Information processing device

Abstract

The present invention provides an information processing device that can accurately acquire vehicle speed. [Solution] The control unit 15 continuously acquires the detection results of the white line D1 within the window W that moves with the vehicle C, from the detection results of the lidar 21 that detects features around the vehicle C, at time intervals of Δt, and detects the position of one end of the white line D1 in the direction of movement of the vehicle C from these multiple detection results. Then, it calculates the average speed v of the vehicle C based on the temporal change in the position of the detected line.

Description

Technical Field

[0001] The present invention relates to an information processing apparatus that performs predetermined processing based on a detection result of a detection unit that detects features around a moving object.

Background Art

[0002] For example, an automatic driving system that has been developed in recent years grasps the situation by recognizing objects existing around a vehicle, generates an optimal target trajectory, and controls the vehicle to travel along the target trajectory. At this time, if the accuracy of the vehicle's self-position estimation is poor, there is a possibility that the actual travel trajectory deviates from the target trajectory, reducing the safety of automatic driving. To ensure the safety of automatic driving, accurate self-position estimation is one of the important factors.

[0003] Self-position estimation in a conventional car navigation system often uses GNSS (Global Navigation Satellite System). Therefore, there is a problem that the accuracy deteriorates in an environment where reception is impossible, such as inside a tunnel, or where multipath occurs frequently, such as between buildings.

[0004] Therefore, a so-called dead reckoning technique for estimating the vehicle position based on the vehicle's driving state (for example, vehicle speed and yaw rate) is known. And in order to improve the estimation accuracy of the vehicle position by dead reckoning, it is necessary to accurately acquire the vehicle's driving state such as the above-mentioned vehicle speed.

[0005] As a technique for accurately acquiring the vehicle speed, for example, it is known to correct the vehicle speed pulse as described in Patent Document 1. Patent Document 1 describes correcting an arithmetic expression for obtaining the traveling speed based on the relationship between the count value Cp of the output pulse of the wheel speed sensor counted between two features and the distance D between the two features.

Prior Art Documents

Patent Documents

[0006] [Patent Document 1] Japanese Patent Publication No. 2008-8783 [Overview of the project] [Problems that the invention aims to solve]

[0007] In the invention described in Patent Document 1, the calculation formula is corrected based on the distance D between two features. Therefore, if the accuracy of the distance D between the two features is low, the accuracy of the correction of the calculation formula may also decrease. Consequently, the accuracy of the calculated vehicle speed may also decrease.

[0008] One example of a problem that this invention aims to solve is accurately acquiring the vehicle speed as described above. [Means for solving the problem]

[0009] The invention described in claim 1, made to solve the above problems, is characterized by comprising: an acquisition unit that continuously acquires, at predetermined time intervals, the detection results of a detection unit that detects features around a moving body, specifically the detection results of features within a predetermined detection area that moves together with the moving body; an extraction unit that extracts positional information of characteristic parts of the features within the detection area in the direction of movement of the moving body from the acquired plurality of detection results; and a calculation unit that calculates the speed of the moving body based on the temporal change of the extracted positional information.

[0010] Furthermore, the invention described in claim 9 is characterized by comprising: a detection unit for detecting features around a moving body; an acquisition unit for continuously acquiring detection results of features within a predetermined detection area that moves together with the moving body at predetermined time intervals from the detection results of the detection unit; an extraction unit for extracting positional information of characteristic parts of the features within the detection area in the direction of movement of the moving body from the acquired plurality of detection results; and a calculation unit for calculating the speed of the moving body based on the temporal change of the extracted positional information.

[0011] Furthermore, the invention described in claim 10 is an information processing method performed by an information processing device that performs predetermined processing based on the detection results of a detection unit that detects features around a moving body, and is characterized by including: an acquisition step of continuously acquiring detection results of features within a predetermined detection area that moves together with the moving body from the detection results of the detection unit at predetermined time intervals; an extraction step of extracting positional information of characteristic parts of features within the detection area in the direction of movement of the moving body from the acquired plurality of detection results; and a calculation step of calculating the speed of the moving body based on the temporal change of the extracted positional information.

[0012] Furthermore, the invention described in claim 11 is characterized in that the information processing method described in claim 10 is executed by a computer. [Brief explanation of the drawing]

[0013] [Figure 1] This is the configuration of a detection device having an information processing device according to the first embodiment of the present invention. [Figure 2] Figure 1 is an explanatory diagram illustrating the detection of white lines by the detection device shown. [Figure 3] Figure 1 is an explanatory diagram of the coordinate transformation of measured values ​​in the detection device shown. [Figure 4] Figure 1 is an explanatory diagram regarding the scan angle in the lidar. [Figure 5] Figure 1 is an explanatory diagram regarding the scan interval in the lidar shown. [Figure 6] Figure 1 is an explanatory diagram regarding the vertical angle in the lidar shown. [Figure 7] Figure 1 is an explanatory diagram of the speed calculation method in the control unit shown. [Figure 8] Figure 1 is an explanatory diagram of the speed calculation method in the control unit shown. [Figure 9] Figure 7 is a flowchart of the speed calculation method. [Figure 10] Figure 9 is a flowchart of the speed calculation process. [Figure 11] It is a flowchart of the speed calculation method shown in FIG. 8. [Figure 12] It is an explanatory diagram of the speed calculation method in the control unit according to the second embodiment of the present invention. [Figure 13] It is an explanatory diagram of the speed calculation method in the control unit according to the second embodiment of the present invention. [Figure 14] It is a flowchart of the speed calculation method shown in FIG. 12. [Figure 15] It is a flowchart of the speed calculation method shown in FIG. 13. [Figure 16] It is an explanatory diagram of the detection of a white line according to the third embodiment of the present invention. [Figure 17] It is an explanatory diagram of the speed calculation method in the control unit shown in FIG. 16. [Figure 18] It is a flowchart of the speed calculation method shown in FIG. 17. [Figure 19] It is an explanatory diagram of another speed calculation method in the control unit shown in FIG. 16. [Figure 20] It is a flowchart of the speed calculation method shown in FIG. 19.

Mode for Carrying Out the Invention

[0014] Hereinafter, an information processing apparatus according to an embodiment of the present invention will be described. In the information processing apparatus according to an embodiment of the present invention, an acquisition unit continuously acquires, at a predetermined time interval, detection results of ground objects within a predetermined detection area that moves together with a moving object, among the detection results of a detection unit that detects ground objects around the moving object. An extraction unit extracts position information in the moving direction of the moving object of a characteristic portion of the ground object within the detection area from a plurality of detection results acquired by the acquisition means. Then, a calculation unit calculates the speed of the moving object based on the temporal change of the extracted position information. By doing so, for example, it becomes possible to calculate the speed based on the position information of a characteristic portion such as a boundary portion of a ground object such as a road marking within the detection area of a detection unit such as a lidar, and the speed of the moving object can be accurately calculated and acquired.

[0015] Furthermore, the characteristic feature of a geographic feature may be just one end of the feature. By doing so, it becomes possible to detect the velocity by detecting a characteristic feature at just one location, such as one end of the feature. Therefore, it becomes unnecessary to consider the accuracy of the distance to other geographic features, and the velocity of the moving object can be calculated with high accuracy.

[0016] Furthermore, the temporal change in location information may also be the elapsed time from the start to the end of feature detection. In this way, the velocity of the moving object can be calculated based on the time the feature was detected.

[0017] Furthermore, the length corresponding to the direction of movement of the moving object is known, and the characteristic parts of the object may be one end and the other end of the object. In this way, when the length corresponding to the direction of movement of the object is known, the speed of the moving object can be detected based on that length.

[0018] Furthermore, the temporal change in positional information may be the elapsed time from the start of detection at one end to the start of detection at the other end, or the elapsed time from the end of detection at one end to the end of detection at the other end. In this way, the velocity of the moving object can be calculated based on the time that the feature portion was detected.

[0019] Furthermore, the detection area consists of two locations separated by a predetermined distance before and after the direction of movement of the moving object, and the characteristic part of the feature may be one end of the feature. In this way, since the predetermined distance is known, the velocity of the moving object can be calculated even for features whose length corresponding to the direction of movement is unknown.

[0020] Furthermore, the temporal change in positional information may be the elapsed time from the end of detection in the detection area in front of the feature portion to the start of detection in the detection area behind it. In this way, the velocity of the moving object can be calculated based on the time the feature portion was detected.

[0021] Furthermore, the detection area may be a rectangular area set within the detectable range of the detection unit. By doing so, only the parts that have the potential to detect features can be designated as the detection area. Therefore, it is possible to prevent a decrease in the accuracy of velocity calculation due to noise caused by the detection of features or other objects other than the target object that is intended to be detected.

[0022] Furthermore, a detection device according to one embodiment of the present invention includes a detection unit that detects features around a moving object. The acquisition unit continuously acquires detection results of features within a predetermined detection area that moves with the moving object at predetermined time intervals, and the extraction unit extracts positional information of feature portions of features within the detection area in the direction of movement of the moving object from a plurality of detection results acquired by the acquisition unit. The calculation unit then calculates the speed of the moving object based on the temporal change of the extracted positional information. In this way, for example, in a detection device equipped with a detection unit such as a lidar, it becomes possible to calculate the speed by detecting positional information of feature portions such as the boundary portions of features of features such as road markings within the detection area, and the speed of the moving object can be calculated and acquired with high accuracy.

[0023] Furthermore, in the information processing method according to one embodiment of the present invention, in the acquisition step, the detection results of features within a predetermined detection area that moves with the moving object are continuously acquired at predetermined time intervals from the detection results of a detection unit that detects features around the moving object. In the extraction step, position information of feature portions of features within the detection area in the direction of movement of the moving object is extracted from the plurality of detection results acquired in the acquisition step. Then, in the calculation step, the speed of the moving object is calculated based on the temporal change of the extracted position information. By doing so, it becomes possible to calculate the speed by detecting the position information of feature portions such as the boundary portions of features such as road markings within the detection area of ​​a detection unit such as a lidar, and the speed of the moving object can be calculated and acquired with high accuracy.

[0024] Alternatively, the above-described information processing method may be implemented as an information processing program that uses a computer to execute it. By doing so, it becomes possible to calculate the speed by using a computer to detect the positional information of feature parts such as the boundaries of road markings and other geographical features within the detection area of ​​the detection unit, thereby enabling the accurate calculation and acquisition of the speed of a moving object. [Examples]

[0025] An information processing device according to the first embodiment of the present invention will be described with reference to Figures 1 to 11. The information processing device according to this embodiment is included in the detection device 1 and moves together with the vehicle as a mobile object.

[0026] Figure 1 shows a schematic block configuration of the detection device 1 according to this embodiment. The detection device 1 comprises a sensor group 11, a storage unit 12, a control unit 15, and an output unit 16.

[0027] The sensor group 11 includes a rider 21, a vehicle speed sensor 22, an acceleration sensor 23, a gyroscope sensor 24, a tilt sensor 25, a temperature sensor 26, and a GPS receiver 27.

[0028] The lidar 21, acting as a detection unit, discretely measures the distance to objects in the external environment by emitting laser light in a pulsed manner. The lidar 21 outputs a point cloud of measurement points, indicated by the pair of the distance to the object to which the laser light was reflected and the emission angle of the laser light. In this embodiment, the lidar 21 is used to detect features present around a vehicle. Features are a concept that includes all natural or artificial objects present on the ground. Examples of features include features on the route (i.e., roads) of a vehicle and features around the road. Examples of features on the route include road signs, traffic lights, guardrails, pedestrian bridges, etc., and also include the road itself. That is, letters and figures drawn on the road surface, as well as the shape of the road (road width and curvature), are also included in features on the route. Examples of features around the road include buildings (houses, shops) and signs located along the road.

[0029] The vehicle speed sensor 22 measures pulses (also called "axle rotation pulses") consisting of pulse signals generated in conjunction with the rotation of the vehicle's wheels and detects the vehicle speed. The acceleration sensor 23 detects the acceleration in the direction of travel of the vehicle. The gyro sensor 24 detects the angular velocity of the vehicle when the vehicle changes direction. The tilt sensor 25 detects the tilt angle (also called "gradient angle") of the vehicle in the pitch direction relative to the horizontal plane. The temperature sensor 26 detects the temperature around the acceleration sensor 23. The GPS (Global Positioning System) receiver 27 has multiple The absolute position of the vehicle is detected by receiving radio waves containing positioning data from GPS satellites. The output of each sensor in the sensor group 11 is supplied to the control unit 15.

[0030] The storage unit 12 stores information processing programs executed by the control unit 15, and information necessary for the control unit 15 to perform predetermined processes. In this embodiment, the storage unit 12 stores a map database (DB) 10 containing road data and feature information. The map DB 10 may be updated periodically. In this case, for example, the control unit 15 receives partial map information relating to the area to which the vehicle's position belongs from an external server device that manages map information via a communication unit (not shown), and reflects it in the map DB 10. Alternatively, instead of the storage unit 12 storing the map DB 10, a server device that can communicate with the detection device 1 may store the map DB 10. In this case, the control unit 15 obtains necessary feature information from the map DB 10 by communicating with the external server device.

[0031] The output unit 16 outputs, for example, the speed information calculated by the control unit 15 to an automatic driving control device or other in-vehicle equipment such as a meter.

[0032] The control unit 15 includes a CPU (Central Processing Unit) and the like that which executes the program. The control unit 15 controls the entire detection device 1. The control unit 15 comprises an acquisition unit 15a, an extraction unit 15b, and a calculation unit 15c. In this embodiment, the control unit 15 calculates the vehicle speed based on the features detected by the lidar 21.

[0033] The acquisition unit 15a continuously acquires the detection results of features detected by the lidar 21, specifically the detection results in the window described later, at predetermined time intervals.

[0034] The extraction unit 15b extracts positional information of the feature portion of the feature within the window in the direction of vehicle movement from the detection results acquired by the acquisition unit 15a.

[0035] The calculation unit 15c calculates the vehicle's speed based on the temporal changes in the position information extracted by the extraction unit 15b.

[0036] Furthermore, the control unit 15 of the detection device 1 with the above configuration functions as an information processing device according to this embodiment.

[0037] Next, the method for detecting speed in the control unit 15 (information processing device) of the detection device 1 with the above configuration will be described. In the following description, a dashed lane boundary line (so-called white line) will be used as the geographical feature. Since the white line is coated with retroreflective material, it has a high reflectivity and is easy to detect with a lidar.

[0038] The detection of white lines in this embodiment will be explained with reference to Figure 2. In Figure 2, vehicle C is assumed to be traveling from left to right. A rider 21L is installed on the left front of vehicle C, and similarly, a rider 21R is installed on the right front of vehicle C.

[0039] Then, if the detection range of riders 21L and 21R is A, a rectangular area called a window W is set within that detection range A. This window W is set in a position within detection range A where white lines D1 and D2 are easily detected. This window W becomes the detection area that moves with the moving object in this embodiment. In this embodiment, the explanation is given using rider 21 installed in front of vehicle C, but it may also be a rider installed in the rear of vehicle C. Furthermore, it may be just rider 21L or 21R.

[0040] Next, the coordinate transformation of the measured values ​​by the lidar 21 will be explained with reference to Figure 3. As mentioned above, the lidar 21 outputs the distance to the object and the emission angle of the laser beam. That is, it outputs the distance to the object to be measured and the horizontal and vertical angles of the object to be measured. Here, as shown on the left side of Figure 3, let xb be the longitudinal axis of vehicle C, yb be the lateral axis of vehicle C, and zb be the vertical axis of vehicle C. At this time, as shown on the right side of Figure 3, if r is the distance from the center of gravity of vehicle C to the object to be measured, α is the horizontal angle to the object to be measured, and β is the vertical angle to the object to be measured, then the position Z(i) of the object to be measured is expressed by the following equation (1).

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[0041] Next, the scanning interval of the lidar 21 will be explained. In this embodiment, the lidar 21 scans an object by sequentially emitting pulsed light from one horizontal direction to the other. Therefore, as shown in the upper part of Figure 4, the scan trajectory appears as a line when viewed from above. Accordingly, the acquisition unit 15a acquires information from the lidar 21 at intervals of these scanned lines. In other words, the acquisition unit 15a continuously acquires the detection results of features at predetermined time intervals.

[0042] Furthermore, common types of lidars include those that obtain multiple lines by moving a horizontally scanning beam up and down vertically, and those that obtain multiple lines by arranging multiple horizontally scanning optical systems vertically. It is known that the scan interval of these types of lidars widens as they move away from the vehicle C (see also Figure 5). This is because the angle between the lidar 21 and the ground (road surface) becomes shallower as it moves away from the vehicle C.

[0043] Here, as shown in Figure 5, the scan lines within window W are S1, S2, ..., S as they approach the vehicle C from a position further away. 10Let the intervals between the lines be d1, d2, ..., d9. Also, as shown in Figure 6, when the vertical angles of each line are β(i), β(i+1), β(i+2), β(i+3), and the distances from the launch point to each line are r(i), r(i+1), r(i+2), r(i+3), the intervals between the lines d(i), d(i+1), d(i+2) are expressed by the following equations (2) to (4).

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[0044] Then, based on the detection of one end of a line constituting the dashed line (the white line) when the white line passes through the range of window W, the speed of vehicle C is calculated. The following explanation will use the symbols for the lines and line spacing shown in Figure 5.

[0045] First, as shown in the upper part of Figure 7, assume that lines S1 and S2 detect one end of the white line D1. Then, as shown in the middle part of Figure 7, assume that lines S1 to S4 detect the white line D1 after time Δt has elapsed. The distance traveled x(k) at this time is expressed by equation (5), and the speed v(k) of vehicle C at this time is expressed by equation (6). Here, in equations (5) and (6), d2 / 2, d4 / 2, etc. are considered to be half the scan interval because the exact position of the end of the white line D1 is unknown when it is between scans, as shown in Figure 7. Therefore, the closer the scan interval, the smaller the error between this 1 / 2 and the actual position.

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[0046] Next, as shown in the lower part of Figure 7, assume that lines S1 to S7 detect the white line D1 after time Δt has elapsed. The distance traveled x(k+1) at this time is given by equation (7), and the speed v(k+1) of vehicle C at this time is given by equation (8).

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[0047] Therefore, the average velocity v of the above velocity v(k) and velocity v(k+1) is expressed by the following equation (9).

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[0048] In Figure 7, the speed was calculated by detecting the end of the white line D1 closer to vehicle C, but as shown in Figure 8, the speed can also be calculated by detecting the end of the white line D1 further away from vehicle C.

[0049] First, as shown in the upper part of Figure 8, line S3~S 10 Let's assume that the white line D1 is detected. Then, as shown in the middle of Figure 8, after time Δt has elapsed, lines S5~S 10 Suppose the vehicle detects the white line D1. The distance traveled x(k) at this time is given by equation (10), and the speed v(k) of vehicle C at this time is given by equation (11).

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[0050] Next, as shown in the lower part of Figure 8, after time Δt has elapsed, line S8~S 10 Suppose the vehicle detects the white line D1. The distance traveled x(k+1) at this time is given by equation (12), and the speed v(k+1) of vehicle C at this time is given by equation (13).

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[0051] Therefore, the average velocity v of the above velocity v(k) and velocity v(k+1) is expressed by the following equation (14).

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[0052] Next, the operation (information processing method) of the control unit 15 (information processing device) with the above configuration will be explained with reference to the flowcharts in Figures 9 to 11. Furthermore, these flowcharts can be configured as information processing programs executed by a computer by configuring the control unit 15 as a computer having a CPU, etc.

[0053] First, the velocity calculation process described in Figure 7 will be explained with reference to the flowchart in Figure 9. In step S101, the extraction unit 15b determines whether or not it has detected the starting end of the white line D1 within the window W. If it has not detected it (No), this step is repeated. If it has detected it (Yes), the velocity calculation process is performed in step S102. Detection of the starting end of the white line D1 refers to a state like the upper part of Figure 7, for example. The velocity calculation process will be described later. In other words, in the case of Figure 7, the starting end (one end) of the white line D1 is the characteristic part of the feature. As mentioned above, the starting end of the white line D1 is the beginning of one of the lines that make up the dashed line.

[0054] Next, in step S103, the extraction unit 15b determines whether the detection of the starting end of the white line D1 has ceased within the window W. If the detection of the starting end has not ceased (No), the process returns to step S102. If the detection of the starting end has ceased (Yes), the process terminates, assuming that the detection of the end of the white line D1 has finished. The state in which the detection of the starting end of the white line D1 has ceased within the window W refers to a state where, for example, more time has passed since the lower part of Figure 7, and one end of the white line D1 can no longer be detected within the window W. In this state, since the end, which is a characteristic part of the white line D1, cannot be detected, the process terminates. That is, in steps S101 and S103, the acquisition unit 15a extracts positional information of the characteristic part of the feature of the feature within the window W (detection area) in the direction of movement of the vehicle C (moving body) from multiple detection results acquired by the acquisition unit 15a.

[0055] Next, the velocity calculation process (step S102) in Figure 9 will be explained with reference to the flowchart in Figure 10. This flowchart is executed in the calculation unit 15c. First, in step S201, the travel distance x(k) is calculated from the line number of the previous cycle and the current line number. The line number of the previous cycle is, for example, line S2 in the upper part of Figure 7, and the current line number is, for example, line S4 in the middle part of Figure 7. Therefore, the travel distance from line S2 to S4 is as shown in equation (5). Also, one cycle refers to Δt when calculating velocity at time intervals of Δt.

[0056] Next, in step S202, the velocity v(k) is calculated from the travel distance x(k) and the scan period Δt.

[0057] Then, in step S203, the speed v is calculated by averaging the k speeds. As explained in the flowchart in Figure 9, the speed calculation process shown in Figure 10 is executed multiple times based on the decision in step S103, so steps S201 and S202 are effectively executed multiple times, and each time they are executed, the speed is averaged in step S203. That is, the flowchart in Figure 10 (step S102) calculates the speed of vehicle C (moving object) based on the temporal change in the extracted position information. Furthermore, the speed calculation process is repeated until No is determined in step S103, so the final calculated speed v is calculated based on the temporal change in the position information, which is calculated based on the elapsed time from the start to the end of feature detection.

[0058] In other words, step S101 functions as an acquisition and extraction process, step S102 functions as a calculation process, and step S103 functions as an extraction process.

[0059] Next, the velocity calculation process described in Figure 8 will be explained with reference to the flowchart in Figure 11. In step S301, the extraction unit 15b determines whether or not it has detected the end of the white line D1 within the window W. If it has not detected it (No), this step is repeated. If it has detected it (Yes), the velocity calculation process is performed in step S302. The end of the white line D1 refers to a state like the one shown in the upper part of Figure 8. The velocity calculation process is the process shown in the flowchart in Figure 10. That is, in the case of Figure 8, the end (one end) of the white line D1 is the characteristic part of the feature.

[0060] Next, in step S303, the extraction unit 15b determines whether the detection of the end of the white line D1 within the window W has ceased. If the detection of the end has not ceased (No), the process returns to step S302. If the detection of the end has ceased (Yes), the process terminates, assuming that the detection of the end of the white line D1 has finished. The state in which the detection of the end of the white line D1 has ceased within the window W refers to, for example, a state in which more time has passed since the lower part of Figure 8 and the white line D1 is no longer detected within the window W. In this state, since one end, which is a characteristic part of the white line D1, cannot be detected, the process terminates.

[0061] In this embodiment, the control unit 15 continuously acquires the detection results of the white line D1 within the window W that moves with the vehicle C, from the detection results of the lidar 21 that detects features around the vehicle C, at time intervals of Δt. From these detection results, it detects the line position of the scan by the lidar 21 at one end of the white line D1 in the direction of movement of the vehicle C. Then, based on the temporal change in the position of the detected scan line, it calculates the speed v of the vehicle C. In this way, it is possible to calculate the speed based on the position of a feature part, such as the boundary of a feature part like the white line D1 within the window W set in the detection unit such as the lidar. Furthermore, the speed of the moving object can be calculated based on the time the feature part was detected. Therefore, the speed of the moving object can be calculated and acquired with high accuracy.

[0062] Furthermore, by detecting a feature at a single point, such as one end of the white line D1, it becomes possible to detect the velocity. Therefore, it becomes unnecessary to consider the accuracy of the distance to other features.

[0063] Furthermore, the detection area is defined as a rectangular window W set within the detection range A of the lidar 21. This allows only the portion that has the potential to detect the white line D1 to be designated as the detection area. Therefore, it is possible to prevent a decrease in the accuracy of velocity calculation due to noise caused by the detection of objects other than the white line D1 that is intended to be detected. [Examples]

[0064] Next, a detection device and information processing device according to a second embodiment of the present invention will be described with reference to Figures 12 to 15. Note that parts identical to those in the first embodiment described above are denoted by the same reference numerals and their descriptions are omitted.

[0065] This embodiment has the same configuration as Figure 1, but it is a method for calculating the speed of vehicle C when the length of one of the lines constituting the dashed white line is known. The length of this one line (hereinafter referred to as the length of the white line) is often determined by laws and regulations depending on the type of road. Therefore, by using this known information, the length of the white line, the speed can be calculated. In this embodiment, the length of the white line is assumed to be included in map DB10.

[0066] Next, a specific example of speed calculation will be explained with reference to Figures 12 and 13. Figure 12 shows the state in which a window W moving with vehicle C detects a white line D1. In Figure 12, it is assumed that the detection of the white line D1 is performed by scanning at intervals of, for example, Δt, as in the first embodiment. In this case, if a1 is the length of the detected portion when the start end of the white line D1 is first detected, x1 to x8 are the distances traveled between each time interval (per Δt), b1 is the length of the undetected portion when the end end of the white line D1 is first detected, and L is the length of the white line D, then the following equation (15) holds. That is, in this embodiment, the characteristic parts of the feature are one end of the feature (the start end of the white line D1) and the other end (the end end of the white line D1).

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[0067] Here, a1 is d1 + d2 / 2, for example, if S1~S2 as shown in Figure 7 detects the white line D1. Similarly, b1 is d1 + d2 / 2, for example, if S3~S2 as shown in Figure 8. 10 If it detects the white line D1, then it becomes d1 + d2 / 2.

[0068] Transforming equation (15) yields equation (16).

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[0069] Therefore, equation (16) represents the scan movement amount for 8 scans. Thus, the velocity v can be calculated by the following equation (17), which divides L-a1+b1 by the scan time for 8 scans. In equation (17), N represents the number of scans. That is, the temporal change in position information is the elapsed time from the start of detection at one end (the beginning of the white line D1) to the start of detection at the other end (the end of the white line D1). Furthermore, by determining the velocity v in this way, it becomes unnecessary to determine the travel distance x(k), etc., for each scan.

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[0070] Note that while a1 is defined as the length immediately after detection of the start end of the white line D1, a detection value slightly later is also acceptable. Similarly, while b1 is defined as the length immediately after detection of the end end of the white line D1, a detection value slightly later is also acceptable. For example, if the length one time interval after detection of the start end of the white line D1 is detected and set as a1, a1 will be a large value, but since the number of scans N decreases by one, the same value will be calculated as a result. Also, for example, if b1 is defined as the length one time interval after detection of the end end of the white line D1, b1 will be a large value, but since the number of scans increases by one, the same value will be calculated as a result.

[0071] Figure 13 shows an example where the white line D1 exits window W. In this case as well, the velocity can be calculated using the same method as in Figure 12. That is, if a2 is the length of the undetected portion of the white line when the start end of the white line D1 is last detected, x1 to x8 are the distances traveled between each time point, b2 is the length of the detected portion when the end end of the white line D1 is last detected, and L is the length of the white line D, then the following equation (18) holds.

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[0072] Here, a2 and b2 can be calculated based on what was explained in Figures 7 and 8, similar to what was explained in Figure 12.

[0073] Transforming equation (18) yields equation (19).

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[0074] Therefore, equation (19) represents the scan movement amount for 8 scans. Thus, the velocity v can be calculated by the following equation (20), which divides L + a2 - b2 by the scan time for 8 scans. In equation (20), N represents the number of scans. Also, in the case of Figure 13, the spacing of the lines is denser at the rear of the window (closer to vehicle C) (see Figure 5), so the accuracy of calculating (measuring) a2 and b2 is higher, and as a result the accuracy of the velocity v is improved.

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[0075] In this case as well, a2 and b2 do not have to be the last detected values; they can be the values ​​from one time point prior. Even if a2 and b2 become large values, the number of scans increases or decreases, so the same result will be calculated.

[0076] Next, the speed calculation process described in Figure 12 will be explained with reference to the flowchart in Figure 14. First, in step S401, the extraction unit 15b initializes the scan count N to "0". Next, in step S402, the extraction unit 15b determines whether or not it has detected the starting end of the white line D1 in the window W. If it has not been detected (No), this step is repeated. If it has been detected (Yes), in step S403, the calculation unit 15c calculates the length a1 of the detected portion when the starting end of the white line D1 was first detected. Detecting the starting end of the white line D1 means, for example, that one end of the white line D1 on the side closer to the vehicle has been detected, as shown in the second row from the top in Figure 12.

[0077] Next, in step S404, the extraction unit 15b counts up the scan count N. In step S405, the extraction unit 15b determines whether or not it has detected the end of the white line D1 within the window. If it has not detected the end of the white line D1, it returns to step S404. If it has detected the end of the white line D1, in step S406, the calculation unit 15c calculates the length b1 of the undetected portion when the end of the white line D1 was first detected. Detecting the end of the white line D1 means, for example, that one end of the white line D1 furthest from the vehicle has been detected, as shown in the bottom row of Figure 12.

[0078] Then, in step S407, the calculation unit 15c obtains the length of the white line (length of white line D1) L from the map DB10, and in step S408, the calculation unit 15c calculates the speed v using equation (17).

[0079] Next, the speed calculation process described in Figure 13 will be explained with reference to the flowchart in Figure 15. First, in step S501, the extraction unit 15b initializes the scan count N to "0". Next, in step S502, the extraction unit 15b determines whether or not it has detected the white line D1 within the window W. If it has not detected it (No), this step is repeated; if it has detected it (Yes), the process proceeds to step S503. Detecting the white line D1 means, for example, that one or more scan lines within the window W have detected the white line D1, as shown in the top row of Figure 13.

[0080] Next, in step S503, the extraction unit 15b determines whether or not the detection of the starting end of the white line D1 has been discontinued. If the detection of the starting end has been discontinued (No), this step is repeated. If the detection of the starting end has been discontinued, in step S504, the calculation unit 15c calculates the length a2 of the previously undetected portion of the white line. The discontinuation of detection of the starting end of the white line D1 means, for example, as shown in the second row from the top of Figure 13, that one end of the white line D1 closer to the vehicle side is not detected from within the window W.

[0081] Next, in step S505, the extraction unit 15b counts up the scan count N. In step S506, the extraction unit 15b determines whether or not the end of the white line D1 is no longer detected within the window W. If the end is still detected, the unit returns to step S505. If the end is no longer detected, in step S507, the unit calculates the length b2 of the detected portion of the white line D1 immediately before the calculation unit 15c. The state where the end of the white line D1 is no longer detected means, for example, that more time has passed since the bottom of Figure 13 and the white line D1 is no longer detected within the window W.

[0082] Then, in step S508, the calculation unit 15c obtains the length of the dashed line (length of the white line D1) L from the map DB10, and in step S509, the calculation unit 15c calculates the speed v using equation (20).

[0083] In this embodiment, the length of the dashed line (white line D1) corresponding to the direction of movement is known, and the feature portion of the feature is one end and the other end of the white line D1. In this way, when the length of the white line D1 corresponding to the direction of movement is known, the speed of vehicle C can be detected based on that length.

[0084] Furthermore, the temporal change in positional information is the elapsed time from the start of detection at one end to the start of detection at the other end, or the elapsed time from the last detection at one end to the last detection at the other end, i.e., the scan time for N scans. In this way, the speed of vehicle C can be calculated based on the time that the feature portion was detected. [Examples]

[0085] Next, a detection device and information processing device according to a third embodiment of the present invention will be described with reference to Figures 16 to 20. Note that parts identical to those in the first and second embodiments described above are denoted by the same reference numerals and their descriptions are omitted.

[0086] In this embodiment, the configuration is the same as in Figure 1. In the first and second embodiments described above, the window W was set only in front of or behind the vehicle C, but in this embodiment, as shown in Figure 16, a front window WF and a rear window WR are set. That is, two windows are set at a predetermined distance apart in the direction of movement of the moving body, in front of and behind.

[0087] In this embodiment, rider 21FL is installed on the front left side of vehicle C, and rider 21FR is installed on the front right side of vehicle C. Furthermore, rider 21RL is installed on the rear left side of vehicle C, and rider 21RR is installed on the rear right side of vehicle C. In this embodiment, riders are installed on both the left and right sides of the vehicle, but they may also be installed only on the right or left side.

[0088] Then, if the detection range of the RIDA 21FL and 21FR is set to AF, then window WF is set to that detection range AF. Furthermore, if the detection range of the RIDA 21RL and 21RR is set to AR, then window WR is set to that detection range AR. These windows WF and WR are set to positions within detection range A where white lines D1 and D2 are easily detected.

[0089] Next, a specific example of speed calculation will be explained with reference to Figure 17. Figure 17 shows the state in which windows WF and WR, which move with vehicle C, detect the white line D1. In Figure 17, as in the first embodiment, it is assumed that the detection of the white line D1 is performed by scanning at intervals of, for example, Δt. At this time, a3 is the length of the last detected portion of the end of the white line D1 in the front window WF, and x1 to x are the distance traveled between each time interval (per Δt). 10If b3 is the length of the undetected portion when the end of the white line D1 in the rear window is first detected, and G is the gap (distance) between the front window WF and the rear window WR, then the following equation (21) holds. Note that the gap G is a value that can be determined in advance from the installation position of each lidar and the detection range of the lidar.

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[0090] Here, a3 and b3 can be calculated based on what was explained in Figures 7 and 8, similar to what was explained in Figure 12. That is, in this embodiment, the characteristic part of the feature is one end of the feature (the end of the white line).

[0091] Transforming equation (21) yields equation (22).

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[0092] Therefore, equation (22) represents the scan movement amount for 10 scans. Thus, the velocity v can be calculated by the following equation (23), which divides G+a3+b3 by the scan time for 10 scans. In equation (23), N represents the number of scans. That is, the temporal change in position information is the elapsed time from the last detection in the detection area in front of the feature portion (window WF) to the start of detection in the detection area in behind it (window WR). In addition, in this embodiment, the velocity v can also be calculated when the length of the dashed line (white line) is unknown.

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[0093] Furthermore, processing may start from the detection point before the end of the front detection area, or from the beginning to the end of the rear detection area. In the latter case, a3 and b3 will be large values, but the scan count N will also increase, so the same values ​​will be calculated as a result.

[0094] Next, the speed calculation process described in Figure 17 will be explained with reference to the flowchart in Figure 18. First, in step S601, the extraction unit 15b initializes the scan count N to "0". Next, in step S602, the extraction unit 15b determines whether or not it has detected the white line D1 in the front window WF. If it has not detected it (No), this step is repeated. If it has detected it (Yes), in step S603, the extraction unit 15b determines whether or not the end of the white line D1 has no longer been detected in the front window WF. If the end of the line is still detected (No), this step is repeated. If the end of the line is no longer detected, in step S604, the calculation unit 15c calculates the length a3 of the immediately preceding white line detection portion. Here, detecting the end of the white line D1 in the front window WF means that one or more scan lines have detected the white line D1 in the front window WF, as shown in the top row of Figure 17. Furthermore, the absence of detection of the end of the white line D1 within the front window WF indicates a state in the front window WF, as shown in the second row from the top in Figure 17, where all scan lines no longer detect the end of the white line D1 furthest from the vehicle.

[0095] Next, in step S605, the extraction unit 15b counts up the scan count N, and in step S606, the extraction unit 15b determines whether or not it has detected the end of the white line D1 in the rear window WR. If it has not detected the end of the white line D1, it returns to step S605. If it has detected the end of the white line D1, in step S607, the calculation unit 15c calculates the length b3 of the undetected portion of the white line D1. Detecting the end of the white line D1 means, for example, that one end of the white line D1 closest to the vehicle has been detected, as shown in the bottom row of Figure 17.

[0096] Then, in step S608, the calculation unit 15c obtains the gap G between the front window WF and the rear window WR, and in step S609, the calculation unit 15c calculates the velocity v using equation (22).

[0097] Next, another specific example of speed detection in this embodiment will be described with reference to Figure 19. Similar to Figure 17, Figure 19 shows the state in which windows WF and WR, moving with vehicle C, detect the white line D1. Also, in Figure 19, as in the first embodiment, it is assumed that the detection of the white line D1 is performed by scanning at intervals of, for example, Δt. In this case, a4 is the length of the undetected portion of the white line in the front window WF when the starting end of the white line D1 is last detected, and x1 to x are the distances traveled between each time interval (per Δt). 10 If b4 is the length of the detection portion when the starting end of the white line D1 in the rear window is first detected, and G is the gap (distance) between the front window WF and the rear window WR, then the following equation (24) holds. Note that the gap G is a value that can be determined in advance from the installation position of each lidar and the detection range of the lidar.

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[0098] Here, a4 and b4 can be calculated based on what was explained in Figures 7 and 8, similar to what was explained in Figure 12. That is, in this embodiment, the characteristic part of the feature is one end of the feature (the starting end of the white line).

[0099] Transforming equation (24) yields equation (25).

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[0100] Therefore, equation (25) represents the scan movement amount for 10 scans. Thus, the velocity v can be calculated by the following equation (26), which divides G+a4+b4 by the scan time for 10 scans. In equation (26), N represents the number of scans. That is, the temporal change in position information is the elapsed time from the last detection in the detection area in front of the feature portion (window WF) to the start of detection in the detection area behind it (window WR). In this example, the velocity v can also be calculated when the length of the dashed line (white line) is unknown.

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[0101] Furthermore, processing may start from the detection point before the end of the front detection area, or from the beginning to the end of the rear detection area. In the latter case, a4 and b4 will be large values, but the scan count N will also increase, so the same values ​​will be calculated as a result.

[0102] Next, the speed calculation process described in Figure 19 will be explained with reference to the flowchart in Figure 20. First, in step S701, the extraction unit 15b initializes the scan count N to "0". Next, in step S702, the extraction unit 15b determines whether or not it has detected the white line D1 in the front window WF. If it has not detected it (No), this step is repeated. If it has detected it (Yes), in step S703, the extraction unit 15b determines whether or not the detection of the starting end of the white line D1 has ceased in the front window WF. If the detection of the starting end has not ceased (No), this step is repeated. If the detection of the starting end has ceased, in step S704, the calculation unit 15c calculates the length a4 of the previously undetected white line portion. Here, detecting the starting end of the white line D1 in the front window WF means that one or more scan lines have detected the white line D1 in the front window WF, as shown in the top row of Figure 19. Furthermore, the absence of detection of the starting end of the white line D1 within the front window WF indicates a state in which all scan lines within the front window WF, as shown in the second row from the top of Figure 19, no longer detect one end of the white line D1 on the vehicle side.

[0103] Next, in step S705, the extraction unit 15b counts up the scan count N, and in step S706, the extraction unit 15b determines whether or not it has detected the starting end of the white line D1 within the rear window WR. If it has not detected the starting end of the white line D1, it returns to step S705. If it has detected the starting end of the white line D1, in step S707, the calculation unit 15c calculates the length b4 of the detected portion of the white line D1. Detecting the starting end of the white line D1 means, for example, that one end of the white line D1 closest to the vehicle has been detected, as shown in the bottom row of Figure 19.

[0104] Then, in step S708, the calculation unit 15c obtains the gap G between the front window WF and the rear window WR, and in step S709, the calculation unit 15c calculates the velocity v using equation (26).

[0105] In this embodiment, the detection area consists of two locations separated by a gap G in the direction of movement of the vehicle C, and the feature portion of the feature is one end of the white line D1. In this way, since the gap G is known, the speed of the vehicle C can be calculated even if the length of the feature corresponding to the direction of movement is unknown.

[0106] Furthermore, the temporal change in positional information is the elapsed time from the last detection in the front detection area of ​​the feature portion to the start of detection in the rear detection area, i.e., the scan time for N scans. By doing this, both the front and rear windows can be detected with high accuracy due to the dense line spacing, and the speed of vehicle C can be calculated from the time the feature portion was detected.

[0107] Furthermore, the above-described embodiments may be combined. Since vehicles experience vertical vibration, pitching, and rolling depending on the road surface conditions they travel on, the method of a single embodiment alone contains errors. Therefore, for example, the speeds calculated using each embodiment method may be averaged to obtain the final speed. Also, since the accuracy of the calculated speed increases with denser line spacing, the weighting of the speed calculated using densely spaced lines may be increased.

[0108] In the above description, lane markings were explained using dashed lines (white lines), but in the first and third embodiments, other road markings or signs may be used. Furthermore, in the above description, a lidar was used as the detection unit, but an on-board camera may also be used.

[0109] Furthermore, the present invention is not limited to the embodiments described above. That is, those skilled in the art can implement the invention in various ways, without departing from the core principles, in accordance with prior art knowledge. As long as such modifications still possess the configuration of the information processing device of the present invention, they are of course included within the scope of the present invention. [Explanation of Symbols]

[0110] 1. Detection device 15. Control Unit (Information Processing Device) 15a Acquisition part 15b Extraction part 15c Calculation part 21. LIDA (detection unit) S101 Detect the starting point of the white line within the window (acquisition process, extraction process) S102 Speed ​​calculation process (calculation step) S103 The entire window is filled with white lines (extraction process)

Claims

[Claim 1] An acquisition unit continuously acquires, at predetermined time intervals, the detection results of a detection unit that detects features around a moving object, specifically the detection results of features within a predetermined detection area that moves together with the moving object. An extraction unit extracts positional information of the feature portion of the feature within the detection area in the direction of movement of the moving body from the multiple detection results obtained, A calculation unit that calculates the speed of the moving object based on the temporal changes in the extracted position information, An information processing device characterized by comprising:

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