Trajectory generation system, trajectory generation device, trajectory generation method, trajectory generation program

Abstract

To provide a track generation system which enables avoidance traveling securing stability.SOLUTION: A track generation system has a processor, and generates a track on which a host vehicle travels. The processor is configured to execute determination whether or not to execute avoidance traveling avoiding an avoidance object in front of the host vehicle. The processor is configured to execute generation of a plurality of track candidates that are candidates of tracks in the avoidance traveling. The processor is configured to execute estimation of each road surface slip degree when traveling on each of the track candidates. The processor is configured to execute establishment of a track where the vehicle travels in the avoidance traveling from the track candidates on the basis of each of the slip degrees.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to a trajectory generation technique for generating a trajectory along which a host vehicle will travel in the future. [Background technology]

[0002] Patent Document 1 discloses an autonomous vehicle that performs autonomous driving taking into account road surface conditions. This autonomous vehicle estimates the friction coefficient of the road surface on which it is currently traveling based on brake hydraulic pressure and acceleration. Furthermore, the autonomous vehicle generates a driving plan to reduce turning acceleration when the friction coefficient is small. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2017-121874 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, situations may arise where it is necessary to generate a trajectory that avoids an obstacle ahead of the vehicle. In such situations, if a trajectory is generated based on the friction coefficient of the current road surface, as in the technology of Patent Document 1, it may be difficult to ensure stable avoidance driving because the state of the road surface on which the vehicle will travel in the future is not taken into consideration.

[0005] An object of the present disclosure is to provide a trajectory generation system capable of avoidance traveling while ensuring stability. Another object of the present disclosure is to provide a trajectory generation device capable of avoidance traveling while ensuring stability. Yet another object of the present disclosure is to provide a trajectory generation method capable of avoidance traveling while ensuring stability. Yet another object of the present disclosure is to provide a trajectory generation program capable of avoidance traveling while ensuring stability. [Means for solving the problem]

[0006] The technical means of the present disclosure for solving the problems will be described below. Note that the claims and the reference characters in parentheses in this section indicate the correspondence with the specific means described in the embodiments described later in detail, and do not limit the technical scope of the present disclosure.

[0007] A first aspect of the present disclosure is a trajectory generation system having a processor (102) for generating a trajectory for a host vehicle (A), The processor determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; configured to run 、 Estimating the degree of slippage is and estimating, as a slippage degree, a braking distance estimated when traveling on each of the trajectory candidates; To determine the trajectory, and determining, as a trajectory, a candidate trajectory whose braking distance satisfies a determined braking condition. . A second aspect of the present disclosure is a trajectory generation system having a processor (102) for generating a trajectory for a host vehicle (A), The processor determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; configured to run Estimating the degree of slippage is The method includes estimating the degree of slipperiness in a road surface region (R) including a predicted contact region between the host vehicle and the road surface when passing through a specific point (P) on the trajectory candidate. A third aspect of the present disclosure is a trajectory generation system having a processor (102) for generating a trajectory for a host vehicle (A), The processor determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; Setting a control parameter for executing avoidance travel based on the determined trajectory based on the degree of slippage; configured to run the control parameters include at least a braking correction value that corrects a braking operation amount; Setting the control parameters is This includes setting a braking correction value for each wheel of the host vehicle.

[0008] The present disclosure four According to one aspect, there is provided a trajectory generation device having a processor (102), configured to be mountable on a host vehicle (A), and configured to generate a trajectory for the host vehicle, The processor determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; configured to run 、 Estimating the degree of slippage is and estimating, as a slippage degree, a braking distance estimated when traveling on each of the trajectory candidates; To determine the trajectory, and determining, as a trajectory, a candidate trajectory whose braking distance satisfies a determined braking condition. . A fifth aspect of the present disclosure is a trajectory generation device having a processor (102), configured to be mountable on a host vehicle (A), and configured to generate a trajectory for the host vehicle to travel, The processor determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; configured to run Estimating the degree of slippage is The method includes estimating the degree of slipperiness in a road surface region (R) including a predicted contact region between the host vehicle and the road surface when passing through a specific point (P) on the trajectory candidate. A sixth aspect of the present disclosure is a trajectory generation device having a processor (102), configured to be mountable on a host vehicle (A), and configured to generate a trajectory for the host vehicle to travel, The processor determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; Setting a control parameter for executing avoidance travel based on the determined trajectory based on the degree of slippage; configured to run the control parameters include at least a braking correction value that corrects a braking operation amount; Setting the control parameters is This includes setting a braking correction value for each wheel of the host vehicle.

[0009] The present disclosure seven The embodiment is a trajectory generation method executed by a processor (102) to generate a trajectory for a host vehicle (A), the method comprising: determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; Including fruit, Estimating the degree of slippage is and estimating, as a slippage degree, a braking distance estimated when traveling on each of the trajectory candidates; To determine the trajectory, and determining, as a trajectory, a candidate trajectory whose braking distance satisfies a determined braking condition. . An eighth aspect of the present disclosure is a trajectory generation method executed by a processor (102) to generate a trajectory for a host vehicle (A), the method comprising: determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; Including, Estimating the degree of slippage is The method includes estimating the degree of slipperiness in a road surface region (R) including a predicted contact region between the host vehicle and the road surface when passing through a specific point (P) on the trajectory candidate. A ninth aspect of the present disclosure is a trajectory generation method executed by a processor (102) to generate a trajectory for a host vehicle (A), the method comprising: determining whether to perform avoidance driving to avoid an avoidance target ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance driving; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled during avoidance travel from a plurality of trajectory candidates based on each degree of slippage; Setting a control parameter for executing avoidance travel based on the determined trajectory based on the degree of slippage; Including, the control parameters include at least a braking correction value that corrects a braking operation amount; Setting the control parameters is This includes setting a braking correction value for each wheel of the host vehicle.

[0010] The present disclosure ten The embodiment is a trajectory generation program stored in a storage medium (101) for generating a trajectory for a host vehicle (A), the trajectory generation program including instructions to be executed by a processor (102), The command is, determining whether to perform avoidance driving to avoid an avoidance target in front of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance traveling; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled in the avoidance travel from among a plurality of trajectory candidates based on each degree of slippage; Including fruit, The degree of slippage can be estimated by The method includes estimating the braking distance estimated when traveling on each of the trajectory candidates as the degree of slippage, To determine the trajectory, and determining, as a trajectory, a candidate trajectory whose braking distance satisfies a determined braking condition. . An eleventh aspect of the present disclosure is a trajectory generation program stored in a storage medium (101) for generating a trajectory for a host vehicle (A), the trajectory generation program including instructions to be executed by a processor (102), the trajectory generation program including: The command is, determining whether to perform avoidance driving to avoid an avoidance target in front of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance traveling; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled in the avoidance travel from among a plurality of trajectory candidates based on each degree of slippage; Including, The degree of slippage can be estimated by This includes estimating the degree of slipperiness in a road surface region (R) including a predicted contact region between the host vehicle and the road surface when passing through a specific point (P) in the trajectory candidate. A twelfth aspect of the present disclosure is a trajectory generation program stored in a storage medium (101) for generating a trajectory for a host vehicle (A), the trajectory generation program including instructions to be executed by a processor (102), the trajectory generation program including: The command is, determining whether to perform avoidance driving to avoid an avoidance target in front of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for trajectories in avoidance traveling; Estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining a trajectory to be traveled in the avoidance travel from among a plurality of trajectory candidates based on each degree of slippage; Setting a control parameter for executing avoidance travel based on the determined trajectory based on the degree of slippage; Including, the control parameters include at least a braking correction value that corrects a braking operation amount; Setting the control parameters This includes having braking correction values ​​set for each wheel of the host vehicle.

[0011] These first to second Twelve According to this aspect, the degree of road surface slipperiness when traveling along each of a plurality of trajectory candidates is predicted, and a trajectory to be traveled during avoidance travel is determined from the trajectory candidates based on each degree of slipperiness. Therefore, a trajectory can be determined from the trajectory candidates after taking into consideration the degree of slipperiness when traveling along each of the plurality of trajectory candidates. Therefore, avoidance travel with ensured stability can be achieved. [Brief explanation of the drawings]

[0012] [Figure 1] FIG. 1 is a block diagram showing the overall configuration of a first embodiment. [Figure 2] FIG. 2 is a schematic diagram showing a traveling environment of a host vehicle to which the first embodiment is applied. [Figure 3] FIG. 1 is a block diagram showing a functional configuration of a trajectory generation system according to a first embodiment. [Figure 4] 3 is a flowchart illustrating a trajectory generation method according to the first embodiment. [Figure 5] 10 is a graph showing an example of the relationship between brightness, contrast ratio, and coefficient of friction in a captured image. [Figure 6] 10 is a flowchart illustrating a trajectory generation method according to a second embodiment. [Figure 7] 10 is a graph showing an example of a relationship between a friction coefficient and a brake gain. [Figure 8] 10 is a graph showing an example of a relationship between a friction coefficient and a steering angle gain. [Figure 9] 10 is a graph showing an example of the relationship between the slip ratio and the friction coefficient based on internal information. [Figure 10] FIG. 11 is a diagram illustrating an example of setting a road surface area in the third embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0013] Hereinafter, multiple embodiments of the present disclosure will be described with reference to the drawings. Note that corresponding components in each embodiment are designated by the same reference numerals, and redundant description may be omitted. Furthermore, when only a portion of the configuration is described in each embodiment, the configuration of another previously described embodiment may be applied to the remaining portions of the configuration. Furthermore, in addition to the combinations of configurations explicitly stated in the description of each embodiment, configurations of multiple embodiments may be partially combined together even if not explicitly stated, provided that there is no particular problem with the combination.

[0014] Hereinafter, several embodiments of the present disclosure will be described with reference to the drawings.

[0015] (First embodiment) The trajectory generation system 100 of the first embodiment shown in FIG. 1 generates a trajectory for a host vehicle A shown in FIG. 2. From a perspective centered on the host vehicle A, the host vehicle A can also be said to be an ego-vehicle. From a perspective centered on the host vehicle A, the target moving body B can also be said to be an other road user. The host vehicle A is a moving body such as an automobile that can travel on a roadway with an occupant on board. From a perspective centered on the host vehicle A, the target moving body 3 can also be said to be an other road user. The target moving body B includes at least one of, for example, an automobile, a truck, a motorcycle, a bicycle, an autonomous robot, a pedestrian, and an animal.

[0016] The host vehicle A is provided with an autonomous driving mode that is classified into levels according to the degree of manual intervention by the occupant in the driving task. The autonomous driving mode may be realized by autonomous driving control, such as conditional driving automation, high driving automation, or full driving automation, in which the system performs all driving tasks when activated. The autonomous driving mode may also be realized by advanced driving assistance control, such as driving assistance or partial driving automation, in which the occupant performs some or all driving tasks. The autonomous driving mode may be realized by either autonomous driving control or advanced driving assistance control, or by a combination of these, or by switching between them.

[0017] The host vehicle A is equipped with a sensor system 10, a communication system 20, a map database (hereinafter referred to as "DB") 30, and a driving system 40, all of which are shown in Fig. 3. The sensor system 10 acquires sensor information that can be used by the trajectory generation system 100 by detecting the external and internal worlds of the host vehicle A. To this end, the sensor system 10 is configured to include an external sensor 11 and an internal sensor 12.

[0018] The external sensor 11 acquires external information that can be used by the trajectory generation system 100 from the external world that is the surrounding environment of the host vehicle A. The external sensor 11 may acquire the external information by detecting targets that exist in the external world of the host vehicle A. The target detection type external sensor 11 is at least one of a camera, LiDAR (Light Detection and Ranging / Laser Imaging Detection and Ranging), radar, sonar, etc.

[0019] The internal sensor 12 acquires internal information that can be used by the trajectory generation system 100 from the internal world, which is the internal environment of the host vehicle A. The internal sensor 12 may acquire the internal information by detecting a specific physical quantity of motion in the internal world of the host vehicle A. The internal sensor 12 of the physical quantity detection type is at least one type of sensor, such as a traveling speed sensor, an acceleration sensor, or a gyro sensor.

[0020] The communication system 20 acquires communication information usable by the trajectory generation system 100 via wireless communication. The communication system 20 may receive positioning signals from artificial satellites of a Global Navigation Satellite System (GNSS) present in the external world of the host vehicle A. The positioning type communication system 20 is, for example, a GNSS receiver. The communication system 20 may transmit and receive communication signals to and from a V2X system present in the external world of the host vehicle A. The V2X type communication system 20 is, for example, at least one of a Dedicated Short Range Communications (DSRC) communication device and a Cellular V2X (C-V2X) communication device. The communication system 20 may transmit and receive communication signals to and from a terminal present in the internal world of the host vehicle A. The terminal communication type communication system 20 is, for example, at least one of a Bluetooth (registered trademark) device, a Wi-Fi (registered trademark) device, an infrared communication device, etc.

[0021] The map DB 30 stores map information that can be used by the trajectory generation system 100. The map DB 30 includes at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium. The map DB 30 may be a database of a locator that estimates the host vehicle A's own state quantities, including its own position. The map DB 30 may be a database of a navigation unit that navigates the host vehicle A's travel route. The map DB 30 may be configured by combining multiple types of these databases.

[0022] The map DB 30 acquires and stores the latest map information, for example, by communicating with an external center via a V2X type communication system 20. Here, the map information is converted into two-dimensional or three-dimensional data as information representing the driving environment of the host vehicle A. In particular, digital data of a high-precision map is preferably used as the three-dimensional map data. The map information may include road information representing at least one of the following: the position, shape, and road surface condition of the road itself. The map information may include marking information representing at least one of the following: the position and shape of signs and lane markings attached to the road. The map information may include structure information representing at least one of the following: the position and shape of buildings and traffic lights facing the road.

[0023] The map DB 30 acquires and stores the latest map information, for example, through communication with an external center. Here, the map information is converted into two-dimensional or three-dimensional data as information representing the driving environment of the host vehicle A. In particular, digital data of a high-precision map is preferably used as the three-dimensional map data. The map information may include road information representing at least one of the following: the position, shape, and road surface condition of the road itself. The map information may also include marking information representing at least one of the following: the position and shape of signs and lane markings attached to the road. The map information may also include structure information representing at least one of the following: the position and shape of buildings and traffic lights facing the road.

[0024] The traveling system 40 is configured to cause the body of the host vehicle A to travel based on commands from the trajectory generation system 100. The traveling system 40 includes a drive unit that drives the host vehicle A, a braking unit that brakes the host vehicle A, and a steering unit that steers the host vehicle A.

[0025] The trajectory generation system 100 is connected to a sensor system 10, a communication system 20, a map DB 30, and a driving system 40 via at least one of, for example, a LAN (Local Area Network) line, a wire harness, an internal bus, or a wireless communication line. The trajectory generation system 100 is configured to include at least one dedicated computer.

[0026] The dedicated computer constituting the trajectory generation system 100 may be a driving control ECU (Electronic Control Unit) that controls the driving of the host vehicle A. The dedicated computer constituting the trajectory generation system 100 may be a navigation ECU that navigates the driving route of the host vehicle A. The dedicated computer constituting the trajectory generation system 100 may be a locator ECU that estimates the self-state quantity of the host vehicle A. The dedicated computer constituting the trajectory generation system 100 may be an actuator ECU that controls the driving actuator of the host vehicle A. The dedicated computer constituting the trajectory generation system 100 may be an HCU (Human Machine Interface Control Unit (HMI)) that controls the presentation of information in the host vehicle A. The dedicated computer constituting the trajectory generation system 100 may be a computer other than the host vehicle A that constitutes an external center or a mobile terminal that can communicate via, for example, a V2X type communication system 20.

[0027] The dedicated computer constituting the trajectory generation system 100 may be an integrated ECU (Electronic Control Unit) that integrates the driving control of the host vehicle A. The dedicated computer constituting the trajectory generation system 100 may be a judgment ECU that judges a driving task in the driving control of the host vehicle A. The dedicated computer constituting the trajectory generation system 100 may be a monitoring ECU that monitors the driving control of the host vehicle A. The dedicated computer constituting the trajectory generation system 100 may be an evaluation ECU that evaluates the driving control of the host vehicle A.

[0028] The dedicated computer constituting the trajectory generation system 100 has at least one memory 101 and one processor 102. The memory 101 is at least one type of non-transitory tangible storage medium, such as a semiconductor memory, a magnetic medium, or an optical medium, that non-temporarily stores computer-readable programs, data, and the like. Here, "storage" may refer to accumulation in which data is retained even when the host vehicle A is turned off, or may refer to temporary storage in which data is erased when the host vehicle A is turned off. The processor 102 includes at least one type of core, such as a central processing unit (CPU), a graphics processing unit (GPU), a reduced instruction set computer (RISC)-CPU, a data flow processor (DFP), or a graph streaming processor (GSP).

[0029] In the trajectory generation system 100, a processor 102 executes a plurality of instructions included in a trajectory generation program stored in a memory 101 in order to generate a trajectory along which the host vehicle A will travel. In this way, the trajectory generation system 100 constructs a plurality of functional blocks for generating a trajectory along which the host vehicle A will travel. The plurality of functional blocks constructed in the trajectory generation system 100 include an avoidance judgment block 110, a trajectory candidate generation block 120, a road surface condition estimation block 130, a trajectory selection block 140, and a cruise control block 150, as shown in FIG. 3 .

[0030] The flow of a trajectory generation method (hereinafter referred to as a trajectory generation flow) in which the trajectory generation system 100 generates a trajectory for the host vehicle A to travel by using these blocks 110, 120, 130, and 140 in cooperation with each other will be described below with reference to Fig. 4. This processing flow is repeatedly executed while the host vehicle A is running. Note that each "S" in this processing flow represents multiple steps that are executed by multiple commands included in the trajectory generation program.

[0031] First, in S100, the avoidance decision block 110 determines whether or not avoidance driving is necessary for the host vehicle A. The avoidance decision block 110 determines that avoidance driving is necessary when there is an object to be avoided ahead in the traveling direction of the host vehicle A. The avoidance driving here is assumed to be, for example, a control to stop after avoiding the object to be avoided.

[0032] For example, the object to be avoided may be an obstacle that exists within a predetermined distance range from the host vehicle A. Or, the object to be avoided may be an obstacle whose time to collision with the host vehicle A is within a predetermined time range. The obstacle is an object that obstructs the travel of the host vehicle A, and includes at least one of the following: a preceding vehicle that has suddenly stopped due to an accident or the like; an object that has fallen on the road; a pedestrian or vehicle that has jumped into the host vehicle's lane; etc. The avoidance decision block 110 may determine whether avoidance travel is necessary based on external information.

[0033] The avoidance decision block 110 repeatedly executes this decision process periodically or at any timing until it determines that avoidance driving is necessary.

[0034] If it is determined that avoidance travel is necessary, the flow proceeds to S110. In S110, the trajectory candidate generation block 120 generates multiple trajectory candidates TC that are candidates for the trajectory (avoidance trajectory) along which the host vehicle A will travel in the avoidance travel control.

[0035] In the example shown in FIG. 2, the trajectory candidate generation block 120 generates two trajectory candidates TC for avoiding a target moving object B, which is an avoidance target and exists ahead of the host vehicle lane La in which the host vehicle is currently traveling. Specifically, the trajectory candidate generation block 120 generates a trajectory candidate TC for avoiding the target moving object B into a lane Lb adjacent to the host vehicle lane La, and a trajectory candidate TC for avoiding the target moving object B into a shoulder S. Note that the avoidance target in FIG. 2 is a preceding vehicle stopped on the road. For example, each trajectory candidate TC is a planned traveling trajectory for achieving emergency avoidance by decelerating from the current position as a start point and avoiding the avoidance target, and stopping at an end point. The trajectory candidate TC specifies at least a plurality of future positions of the host vehicle A. The trajectory candidate TC may further specify the motion state of the host vehicle A, such as the speed and acceleration, at each future position. Note that three or more trajectory candidates TC may be generated. For example, in the situation shown in FIG. 2, two or more candidate trajectories TC for avoiding onto the adjacent lane Lb and two or more candidate trajectories TC for avoiding onto the shoulder S may be generated.

[0036] In the next step S120, the road surface condition estimation block 130 identifies a road surface region R for predicting the degree of slipperiness of the road surface when traveling along each trajectory candidate TC. The degree of slipperiness is a parameter that indicates the slipperiness of the road surface. The road surface region R is a region that includes an area where the host vehicle A is expected to come into contact with the road surface when traveling along the trajectory candidate TC. For example, the road surface region R is a region that has a predetermined width in the vehicle width direction and is centered on the trajectory candidate TC. The width of the road surface region R is, for example, the length of the vehicle width plus a margin. The road surface region R is, for example, the region from the start point to the end point of the trajectory candidate TC.

[0037] Then, in S130, the road surface condition estimation block 130 estimates the friction coefficient μ of the road surface in each road surface region R as the degree of slipperiness. A larger friction coefficient μ means a smaller degree of slipperiness. The road surface condition estimation block 130 estimates the friction coefficient μ based on external information. For example, the road surface condition estimation block 130 estimates the friction coefficient μ based on an image of the road surface in the road surface region R captured by a camera. Specifically, the road surface condition estimation block 130 estimates the friction coefficient μ based on the brightness (e.g., the average value of the entire road surface region R) and the contrast ratio in the road surface region R of the image. For example, the road surface condition estimation block 130 estimates the friction coefficient μ by retaining or acquiring relationship information between the brightness, the contrast ratio, and the friction coefficient μ, such as the graph shown in FIG. 5. This relationship information may be represented as a graph, a table, or a mathematical formula. 5 is a two-dimensional graph showing the relationship between the friction coefficient μ and the brightness and contrast ratio when the combinations shown by the dotted lines are used. In this case, the higher the brightness and the lower the contrast ratio, the larger the friction coefficient μ becomes, and vice versa.

[0038] In the next step S140, the trajectory selection block 140 selects an avoidance trajectory from the trajectory candidates TC based on the friction coefficient μ in the road surface region R of each trajectory candidate TC. The trajectory selection block 140 determines, as the avoidance trajectory, the trajectory candidate TC whose friction coefficient μ satisfies the confirmed friction condition. For example, the confirmed friction condition may be the largest value among multiple friction coefficients μ. Alternatively, the confirmed friction condition may be that the friction coefficient μ reaches a confirmed range. Here, the confirmed range is a numerical range in which the friction coefficient μ is equal to or greater than a threshold value. Note that if there are multiple friction coefficients μ that reach the confirmed range, the trajectory selection block 140 may determine, as the avoidance trajectory, the trajectory selection block 140 selects the trajectory candidate TC corresponding to the largest friction coefficient μ. Alternatively, the trajectory selection block 140 may determine, based on other conditions, an avoidance trajectory from the trajectory candidates TC whose friction coefficient μ reaches the confirmed range.

[0039] Furthermore, in S150, the cruise control block 150 executes avoidance cruise control based on the determined avoidance trajectory. In the avoidance cruise control, the cruise control block 150 sequentially sets target braking and steering amounts to realize cruise along the avoidance trajectory, and controls the cruise system 40 based on each parameter. When the host vehicle A reaches the end point of the avoidance trajectory, this flow ends.

[0040] According to the first embodiment described above, the degree of slipperiness of the road surface when traveling along each of the multiple trajectory candidates TC is predicted, and a trajectory to be traveled during avoidance traveling is determined from the trajectory candidates TC based on each degree of slipperiness. Therefore, a trajectory can be determined from the trajectory candidates TC after taking into consideration the degree of slipperiness when traveling along each of the multiple trajectory candidates TC. Therefore, avoidance traveling while ensuring stability can be achieved.

[0041] Second Embodiment As shown in FIG. 9, the second embodiment is a modification of the first embodiment.

[0042] A trajectory generation flow in which the trajectory generation system 100 generates a trajectory for the host vehicle A in the second embodiment will be described below with reference to Fig. 6. This processing flow is repeatedly executed while the host vehicle A is running.

[0043] Steps S200, S210, S220, and S230 are the same processes as steps S100, S110, S120, and S130 in the first embodiment.

[0044] In S240, which follows S230, the cruise control block 150 sets control parameters for when executing avoidance cruise along each candidate trajectory TC. The cruise control block 150 sets the control parameters based on the estimated friction coefficient μ. For example, the cruise control block 150 sets a brake gain and a steering angle gain based on the friction coefficient μ. The brake gain is an example of a braking correction value that corrects the braking operation amount. The brake gain is set as the ratio of the braking operation amount (e.g., braking torque) actually output to the braking operation amount. The steering angle gain is an example of a steering correction value that configures the steering operation amount. The steering angle gain is set as the ratio of the steering operation amount (e.g., steering angle) actually output to the steering operation amount. Specifically, as shown in FIG. 7, the cruise control block 150 sets a larger brake gain as the friction coefficient μ decreases. In addition, as shown in FIG. 8, the cruise control block 150 sets a smaller steering angle gain as the friction coefficient μ decreases.

[0045] The cruise control block 150 may change the brake gain for each wheel of the host vehicle A. For example, when the friction coefficient μ is equal to or less than a threshold value, the cruise control block 150 may set the brake gain for the wheels located on the inside of the curve on the avoidance trajectory to be greater than the brake gain for the wheels located on the outside. Alternatively, the cruise control block 150 may set the brake gain according to the longitudinal acceleration acting on the host vehicle A. In other words, the cruise control block 150 may set different brake gains for the front wheels and the rear wheels so as to eliminate tilt of the vehicle body in the pitch direction.

[0046] In the next step S250, the trajectory selection block 140 calculates the braking distance required to perform avoidance driving along each of the trajectory candidates TC as the degree of slippage. A larger braking distance indicates a larger degree of slippage. For example, the trajectory selection block 140 may calculate the braking distance based on the estimated friction coefficient μ and the vehicle speed at the start of avoidance driving. Then, in step S260, the trajectory selection block 140 determines a trajectory from the trajectory candidates TC based on the braking distance.

[0047] The trajectory selection block 140 determines, as the avoidance trajectory, a trajectory candidate TC whose braking distance satisfies the determined braking condition. In this embodiment, the determined braking condition may be that the braking distance is the smallest value. Alternatively, the determined braking condition may be that the braking distance falls within a determined range. Here, the determined range is a numerical range in which the braking distance is equal to or less than a threshold value. Note that, if there are multiple braking distances that fall within the determined range, the trajectory selection block 140 may determine, as the avoidance trajectory, the trajectory candidate TC corresponding to the smallest braking distance. Alternatively, the trajectory selection block 140 may determine, based on other conditions, an avoidance trajectory from among the trajectory candidates TC whose braking distance falls within the determined range.

[0048] Furthermore, in S270, the cruise control block 150 executes avoidance cruise control based on the determined avoidance trajectory. In the avoidance cruise control, the cruise control block 150 controls the cruise system 40 based on the control parameters set in S240.

[0049] Furthermore, the cruise control block 150 may reset the control parameters based on the friction coefficient μ calculated according to the motion state of the host vehicle A after the start of the avoidance travel. Specifically, the cruise control block 150 may calculate the friction coefficient μ based on the relationship between the slip ratio and the friction coefficient μ as shown in FIG. 9. The slip ratio is a value obtained by dividing the wheel speed change rate by the acceleration. In other words, the cruise control block 150 may calculate the friction coefficient μ based on internal information acquired from the wheel speed sensor, the inertial sensor, etc. When the host vehicle A has completed traveling along the avoidance trajectory, this flow ends.

[0050] According to the second embodiment described above, the braking distance estimated when traveling along each trajectory candidate TC is estimated as the degree of slippage, and the trajectory candidate TC whose braking distance satisfies the confirmed braking condition is confirmed as the trajectory. Therefore, the trajectory is confirmed after taking into consideration the braking distance as the influence that the road surface condition has on the host vehicle A. Therefore, stability in avoidance traveling can be more reliably ensured.

[0051] Furthermore, according to the second embodiment, the control parameters for executing avoidance maneuvering on the determined trajectory are set based on the degree of slippage. Therefore, the estimated degree of slippage can be used for the control parameters for avoidance maneuvering. Therefore, stability in avoidance maneuvering can be more reliably ensured.

[0052] Furthermore, according to the second embodiment, the brake gain can be set for each wheel of the host vehicle A. Therefore, by adjusting the brake gain, it may be possible to make the avoidance driving of the host vehicle A more stable.

[0053] (Third embodiment) As shown in FIG. 10, the third embodiment is a modification of the first embodiment.

[0054] At S120 in the third embodiment, the road surface condition estimation block 130 identifies the road surface region R as a region including a region of the trajectory candidate TC that is expected to contact the road surface when passing through the specific point P (see FIG. 10 ). In this case, the road surface region R is a region corresponding to a limited section of the trajectory candidate TC that includes the specific point P. That is, the length of the road surface region R in the extension direction of the trajectory candidate TC is shorter than that of the trajectory candidate TC. For example, the road surface condition estimation block 130 may determine the point at which the yaw rate acting on the host vehicle A is greatest as the specific point P. Alternatively, the road surface condition estimation block 130 may determine the point at which the steering angle is greatest as the specific point P. Alternatively, the road surface condition estimation block 130 may determine the point at which braking starts as the specific point P. Alternatively, the road surface condition estimation block 130 may determine the point at which the amount of brake application is greatest as the specific point P.

[0055] According to the third embodiment described above, the degree of slipperiness in the road surface region R including the expected contact region of the host vehicle A with the road surface when passing through the specific point P in the trajectory candidate TC is predicted. Therefore, the trajectory can be determined taking into consideration the degree of slipperiness when passing through the specific point P, where the degree of slipperiness should be particularly taken into consideration in avoidance driving. Therefore, it may be possible to make the avoidance driving of the host vehicle A more stable.

[0056] (Other embodiments) Although multiple embodiments have been described above, the present disclosure should not be construed as being limited to those embodiments, and can be applied to various embodiments and combinations within the scope that does not deviate from the gist of the present disclosure.

[0057] In a modified example, the cruise control block 150 may include the regeneration rate of the regenerative brake in the control parameters that are set according to the degree of slippage.

[0058] In a modified example, the trajectory selection block 140 may select whether to determine as a trajectory a trajectory candidate TC whose friction coefficient μ satisfies a fixed friction condition or a trajectory candidate TC whose braking distance satisfies a fixed braking condition, depending on the urgency of the avoidance driving. Specifically, when the urgency of the avoidance driving is high, the trajectory selection block 140 selects as a trajectory a trajectory candidate TC whose friction coefficient μ satisfies a fixed friction condition. The urgency of the avoidance driving includes at least one of the following: the distance to the avoidance target, the magnitude of the overall degree of slipperiness of the multiple trajectory candidates TC, etc. For example, the shorter the distance to the avoidance target, the higher the urgency is determined to be. Alternatively, the greater the overall degree of slipperiness of the multiple trajectory candidates TC, the higher the urgency is determined to be. The overall degree of slipperiness of the multiple trajectory candidates TC may be, for example, the largest of the respective degrees of slipperiness.

[0059] In a modified example, the road surface condition estimation block 130 may identify the road surface region R corresponding to one trajectory candidate TC as a pair of regions set on the left and right of the trajectory candidate TC. In this case, the pair of regions set on the left and right of the trajectory candidate TC is a pair of a region where the left wheel of the host vehicle A is estimated to come into contact and a region where the right wheel of the host vehicle A is estimated to come into contact.

[0060] In a modified example, the dedicated computer constituting the trajectory generation system 100 may have at least one of a digital circuit and an analog circuit as a processor. Here, the digital circuit is at least one of the following: an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a system on a chip (SOC), a programmable gate array (PGA), and a complex programmable logic device (CPLD). Such a digital circuit may also have a memory that stores a program.

[0061] In addition to the forms described above, the trajectory generation system 100 according to the above-mentioned embodiments and modifications may be implemented as a trajectory generation device that is a processing device (e.g., a processing ECU) mounted on the host vehicle A. Furthermore, the above-mentioned embodiments and modifications may be implemented as a semiconductor device (e.g., a semiconductor chip) having at least one processor 102 and one memory 101 of the trajectory generation system 100. [Explanation of symbols]

[0062] 100: Trajectory generation system, 101: Memory (storage medium), 102: Processor, A: Host vehicle, TC: Trajectory candidate, R: Road surface area, P: Specific point.

Claims

1. A trajectory generation system having a processor (102) for generating a trajectory for a host vehicle (A), The processor: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; configured to run The estimation of the degree of slippage is performed by: estimating, as the degree of slippage, a braking distance estimated when traveling on each of the trajectory candidates; determining the trajectory includes: and determining, as the trajectory, the trajectory candidate whose braking distance satisfies a determined braking condition.

2. The estimation of the degree of slippage is performed by: The trajectory generation system according to claim 1, further comprising estimating the degree of slipperiness in a road surface region (R) including an expected contact region between the host vehicle and the road surface when passing through a specific point (P) in the trajectory candidate.

3. A trajectory generation system having a processor (102) for generating a trajectory for a host vehicle (A), comprising: The processor: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; configured to run The estimation of the degree of slippage is performed by: A trajectory generation system that includes estimating the degree of slipperiness in a road surface region (R) that includes an expected contact region between the host vehicle and the road surface when passing through a specific point (P) in the trajectory candidate.

4. The trajectory generation system according to claim 1 , further comprising: setting, based on the degree of slippage, a control parameter for executing the avoidance traveling based on the determined trajectory.

5. 5. The trajectory generation system according to claim 4, wherein the control parameters include at least one of a braking correction value for correcting a braking operation amount and a steering correction value for correcting a steering operation amount.

6. A trajectory generation system having a processor (102) for generating a trajectory for a host vehicle (A), comprising: The processor: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; setting a control parameter for executing the avoidance travel based on the determined trajectory based on the degree of slippage; configured to run the control parameters include at least a braking correction value that corrects a braking operation amount, setting the control parameters A trajectory generation system including setting the braking correction value for each wheel of the host vehicle.

7. The estimation of the degree of slippage is performed by: estimating, as the degree of slippage, a braking distance estimated when traveling on each of the trajectory candidates; determining the trajectory includes: The trajectory generation system according to claim 6 , further comprising determining, as the trajectory, the trajectory candidate whose braking distance satisfies a determined braking condition.

8. Each of the estimations of the degree of slippage comprises:

8. The trajectory generation system according to claim 6 or claim 7, further comprising estimating the degree of slipperiness in a road surface region (R) including an expected contact region between the host vehicle and the road surface when passing through a specific point (P) in the trajectory candidate.

9. The estimation of the degree of slippage is performed by: estimating a friction coefficient of the road surface as the degree of slipperiness; determining the trajectory includes: The trajectory generation system according to claim 1 , further comprising determining, as the trajectory, the trajectory candidate whose friction coefficient satisfies a determined friction condition.

10. The estimation of the degree of slippage is performed by: The trajectory generation system according to any one of claims 1 to 9, further comprising estimating the degree of slipperiness in a road surface region (R) including an expected contact region between the host vehicle and the road surface when traveling along the trajectory candidate.

11. A trajectory generation device having a processor (102), configured to be mountable on a host vehicle (A), and configured to generate a trajectory for the host vehicle (A), The processor: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; configured to run The estimation of the degree of slippage is performed by: estimating, as the degree of slippage, a braking distance estimated when traveling on each of the trajectory candidates; determining the trajectory includes: and determining, as the trajectory, the trajectory candidate whose braking distance satisfies a determined braking condition.

12. A trajectory generation device having a processor (102), configured to be mountable on a host vehicle (A), and generating a trajectory for the host vehicle, comprising: The processor: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; configured to run The estimation of the degree of slippage is performed by: A trajectory generation device that includes estimating the degree of slipperiness in a road surface area (R) that includes an expected contact area between the host vehicle and the road surface when passing through a specific point (P) in the trajectory candidate.

13. A trajectory generation device having a processor (102), configured to be mountable on a host vehicle (A), and generating a trajectory for the host vehicle, comprising: The processor: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; setting a control parameter for executing the avoidance travel based on the determined trajectory based on the degree of slippage; configured to run the control parameters include at least a braking correction value that corrects a braking operation amount, setting the control parameters A trajectory generation device that includes setting the braking correction value for each wheel of the host vehicle.

14. A trajectory generation method executed by a processor (102) to generate a trajectory for a host vehicle (A), comprising: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; Including, The estimation of the degree of slippage is performed by: estimating, as the degree of slippage, a braking distance estimated when traveling on each of the trajectory candidates; determining the trajectory includes: and determining, as the trajectory, the trajectory candidate whose braking distance satisfies a determined braking condition.

15. A trajectory generation method executed by a processor (102) to generate a trajectory for a host vehicle (A), comprising: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; Including, The estimation of the degree of slippage is performed by: A trajectory generation method including estimating the degree of slipperiness in a road surface region (R) including an expected contact region between the host vehicle and the road surface when passing through a specific point (P) in the trajectory candidate.

16. A trajectory generation method executed by a processor (102) to generate a trajectory for a host vehicle (A), comprising: determining whether to perform avoidance driving to avoid an object to be avoided ahead of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance travel from the plurality of trajectory candidates based on each of the degrees of slippage; setting a control parameter for executing the avoidance travel based on the determined trajectory based on the degree of slippage; Including, the control parameters include at least a braking correction value that corrects a braking operation amount, setting the control parameters A trajectory generation method comprising: setting the braking correction value for each wheel of the host vehicle.

17. A trajectory generation program stored in a storage medium (101) for generating a trajectory for a host vehicle (A) and including instructions to be executed by a processor (102), The instruction: determining whether to perform avoidance driving to avoid an object to be avoided in front of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance traveling from among the plurality of trajectory candidates based on each of the degrees of slippage; Including, The estimation of the degree of slippage is performed by: and estimating, as the degree of slippage, a braking distance estimated when traveling on each of the trajectory candidates, determining the trajectory includes: and determining, as the trajectory, the trajectory candidate whose braking distance satisfies a determined braking condition.

18. A trajectory generation program stored in a storage medium (101) for generating a trajectory for a host vehicle (A) and including instructions to be executed by a processor (102), The instruction: determining whether to perform avoidance driving to avoid an object to be avoided in front of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance traveling from among the plurality of trajectory candidates based on each of the degrees of slippage; Including, The estimation of the degree of slippage is performed by: A trajectory generation program that includes estimating the degree of slipperiness in a road surface region (R) that includes an expected contact region between the host vehicle and the road surface when passing through a specific point (P) in the trajectory candidate.

19. A trajectory generation program stored in a storage medium (101) for generating a trajectory for a host vehicle (A) and including instructions to be executed by a processor (102), The instruction: determining whether to perform avoidance driving to avoid an object to be avoided in front of the host vehicle; generating a plurality of trajectory candidates (TC) that are candidates for the trajectory in the avoidance traveling; estimating the degree of slipperiness of the road surface when traveling along each of the trajectory candidates; determining the trajectory to be traveled during the avoidance traveling from among the plurality of trajectory candidates based on each of the degrees of slippage; setting a control parameter for executing the avoidance travel based on the determined trajectory based on the degree of slippage; Including, the control parameters include at least a braking correction value that corrects a braking operation amount, setting the control parameters a trajectory generation program that causes the braking correction value to be set for each wheel of the host vehicle;

Images