Device for detecting an external environment

Abstract

An external environment detection device 1 is provided, capable of accurately estimating the relative relationship between a map feature and a target detected by a sensor. The external environment detection device 1 comprises a self-position estimation unit 3, which estimates self-positions 10P1 to 10P4, the positions of a host vehicle 10 on a map, based on external environment information acquired by an external environment sensor 2 attached to the host vehicle 10, and a target detection unit 4, which detects targets around the host vehicle 10 based on the external environment information.Furthermore, the device 1 for detecting an external environment comprises a map information acquisition unit 5, which acquires map information comprising a map point group 16G, which is a set of feature points on the map, and feature information comprising information about a position and type of feature; a sensor point group acquisition unit 6, which acquires a sensor point group 15G around the targets detected by the target detection unit 4; and a point group matching unit 7, which estimates positions of the targets on the map by matching between the sensor point group 15G acquired by the sensor point group acquisition unit 6 and the map point group 16G.

Description

Technical area

[0001] The present invention relates to an external environment detection device mounted on a vehicle. State of the art

[0002] In recent years, both driving assistance devices and automatic driving devices for vehicles have been developed. In driving assistance devices or automatic driving devices, estimating the position of a target is important.

[0003] PTL 1 describes a technique for detecting a target from two points using a camera or sensor mounted on a host vehicle, obtaining two error distributions, and comparing standard errors E1 and E2.

[0004] The technique described in PTL 1 is a technique for determining whether or not to select a sampling point of two error distributions or a sampling point of an overlapping region of two error distributions from a magnitude relationship between the standard errors E1 and E2, and estimating the target using the selected sampling point. Citation listPatent literature

[0005] PTL 1: JP 2018-185156 A Summary of the inventionTechnical problem

[0006] However, PTL 1 does not consider a case where there is an error between a sensor point group acquired using the camera or the sensor and a map point group, and has a difficulty in accurately estimating a relative relationship between a map feature and a target by the sensor in a case where the error exists between the sensor point group and the map point group.

[0007] If it is difficult to accurately estimate the relative relationship between the map feature and the target detected by the sensor, it is difficult to perform driving assistance and automatic driving of the vehicle with high accuracy.

[0008] An object of the present invention is to provide an external environment recognition apparatus and an external environment recognition method capable of accurately estimating a relative relationship between a map feature and a target detected by a sensor. Solution to the problem

[0009] To achieve the above object, the present invention is configured as follows.

[0010] An external environment detection device includes a self-position estimation unit that estimates a self-position, which is a position of a host vehicle on a map stored in a map database, based on external environment information detected by an external environment sensor mounted on the host vehicle; a target detection unit that detects targets around the host vehicle based on the external environment information; a map information acquisition unit that acquires map information including a map point group, which is a set of feature points on the map, and feature information including information about a position and a type of a feature; a sensor point group acquisition unit that acquires a sensor point group around the targets detected by the target detection unit; and a point group matching unit.estimates the positions of the targets on the map by comparing the sensor point group detected by the sensor point group detection unit with the map point group.

[0011] In addition, a method for recognizing an external environment includes estimating a self-position, which is a position of a host vehicle on a map stored in a map database, based on external environment information acquired by an external environment sensor mounted on the host vehicle, detecting targets around the host vehicle based on the external environment information, acquiring map information including a map point group, which is a set of feature points on the map, and feature information including information about a position and a type of a feature, acquiring a sensor point group around the detected targets, and estimating a position of the target on the map by matching between the acquired sensor point group and the map point group. Advantageous effects of the invention

[0012] According to the present invention, it is possible to provide the external environment recognition apparatus and the external environment recognition method capable of accurately estimating the relative relationship between the map feature and the target detected by the sensor.

[0013] In the present invention, a position of a sensor target on a map is estimated by extracting a point group around a sensor target (a target detected by a sensor) and performing point group matching between a map point group and the sensor target, and a relative relationship between a map feature and a sensor target is accurately estimated. Short description of the drawings Fig. 1 is a schematic configuration diagram of an external environment detection apparatus according to a first embodiment of the present invention. Fig. Figure 2 is an explanatory diagram of a scale error. Fig. Figure 3 is an explanatory diagram of the scale error. Fig. 4 is an explanatory diagram of an angular error. Fig. 5 is an explanatory diagram of the angular error. Fig. 6 is an operational explanatory diagram of point group matching according to the first embodiment. Fig. 7 is a diagram illustrating an information table of an example in which a combination of a sensor target and a map feature is set. Fig. 8 is an explanatory diagram of a sensor target selection. Fig. 9 is a diagram illustrating an information table showing a relationship between the sensor target, the map feature, and a threshold. Fig. 10 is a schematic configuration diagram of an external environment detection apparatus according to a second embodiment of the present invention. Fig. 11 is an operational explanatory diagram of a speed prediction unit. Fig. 12 is a schematic configuration diagram of an external environment detection apparatus according to a third embodiment of the present invention. Fig. 13 is an operational explanatory diagram of an intended speed prediction unit. Fig. 14 is a schematic configuration diagram of an external environment detection apparatus according to a fourth embodiment of the present invention. Fig. 15 is an explanatory diagram illustrating that there is a high-accuracy range in a result of detection of an external environment by a target detection unit. Fig. 16 is an explanatory diagram of self-map generation according to the fourth embodiment. Fig. 17 is a schematic configuration diagram of an external environment detection apparatus according to a fifth embodiment of the present invention. Fig. 18 is an operational explanatory diagram of a trajectory prediction unit according to the fifth embodiment. DESCRIPTION OF EMBODIMENTS

[0014] Embodiments of the present invention will be described in detail with reference to the accompanying drawings. Embodiments (First Embodiment)

[0015] Fig. 1 is a schematic configuration diagram of an external environment detection apparatus 1 according to a first embodiment of the present invention.

[0016] In Fig. 1, the external environment recognition device 1 comprises a self-position estimation unit 3, a target recognition unit 4, a map information acquisition unit 5, a sensor point group acquisition unit 6, a point group matching unit 7, and a target selection unit 8.

[0017] The self-position estimation unit 3 estimates a self-position, which is a position of a host vehicle 10 on a map, based on external environment information acquired by an external environment sensor 2 mounted on the host vehicle 10 (in Fig. 2).

[0018] The target detection unit 4 detects targets around the host vehicle 10 based on external environmental information acquired by the external environmental sensor 2. The external environmental sensor 2 is a sensor such as a camera or radar and acquires information about the external environment of the host vehicle 10.

[0019] The map information acquisition unit 5 acquires map information including map information stored in a storage unit attached to the host vehicle 10, a map point group, which is a set of feature points on a map transmitted from the outside, and feature information, which includes information about a position and a type of a feature. Fig. 1, the map information is stored in a map database 9. The map database 9 may be stored in the storage unit attached to the host vehicle 10 or may be information transmitted from the outside.

[0020] The sensor point group detecting unit 6 detects sensor point groups, which are a plurality of positions around the targets detected by the target detecting unit 4, from the external environment information detected by the external environment sensor 2.

[0021] The point group matching unit 7 estimates positions of the targets on the map detected by the target detection unit 4 by matching between the sensor point group detected by the sensor point group detection unit 6 and the map point group detected by the map information detection unit 5. That is, the point group matching unit 7 determines whether the point group matching was successful or not and outputs the positions of the targets on the map estimated by the matching between the sensor point group and the map point group in a case where the point group matching was successful. In a case where the point group matching fails, the point group matching unit 7 outputs the positions of the targets on the map calculated using the self-position and a detection result of the targets detected by the target detection unit 4.

[0022] The target selection unit 8 selects a target satisfying a predetermined condition from among the targets detected by the target detection unit 4 based on the self-position of the host vehicle 10 estimated by the self-position estimation unit 3, the detection result of the targets detected by the target detection unit 4, and the feature information acquired by the map information acquisition unit 5.

[0023] A positional relationship between the host vehicle 10 and another vehicle 11, and a positional relationship between the host vehicle 10, the other vehicle 11, and a road will be described. Regarding an actual positional relationship (true positional relationship) with a target in an external environment of the host vehicle 10, a positional relationship between a self-generated map by odometry (a method of estimating a current position from a rotation angle of a wheel of the vehicle) and the target in the external environment detected using the external environment sensor 2 may be inaccurate.

[0024] It should be noted that the other vehicle 11 is also included in the destination.

[0025] A scale error is determined by reference to the Fig. 2 and Fig. 3 described.

[0026] (a) of Fig. 2 is a diagram illustrating a true position map 12T1, which is an actual positional relationship (true positional relationship) with the target in the external environment of the host vehicle 10. In (b) of Fig. 2, the other vehicle 11 moves forward in front of the host vehicle 10 in a direction opposite to a direction of movement of the host vehicle 10. The host vehicle 10 and the other vehicle 11 move within a road boundary 12, and the other vehicle 11 moves at a position on the way via a retreat area position 17.

[0027] (b) of Fig. 2 is a diagram illustrating a self-generated map 12G1 generated by the host vehicle 10 using odometry.

[0028] By comparing the true position map 12T1 shown in (a) of Fig. 2, with the self-generated map 12G1 in (b) of Fig. 2, a position of the retraction range position 17 is shifted. This is because there is a scale error due to an odometry error.

[0029] (a) of Fig. 3 is a diagram illustrating a detection result 12S1 indicating the positional relationship between the host vehicle 10 and the other vehicle 11 detected using the external environment sensor 2 of the host vehicle 10.

[0030] (b) of Fig. 3 is a diagram illustrating a synthesis result card 12GS1 obtained by synthesizing the detection result 12S1 shown in (a) of Fig. 3, and the self-generated map 12G1 shown in (b) by Fig. 2 is obtained.

[0031] By comparing the true position map 12T1 shown in (a) of Fig. 2, with the composite result map 12G1 shown in (b) by Fig. 3, the position of the retreat area position 17 is shifted and a relative relationship between the other vehicle 11 and the retreat area position 17 is inaccurate.

[0032] That is, in the true position map 12T1 shown in (a) of Fig. 2, the other vehicle 11 crosses the retreat area position 17, whereas in the synthesis result map 12GS1 shown in (b) of Fig. 3, a position 11P1 of the other vehicle is in a state before crossing the retreat area position 17.

[0033] When the relative relationship between the other vehicle 11 and the retreat area 17 is inaccurate due to the scale error, it is difficult to perform driving assistance or automatic driving of the host vehicle 10 with high accuracy.

[0034] An angular error is determined by reference to the Fig. 4 and Fig. 5 described.

[0035] (a) of Fig. 4 is a diagram illustrating a true position map 12T2, which is an actual positional relationship (true positional relationship) with the target in the external environment of the host vehicle 10. In (a) of Fig. 4, the other vehicle 11 moves on a road substantially orthogonal to a road on which the host vehicle 10 moves in a direction substantially orthogonal to the direction of movement of the host vehicle 10.

[0036] (b) of Fig. 4 is a diagram illustrating a self-generated map 12G2 generated by the host vehicle 10 using odometry.

[0037] By comparing the true position map 12T2 shown in (a) of Fig. 4, with the self-generated map 12G2, which in (b) is Fig. 4, an angle of a dividing line 14 is shifted. This is because an angle error exists due to an odometry error.

[0038] (a) of Fig. 5 is a diagram illustrating a detection result 13R2 indicating a positional relationship between the host vehicle 10 and the other vehicle 11 detected using the external environment sensor 2 of the host vehicle 10.

[0039] (b) of Fig. 5 is a diagram illustrating a synthesis result card 12GS2 obtained by synthesizing the detection result 13R2 shown in (a) of Fig. 5, and the self-generated map 12G2 shown in (b) by Fig. 4 is obtained.

[0040] By comparing the true position map 12T2 shown in (a) of Fig. 4, with the synthesis result card 12GS2 in (b) of Fig. 5, a relative relationship between another vehicle position 11P2 on the map and the dividing line 14 is inaccurate.

[0041] That is, the other vehicle 11 is a position of moving forward in a direction parallel to the dividing line 14 in the true position map 12T2 shown in (a) of Fig. 4, and the position 11P2 of the other vehicle is a position of moving forward in a direction intersecting the dividing line 14 in the synthesis result map 12GS2 shown in (b) of Fig. 5 is illustrated.

[0042] When the relative relationship between the other vehicle 11 and the dividing line 14 is inaccurate due to the angle error, it is difficult to perform driving assistance or automatic driving of the host vehicle 10 with high accuracy, similar to the scale error.

[0043] In the first embodiment of the present invention, the positions of the targets detected by the external environment sensor 2 on the map are estimated by extracting the point groups around the targets detected by the external environment sensor 2 and performing point group matching between the extracted point groups around the targets and the map point groups acquired from the map information. Thus, the scale error and the angle error are corrected to accurately estimate the relative relationship between the host vehicle 10, the other vehicle 11, and the feature on the map.

[0044] Fig. 6 is an operational explanatory diagram of point group matching according to the first embodiment of the present invention.

[0045] In (a) of Fig. 6 a variety of points 15 (in Fig. 6) in a sensor point group 15G around the other vehicle 11 is extracted from the external environment information acquired by the external environment sensor 2. The sensor point 15 has a relatively high accuracy in a region of the sensor point group 15G around the other vehicle 11.

[0046] The extraction processing of the plurality of points 15 in the sensor point group 15G is performed by the external environment detection device 1. That is, the target detection unit 4 detects the targets from the external environment information acquired by the external environment sensor 2. In addition, the self-position estimation unit 3 estimates the map information acquired by the map information acquisition unit 5 and the self-position, which is the position of the host vehicle 10 detected by the external environment sensor 2. The target selection unit 8 selects the target from the map information acquired by the map information acquisition unit 5, the self-position estimated by the self-position estimation unit 3, and the targets detected by the target detection unit 4.The sensor point group detecting unit 6 extracts the sensor point group 15G based on the targets detected by the target detecting unit 4 and the target selected by the target selecting unit 8.

[0047] Subsequently, the point group matching unit 7 performs matching processing between the sensor points 15 and a map point group 16 based on the map information acquired by the map information acquiring unit 5 and the sensor point group 15G extracted by the sensor point group acquiring unit 6.

[0048] (b) of Fig. 6 is an explanatory diagram of the matching processing between the sensor point 15 and the map point 16. Circles in (b) of Fig. 6 indicate the map points 16. A map point group 16G comprising the map points 16 has a relatively high accuracy in a local area.

[0049] The matching processing is performed to overlap the sensor points 15 and the map points 16, a relative relationship between another vehicle position 11P3 on the map and the retreat area 17 is made accurate.

[0050] For the Fig. 5, the matching processing described above is performed, and thus the relative relationship between the other vehicle position 11P2 on the map and the dividing line 14 is also made accurate.

[0051] The determination of whether the matching processing in the point group matching unit 7 was successful or not is described.

[0052] If the number of points contained in the map is less than or equal to a predetermined threshold, it is determined that the matching failed. In this case, the matching processing is not executed from the beginning.

[0053] Subsequently, in a case where the number of points included in the map exceeds the predetermined threshold, in a case where an average value of the distances between the sensor points 15 and the map points 16 that correspond to each other is greater than or equal to a predetermined threshold, it is determined that the matching has failed. In addition, in a case where the number of corresponding points between the sensor points 15 and the map points 16 is less than or equal to a predetermined threshold, it is also determined that the matching has failed.

[0054] If the matching processing is successful, the point group matching unit 7 outputs a position and posture of a sensor target on the map estimated by the matching processing. The host vehicle 10 performs driving assistance and automatic driving processing based on the position and posture of the sensor target output by the point group matching unit 7.

[0055] In a case where the matching processing has failed, the point group matching unit 7 may output the position and attitude of the sensor target on the map (a preliminary position of the target selection unit 8) estimated from the self-position estimation result and the detection result.

[0056] Next, an operation of the target selection unit 8 will be described.

[0057] A combination of the sensor target and the map feature whose relative relationship is important is set in advance. Fig. 7 is a diagram illustrating an information table 19 of an example in which the combination of the sensor target and the map feature is set.

[0058] Fig. 8 is an explanatory diagram of a sensor target selection.

[0059] The target selection unit 8 calculates a preliminary position 11P4 of a sensor feature from a self-position estimation result 10P3 and a detection result 13R1. The target selection unit 8 selects a sensor target whose distance from a corresponding map feature (a retreat area in the Fig. 8) is less than or equal to a predetermined threshold.

[0060] As in Fig. As illustrated in Figure 7, the combination of the sensor target and the map feature whose relative relationship is important is set in advance, and the combination of the sensor target and the map feature whose relative relationship is important for the distance between the feature and the sensor target is set in advance. As a result, processing time for target selection can be shortened.

[0061] The target selection unit 8 may be configured to be able to change the threshold of target selection (the distance between the feature and the sensor target) according to a size, a speed, a weather, and a brightness of the sensor target such that the target can be appropriately selected.

[0062] For example, if the size of the sensor target is larger, the threshold is set higher. If the speed of the sensor target is higher, the threshold is set higher (the arrival time of the sensor target at the map feature can be used instead of the distance).

[0063] Additionally, if the weather is worse, the threshold can be set higher. If the brightness is darker, the threshold can be set higher.

[0064] Fig. 9 is a diagram illustrating an information table 19A of an example in which threshold values ​​are added to and retained for the sensor target and the map feature in the information table 19, and a relationship between the sensor target, the map feature, and the threshold values ​​is illustrated. Predetermined threshold values ​​am, bm, cm, dm··· can be changed according to the size, speed, weather, and / or brightness of the sensor target.

[0065] A method for recognizing an external environment according to the first embodiment of the present invention will be described.

[0066] The self-position, which is the position of the host vehicle 10 on the map stored in the map database 9, is estimated based on the external environment information acquired by the external environment sensor 2 mounted on the host vehicle 10, the target around the host vehicle 10 is detected based on the external environment information, the map information including the map point group, which is the set of feature points on the map, and the feature information including the information about the position and type of the feature is acquired, the sensor point group around the detected target is acquired, and the position of the target on the map is estimated by matching the acquired sensor point group and the map point group.

[0067] As described above, according to the first embodiment of the present invention, since the position of the sensor target on the map is estimated by extracting the point group around the sensor target and performing the point group matching between the map point group and the sensor target, it is possible to provide the external environment recognition apparatus and the external environment recognition method capable of accurately estimating the relative relationship between the map feature and the target detected by the sensor. (Second embodiment)

[0068] Next, a second embodiment of the present invention will be described.

[0069] Fig. Fig. 10 is a schematic configuration diagram of an external environment detection device 1 according to the second embodiment of the present invention. A difference between the second embodiment and the Fig. The first embodiment illustrated in Figure 1 is that a speed prediction unit 20 is connected to the point group matching unit 7 of the first embodiment. Other configurations of the second embodiment are similar to the configurations of the first embodiment.

[0070] The speed prediction unit 20 predicts a position and a speed of another preceding vehicle 11 on a map estimated by a point group matching unit 7, is used for an adaptive cruise control (ACC) function, and appropriately controls a speed of the host vehicle 10.

[0071] Fig. 11 is an operational explanatory diagram of the speed prediction unit 20.

[0072] In (a) of Fig. 11, a host vehicle 10P3 automatically moves at a constant speed while maintaining a constant inter-vehicle distance with respect to another preceding vehicle 11P4 by an adaptive cruise control function. The speed prediction unit 20 can predict that a speed of the other preceding vehicle 11P4 will decrease as the other preceding vehicle 11P4 approaches a temporary stop line 21.

[0073] When a speed of the host vehicle 10P3 is controlled based on the speed prediction of the speed prediction unit 20, the adaptive cruise control function can be performed with high accuracy.

[0074] As described above, according to the second embodiment of the present invention, effects similar to the effects of the first embodiment can be obtained, and the adaptive cruise control function can be performed with high accuracy. (Third embodiment)

[0075] Next, a third embodiment of the present invention will be described.

[0076] Fig. Fig. 12 is a schematic configuration diagram of an external environment detection device 1 according to the third embodiment of the present invention. A difference between the third embodiment and the Fig. The first embodiment illustrated in Figure 1 is that an intention prediction unit 24 is connected to the point group matching unit 7 of the first embodiment. Other configurations of the third embodiment are similar to the configurations of the first embodiment.

[0077] The intention prediction unit 24 is used in an automatic driving device and predicts an intention of a pedestrian based on a relative relationship between a pedestrian position on the map estimated by a point group matching unit 7 and a pedestrian path on the map.

[0078] As a result, it is possible to accurately predict the pedestrian's intention and appropriately plan an action of a host vehicle 10.

[0079] Fig. 13 is an operational explanatory diagram of the intended speed prediction unit 20.

[0080] In (a) of Fig. 13, another vehicle 11P5 is positioned in front of a host vehicle 10P4 on a pedestrian path 23, and a pedestrian 22 on a map estimated by point group matching is positioned in front of the pedestrian path 23. In this case, the intention prediction unit 24 predicts an intention of the pedestrian 22 to cross the pedestrian path 23. Based on the prediction of the intention prediction unit 24, the automatic driving device (not illustrated) can perform control such that the host vehicle 10P4 stops in front of the pedestrian path.

[0081] In (b) of Fig. 13, the other vehicle 11P5 is positioned in front of the host vehicle 10P4 on the pedestrian path 23, and the pedestrian 22 on the map estimated by point group matching is not positioned in front of the pedestrian path 23, but substantially midway between the host vehicle 10P4, which is separated from the pedestrian path 23, and the pedestrian path 23. In this case, the intention prediction unit 24 predicts that the pedestrian 22 will stop, and the automatic driving device (not illustrated) can perform control such that the host vehicle 10P4 travels without stopping in front of the pedestrian path based on the prediction of the intention prediction unit 24.

[0082] Similarly, for a stopped vehicle, which is a sensor target, and a time-limited parking zone, which is a map feature, the intention prediction unit 24 predicts an intention of the stopped vehicle or the like, and the automatic driving device can perform control based on the result. For example, the intention prediction unit 24 predicts whether the stopped vehicle is a parked vehicle or a vehicle temporarily stopped at a traffic light or the like.

[0083] In a case where the other vehicle 11P5 is positioned on the pedestrian path 23, an external environment sensor 2 may not be able to detect the pedestrian path 23. In this case, it is necessary to acquire information from a map database 9 and determine whether the pedestrian path on which the vehicle is positioned is the pedestrian path 23 or not.

[0084] The intention prediction unit 24 can accurately predict the intention of the pedestrian 22 based on the relative relationship between the position of the pedestrian 22 on the map estimated by the point group matching unit 7 and the pedestrian path 23 on the map.

[0085] As described above, according to the third embodiment of the present invention, it is possible to obtain effects similar to the effects of the first embodiment, and it is possible to appropriately control the host vehicle 10 by automatic driving by predicting the intention of the pedestrian or the like. (Fourth embodiment)

[0086] Next, a fourth embodiment of the present invention will be described.

[0087] Fig. Fig. 14 is a schematic configuration diagram of an external environment detection device 1 according to the fourth embodiment of the present invention. A difference between the fourth embodiment and the Fig. 1 is that a self-map generation unit 26 is connected to the target recognition unit 4 of the first embodiment, and the self-map generation unit 26 generates a self-map from information from an odometry unit 25 and target information from a target recognition unit 4, and stores the self-map in a map database 9. Other configurations of the fourth embodiment are similar to the configurations of the first embodiment.

[0088] As in Fig. 15, in the fourth embodiment, in a case where there is a high-accuracy area 27 in a result of detection of an external environment by the target detection unit 4 at any time, the map by the odometry unit 25 and the information by the target detection unit 4 regarding the area 27 are combined to generate the self-map.

[0089] Since, as in (a) of Fig. 16 illustrates, an external environment detection result 29 of a host vehicle 10P in an external environment detection result 29 before a time point is not in the high-accuracy range, the map is not generated by the self-map generation unit 26.

[0090] Since a retreat area 17A in a host vehicle 10N is located in the high-accuracy area at a subsequent time, the self-map generation unit 26 generates the self-map from an odometry estimated position 28 and the target information from the target recognition unit 4 and stores the self-map in the map database 9.

[0091] A state in which a period of time from that in Fig. 16 (a) has elapsed, the period of validity of the Fig. 16(b). Since a retreat area 17 is located near a host vehicle 10N1 in the state shown in (b) of Fig. 16 is a high-accuracy area, the self-map generating unit 26 generates the self-map from the odometry estimated position 28 and the target information from the target recognition unit 4, and stores the self-map in the map database 9. The self-map generating unit 26 stores a sensor point group 15G as a map around a feature described in an information table 19 in which a type of target and a type of feature are associated with each other.

[0092] In this way, a relatively high-accuracy map is created and stored in the map database 9.

[0093] In the example described above, although the relatively high-accuracy map is generated and stored in map database 9, it is also possible to store a point group only around a map feature whose relative positional relationship to the sensor target is important. For example, it is also possible to store the point group only around the retreat area.

[0094] This can reduce the map capacity stored in the map database 9.

[0095] As described above, according to the fourth embodiment of the present invention, it is possible to obtain effects similar to the effects of the first embodiment, create a relatively high-accuracy map, and further reduce the map capacity stored in the map database 9. (Fifth embodiment)

[0096] Next, a fifth embodiment of the present invention will be described.

[0097] Fig. Fig. 17 is a schematic configuration diagram of an external environment detection device 1 according to a fifth embodiment of the present invention. A difference between the fifth embodiment and the Fig. The first embodiment illustrated in Figure 1 is that a trajectory prediction unit 30 is connected to the point group matching unit 7 of the first embodiment. Other configurations of the fifth embodiment are similar to the configurations of the first embodiment.

[0098] As in (a) and (b) of Fig. 18, the trajectory prediction unit 30 predicts a trajectory of an oncoming vehicle 11P6 based on a relative relationship between the position 11P6 of the oncoming vehicle on a map estimated by the point group matching unit 7 and a retreat area 17 on the map.

[0099] In the case of (a) of Fig. 18, the trajectory prediction unit 30 predicts that the oncoming vehicle can enter the retreat area 17. In the state illustrated in (b) of Fig. 18, the trajectory prediction unit 30 predicts that the oncoming vehicle will not enter the retreat area 17.

[0100] Since the trajectory prediction unit 30 can accurately predict the trajectory of the oncoming vehicle, an action of the host vehicle can be appropriately planned.

[0101] As described above, according to the fifth embodiment of the present invention, since effects similar to the effects of the first embodiment can be obtained and the trajectory of the oncoming vehicle can be accurately predicted, the action of the host vehicle can be appropriately planned.

[0102] It should be noted that in the first to fifth embodiments described above, although a target selection unit 8 is an inventory requirement of the external environment recognition device 1, the article selection unit 8 may be omitted, and an example in which the article selection unit 8 is omitted is also included in the embodiment of the present invention.

[0103] In the example where the article selection unit 8 is omitted, a self-position estimated by a self-position estimation unit 3 is output to the point group matching unit 7, and target information detected by a target detection unit 4 is output only to a sensor point group acquisition unit 6. Then, the point group matching unit 7 performs point group matching based on the self-position estimated by the self-position estimation unit 3, the sensor point group acquired by the sensor point group acquisition unit 6, and map information from a map information acquisition unit 5. List of reference symbols 1 Device for detecting an external environment 2 Sensor for an external environment 3 Self-position estimation unit 4 Target detection unit 5 Map information acquisition unit 6 sensor point group detection unit 7 Point group matching unit 8 Target selection unit 9 Map database 10 Host vehicle 10P1, the card10P2, 10P3, 10P4, 10P5 host vehicle position on 11 other vehicle 11P1, 11P2, 11P3, 11P4, 11P5, 11P6 other vehicle position on the map 11P4 temporary other vehicle position 12 Road boundary 12G1, 12G2 self-generated map 12GS1, 12GS2 card acquisition synthesis result state 12S1 Acquisition result state 12T1, 12T2 true state 13R1, 13R2 relative position of the host vehicle to 14 another vehicle dividing line 15 extracted point 15G extracted point group 16 map points 17, 17A Retreat area position 18TH threshold distance 19, 19A Information table 20 Speed ​​prediction unit 21 temporary stop line 22 Pedestrian position 23 Pedestrian path 24 Intention prediction unit 25 Odometry unit 26 Self-card generation unit 27 range with good accuracy 28 odometry estimated position 29 Result of a detection of an external environment before a point in time 30 Trajectory prediction unit QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] JP 2018-185156 A

[0005]

Claims

[1] Device for detecting an external environment, comprising: a self-position estimation unit that estimates a self-position, which is a position of a host vehicle on a map stored in a map database, based on external environment information acquired by an external environment sensor mounted on the host vehicle; a target detection unit that detects targets around the host vehicle based on the information about the external environment; a map information acquiring unit that acquires map information including a map point group, which is a set of feature points on the map, and feature information, which includes information about a position and a type of a feature; a sensor point group detecting unit that detects a sensor point group around the targets detected by the target detecting unit; and a point group matching unit that estimates positions of the targets on the map by matching between the sensor point group detected by the sensor point group detecting unit and the map point group. [2] An external environment detection device according to claim 1, further comprising: a target selection unit that selects the target that satisfies a predetermined condition from among the targets detected by the target detection unit based on the self-position estimated by the self-position estimation unit, a detection result of the targets detected by the target detection unit, and the feature information, wherein the sensor point group detecting unit detects a sensor point group around the selected target from the target detecting unit. [3] An external environment recognition device according to claim 2, wherein the target selection unit selects the target in a case where there is the corresponding feature around the targets recognized by the target recognition unit, while referring to an information table in which a type of the target and the type of the feature are associated with each other. [4] An external environment detection device according to claim 3, wherein the target selection unit estimates a preliminary position of the target on the map using the self-position and the detection result of the targets detected by the target detection unit, and selects the target in a case where a distance between the preliminary position and a position of the feature corresponding to the target in the information table is less than or equal to a threshold value. [5] An external environment detection device according to claim 4, wherein the information table holds a threshold value used by the target selection unit for each assignment of the type of target to the type of feature, and the target selection unit changes the threshold value held in the information table based on the detection result of the targets, the weather and / or the brightness. [6] The external environment recognition device according to claim 1, wherein the point group matching unit determines whether or not point group matching has been successful, and outputs a position of the target on the map estimated by the matching between the sensor point group and the map point group in a case where the point group matching has been successful. [7] The external environment recognition device according to claim 1, wherein the point group matching unit determines whether or not point group matching has succeeded and outputs a position of the target on the map calculated using the self position and a recognition result of the targets recognized by the target recognition unit in a case where the point group matching has failed. [8] An external environment detection device according to claim 1, further comprising: a prediction unit that predicts a trajectory, a speed, and / or an intention of the target based on a position of the target on the map estimated by the point group matching unit and a position of the feature on the map. [9] The external environment recognition device according to claim 1, further comprising a self-map generating unit that generates the map using a relative position and posture of the host vehicle estimated by odometry, a detection result of the targets detected by the target detecting unit, and the sensor point group. [10] The external environment recognition device according to claim 9, wherein the self-map generating unit stores the sensor point group as the map in the map database around the feature described in an information table in which a type of the target and the type of the feature are associated with each other. [11] A method for detecting an external environment, comprising: Estimating a self-position, which is a position of a host vehicle on a map stored in a map database, based on external environment information acquired by an external environment sensor mounted on the host vehicle; Detecting targets around the host vehicle based on the information about the external environment; Acquiring map information including a map point group, which is a set of feature points on the map, and feature information, which includes information about a position and a type of a feature; Detecting a sensor point group around the detected targets; and Estimating a position of the target on the map by matching between the detected sensor point group and the map point group. [12] A method for detecting an external environment according to claim 11, further comprising: Selecting the target that satisfies a predetermined condition among the detected targets based on the estimated self-position, a detection result of the detected targets, and the feature information; and Detecting a group of sensor points around the selected target.

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