Self-position estimation device, self-position estimation system, self-position estimation method, and self-position estimation program

Abstract

To provide a self-position estimation device that can improve self-position estimation accuracy by dead reckoning, a self-position estimation system, a self-position estimation method, and a self-position estimation program.SOLUTION: A tire data acquisition part 22 acquires tire data including tire cornering power as tire information, a vehicle travel data acquisition part 24 acquires vehicle travel data including a vehicle longitudinal speed detected by a vehicle travel data detection part as vehicle information, a calculation part 26 calculates a vehicle lateral speed on the basis of the tire data and the vehicle travel data, and an estimation part 28 estimates a self-position of a vehicle 12 on the basis of the vehicle longitudinal speed and the vehicle lateral speed.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present disclosure relates to a self-location estimation device, a self-location estimation system, a self-location estimation method, and a self-location estimation program. [Background technology]

[0002] Patent Document 1 proposes an autonomous driving method in which the state variables of a vehicle's control model are updated in real time while being controlled, and the state variables are the vehicle's own weight, gradient, center of gravity position, and tire cornering coefficient, and the vehicle's own weight and gradient are estimated using an equation for the acceleration generated in response to accelerator input, while the center of gravity position and tire cornering coefficient are estimated using GPS and wheel speed, and these estimated changed state variables are substituted into the control model.

[0003] Patent Document 2 proposes a manager mounted on a vehicle, which includes a reception unit that receives a plurality of action plans from a plurality of ADAS applications, each of which includes first information that represents the lateral movement of the vehicle; an arbitration unit that arbitrates the plurality of action plans; a calculation unit that calculates a movement request based on the arbitration result by the arbitration unit; a first output unit that distributes the movement request to at least one of a plurality of actuator systems; and a second output unit that outputs second information used to generate the first information to at least one of the plurality of ADAS applications. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2023-030490 [Patent Document 2] Japanese Patent Publication No. 2022-187753 Summary of the Invention [Problem to be solved by the invention]

[0005] Various technologies related to autonomous driving have been proposed, as in Patent Documents 1 and 2, but currently, scan matching methods using LiDAR (Light Detection and Ranging) are commonly used to estimate the self-position of autonomous vehicles. However, scan matching methods using LiDAR are not good at handling tunnels and open areas with few distinctive features, or areas with lush vegetation where the environment is subject to change.

[0006] On the other hand, dead reckoning is known, which estimates the vehicle's position by calculating the distance traveled from the vehicle's direction and speed. Although dead reckoning is less susceptible to the influence of the surrounding environment, it depends on the accuracy of the calculation method and measurements, and calculation errors accumulate, making it less accurate than self-position estimation methods such as LiDAR and GPS.

[0007] The present disclosure aims to provide a self-location estimation device, a self-location estimation system, a self-location estimation method, and a self-location estimation program that can be expected to improve errors through calculation methods and that can improve the accuracy of self-location estimation through dead reckoning. [Means for solving the problem]

[0008] In order to achieve the above object, the self-position estimation device according to the first aspect includes a first acquisition unit that acquires tire information including tire cornering power, a second acquisition unit that acquires vehicle information including vehicle longitudinal speed, a calculation unit that calculates vehicle lateral speed based on the tire information and the vehicle information, and an estimation unit that estimates the vehicle's self-position based on the vehicle longitudinal speed and the vehicle lateral speed.

[0009] The self-location estimation device according to the second aspect is the self-location estimation device according to the first aspect, wherein the calculation unit calculates a sideslip angle at the position of the vehicle's center of gravity based on the tire information and the vehicle information, and calculates the vehicle lateral speed based on the sideslip angle at the position of the vehicle's center of gravity and the vehicle longitudinal speed.

[0010] A self-location estimation device according to a third aspect is the self-location estimation device according to the second aspect, wherein the first acquisition unit acquires the tire information including the tire cornering power according to a wear state of the tire.

[0011] A self-position estimation device according to a fourth aspect is the self-position estimation device according to any one of the first to third aspects, wherein the vehicle longitudinal speed is derived by multiplying the circumference of a tire by the number of rotations of the tire.

[0012] A self-position estimation device according to a fifth aspect is a self-position estimation device according to any one of the first to fourth aspects, wherein the tire information further includes tire wear information, and the estimation unit corrects the vehicle longitudinal speed according to the wear information, and estimates the vehicle's self-position based on the corrected vehicle longitudinal speed and vehicle lateral speed.

[0013] A self-position estimation device according to a sixth aspect is the self-position estimation device according to the second aspect, wherein the calculation unit calculates the side slip angle of the vehicle center of gravity position based on specification information determined for each vehicle, the tire information, and the vehicle information.

[0014] A self-location estimation system according to a seventh aspect includes a first self-location estimation device that estimates a self-location based on image information and a self-location estimation device according to the first aspect, and when a predetermined condition is met, switches from the first self-location estimation device to the self-location estimation device to estimate the vehicle's self-location.

[0015] In a self-position estimation method according to an eighth aspect, a computer acquires tire information including tire cornering power, acquires vehicle information including vehicle longitudinal speed, calculates vehicle lateral speed based on the tire information and the vehicle information, and performs processing to estimate the vehicle's self-position based on the vehicle longitudinal speed and the vehicle lateral speed.

[0016] A self-position estimation program according to a ninth aspect causes a computer to acquire tire information including tire cornering power, acquire vehicle information including vehicle longitudinal speed, calculate vehicle lateral speed based on the tire information and the vehicle information, and execute a process of estimating the vehicle's self-position based on the vehicle longitudinal speed and the vehicle lateral speed. [Effects of the Invention]

[0017] The present disclosure has an effect of providing a self-location estimation device, a self-location estimation system, a self-location estimation method, and a self-location estimation program that can improve the self-location estimation accuracy by dead reckoning. [Brief explanation of the drawings]

[0018] [Figure 1] 1 is a block diagram showing a schematic configuration of a vehicle equipped with a self-position estimation unit as a self-position estimation device according to the present embodiment. [Figure 2] FIG. 2 is a block diagram showing the configuration of a self-position estimation unit and an automatic driving control unit. [Figure 3] FIG. 2 is a functional block diagram showing the functional configuration of a self-position estimation unit according to the present embodiment. [Figure 4] 10A and 10B are diagrams illustrating self-position estimation by dead reckoning when only the vehicle longitudinal speed Vx is taken into consideration, and when both the vehicle longitudinal speed Vx and the vehicle lateral speed Vy are taken into consideration. [Figure 5] 10 is a flowchart showing an example of the flow of processing performed by a self-position estimation unit according to the present embodiment. [Figure 6] FIG. 2 is a block diagram showing an outline of self-position estimation by a self-position estimation unit according to the present embodiment. [Figure 7] FIG. 10 is a diagram showing the results of dead reckoning using the vehicle longitudinal velocity Vx and the vehicle lateral velocity vy estimated using tire information in addition to Vx. DETAILED DESCRIPTION OF THE INVENTION

[0019] An example of an embodiment that realizes the technology of the present disclosure will be described in detail below with reference to the drawings. Note that in each drawing, the same or equivalent components and parts are assigned the same reference numerals. Furthermore, the dimensional proportions in the drawings are exaggerated for the convenience of explanation and may differ from the actual proportions. Furthermore, the present disclosure is not limited to the following embodiment, and can be implemented by making appropriate modifications within the scope of the object of the present disclosure.

[0020] FIG. 1 is a block diagram showing a schematic configuration of a vehicle equipped with a self-position estimation unit as a self-position estimation device according to this embodiment.

[0021] The self-position estimation unit 10 according to this embodiment is mounted on a vehicle 12 capable of autonomous driving, and estimates the position of the vehicle 12 during autonomous driving. Specifically, the self-position estimation unit 10 estimates the self-position by calculating the travel distance from the orientation and speed of the vehicle 12 using dead reckoning.

[0022] In addition to the self-position estimation unit 10, the vehicle 12 is equipped with an automatic driving control unit 14, a LiDAR (Light Detection and Ranging) 16, a GPS (Global Positioning System) 18, and a vehicle driving data detection unit 20.

[0023] The vehicle travel data detection unit 20 detects vehicle travel data including the vehicle longitudinal speed. In addition to the vehicle longitudinal speed, the vehicle travel data detected includes, for example, the steering angle, yaw rate, lateral acceleration, and longitudinal acceleration. The vehicle travel data detection unit 20 detects the vehicle travel data using various sensors mounted on the vehicle.

[0024] The GPS 18 is an example of a Global Navigation Satellite System (GNSS), and detects the position of the vehicle 12 by receiving signals from positioning satellites.

[0025] LiDAR16 emits laser light and measures the distance to an object and the shape of the object based on the information from the reflected light.

[0026] The autonomous driving control unit 14 controls autonomous driving of the vehicle 12 based on the detection results of the self-position estimation unit 10, the LiDAR 16, the GPS 18, and the vehicle driving data detection unit 20. In this embodiment, the self-position estimation unit 10 detects the vehicle's position using scan matching by the LiDAR 16, which is an example of a first self-position estimation device, or the GPS 18, and controls the autonomous driving. The detection of the vehicle's position is performed by switching between the self-position estimation unit 10, scan matching by the LiDAR 16, and the GPS 18, depending on predetermined conditions. For example, if the position of the vehicle 12 cannot be detected using scan matching by the LiDAR 16 or the GPS 18, the self-position estimation unit 10 estimates the position of the vehicle 12. Note that scan matching by the LiDAR 16 senses the surrounding environment using the LiDAR 16 to generate image information including distance information, and compares the image information with a 3D map to estimate the position of the vehicle 12. This corresponds to the first self-position estimation device that estimates the vehicle's position based on image information.

[0027] 2 is a block diagram showing the configuration of the self-position estimation unit 10 and the automatic driving control unit 14. Note that since the self-position estimation unit 10 and the automatic driving control unit 14 have a general computer configuration, the self-position estimation unit 10 will be described as a representative example.

[0028] The self-position estimation unit 10 is composed of a general microcomputer including a CPU (Central Processing Unit) 10A, a ROM (Read Only Memory) 10B, a RAM (Random Access Memory) 10C, storage 10D, an interface (I / F) 10E, and a bus 10F.

[0029] I / F10E is connected to a vehicle driving data detection unit 20 and an automatic driving control unit 14, and the self-position estimation unit 10 estimates the position of the vehicle 12 based on the detection results of the vehicle driving data detection unit 20 and outputs the estimation results to the automatic driving control unit 14.

[0030] Next, a functional configuration will be described in which the CPU 10A of the self-location estimation unit 10 loads the self-location estimation program stored in the ROM 10B or the storage 10D into the RAM 10C and executes it. Fig. 3 is a functional block diagram showing the functional configuration of the self-location estimation unit 10 according to this embodiment.

[0031] The self-position estimation unit 10 has the functions of a tire data acquisition unit 22 as an example of a first acquisition unit, a vehicle driving data acquisition unit 24 as an example of a first acquisition unit, a calculation unit 26, and an estimation unit .

[0032] The tire data acquisition unit 22 acquires tire data including tire cornering power as tire information. In addition to tire cornering power, the tire information may also include information about the tire, such as tire size. The tire cornering power may be a value determined in advance for each vehicle and tire through experiments or the like, or a value simply measured for each tire by applying a predetermined load to the tire. The tire cornering power may be acquired according to the tire wear state. For example, the tire cornering power according to the tire wear state may be set in advance using a map or the like, or may be calculated using a function or the like.

[0033] The vehicle travel data acquisition unit 24 acquires vehicle travel data including the vehicle longitudinal speed detected by the vehicle travel data detection unit 20 as vehicle information. The vehicle longitudinal speed is calculated by multiplying the tire circumference by the tire rotation speed. In addition to the longitudinal speed, the vehicle travel data acquired may also include, for example, steering angle, yaw rate, lateral acceleration, longitudinal acceleration, etc., detected by the vehicle travel data detection unit 20.

[0034] The calculation unit 26 calculates the vehicle lateral speed based on the tire data acquired by the tire data acquisition unit 22 and the vehicle driving data acquired by the vehicle driving data acquisition unit 24. Specifically, the calculation unit calculates the side slip angle at the vehicle center of gravity position based on the tire data and the vehicle driving data, and calculates the vehicle lateral speed based on the calculated side slip angle at the vehicle center of gravity position and the vehicle longitudinal speed. When calculating the side slip angle at the vehicle center of gravity position, the side slip angle at the vehicle center of gravity position may be calculated based on specification information determined for each vehicle (e.g., vehicle weight, distance from the vehicle center of gravity position to the front and rear axles, etc.), the tire data, and the vehicle driving data. Alternatively, the side slip angle at the vehicle center of gravity position may be acquired from the vehicle 12, and calculated based on the tire data and the vehicle driving data.

[0035] The estimation unit 28 estimates the own position of the vehicle 12 based on the vehicle longitudinal speed acquired by the vehicle travel data acquisition unit 24 and the vehicle lateral speed calculated by the calculation unit 26.

[0036] Here, the calculation of the vehicle lateral speed by the calculation unit 26 and the estimation of the vehicle's own position by the estimation unit 28 will be explained in more detail.

[0037] Calculation unit 26 calculates the slip angle at the vehicle center of gravity (hereinafter referred to as the side slip angle at the vehicle center of gravity) from the following two-wheel vehicle model equation using the previously measured tire cornering power, vehicle weight, distance from the vehicle center of gravity to the front and rear wheel axles, vehicle moment of inertia, and the longitudinal speed, actual steering angle (tire wheel angle), yaw rate, and lateral acceleration measured by the autonomously driven vehicle. Then, lateral speed is estimated from the product of the side slip angle at the vehicle center of gravity and the vehicle longitudinal speed.

[0038]

number

[0039] where m is the vehicle mass, I is the vehicle moment of inertia, V is the vehicle speed, β is the side slip angle at the vehicle center of gravity, Cpf is the front wheel equivalent cornering power, Cpr is the rear wheel equivalent cornering power, If and Ir are the distances from the center of gravity to the front and rear wheel axle centers, respectively, γ is the vehicle yaw rate, δ is the actual steering angle (tire wheel angle), and ay is the vehicle lateral acceleration.

[0040] The equation for the side slip angle β at the center of gravity of the vehicle can be derived from the equation for the two-wheel vehicle model above, as follows:

[0041]

number

[0042] Assuming V = Vx, then Vy = Vx β, where Vy is the lateral velocity of the vehicle and Vx is the longitudinal velocity of the vehicle.

[0043] When calculating the vehicle body slip angle β, information relating to vehicle specifications such as the vehicle mass and the distance from the center of gravity to the center of the front and rear wheel axles may be calculated using predetermined fixed values.

[0044] In addition, factors that cause cornering power to fluctuate include internal pressure, aspect ratio, rim, wear state, etc., and cornering power corresponding to the factors may be used. For example, cornering power corresponding to changes in internal pressure may be calculated and used.

[0045] Furthermore, since the vehicle longitudinal speed is detected by detecting the number of rotations of the axle, if the tire diameter changes due to tire wear or replacement, the tire circumference also changes, resulting in an error in the detected vehicle longitudinal speed.

[0046] Therefore, the vehicle longitudinal speed Vx may be corrected based on the tire circumference ratio. Specifically, as shown in the following equation, the vehicle longitudinal speed is corrected by multiplying the detected vehicle longitudinal speed (Vx_exp) by the tire circumference ratio (l / lB).

[0047]

number

[0048] The estimation unit 28 estimates the position of the vehicle 12 by dead reckoning based on the longitudinal speed and lateral speed of the vehicle 12. That is, the estimation unit 28 measures the speed and direction of the vehicle 12, calculates the amount of movement from the initial position, and integrates the movement to estimate the position of the vehicle 12.

[0049] FIG. 4 is a diagram showing self-position estimation by dead reckoning when only the vehicle longitudinal velocity Vx is taken into consideration, and when both the vehicle longitudinal velocity Vx and the vehicle lateral velocity Vy are taken into consideration.

[0050] In this embodiment, as shown by the solid arrow in Fig. 4, the vehicle lateral speed Vy is calculated and the movement due to the vehicle longitudinal speed Vx and the vehicle lateral speed Vy is taken into consideration, thereby improving the accuracy of estimating the position of the vehicle 12. Note that the dashed-dotted arrow in Fig. 4 indicates the case where only the vehicle longitudinal speed Vx is taken into consideration, and it can be seen that a deviation occurs in the estimated position of the vehicle 12 between times t1 and t3.

[0051] Next, specific processing performed by the self-position estimation unit 10 according to this embodiment will be described. Fig. 5 is a flowchart showing an example of the flow of processing performed by the self-position estimation unit 10 according to this embodiment. Note that the processing in Fig. 5 starts when, for example, as an example of a case where a predetermined condition is met, the position of the vehicle 12 cannot be detected by scan matching using the LiDAR 16 and the GPS 18, or when the detection accuracy becomes equal to or lower than a predetermined threshold.

[0052] In step 100, the CPU 10A acquires tire data and proceeds to step 102. That is, the tire data acquisition unit 22 acquires tire data including tire cornering power as tire information. For example, values ​​obtained in advance by experiments or the like for each vehicle and each tire are stored in a server or the like, and tire information corresponding to the vehicle 12 is acquired.

[0053] In step 102, CPU 10A acquires vehicle travel data and proceeds to step 104. That is, vehicle travel data acquisition unit 24 acquires, as vehicle information, vehicle travel data including the vehicle longitudinal speed detected by vehicle travel data detection unit 20. In addition to the longitudinal speed, the vehicle travel data may also include, for example, steering angle, yaw rate, lateral acceleration, longitudinal acceleration, etc., detected by vehicle travel data detection unit 20.

[0054] In step 104, CPU 10A calculates the side slip angle at the position of the vehicle center of gravity, and proceeds to step 106. That is, calculation unit 26 calculates the vehicle body slip angle based on the tire data acquired by tire data acquisition unit 22 and the vehicle driving data acquired by vehicle driving data acquisition unit 24. The side slip angle at the position of the vehicle center of gravity is calculated using the equation for side slip angle β at the position of the vehicle center of gravity derived from the above-mentioned two-wheel vehicle model.

[0055] In step 106, CPU 10A calculates the vehicle lateral speed and proceeds to step 108. That is, calculation unit 26 calculates the vehicle lateral speed based on the side slip angle at the vehicle center of gravity position. Assuming that V=Vx, vehicle lateral speed Vy is calculated by Vy=Vx·β.

[0056] In step 108, CPU 10A determines whether or not the tire circumference needs to be corrected. This determination is made based on a physical quantity that can detect whether or not the tire circumference has changed. For example, it may be determined whether or not the travel distance of vehicle 12 has reached a predetermined threshold, or it may be determined by estimating the tire wear state from the behavior of vehicle 12. Alternatively, inspection results of the tire wear state and the like may be stored in storage 10D, a server, or the like as wear information, and the determination may be made based on the stored wear information. If the determination is affirmative, the process proceeds to step 110; if the determination is negative, the process proceeds to step 112.

[0057] In step 110, the CPU 10A corrects the vehicle longitudinal speed and proceeds to step 112. That is, the calculation unit 26 corrects the vehicle longitudinal speed by multiplying the detected vehicle longitudinal speed by the tire circumference ratio using the above-mentioned equation for correcting the vehicle longitudinal speed. For example, the circumference, which changes with each mileage, may be determined in advance, and the current tire circumference corresponding to the mileage may be read out to correct the vehicle longitudinal speed. Alternatively, the tire wear state may be estimated from the behavior of the vehicle 12 detected by various sensors mounted on the vehicle 12, and the tire circumference may be estimated and used to correct the vehicle longitudinal speed. Alternatively, the results of inspecting the tire wear state may be stored in advance as wear information in the storage 10D or a server, and the tire circumference may be derived by reading out the wear information to correct the vehicle longitudinal speed.

[0058] In step 112, CPU 10A estimates its own position based on the vehicle longitudinal speed and vehicle lateral speed, and proceeds to step 114. That is, estimation unit 28 measures the speed and direction of vehicle 12 by dead reckoning based on the vehicle longitudinal speed acquired by vehicle traveling data acquisition unit 24 and the vehicle lateral speed calculated by calculation unit 26, calculates the amount of movement from the initial position, and adds up the movement amount to estimate the position of vehicle 12.

[0059] In step 114, the CPU 10A determines whether to end self-position estimation using dead reckoning. This determination is made based on whether the position of the vehicle 12 can be detected by scan matching using the LiDAR 16 or the GPS 18, or whether the detection accuracy of scan matching using the LiDAR 16 exceeds a predetermined threshold. If the determination is negative, the process returns to step 100 and the above-described processing is repeated, and the series of processing ends when the determination is positive.

[0060] FIG. 6 is a block diagram showing an outline of the self-position estimation performed by the self-position estimation unit 10 according to this embodiment.

[0061] As described above, the self-position estimation unit 10 uses tire information and vehicle information to calculate the side slip angle β at the position of the vehicle's center of gravity, and calculates the vehicle lateral speed based on the side slip angle at the position of the vehicle's center of gravity. At this time, since the cornering power of the tires as tire information varies depending on the state of tire wear, the side slip angle β at the position of the vehicle's center of gravity may be calculated by correcting the cornering power of the tires as tire information using the tire wear information, as shown by the dotted line in Fig. 6. This makes it possible to calculate the vehicle lateral speed taking the state of tire wear into consideration.

[0062] Furthermore, the vehicle information and tire wear information are used to correct the vehicle longitudinal speed acquired from the vehicle 12. This makes it possible to suppress errors in the vehicle longitudinal speed that are caused by changes in tire circumference due to tire wear.

[0063] Then, the vehicle 12 performs self-location estimation using the vehicle lateral speed and the corrected vehicle longitudinal speed, thereby improving the accuracy of estimating the position of the vehicle 12 compared to when the vehicle 12 performs self-location estimation using only the vehicle longitudinal speed.

[0064] Fig. 7 shows the results of dead reckoning using the vehicle longitudinal speed Vx and the vehicle lateral speed vy estimated using tire information in addition to Vx. Fig. 7 shows the GNSS position information from GPS18 (dashed line in Fig. 7), the trajectory of self-localization without considering the vehicle lateral speed Vy (dotted line in Fig. 7), and the trajectory of self-localization using the vehicle lateral speed Vy (solid line in Fig. 7) when driving autonomously on a test course.

[0065] As shown in Figure 7, by taking into account the amount of movement due to the vehicle lateral speed Vy, the self-position estimation accuracy can be improved, as it is closer to the GNSS position information considered to be the true value compared to self-position estimation that does not take the vehicle lateral speed Vy into account. As shown in the enlarged view 40 of the corner in Figure 7, the self-position estimation accuracy can be improved, especially when entering a corner.

[0066] In the above embodiment, as an example of a case where a predetermined condition is satisfied, the self-location estimation unit 10 performs self-location estimation when the position of the vehicle 12 cannot be detected by scan matching using the LiDAR 16 and the GPS 18, or when the detection accuracy falls below a predetermined threshold. However, this is not limiting. For example, the self-location estimation unit 10 may perform self-location estimation when there is little need for precise self-location estimation by scan matching using the LiDAR 16, such as on a straight, smooth road or a wide road. Alternatively, the self-location estimation unit 10 may perform self-location estimation when the weather worsens, when traveling in a place without features, such as inside a tunnel, when traveling in a place where features change significantly, such as in nature, or when the LiDAR 16 malfunctions.

[0067] Furthermore, the technical scope of the present disclosure is not limited to the scope described in the above embodiments. Various modifications or improvements can be made to the above embodiments without departing from the gist of the present disclosure, and such modifications or improvements are also included in the technical scope of the present disclosure.

[0068] In the above embodiment, the processes performed by the self-position estimation unit 10 may be realized by a software configuration, or each process may be realized by a hardware configuration, or may be realized by a combination of a software configuration and a hardware configuration. [Explanation of symbols]

[0069] 10 self-position estimation unit, 12 vehicle, 14 automatic driving control unit, 16 LiDAR, 18 GPS, 20 vehicle driving data detection unit, 22 tire data acquisition unit, 24 vehicle driving data acquisition unit, 26 calculation unit, 28 estimation unit

Claims

1. a first acquisition unit that acquires tire information including tire cornering power; a second acquisition unit that acquires vehicle information including a vehicle longitudinal speed; a calculation unit that calculates a vehicle lateral speed based on the tire information and the vehicle information; an estimation unit that estimates a vehicle's own position based on the vehicle longitudinal speed and the vehicle lateral speed; A self-location estimation device including:

2. 2. The self-position estimation device according to claim 1, wherein the calculation unit calculates a sideslip angle at the position of the center of gravity of the vehicle based on the tire information and the vehicle information, and calculates the vehicle lateral speed based on the sideslip angle at the position of the center of gravity of the vehicle and the vehicle longitudinal speed.

3. The self-position estimation device according to claim 2 , wherein the first acquisition unit acquires the tire information including the tire cornering power according to a state of wear of the tire.

4. The self-position estimation device according to claim 1 , wherein the vehicle longitudinal speed is derived by multiplying the circumference of a tire by the number of revolutions of the tire.

5. The tire information further includes tire wear information, The self-position estimation device according to claim 1 , wherein the estimation unit corrects the vehicle longitudinal speed in accordance with the wear information, and estimates the vehicle's own position based on the corrected vehicle longitudinal speed and the vehicle lateral speed.

6. The self-position estimation device according to claim 2 , wherein the calculation unit calculates the side slip angle of the vehicle center of gravity position based on specification information determined for each vehicle, the tire information, and the vehicle information.

7. a first self-location estimation device that estimates a self-location based on image information; The self-location estimation device according to claim 1 ; Equipped with A self-location estimation system that, when a predetermined condition is met, switches from the first self-location estimation device to the self-location estimation device to estimate a self-location of a vehicle.

8. The computer Get tire information including tire cornering power, Acquire vehicle information including the vehicle's longitudinal speed, Calculating a vehicle lateral speed based on the tire information and the vehicle information; A self-position estimation method for estimating a vehicle's own position based on the vehicle longitudinal speed and the vehicle lateral speed.

9. On the computer, Get tire information including tire cornering power, Acquire vehicle information including the vehicle's longitudinal speed, Calculating a vehicle lateral speed based on the tire information and the vehicle information; a self-position estimation program for executing a process of estimating a self-position of a vehicle based on the vehicle longitudinal speed and the vehicle lateral speed;

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