually reduced.

The search instruction unit 202a preferably transmits an instruction to search for a recommended route corresponding to the state of the co-passenger estimated by the co-passenger state estimation unit 213 to the automated driving ECU8, as described in embodiment 1. In other words, the search instruction unit 202a is preferably configured to, as in embodiment 1, not to instruct the driver to search for a recommended route corresponding to the state of the co-passenger, but to instruct the driver to search for a recommended route corresponding to an important factor of the driver's pressure, when the degree of the driver's pressure estimated by the driver state estimating unit 211a is equal to or greater than a predetermined threshold. On the other hand, when the degree of the driver's pressure estimated by the driver state estimating unit 211a is smaller than the predetermined threshold, the search instructing unit 202a is preferably configured to instruct to search for a recommended route corresponding to the state of the co-passenger without instructing to search for a recommended route corresponding to an important factor of the driver's pressure. In addition, as in embodiment 1, the automatic driving ECU8 may be configured to search for a recommended route satisfying both of the recommended route corresponding to the important factor of the driver's pressure and the recommended route corresponding to the state of the co-passenger by performing both of the instruction to search for the recommended route and the instruction to search for the recommended route corresponding to the state of the co-passenger.

In the automated driving ECU8, the recommended route satisfying the search condition sent from the search instruction unit 202a is searched for and returned to the HCU20 as described in embodiment 1. In other words, the automated driving ECU8 searches for a recommended route corresponding to the degree of deterioration of the mental state estimated by the driver state estimating portion 211 a. The automated driving ECU8 also searches for a recommended route corresponding to at least one of the type and the state of the co-passenger estimated by the co-passenger state estimating unit 213. In this case, the automated driving ECU8 may search for a recommended route satisfying the search condition sent from the search instruction unit 202 in preference to time and distance.

The recommended route determination unit 203 determines the recommended route searched for by the automated driving ECU8 according to the instruction of the search instruction unit 202a as the recommended route. In other words, the recommended route determination unit 203 determines a recommended route corresponding to three or more stages of deterioration of the mental state estimated by the driver state estimation unit 211 a. The recommended route determination unit 203 determines, as the recommended route, a route satisfying more types of mild search conditions, based on the degree of deterioration of the mental state of the driver estimated by the driver state estimation unit 211a becoming greater.

< State improvement association Process in HCU20a >)

Here, an example of the flow of the state improvement relating process in the HCU20a will be described with reference to the flowchart of fig. 5. The flowchart of fig. 5 may be configured to start in the same manner as the flowchart of fig. 3.

First, in step S21, the estimating unit 201a performs estimation associated with the state of the passenger of the host vehicle. In S21, the driver state estimating unit 211a estimates the degree of pressure of the driver in three or more stages. In S21, the co-occupant state estimation unit 213 estimates the state of the co-occupant.

In step S22, when the degree of the driver' S pressure estimated in S21 is equal to or greater than a predetermined threshold (S22: yes), the routine proceeds to step S23, as in S2. On the other hand, if the degree of the driver' S pressure estimated in S21 is smaller than the predetermined threshold (S22: no), the routine proceeds to step S24.

In step S23, the search instruction unit 202a determines a search condition for preferentially searching for a route estimated to be effective for alleviating the stress corresponding to the degree of stress estimated in step S21, and proceeds to step S26. In S23, the type of the determined search condition may be increased according to the increase in the degree of the pressure.

In step S24, if the host vehicle has a fellow passenger (S24: yes), the process proceeds to step S25, similarly to step S5. On the other hand, if the host vehicle does not have a fellow passenger (S24: NO), the routine proceeds to step S33. In step S25, the search instruction unit 202a determines a search condition corresponding to at least one of the type and the state of the co-occupant estimated in step S21, and proceeds to step S26.

In step S26, the search instruction unit 202a sends an instruction to search for the recommended route under the search condition determined in S23 or S25 to the automated driving ECU8. The processing of step S27 to step S32 is performed in the same manner as the processing of S8 to S13. In step S33, when the end timing of the state improvement relating process is set (S33: yes), the state improvement relating process is ended. On the other hand, if the end timing of the state improvement relating process is not set (S33: NO), the process returns to S21 to repeat the process.

Summary of embodiment 2

According to the configuration of embodiment 2, it is possible to present the driver with a route for improving the pressure corresponding to three or more stages of pressure more detailed than the presence or absence of the simple pressure. In detail, the following is described. Even when the driver feels the pressure, there are cases where the route for effective relief of the pressure is different depending on the height of the pressure. In contrast, according to the configuration of embodiment 2, a recommended route estimated to alleviate the pressure is proposed based on the heights of three or more stages of the pressure. This can alleviate the pressure with higher accuracy. As a result, when the mental state of the driver of the vehicle is deteriorated, the mental state can be improved with higher accuracy.

In addition, according to the configuration of embodiment 2, as the degree of the driver's pressure increases, a route satisfying a wider variety of mild search conditions is determined as a recommended route and proposed. Accordingly, if the driver's pressure is higher and a plurality of important factors become the cause of the pressure, the possibility of the pressure being relaxed with higher accuracy increases.

In the configuration of embodiment 2, even when the driver does not want the guidance of the recommended route corresponding to the important factor of the pressure, the pressure can be relaxed by automatically starting the operation of the state improvement device 27.

In embodiment 2, the case where the deterioration of the mental state is stress has been described as an example, but the deterioration of the mental state other than stress may be improved from the deterioration of the mental state with a better accuracy by presenting a recommended route corresponding to the degree of three or more stages of the deterioration of the mental state.

Embodiment 3

The following embodiment 3 may be adopted. An example of embodiment 3 will be described below with reference to the drawings. The driving support system 1 of embodiment 3 is the same as the driving support system 1 of embodiment 1 except that the HCU20b is included instead of the HCU 20.

< schematic structure of HCU20b >

Here, a schematic structure of the HCU20b will be described with reference to fig. 6. The HCU20b includes, as functional blocks, an estimating unit 201b, a search instructing unit 202b, a recommended route determining unit 203, a presentation control unit 204, a voice recognition unit 205, a selecting unit 206, and a stimulus control unit 207 as shown in fig. 6 for the state improvement related process. The HCU20b is the same as the HCU20 of embodiment 1 except that an estimating unit 201b and a search instructing unit 202b are provided instead of the estimating unit 201 and the search instructing unit 202. The HCU20b also corresponds to an auxiliary device for a vehicle. Further, executing the processing of each functional module of the HCU20b by a computer corresponds to executing the vehicle assistance method.

The estimating unit 201b includes a driver state estimating unit 211, an importance factor estimating unit 212, and a co-passenger state estimating unit 213b as sub-functional blocks. The estimation unit 201b is the same as the estimation unit 201 of embodiment 1, except that a co-passenger state estimation unit 213b is provided instead of the co-passenger state estimation unit 213. In the example of the present embodiment, the degree to which the driver state estimating unit 211 estimates the pressure of the driver will be described as an example.

The co-rider state estimation unit 213b estimates the type and state of the co-rider. The co-passenger state estimating unit 213b is similar to the co-passenger state estimating unit 213 of embodiment 1, except that it is limited to the configuration for estimating the type and state of the co-passenger.

The search instruction unit 202b may also send an instruction to the automated driving ECU8 to search for a recommended route corresponding to the state of the co-passenger estimated by the co-passenger state estimation unit 213. The search instruction unit 202b is the same as the search instruction unit 202 of embodiment 1, except that the search condition is determined by which passenger is prioritized.

The search instruction unit 202b switches the search conditions for determining the recommended route corresponding to the priority of the importance factor of the driver's pressure estimated by the importance factor estimating unit 212 and the state of the co-passenger estimated by the co-passenger state estimating unit 213b, based on the type of co-passenger estimated by the co-passenger state estimating unit 213 b. The state of the co-passenger to be the priority target may be set in advance for each type of co-passenger. For example, in the case of the elderly, fatigue may be a priority object. If the child is a child, sleep may be given priority.

The search instruction unit 202b may be configured to give priority to the order of child > elderly > driver, as an example. For example, when the driver state estimating unit 211 estimates the degree of pressure equal to or higher than the threshold value of the driver, the passenger state estimating unit 213b estimates fatigue of the aged, and the passenger state estimating unit 213b estimates sleep of the child, a search condition for preferentially searching for a recommended route corresponding to the sleep of the child may be determined. For example, a search condition for preferentially searching a route far around may be decided. In addition, when the driver state estimating unit 211 estimates the degree of the pressure equal to or higher than the threshold value of the driver, the passenger state estimating unit 213b estimates the fatigue of the old, and the passenger state estimating unit 213b does not estimate the sleep of the child, a search condition for preferentially searching the recommended route corresponding to the fatigue of the old may be determined. For example, a search condition may be decided to preferentially search for an air freshening path capable of opening a window of the host vehicle. When the driver state estimating unit 211 estimates the degree of pressure equal to or higher than the threshold value of the driver, the passenger state estimating unit 213b does not estimate the fatigue of the old, and the passenger state estimating unit 213b does not estimate the sleep of the child, it is sufficient to determine a search condition for preferentially searching a route estimated to be effective for alleviating the pressure of the driver.

The recommended route determination unit 203 determines the recommended route searched for by the automated driving ECU8 according to the instruction of the search instruction unit 202b as the recommended route. In other words, the recommended route corresponding to one of the importance factor of the pressure of the driver estimated by the importance factor estimating unit 212 and the state of the co-passenger estimated by the co-passenger state estimating unit 213b is switched and determined in priority according to the type of co-passenger estimated by the co-passenger state estimating unit 213 b.

With the above configuration, the recommended route corresponding to the state of the passenger to be prioritized can be preferentially presented. The configuration of embodiment 3 may be combined with the configuration of embodiment 2 instead of the configuration of embodiment 1.

Embodiment 4

In embodiment 3, the recommended route configuration is described in which the passenger is switched to be determined to which passenger is prioritized according to the type of the passenger, but the present invention is not limited to this. For example, the recommended route for deciding which passenger to take priority may be switched according to an input received from the driver via the microphone 25 or the operation device 26. Accordingly, the driver can select and determine the recommended route corresponding to which passenger is prioritized.

Embodiment 5

In the above-described embodiment, the configuration in which the recommended route corresponding to the state of the co-occupant is proposed is shown, but this is not necessarily the case. For example, the recommended route corresponding to the state of the co-occupant may not be set. In this case, the estimation units 201, 201a, and 201b may not include the co-occupant state estimation units 213 and 213 b.

Embodiment 6

In the above-described embodiment, the configuration in which the search instruction units 202, 202a, 202b instruct the automated driving ECU8 to search for the recommended route is shown, but the present invention is not limited to this. For example, the HCUs 20, 20a, and 20b may be provided with a function module for searching for a recommended route, and the HCUs 20, 20a, and 20b may search for a recommended route. In this case, the recommended route determination unit 203 may be configured to search for a recommended route to determine the recommended route. The search instruction units 202, 202a, 202b may send the search conditions to the recommended route determination unit 203 to search for the recommended route. In addition, the HCUs 20, 20a, 20b may not include the search instruction units 202, 202a, 202b, and the recommended route determination unit 203 may include the function of the search instruction units 202, 202a, 202b and the function of searching for a recommended route.

Embodiment 7

In the above-described embodiment, the configuration in which the operation of the state improvement device 27 is automatically started when the selection unit 206 selects the guidance for which the recommended route is not necessary has been described, but the present invention is not limited to this. For example, even when the selection unit 206 selects guidance that does not require a recommended route, the operation of the state improvement device 27 may not be automatically started. The state improvement device 27 may not be provided in the host vehicle, and the stimulation control unit 207 may not be provided in the HCUs 20, 20a, and 20 b.

The present disclosure is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims, and embodiments in which the disclosed technical means are appropriately combined with different embodiments are also included in the technical scope of the present disclosure. The control unit and the method thereof described in the present disclosure may be implemented by a special purpose computer constituting a processor programmed to execute one or more functions embodied by a computer program. Alternatively, the apparatus and method described in the present disclosure may be implemented by dedicated hardware logic circuits. Alternatively, the apparatus and method described in the present disclosure may be implemented by one or more special purpose computers comprising a combination of one or more hardware logic circuits and a processor executing a computer program. The computer program may be stored in a non-transitory tangible recording medium readable by a computer as instructions executable by the computer.

Claims (10)

1. An auxiliary device for a vehicle, comprising:

a mental state estimation unit (211, 211a, 212) that estimates a mental state, which is at least one of the degree of deterioration of the mental state of a driver of a vehicle and an important factor of the deterioration of the mental state of the driver;

a recommended route determination unit (203) that determines a recommended route including a plurality of links corresponding to the mental association state estimated by the mental association state estimation unit; and

and a presentation control unit (204) that presents the recommended route determined by the recommended route determination unit to the driver.

2. The auxiliary device for a vehicle according to claim 1, wherein,

the mental state estimation unit estimates at least an important factor of deterioration of the mental state of the driver as the mental state,

the recommended route determination unit determines the recommended route estimated to improve the mental state corresponding to an important factor of the deterioration of the mental state of the driver estimated by the mental state estimation unit.

3. The auxiliary device for a vehicle according to claim 1, wherein,

The mental state estimation unit estimates at least the degree of deterioration of the mental state of the driver as the mental state,

the recommended route determination unit determines the recommended route estimated to improve the mental state corresponding to three or more stages of deterioration of the mental state of the driver estimated by the mental state estimation unit.

4. The auxiliary device for a vehicle according to claim 3, wherein,

the search conditions of the paths estimated to improve the above-described mental state are of a plurality of kinds,

the recommended route determining unit determines, as the recommended route, a route satisfying the search condition of a greater variety based on the degree of deterioration of the mental state of the driver estimated by the mental state estimating unit.

5. The vehicle assist device according to any one of claims 1 to 4, comprising:

a selection unit (206) for selecting whether or not guidance of the recommended route determined by the recommended route determination unit is required, based on an input received from the driver via input devices (25, 26); and

a stimulus control unit (207) for controlling the operation of a state improvement device (27) provided in the vehicle and giving the driver a stimulus for improving the mental state from the deterioration of the mental state,

The presentation control unit may guide the recommended route,

the presentation control unit does not perform guidance of the recommended route when guidance that does not require the recommended route is selected by the selection unit, and the stimulation control unit automatically starts the operation of the state improvement device, whereas the stimulation control unit does not automatically start the operation of the state improvement device and performs guidance of the recommended route when guidance that does require the recommended route is selected by the selection unit.

6. The auxiliary device for a vehicle according to any one of claims 1 to 5, wherein,

the above-mentioned deterioration of mental state is stress.

7. The auxiliary device for a vehicle according to any one of claims 1 to 6, wherein,

comprises a passenger state estimating unit (213, 213 b) for estimating the state of a passenger other than the driver of the vehicle,

the recommended route determination unit also determines the recommended route based on the state of the co-passenger estimated by the co-passenger state estimation unit.

8. The auxiliary device for a vehicle according to claim 7, wherein,

The mental state estimation unit estimates at least the degree of deterioration of the mental state of the driver as the mental state,

the recommended route determining unit determines the recommended route corresponding to the state of the passenger estimated by the state of the passenger estimating unit, in preference to the state of the passenger estimated by the state of the passenger estimating unit, when the degree of deterioration of the state of the driver estimated by the state of the passenger estimating unit is equal to or greater than a predetermined threshold, and determines the recommended route corresponding to the state of the passenger estimated by the state of the passenger estimating unit, in preference to the state of the passenger estimated by the state of the passenger, when the degree of deterioration of the state of the driver estimated by the state of the passenger estimating unit is less than the predetermined threshold.

9. The auxiliary device for a vehicle according to claim 7, wherein,

the co-passenger state estimating unit (213 b) also estimates the type of the co-passenger,

the recommended route determination unit switches and determines the recommended route to which one of the mental association state of the driver estimated by the mental association state estimation unit and the state of the co-passenger estimated by the co-passenger state estimation unit corresponds preferentially according to the type of the co-passenger estimated by the co-passenger state estimation unit.

10. A vehicle assistance method, comprising the following steps performed by at least one processor:

a mental state estimation step of estimating a mental state, which is at least one of a degree of deterioration of a mental state of a driver of a vehicle and an important factor of deterioration of the mental state of the driver;

a recommended route determining step of determining a recommended route including a plurality of links corresponding to the mental association state estimated by the mental association state estimating step; and

and a presentation control step of presenting the recommended route determined in the recommended route determination step to the driver.