Patent Description:
A vehicle travel control device known to date controls a plurality of on-vehicle device for travel mounted on a vehicle.

As such a vehicle travel control device, <CIT>, for example, describes a control system that is hierarchized into device control parts for controlling a plurality of on-vehicle devices and domain control parts for managing the device control parts in a plurality of domains previously defined in accordance with functions of the on-vehicle devices. The control system also includes an integrated control part located at a higher rank than the domain control parts and manages the domain control parts.

In <CIT>, the device control parts calculate controlled variables of the corresponding on-vehicle devices, and output control signals for achieving the controlled variables to the on-vehicle devices. Document <CIT> shows a method for improving the safety and comfort of a vehicle driving over a railroad track, cattle guard, or the like. The method may include receiving, by a computer system, one or more inputs corresponding to one or more forward looking sensors. The computer system may also receive data characterizing a motion of the vehicle. The computer system may estimate, based on the one or more inputs and the data, a motion of a vehicle with respect to a railroad track, cattle guard, or the like extending across a road ahead of the vehicle. Accordingly, the computer system may change a suspension setting, steering setting, or the like of the vehicle to more safely or comfortably drive over the railroad track, cattle guard, or the like. <CIT> shows a driver assistance system is presented, including an event detector which comprises a set of rules defining a surprising event based on signals reflecting a vehicle operator input and signals from an object detection sensor. An event data file generator is configured to generate an event data file according to rules comprised in the event detector, the event data file comprising a video signal received from a camera, a signal from at least one dynamic vehicle sensor, and target object information received from the object detection sensor. The event data file generator is further configured to initiate data file generation responsive to a surprising event being detected by the event generator, and wherein the contents of the data file are specified by the rules comprised in the event detector. In this way, surprising events may be collected and analyzed off-board in order to generate updates for the driver assistance system. <CIT> shows a travel route control unit 10a that updates a target travel route of a vehicle, and an automatic emergency avoidance control unit that executes an automatic emergency avoidance control process for automatically operating a prescribed control system in order to avoid a collision with an obstacle. The travel route control unit calculates a plurality of correction travel routes that correct the target travel route R and avoid the obstacle, evaluates the correction travel routes using a prescribed evaluation function, and selects one correction travel route. The travel route control unit generates a first requirement signal so that the vehicle travels on the correction travel route. The automatic emergency avoidance control unit generates a second requirement signal. An output control unit outputs the first or second requirement signal to a prescribed control system.

In recent years, development of automated driving systems has been promoted on a national scale. In general, in an automated driving system, vehicle outdoor environment information is acquired by a camera or other devices, and based on the acquired vehicle outdoor environment information, a route on which a vehicle is to travel is calculated. In the automated driving system, traveling devices are controlled in order to follow the route to be traveled.

Here, it takes time to calculate a route on which a vehicle is to travel because of necessity for processing a vast amount of vehicle outdoor environment information. On the other hand, from the viewpoint of stability in travel, traveling devices need to be controlled precisely in accordance with conditions such as a road condition, and thus, the control of the traveling devices needs to be performed as fast as possible. A large number of traveling devices are employed, such as an ignition plug, a fuel injection valve, and a valve mechanism for intake and exhaust valves, in an engine alone. Thus, in calculating controlled variables of these devices as well as calculating a route by one computation device, the amount of calculation is enormous, and it takes time before outputting control signals to the traveling devices. Consequently, responsiveness of the traveling devices to vehicle outdoor environments might degrade.

The technique disclosed here has been made in view of the foregoing situations, and has an object of enhancing responsiveness of a traveling device to vehicle outdoor environments in a vehicle travel control device that controls actuation of the traveling device so as to follow a route calculated by a computation device.

An object of the present invention is a vehicle travel control device according to independent claim <NUM>.

To solve problems described above, the technique disclosed here provides a configuration of a vehicle travel control device including: a computation device; and a device controller configured to control actuation of a traveling device mounted on the vehicle, based on a computation result of the computation device, wherein the computation device includes a vehicle outdoor environment identifier configured to identify a vehicle outdoor environment based on an output from an image acquirer, the image acquirer being configured to acquire a vehicle outdoor environment, a route setter configured to set a route on which the vehicle is to be travel, in accordance with the vehicle outdoor environment identified by the vehicle outdoor environment identifier, a target motion determiner configured to determine a target motion of the vehicle for following the route set by the route setter, and a physical quantity calculator configured to calculate a target physical quantity to be generated by the traveling device, in order to achieve the target motion determined by the target motion determiner, and the device controller calculates a controlled variable of the traveling device such that the target physical quantity calculated by the physical quantity calculator is achieved, and the device controller outputs a control signal to the traveling device.

With this configuration, the computation device only calculates the physical quantity to be achieved, and an actual controlled variable of the traveling device is calculated by the device controller. Accordingly, the amount of calculation of the computation device decreases so that the calculation speed of the computation device can be increased. The device controller only needs to calculate an actual controlled variable and output a control signal to the traveling device. Thus, a processing speed thereof is high. Consequently, responsiveness of the traveling device to the vehicle outdoor environment can be increased.

Since the device controller calculates the controlled variable, the computation device only needs to calculate a rough physical quantity. Thus, the computation speed may be lower than that of the device controller. As a result, computation accuracy of the computation device can be enhanced.

Since the device controller calculates a controlled variable, the vehicle travel control device can cope with a slight change of the vehicle outdoor environment by adjusting the controlled variable by using the device controller without using the computation device.

The "traveling devices" herein refer to devices such as actuators and sensors that are controlled while the vehicle travels.

The vehicle travel control device for automobile of the present invention further includes an abnormality detector configured to detect an abnormality in traveling of the vehicle, wherein the abnormality detector is a sensor and inputs a detection signal to the device controller when detecting an abnormality and when a detection signal is input from the abnormality detector to the device controller, the device controller calculates a controlled variable of the traveling device in order to eliminate or reduce the abnormality, and outputs a control signal to traveling device.

That is, when slipping of wheels occur, for example, it is desirable to take immediate action in order to stabilize traveling. With this configuration, when an abnormality is detected, the device controller calculates a controlled variable for eliminating or reducing the abnormality without waiting for calculation of the computation device, and outputs a control signal to the traveling device. Consequently, responsiveness of the traveling device to the vehicle outdoor environment can be further enhanced.

In the computation device for automobile, the traveling device comprises a power train-related device constituting a power train device, a brake-related device constituting a braking device, and a steering-related device constituting a steering device, the device controller comprises a power train controller configured to control actuation of the power train-related device, a brake controller configured to control actuation of the brake-related device, and a steering controller configured to control actuation of the steering-related device, and the power train controller, the brake controller, and the steering controller are configured to be communicable with one another, and share information on physical quantities generated by the power train controller, the brake controller, and the steering controller.

With this configuration, stability in traveling of the vehicle can be enhanced. For example, in a state where a road is slippery, for example, it is required to reduce the rotation speed of the wheels (i.e., so-called traction control) in order to prevent idle rotation of the wheels. To reduce idle rotation of the wheels, an output of the power train device is reduced or a braking force of the braking device is used. However, when the power train controller and the brake controller are communicable with each other, optimum action using both the power train device and the braking device can be taken. In turning a corner, for example, controlled variables of the power train-related device and the brake-related device are finely adjusted in accordance with a target steering variable of the steering device so that an appropriate lateral force can be applied to the vehicle, and smooth cornering can be performed. As a result, responsiveness of the traveling device to the vehicle outdoor environment can be further enhanced.

In the computation device for automobile, the vehicle outdoor environment identifier may identify a vehicle outdoor environment by using deep learning.

With this configuration, since the vehicle outdoor environment identifier identify the vehicle outdoor environment by using deep learning, the computation device performs an especially large amount of computation. In view of this, the controlled variable of the traveling device is calculated by the device controller other than the computation device so that the advantage of further enhancing responsiveness of the traveling device to the vehicle outdoor environment can be more appropriately obtained.

As described above, the technique disclosed here can enhance responsiveness of a traveling device to vehicle outdoor environment in a vehicle travel control device that controls operation of the traveling device so as to follow a route calculated by a computation device.

An exemplary embodiment will be described hereinafter in detail with reference to the drawings. A "traveling device" described later in this embodiment refers to devices such as actuators and sensors that are controlled when a vehicle <NUM> travels.

<FIG> schematically illustrates a configuration of the vehicle <NUM> that is controlled by a vehicle travel control device <NUM> (hereinafter referred to as a travel control device <NUM>) according to this embodiment. The vehicle <NUM> is an automobile capable of operating in manual driving of traveling in accordance with an operation of, for example, an accelerator by a driver, assisted driving of traveling while assisting an operation by the driver, and automated driving of traveling without an operation by the driver.

The vehicle <NUM> includes an engine <NUM> serving as a driving source and including a plurality of (four in this embodiment) cylinders <NUM>, a transmission <NUM> coupled to the engine <NUM>, a braking device <NUM> that brakes rotation of front wheels <NUM> as driving wheel, and a steering device <NUM> that steers the front wheels <NUM> as steered wheels.

The engine <NUM> is, for example, a gasoline engine. As illustrated in <FIG>, each of the cylinders <NUM> of the engine <NUM> is provided with an injector <NUM> for supplying fuel into the cylinder <NUM> and an ignition plug <NUM> for igniting an air-fuel mixture of fuel and intake air supplied into the cylinder <NUM>. The engine <NUM> includes, for each of the cylinders <NUM>, an intake valve <NUM>, an exhaust valve <NUM>, and a valve mechanism <NUM> for adjusting opening/closing operations of the intake valve <NUM> and the exhaust valve <NUM>. The engine <NUM> also includes a piston <NUM> that reciprocates in the cylinders <NUM>, and a crankshaft <NUM> coupled to the piston <NUM> through a connecting rod. The engine <NUM> may be a diesel engine. In the case where the engine <NUM> is the diesel engine, the ignition plug <NUM> may not be provided. The injector <NUM>, the ignition plug <NUM>, and the valve mechanism <NUM> are examples of power train-related devices.

The transmission <NUM> is, for example, a multistep automatic transmission. The transmission <NUM> is disposed at one side of the cylinder line of the engine <NUM>. The transmission <NUM> includes an input shaft (not shown) coupled to the crankshaft <NUM> of the engine <NUM> and an output shaft (not shown) coupled to the input shaft through a plurality of speed-reducing gears (not shown). The output shaft is coupled to an axle <NUM> of the front wheels <NUM>. Rotation of the crankshaft <NUM> is subjected to a gear shift by the transmission <NUM>, and is transferred to the front wheels <NUM>. The transmission <NUM> is an example of a power train-related device.

The engine <NUM> and the transmission <NUM> are power train devices that generate a driving force for enabling the vehicle <NUM> to travel. Actuation of the engine <NUM> and the transmission <NUM> is controlled by a power train electric control unit (ECU) <NUM>. For example, while the vehicle <NUM> is in the manual driving, the power train ECU <NUM> controls, for example, a fuel injection amount and a fuel injection timing by the injector <NUM>, an ignition timing by the ignition plug <NUM>, and valve open timings and valve open periods of the intake and exhaust valves <NUM> and <NUM> by the valve mechanism <NUM>, based on detection values of, for example, an accelerator opening sensor SW1 for detecting an accelerator opening corresponding to a manipulated variable of an accelerator pedal by the driver. While the vehicle <NUM> is in the manual driving, the power train ECU <NUM> adjusts a gear stage of the transmission <NUM> based on a detection result of a shift sensor SW2 for detecting an operation of a shift lever by the driver and a required driving force calculated from an accelerator opening. While the vehicle <NUM> is in the assisted driving or the automated driving, the power train ECU <NUM> basically calculates controlled variables of traveling devices (e.g., the injector <NUM> in this embodiment) such that a target driving force calculated by a computation device <NUM> described later can be obtained, and outputs a control signal to the traveling devices. The power train ECU <NUM> is an example of a device controller.

The braking device <NUM> includes a brake pedal <NUM>, a brake actuator <NUM>, a booster <NUM> connected to the brake actuator <NUM>, a master cylinder <NUM> connected to the booster <NUM>, a dynamic stability control (DSC) device <NUM> for adjusting a braking force, and brake pads <NUM> for actually braking rotation of the front wheels <NUM>. The axle <NUM> of the front wheels <NUM> is provided with disc rotors <NUM>. The braking device <NUM> is an electric brake, and actuates the brake actuator <NUM> in accordance with a manipulated variable of the brake pedal <NUM> detected by a brake sensor SW3, and actuates the brake pads <NUM> through the booster <NUM> and the master cylinder <NUM>. The braking device <NUM> causes the disc rotors <NUM> to be sandwiched by the brake pads <NUM> and brakes rotation of the front wheels <NUM> by a friction force occurring between the brake pads <NUM> and the disc rotors <NUM>. The brake actuator <NUM> and the DSC device <NUM> are examples of brake-related devices.

Actuation of the braking device <NUM> is controlled by a brake microcomputer <NUM> and a DSC microcomputer <NUM>. For example, while the vehicle <NUM> is in the manual driving, the brake microcomputer <NUM> controls a manipulated variable of the brake actuator <NUM> based on detection values of, for example, the brake sensor SW3 for detecting a manipulated variable of the brake pedal <NUM> by the driver. The DSC microcomputer <NUM> controls actuation of the DSC device <NUM> irrespective of operation of the brake pedal <NUM> by the driver, and applies a braking force to the front wheels <NUM>. While the vehicle <NUM> is in the assisted driving or the automated driving, the brake microcomputer <NUM> basically calculates controlled variables of traveling devices (e.g., the brake actuator <NUM> in this embodiment) such that a target braking force calculated by the computation device <NUM> described later can be obtained, and outputs control signals to the traveling devices. The brake microcomputer <NUM> and the DSC microcomputer <NUM> are examples of a device controller. The brake microcomputer <NUM> and the DSC microcomputer <NUM> may be constituted by one microcomputer.

The steering device <NUM> includes a steering wheel <NUM> that is operated by the driver, an electronic power assist steering (EPAS) device <NUM> for assisting a steering operation by the driver, and a pinion shaft <NUM> coupled to the EPAS device <NUM>. The EPAS device <NUM> includes an electric motor 42a and a speed reducer 42b that reduces the speed of a driving force of the electric motor 42a and transfers the resulting driving force to the pinion shaft <NUM>. The steering device <NUM> is a steer-by-wire steering device, and actuates the EPAS device <NUM> in accordance with a manipulated variable of the steering wheel <NUM> detected by a steering angle sensor SW4, and operates the front wheels <NUM> by rotating the pinion shaft <NUM>. The pinion shaft <NUM> and the front wheels <NUM> are coupled to each other by an unillustrated rack bar, and rotation of the pinion shaft <NUM> is transferred to the front wheels through the rack bar. The EPAS device <NUM> is an example of a steering-related device.

Actuation of the steering device <NUM> is controlled by the EPAS microcomputer <NUM>. For example, while the vehicle <NUM> is in the manual driving, the EPAS microcomputer <NUM> controls a manipulated variable of the electric motor 42a based on detection values of, for example, the steering angle sensor SW4. While the vehicle <NUM> is in the assisted driving or the automated driving, the EPAS microcomputer <NUM> basically calculates controlled variables of traveling devices (e.g., the EPAS device <NUM> in this embodiment) such that a target steering variable calculated by the computation device <NUM> described later can be obtained, and outputs control signals to the traveling devices. The EPAS microcomputer <NUM> is an example of a device controller.

Although specifically described later, in this embodiment, the power train ECU <NUM>, the brake microcomputer <NUM>, the DSC microcomputer <NUM>, and the EPAS microcomputer <NUM> are configured to be communicable with one another. In the following description, the power train ECU <NUM>, the brake microcomputer <NUM>, the DSC microcomputer <NUM>, and the EPAS microcomputer <NUM> will be simply referred to as device controllers.

In this embodiment, to enable the assisted driving and the automated driving, the travel control device <NUM> includes a computation device <NUM> that calculates a route on which the vehicle <NUM> is to travel and that determines a motion of the vehicle <NUM> for following the route. The computation device <NUM> is a computation hardware including one or more chips. Specifically, as illustrated in <FIG>, the computation device <NUM> includes a memory storing a processor including a CPU and a plurality of modules.

<FIG> illustrates a configuration for implementing a function (a route generating function described later) according to this embodiment in further detail. <FIG> does not illustrate all the functions of the computation device <NUM>.

The computation device <NUM> determines a target motion of the vehicle <NUM> and controls actuation of a device base on an output from, for example, a plurality of sensors. The sensors for outputting information to the computation device <NUM>, for example, include: a plurality of cameras <NUM> disposed on, for example, the body of the vehicle <NUM> and used for taking images of vehicle outdoor environments; a plurality of radars <NUM> disposed on, for example, the body of the vehicle <NUM> and used for detecting an object outside the vehicle and other objects; a position sensor SW5 for detecting a position of the vehicle <NUM> (vehicle position information) by utilizing global positioning system (GPS); a vehicle state sensor SW6 constituted by outputs of sensors for detecting a vehicle behavior, such as a vehicle speed sensor, an acceleration sensor, and a yaw rate sensor, and used for acquiring a state of the vehicle <NUM>; and an occupant state sensor SW7 constituted by, for example, an in-car camera and used for acquiring a state of an occupant of the vehicle <NUM>. The computation device <NUM> receives communication information received by a vehicle outside communicator <NUM> and sent from another vehicle around the own vehicle, and traffic information received by the vehicle outside communicator <NUM> and sent from a navigation system.

Each of the cameras <NUM> is disposed to capture an image around the vehicle <NUM> by <NUM>° horizontally. Each camera <NUM> captures an optical image representing vehicle outdoor environments and generates image data. Each camera <NUM> outputs the generated image data to the computation device <NUM>. The cameras <NUM> are examples of an image acquirer that acquires information on vehicle outdoor environments.

The image data acquired by the cameras <NUM> is input to a human machine interface (HMI) unit <NUM> as well as the computation device <NUM>. The HMI unit <NUM> displays information based on the acquired image data on, for example, a display device.

In a manner similar to the cameras <NUM>, each of the radars <NUM> is disposed to detect an image in a range around the vehicle <NUM> by <NUM>° horizontally. The radars <NUM> are not limited to a specific type, and a millimeter wave radar or an infrared ray radar may be employed. The radars <NUM> are an example of the image acquirer that acquires vehicle outdoor environments.

While the vehicle <NUM> is in the assisted driving or the automated driving, the computation device <NUM> sets a travel route of the vehicle <NUM> and sets a target motion of the vehicle <NUM> such that the vehicle <NUM> follows the travel route. The computation device <NUM> includes: an vehicle outdoor environment identifier <NUM> that identifies a vehicle outdoor environment based on an output from, for example, the cameras <NUM> in order to set a target motion of the vehicle <NUM>; a candidate route generator <NUM> that calculates one or more candidate routes on which the vehicle <NUM> is capable of traveling, in accordance with the vehicle outdoor environment determined by the vehicle outdoor environment identifier <NUM>; a vehicle behavior estimator <NUM> that estimates a behavior of the vehicle <NUM> based on an output from the vehicle state sensor SW6; an occupant behavior estimator <NUM> that estimates a behavior or an occupant of the vehicle <NUM> based on an output from the occupant state sensor SW7; a route determiner <NUM> that determines a route on which the vehicle <NUM> is to travel; a vehicle motion determiner <NUM> that determines a target motion of the vehicle <NUM> in order to allow the vehicle <NUM> to follow the route set by the route determiner <NUM>; and a driving force calculator <NUM>, a braking force calculator <NUM>, and a steering variable calculator <NUM> that calculate target physical quantities (e.g., a driving force, a braking force, and a steering angle) to be generated by the traveling devices in order to obtain the target motion determined by the driving force calculator vehicle motion determiner <NUM>. The candidate route generator <NUM>, the vehicle behavior estimator <NUM>, the occupant behavior estimator <NUM>, and the route determiner <NUM> constitute a route setter that sets a route on which the vehicle <NUM> is to travel, in accordance with the vehicle outdoor environment identified by the vehicle outdoor environment identifier <NUM>.

The computation device <NUM> also includes, as safety functions, a rule-based route generator <NUM> that identifies an object outside the vehicle according to a predetermined rule and generates a travel route that avoids the object, and a backup <NUM> that generates a travel route for guiding the vehicle <NUM> to a safe area such as a road shoulder.

The vehicle outdoor environment identifier <NUM>, the candidate route generator <NUM>, the vehicle behavior estimator <NUM>, the occupant behavior estimator <NUM>, the route determiner <NUM>, the vehicle motion determiner <NUM>, the driving force calculator <NUM>, the braking force calculator <NUM>, the steering variable calculator <NUM>, the rule-based route generator <NUM>, and the backup <NUM> are examples of modules stored in a memory <NUM>.

The vehicle outdoor environment identifier <NUM> receives outputs from, for example, the cameras <NUM> and the radars <NUM> mounted on the vehicle <NUM>, and identifies a vehicle outdoor environment. The identified vehicle outdoor environment includes at least a road and an obstacle. In this embodiment, the vehicle outdoor environment identifier <NUM> compares three-dimensional information on surroundings of the vehicle <NUM> with a vehicle outdoor environment model, based on data of the cameras <NUM> and the radars <NUM> to thereby estimate a vehicle environment including a road and an obstacle. The vehicle outdoor environment model is, for example, a learned model generated by deep learning, and is capable of recognizing a road, an obstacle, and other objects with respect to three-dimensional information on surroundings of the vehicle.

For example, the vehicle outdoor environment identifier <NUM> specifies free space or a region where no objects are present through image processing, from images captured by the cameras <NUM>. In the image processing here, a learned model generated by, for example, deep learning is used. Then, a two-dimensional map representing a free space is generated. The vehicle outdoor environment identifier <NUM> acquires information on a target around the vehicle <NUM>, from outputs of the radars <NUM>. This information is positioning information including a position and a speed, for example, of the target. Thereafter, the vehicle outdoor environment identifier <NUM> combines the generated two-dimensional map with the positioning information of the target, and generates a three-dimensional map representing surroundings around the vehicle <NUM>. In this embodiment, information on locations and image-capturing directions of the cameras <NUM>, and information on locations and transmission directions of the radars <NUM> are used. The vehicle outdoor environment identifier <NUM> compares the generated three-dimensional map with the vehicle outdoor environment model to thereby estimate a vehicle environment including a road and an obstacle. In deep learning, a deep neural network (DNN) is used. Examples of the DNN include convolutional neural network (CNN).

The candidate route generator <NUM> generates candidate routes on which the vehicle <NUM> can travel, based on, for example, an output of the vehicle outdoor environment identifier <NUM>, an output of the position sensor SW5, and information transmitted from the vehicle outside communicator <NUM>. For example, the candidate route generator <NUM> generates a travel route that avoids an obstacle identified by the vehicle outdoor environment identifier <NUM> on a road identified by the vehicle outdoor environment identifier <NUM>. The output of the vehicle outdoor environment identifier <NUM> includes travel route information on a travel route on which the vehicle <NUM> travels, for example. The travel route information includes information on a shape of the travel route itself and information on an object on the travel route. The information on the shape of the travel route includes, for example, a shape (linear, curve, or curve curvature), a travel route width, the number of lanes, and a lane width of the travel route. The information on the object includes, for example, a relative position and a relative speed of the object with respect to the vehicle, and attributes (e.g., types and direction of movement) of the object. Examples of the type of the object include a vehicle, a pedestrian, a road, and a mark line.

In this embodiment, the candidate route generator <NUM> calculates a plurality of candidate routes by a state lattice method, and based on a route cost of each of the calculated candidate routes, selects one or more candidate routes. The routes may be calculated by other methods.

The candidate route generator <NUM> sets an imaginary grid area on a travel route based on travel route information. This grid area includes a plurality of grid points. With each of the grid points, a position on the travel route is specified. The candidate route generator <NUM> sets a predetermined grid point as a target arrival position. Then, the candidate route generator <NUM> computes a plurality of candidate routs by a route search using the plurality of grid points in the grid area. In the state lattice method, a route is branched from a given grid point to another grid point located ahead of the given grid point in the traveling direction of the vehicle. Thus, the candidate routes are set so as to pass the plurality of grid points sequentially. The candidate routes include, for example, time information representing times when the vehicle passes the grid points, speed information on, for example, speeds and accelerations at the grid points, and information on other vehicle motions.

The candidate route generator <NUM> selects one or more travel routes based on a route cost from the plurality of candidate routes. Examples of the route cost include the degree of lane centering, an acceleration of the vehicle, a steering angle, and possibility of collision. In a case where the candidate route generator <NUM> selects a plurality of travel routes, the route determiner <NUM> selects one travel route.

The vehicle behavior estimator <NUM> measures a state of the vehicle from outputs of sensors for detecting a behavior of the vehicle, such as a vehicle speed sensor, an acceleration sensor, and a yaw rate sensor. The vehicle behavior estimator <NUM> generates a vehicle <NUM>-axis model showing a behavior of the vehicle.

The vehicle <NUM>-axis model here is a model of accelerations in <NUM>-axis directions of "front and rear," "left and right," and "upward and downward" of the traveling vehicle and angular velocities in <NUM>-axis directions of "pitch," "roll," and "yaw. " That is, the vehicle <NUM>-axis model is a numerical model obtained by capturing a motion of the vehicle not only in a plane in terms of a classical vehicle motion engineering, and but also by reproducing a behavior of the vehicle by using a total of six axes of pitching (Y axis), roll (X axis) motion, and movement along a Z axis (upward and downward motion of the vehicle body) of the vehicle body on which an occupant is seated on four wheels with suspensions interposed therebetween.

The vehicle behavior estimator <NUM> applies the vehicle <NUM>-axis model to the travel route generated by the candidate route generator <NUM>, and estimates a behavior of the vehicle <NUM> in following the travel route.

The occupant behavior estimator <NUM> estimates especially physical conditions and feelings of a driver, from a detection result of the occupant state sensor SW7. Examples of the physical conditions include good health, mild fatigue, ill health, and loss of consciousness. Examples of the feelings include pleasant, normal, bored, frustrated, and unpleasant.

For example, the occupant behavior estimator <NUM> extracts a face image of a driver from images captured by, for example, a camera placed in a cabin, and specifies the driver. The extracted face image and information on the specified driver are applied to a human model as inputs. The human model is a learned model generated by deep learning, for example, and physical conditions and feelings are output for each person that can be a driver of the vehicle <NUM>. The occupant behavior estimator <NUM> outputs the physical conditions and the feelings of the driver output from the human model.

In a case where biometric sensors such as a skin temperature sensor, a heart rate sensor, a blood flow sensor, and a sweat sensor, are used for the occupant state sensor SW7 for acquiring information on a driver, the occupant behavior estimator measures biometric of the driver from outputs of the biometric sensors. In this case, the human model receives biometrics of each person who can be a driver of the vehicle <NUM>, and outputs physical conditions and feelings of the person. The occupant behavior estimator <NUM> outputs the physical conditions and the feelings of a driver output from the human model.

As the human model, a model that estimates feelings of a human to a behavior of the vehicle <NUM> may be used with respect to a person who can be a driver of the vehicle <NUM>. In this case, an output of the vehicle behavior estimator <NUM>, biometrics of the driver, and estimated feelings are managed chronologically to constitute a model. This model enables, for example, a relationship between a heightened emotion (consciousness) of a driver and a behavior of the vehicle to be predicted.

The occupant behavior estimator <NUM> may include a human body model as a human model. The human body model specifies, for example, a neck muscle strength supporting a head mass (e.g., <NUM>) and front, rear, left, and right G. When receiving a motion (acceleration G and jerk) of the vehicle body, the human body model outputs predicted physical feelings and subjective feelings of an occupant. Examples of the physical feelings of the occupant include comfortable/moderate/uncomfortable, and examples of subjective feelings include unpredicted/predictable. Since a vehicle body behavior in which the head of the occupant is bent over backward even slightly, for example, is uncomfortable to the occupant, a travel route causing such a behavior is not selected by referring to the human body model. On the other hand, a vehicle body behavior with which the head moves forward as if the occupant makes a bow allows the occupant to take a posture against this vehicle body behavior easily, and thus, does not make the occupant feel uncomfortable immediately. Thus, a travel route causing such a behavior can be selected. Alternatively, a target motion may be determined to avoid shaking of the head of the occupant or may be dynamically determined to make the head active, by referring to the human body model.

The occupant behavior estimator <NUM> applies the human model to a vehicle behavior estimated by the vehicle behavior estimator <NUM>, and estimates a change of physical conditions and a change of feelings of the current driver with respect to the vehicle behavior.

The route determiner <NUM> determines a route on which the vehicle <NUM> is to travel, based on an output of the occupant behavior estimator <NUM>. In a case where the candidate route generator <NUM> generates one generated route, the route determiner <NUM> sets this route as a route on which the vehicle <NUM> is to travel. In a case where the candidate route generator <NUM> generates a plurality of generated routes, in consideration of an output of the occupant behavior estimator <NUM>, the route determiner <NUM> selects a route on which an occupant (especially a driver) feels comfortable most, that is, a route on which a driver does not feel redundancy such as excessive caution in avoiding an obstacle, among a plurality of candidate routes.

The rule-based route generator <NUM> identifies an object outside the vehicle according to a predetermined rule and generates a travel route avoiding the object, based on outputs from the cameras <NUM> and the radars <NUM>, without using deep learning. In a manner similar to the candidate route generator <NUM>, the rule-based route generator <NUM> calculates a plurality of candidate routes by a state lattice method, and based on a route cost of each of the candidate routes, selects one or more candidate routes. The rule-based route generator <NUM> calculates a route cost based on, for example, a rule in which the vehicle does not enter within a few or several meters around the object. The rule-based route generator <NUM> may also calculate a route by other methods.

Information of routes generated by the rule-based route generator <NUM> is input to the vehicle motion determiner <NUM>.

The backup <NUM> generates a route for guiding the vehicle <NUM> to a safe area such as a road shoulder based on outputs from the cameras <NUM> and the radars <NUM>, in a case where a sensor, for example, is out of order or an occupant is not in good physical condition. For example, the backup <NUM> sets a safe region in which the vehicle <NUM> can be brought to an emergency stop from information of the position sensor SW5, and generates a travel route to the safe area. In a manner similar to the candidate route generator <NUM>, the backup <NUM> calculates a plurality of candidate routes by a state lattice method, and based on a route cost of each of the candidate routes, selects one or more candidate routes. This backup <NUM> may also calculate a route by other methods.

Information on the routes generated by the backup <NUM> is input to the vehicle motion determiner <NUM>.

The vehicle motion determiner <NUM> sets a target motion for a travel route determined by the route determiner <NUM>. The target motion refers to steering and acceleration and speed-reduction that allow the vehicle to follow the travel route. The target motion determiner <NUM> computes a motion of the vehicle body by referring to the vehicle <NUM>-axis model, with respect to the travel route selected by the route determiner <NUM>.

The vehicle motion determiner <NUM> determines a target motion that allows the vehicle to follow the travel route generated by the rule-based route generator <NUM>.

The vehicle motion determiner <NUM> determines a target motion that allows the vehicle to follow the travel route generated by the backup <NUM>.

If the travel route determined by the route determiner <NUM> greatly significantly deviates from the travel route generated by the rule-based route generator <NUM>, the vehicle motion determiner <NUM> selects the travel route generated by the rule-based route generator <NUM> as a route on which the vehicle <NUM> is to travel.

If sensors (especially the cameras <NUM> or the radars <NUM>), for example, are out of order or a poor physical condition of an occupant is estimated, the vehicle motion determiner <NUM> selects the travel route generated by the backup <NUM> as a route on which the vehicle <NUM> is to travel.

The physical quantity calculator is constituted by the driving force calculator <NUM>, the braking force calculator <NUM>, and the steering variable calculator <NUM>. To achieve a target motion, the driving force calculator <NUM> calculates a target driving force to be generated by the power train devices (the engine <NUM> and the transmission <NUM>). To achieve the target motion, the braking force calculator <NUM> calculates a target braking force to be generated by the braking device <NUM>. To achieve the target motion, the steering variable calculator <NUM> calculates a target steering variable to be generated by the steering device <NUM>.

The peripheral equipment operation setter <NUM> sets operations of devices related to the body of the vehicle <NUM>, such as a lamp and doors, based on an output of the vehicle motion determiner <NUM>. For example, the peripheral equipment operation setter <NUM> sets a direction of the lamp when the vehicle <NUM> follows the travel route determined by the route determiner <NUM>, for example. In the case of guiding the vehicle <NUM> to a safe area set by the backup <NUM>, for example, the peripheral equipment operation setter <NUM> turns hazard flashers on or unlocks the doors, after the vehicle <NUM> has reached the safe area.

A computation result in the computation device <NUM> is output to the power train ECU <NUM>, the brake microcomputer <NUM>, the EPAS microcomputer <NUM>, and a body-related microcomputer <NUM>. Specifically, the power train ECU <NUM> receives information on a target driving force calculated by the driving force calculator <NUM>, the brake microcomputer <NUM> receives information on a target braking force calculated by the braking force calculator <NUM>, the EPAS microcomputer <NUM> receives information on a target steering variable calculated by the steering variable calculator <NUM>, and the body-related microcomputer <NUM> receives information on operations of devices related to the body and set by the peripheral equipment operation setter <NUM>.

As described above, the power train ECU <NUM> basically calculates a fuel injection timing of the injector <NUM> and an ignition timing of the ignition plug <NUM> such that a target driving force is achieved, and outputs control signals to these traveling devices. The brake microcomputer <NUM> basically calculates a controlled variable of the brake actuator <NUM> such that a target driving force is achieved, and outputs a control signal to the brake actuator <NUM>. The EPAS microcomputer <NUM> basically calculates the amount of current to be supplied to the EPAS device <NUM> such that a target steering variable is achieved, and outputs a control signal to the EPAS device <NUM>.

As described above, in this embodiment, the computation device <NUM> only calculates target physical quantities to be output from the traveling devices, and controlled variables of the traveling devices are calculated by the device controllers <NUM> to <NUM>. Accordingly, the amount of calculation of the computation device <NUM> decreases so that the calculation speed of the computation device <NUM> can be increased. The device controllers <NUM> to <NUM> only need to calculate actual controlled variables and output control signals to the traveling devices (e.g., the injector <NUM>), and thus, processing speeds thereof are high. Consequently, responsiveness of the traveling device to vehicle outdoor environments can be increased.

Since the device controllers <NUM> to <NUM> calculate the controlled variables, the computation device <NUM> only needs to calculate rough physical quantities. Thus, computation speeds may be lower than those of the device controllers <NUM> to <NUM>. As a result, computation accuracy of the computation device <NUM> can be enhanced.

As illustrated in <FIG>, in this embodiment, the power train ECU <NUM>, the brake microcomputer <NUM>, the DSC microcomputer <NUM>, and the EPAS microcomputer <NUM> are configured to be communicable with one another. The power train ECU <NUM>, the brake microcomputer <NUM>, the DSC microcomputer <NUM>, and the EPAS microcomputer <NUM> are configured to share information on controlled variables of the traveling devices and to be capable of executing control for using the information in cooperation with one another.

For example, in a state where a road is slippery, for example, it is required to reduce the rotation speed of the wheels (i.e., so-called traction control) so as not to rotate the wheels idly. To reduce idle rotation of the wheels, an output of the power train is reduced or a braking force of the braking device <NUM> is used. Since the power train ECU <NUM> and the brake microcomputer <NUM> are communicable with each other, an optimum measure using both the power train and the braking device <NUM> can be taken.

In cornering of the vehicle <NUM>, for example, the controlled variables of the power train and the braking device <NUM> (including the DSC device <NUM>) are finely adjusted in accordance with a target steering variable so that rolling and pitching in which a front portion of the vehicle <NUM> sinks are caused to occur in synchronization to cause a diagonal roll position. By causing the diagonal roll position, loads on the outer front wheels <NUM> increase so that the vehicle <NUM> is allowed to turn with a small steering angle. Thus, it is possible to reduce a rolling resistance on the vehicle <NUM>.

As another example, in vehicle stability control (dynamic vehicle stability), based on a current steering angle and a current vehicle speed, if a difference occurs between a target yaw rate and a target lateral acceleration calculated as ideal turning state of the vehicle <NUM> and a current yaw rate and a current lateral acceleration, the braking devices <NUM> for the four wheels are individually actuated or an output of the power train is increased or reduced so as to cause the current yaw rate and the current lateral acceleration to return to the target values. In techniques employed to date, the DSC microcomputer <NUM> has to comply with a communication protocol, information on instability of the vehicle is acquired from yaw rate sensors and wheel speed sensors through a relatively low-speed CAN, and actuation is instructed to the power train ECU <NUM> and the brake microcomputer <NUM> also through the CAN. These techniques take time, disadvantageously. In this embodiment, information on controlled variables can be directly transmitted among these microcomputers. Thus, brake actuation of the wheels and start of output increase/decrease, which are stability control, can be performed significantly early from detection of a vehicle instability state. Reduction of stability control in a case where a driver performs counter steering can also be conducted in real time with reference to a steering angle speed and other information of the EPAS microcomputer <NUM>.

As yet another example, a front wheel driving vehicle with high power can employ steering angle-linked output control that reduces an output of the power train to avoid an instable state of the vehicle when an accelerator is pressed with a large steering angle. In this control, the power train ECU <NUM> refers to a steering angle and a steering angle signal of the EPAS microcomputer <NUM>, and reduces an output immediately. Thus, a driving field preferable for a driver without a sense of sudden intervention can be achieved.

Here, during traveling of the vehicle <NUM>, abnormalities concerning traveling of the vehicle <NUM>, such as knocking in the engine <NUM> or slipping of the front wheels <NUM>, occur in some cases. At occurrence of such abnormalities, traveling devices need to be controlled quickly in order to eliminate or reduce these abnormalities. As described above, the computation device <NUM> identifies vehicle outdoor environment using deep learning, and performs a huge amount of computation in order to calculate routes of the vehicle <NUM>. Thus, when computation for eliminating or reducing the abnormalities is performed through the computation device <NUM>, measures might be taken with a delay.

In view of this, in this embodiment, when an abnormality concerning traveling of the vehicle <NUM> is detected, the device controllers <NUM> to <NUM> calculate the controlled variables of the traveling devices in order to eliminate or reduce the abnormality and cause the traveling devices to output control signals, without using the computation device <NUM>.

<FIG> shows, as an example, a relationship between sensors SW5, SW8, and SW9 for detecting abnormalities in traveling of the vehicle <NUM> and the device controllers <NUM>, <NUM>, and <NUM>. In <FIG>, sensors for detecting abnormalities in traveling of the vehicle <NUM> are the position sensor SW5, a knocking sensor SW8, and a slipping sensor SW9, but other sensors may be provided. The knocking sensor SW8 and the slipping sensor SW9 may be known sensors. The position sensor SW5, the knocking sensor SW8, and the slipping sensor SW9 correspond to abnormality detectors, and the sensors themselves detect an abnormality in traveling of the vehicle <NUM>.

For example, when the knocking sensor SW8 detects knocking, a detection signal is input to each of the device controllers <NUM> to <NUM> (especially the power train ECU <NUM>). After the detection signal has been input, the power train ECU <NUM> reduces knocking by adjusting a fuel injection timing of the injector <NUM> and an ignition timing of the ignition plug <NUM>. At this time, the power train ECU <NUM> calculates controlled variables of the traveling device while allowing a shift of a driving force output from the power train from a target driving force.

<FIG> illustrates an example of a behavior of the vehicle <NUM> when slipping occurs. In <FIG>, the solid line is an actual travel route of the vehicle <NUM>, and a dotted line is a travel route set by the computation device <NUM> (hereinafter referred to as a theoretical travel route R). In <FIG>, the solid line and the dotted line partially overlap each other. In <FIG>, a black circle indicates a goal of the vehicle <NUM>.

As illustrated in <FIG>, supposing a puddle W is present in the middle of the travel route of the vehicle <NUM> and the front wheels of the vehicle <NUM> go into the puddle W to cause slipping. At this time, as illustrated in <FIG>, the vehicle <NUM> temporarily deviates from the theoretical travel route R. Slipping of the front wheels of the vehicle <NUM> is detected by the slipping sensor SW9 (see <FIG>), and deviation from the theoretical travel route R is detected by the position sensor SW5 (see <FIG>). These detection signals are input to the device controllers <NUM> to <NUM>. Thereafter, for example, the brake microcomputer <NUM> actuates the brake actuator <NUM> so as to increase a braking force of the front wheels. The EPAS microcomputer <NUM> actuates the EPAS device <NUM> so as to cause the vehicle <NUM> to return to the theoretical travel route R. At this time, communication between the brake microcomputer <NUM> and the EPAS microcomputer <NUM> can optimize a controlled variable of the EPAS device <NUM> in consideration of a braking force by the braking device <NUM>. In the manner described above, as illustrated in <FIG>, the vehicle <NUM> can return to the theoretical travel route R smoothly and quickly so that traveling of the vehicle <NUM> can be stabilized.

As described above, when an abnormality in traveling of the vehicle <NUM> is detected, the device controllers <NUM> to <NUM> calculate controlled variables of the traveling device in order to eliminate or reduce the abnormality without using the computation device <NUM>, and output control signals to the traveling devices. Accordingly, responsiveness of the traveling devices to vehicle outdoor environments can be enhanced.

Therefore, in this embodiment, the vehicle travel control device includes: the computation device <NUM>; and the device controllers <NUM> to <NUM> configured to control actuation of the traveling devices (e.g., the injector <NUM>) mounted on the vehicle <NUM> based on a computation result of the computation device <NUM>. The computation device <NUM> includes: the vehicle outdoor environment identifier <NUM> configured to identify a vehicle outdoor environment based on outputs from the cameras <NUM> and the radars <NUM> configured to acquire information on the vehicle outdoor environment; the route setter (e.g., the route calculator <NUM>) configured to set a route on which the vehicle <NUM> is to travel in accordance with the vehicle outdoor environment identified by the vehicle outdoor environment identifier <NUM>; the vehicle motion determiner <NUM> configured to determine a target motion of the vehicle <NUM> in order to follow the route set by the route setter; and the physical quantity calculators <NUM> to <NUM> configured to calculate target physical quantities in order to achieve the target motion determined by the vehicle motion determiner <NUM>. The device controllers <NUM> to <NUM> calculate controlled variables of the traveling devices such that the target physical quantities calculated by the physical quantity calculators <NUM> to <NUM> is achieved, and outputs control signals to the traveling devices. In this manner, the computation device <NUM> only calculates physical quantities to be achieved, and actual controlled variables of the traveling devices are calculated by the device controllers <NUM> to <NUM>. Accordingly, the amount of calculation of the computation device <NUM> decreases so that the calculation speed of the computation device <NUM> can be increased. The device controllers <NUM> to <NUM> only need to calculate actual controlled variables and output control signals to the traveling devices, and thus, processing speeds thereof are high. Consequently, responsiveness of the traveling devices to the vehicle outdoor environment can be increased.

In particular, in this embodiment, the vehicle outdoor environment identifier <NUM> identifies a vehicle outdoor environment by using deep learning, and thus, especially the computation device <NUM> performs a large amount of calculation. Thus, the controlled variables of the traveling devices are calculated by the device controllers <NUM> to <NUM> other than the computation device <NUM> so that the advantage of further enhancing responsiveness of the traveling devices to the vehicle outdoor environment can be more appropriately obtained.

During assisted driving of the vehicle <NUM>, the driving force calculator <NUM>, the braking force calculator <NUM>, and the steering variable calculator <NUM> may change a target driving force, for example, in accordance with the state of a driver of the vehicle <NUM>. For example, while the driver enjoys driving (e.g., feeling of the driver is "enjoy"), the target driving force, for example, may be reduced so that the driving state approaches manual driving as close as possible. On the other hand, if the driver is in a poor physical condition, the target driving force, for example, is increased so that the driving state approaches automated driving as close as possible.

The technique disclosed here is not limited to the embodiment described above, and can be changed without departing from the gist of claims.

For example, in the embodiment described above, the route determiner <NUM> determines a route on which the vehicle <NUM> is to travel. However, the technique is not limited to this example, and the route determiner <NUM> may be omitted, and the vehicle motion determiner <NUM> may determine a route on which the vehicle <NUM> is to travel. That is, the vehicle motion determiner <NUM> may serve as both a part of the route setter and the target motion determiner.

In the embodiment described above, the driving force calculator <NUM>, the braking force calculator <NUM>, and the steering variable calculator <NUM> calculate target physical quantities such as a target driving force. However, the technique is not limited to this example, and the driving force calculator <NUM>, the braking force calculator <NUM>, and the steering variable calculator <NUM> may be omitted, and the vehicle motion determiner <NUM> may calculate a target physical quantity. That is, the vehicle motion determiner <NUM> may serve as both the target motion determiner and the physical quantity calculator.

Claim 1:
A vehicle travel control device (<NUM>) configured to control traveling of a vehicle (<NUM>), the vehicle travel control device (<NUM>) comprising:
a computation device (<NUM>);
a device controller (<NUM>, <NUM>, <NUM>) configured to control actuation of a traveling device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) mounted on the vehicle (<NUM>), based on a computation result of the computation device (<NUM>); and
an abnormality detector (SW5, SW6, SW7) configured to detect an abnormality occurring in traveling of the vehicle (<NUM>), wherein
the computation device (<NUM>) includes
a vehicle outdoor environment identifier (<NUM>) configured to identify a vehicle outdoor environment based on an output from an image acquirer, the image acquirer being configured to acquire a vehicle outdoor environment,
a route setter (<NUM>, <NUM>) configured to set a route on which the vehicle (<NUM>) is to travel, in accordance with the vehicle outdoor environment identified by the vehicle outdoor environment identifier (<NUM>),
a target motion determiner (<NUM>) configured to determine a target motion of the vehicle (<NUM>) for following the route set by the route setter (<NUM>, <NUM>), and
a physical quantity calculator (<NUM>, <NUM>, <NUM>) configured to calculate a target physical quantity to be generated by the traveling device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>), in order to achieve the target motion determined by the target motion determiner (<NUM>),
characterized in that
the abnormality detector (SW5, SW6, SW7) is a sensor and inputs a detection signal to the device controller (<NUM>, <NUM>, <NUM>) when detecting an abnormality,
the device controller (<NUM>, <NUM>, <NUM>) calculates a controlled variable of the traveling device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>) such that the target physical quantity calculated by the physical quantity calculator (<NUM>, <NUM>, <NUM>) is achieved, and the device controller outputs a control signal to the traveling device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>), and
when a detection signal is input from the abnormality detector (SW5, SW6, SW7) to the device controller (<NUM>, <NUM>, <NUM>), the device controller (<NUM>, <NUM>, <NUM>) calculates a controlled variable of the traveling device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>), not using the computation device (<NUM>), in order to eliminate or reduce the abnormality, and outputs a control signal to the traveling device (<NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>).