Driving assistance method and driving assistance device

Abstract

To provide a driving support method and a driving support device that can estimate lanes, and add information about the lanes to map data (SD-MAP: Standard Map) not including information of lane units of a road.SOLUTION: A travel support device includes coordinate acquisition units (10 and 20) that identify a plurality of positions on map data of a vehicle, and a road estimation unit 30a. The road estimation unit 30a identifies a link closest to each of the plurality of positions, and generates a histogram aggregating distances between each of the plurality of positions and the link. The road estimation unit 30a estimates that there is a lane within a predetermined range from a position on the map data that is away from the link by a distance corresponding to a peak of the histogram, and adds information about the estimated lane on the map data.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a driving assistance method and a driving assistance device. [Background technology]

[0002] Conventionally, there has been known a driving assistance method that uses high definition map data (HD-MAP: High Definition Map) including information on each lane of a road to assist driving of a vehicle (Patent Document 1). The driving assistance method described in Patent Document 1 outputs a plurality of recommended lane candidates for which the vehicle is recommended to travel, based on the number of lanes included in the high definition map data, and outputs a priority indicating the degree to which the vehicle is recommended to travel for each recommended lane candidate. This allows the vehicle to continue autonomous driving even if an unexpected event occurs that prevents the vehicle from traveling in the recommended lane with the highest priority. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] International Publication No. 2019 / 198481 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the driving assistance method disclosed in Patent Document 1 is based on lane-by-lane information contained in high-precision map data, and cannot be used on roads for which high-precision map data has not been generated. Furthermore, because updating high-precision map data is costly, it is difficult to update the high-precision map in response to changes in the number of lanes due to construction or other reasons.

[0005] The present invention has been made in consideration of the above-mentioned problems, and has an object to provide a driving assistance method and a driving assistance device that can estimate lanes and add information about lanes to map data (SD-MAP: Standard Map) that does not include information on individual lanes of roads. [Means for solving the problem]

[0006] A driving assistance method according to one aspect of the present invention is a driving assistance method that assists driving of a vehicle by using map data in which roads on which the vehicle travels are represented by lines made up of a plurality of nodes and links connecting the nodes. The driving assistance method identifies a plurality of positions on the map data of the vehicle, and identifies a link that is closest to the plurality of positions. A histogram is generated by aggregating the distances between the plurality of positions and the links, and a lane is estimated to exist within a predetermined range from a position on the map data that is a distance away from the link that corresponds to the peak of the histogram, and information about the estimated lane is added to the map data. Effect of the Invention

[0007] According to the present invention, lanes can be estimated and information about lanes can be added to map data that does not include information on individual lanes of roads. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a block diagram showing the configuration of a driving assistance device according to the first embodiment. [Figure 2A] FIG. 2A is a schematic diagram showing a process in which the driving assistance device according to the first embodiment measures distances between a plurality of positions and a link. [Figure 2B] FIG. 2B is a diagram showing a histogram generated by the driving assistance device according to the first embodiment by tallying up distances between a plurality of positions and links. [Figure 2C] FIG. 2C is a conceptual diagram showing a process in which the driving assistance device according to the first embodiment estimates a lane boundary. [Figure 2D] FIG. 2D is a schematic diagram showing lane boundaries estimated by the driving assistance device according to the first embodiment. [Diagram 3] FIG. 3 is a conceptual diagram showing a process in which the driving assistance device according to the first embodiment estimates a lane. [Figure 4A] FIG. 4A is a flowchart showing a process in which the driving assistance device according to the first embodiment estimates a lane and assists driving of the vehicle based on information relating to the estimated lane. [Figure 4B] FIG. 4B is a flowchart showing details of the process in which the driving assistance device according to the first embodiment estimates a lane. [Diagram 5] FIG. 5 is a block diagram showing the configuration of a driving assistance device according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] The embodiments will be described with reference to the drawings. In the description of the drawings, the same parts are given the same reference numerals and the description will be omitted.

[0010] (First embodiment) [Configuration of driving support device] The configuration of a driving support device according to the first embodiment will be described with reference to Fig. 1. The driving support device estimates lanes, adds information about the estimated lanes to map data (SD-MAP: Standard Map) that does not include information on individual lanes of a road, and supports driving of a vehicle using the information about the estimated lanes.

[0011] The driving assistance device according to the first embodiment is mounted on a vehicle 1, and includes a vehicle position acquisition unit 10, an object detection unit 20, a control unit 30, and a storage unit .

[0012] The vehicle position acquisition unit 10 and the object detection unit 20 are coordinate acquisition units (10, 20) that acquire the positions of the vehicle 1 and other vehicles (hereinafter referred to as "vehicles") traveling around the vehicle 1. The coordinate acquisition units (10, 20) transfer the acquired vehicle positions to the control unit 30 and the storage unit 40.

[0013] The vehicle position acquisition unit 10 includes a receiving device that receives GPS (Global Positioning System) signals and measures the position (absolute position) and attitude (absolute attitude) of the vehicle 1 in the Earth coordinates. Furthermore, the vehicle position acquisition unit 10 includes a calculation processing circuit that performs odometry and dead reckoning. Specifically, the calculation processing circuit measures the relative position, relative attitude, and speed of the vehicle 1 with respect to a predetermined reference point based on information acquired from a wheel speed sensor that detects the wheel speed of each wheel of the vehicle 1 and a steering angle sensor that detects the steering angle of the steering wheel.

[0014] Even if the reception conditions of the GPS signal deteriorate, the vehicle position acquisition unit 10 can continue to measure the absolute position and absolute attitude of the vehicle 1 from the relative position and relative attitude of the vehicle 1 with respect to a specified reference point measured by the arithmetic processing circuit.

[0015] The object detection unit 20 includes a plurality of different types of object detection sensors that detect objects around the vehicle 1, such as a laser radar, a millimeter wave radar, a camera, and a LiDER (Light Detection and Ranging) mounted on the vehicle.

[0016] The object detection unit 20 can detect moving objects including motorcycles and vehicles, and stationary objects on or around the roadway, such as road signs, road signs, curbs, guardrails, walls, and parked vehicles. For example, the object detection unit 20 detects the position (relative position), attitude, size, speed, acceleration, deceleration, and yaw rate of a moving or stationary object relative to the vehicle 1. The object detection unit 20 continuously detects the roadway on which the vehicle 1 runs and objects around the roadway at a predetermined period. Note that the position of an object detected by the object detection unit 20 is a position (relative position) relative to the vehicle 1, but by combining it with the absolute position of the vehicle 1 detected by the vehicle position acquisition unit 10, it is possible to detect the absolute position of the object detected by the object detection unit 20.

[0017] The object detection unit 20 integrates multiple detection results obtained from multiple object detection sensors and outputs one detection result for each object. Specifically, by using a known sensor fusion technique, the detection results obtained by each sensor can be comprehensively evaluated to obtain a more accurate detection result.

[0018] The control unit 30 acquires the current absolute position of the vehicle from the coordinate acquisition unit (a collective term for the vehicle position acquisition unit 10 and the object detection unit 20). Furthermore, the control unit 30 acquires from the storage unit 40 a plurality of absolute positions of the vehicle, such as the positions of the vehicle itself or a plurality of other vehicles measured in the past, or the positions of the vehicle itself or the other vehicles measured multiple times at different times in the past, and map data (hereinafter referred to as "map data") that does not include information on lane units of the road. The control unit 30 estimates the lane based on the plurality of absolute positions of the vehicle and the map data. The control unit 30 adds information on the estimated lane to the map data, and transfers the updated map data to the storage unit 40. The control unit 30 assists the driving of the vehicle using the map data to which the information on the lane stored in the storage unit 40 has been added. Note that the road here means a vehicle driving area including a single lane or a plurality of lanes with a common driving direction, and the information on the road units is information on each road including nodes set along the extension direction of the road on the map and links connecting the nodes. In addition, a lane refers to a driving area on a road that is divided into one or more sections by lane dividing lines formed by lane markers, poles, etc., and lane-by-lane information refers to information for each lane, including the position and shape of the lane dividing lines on a map, or the position and shape of the lanes divided by the lane dividing lines.

[0019] The control unit 30 is a general-purpose microcomputer equipped with a CPU (Central Processing Unit), memories (storage units) such as RAM and ROM, and an input / output unit. A computer program for functioning as a driving support device is installed in the microcomputer. By executing the computer program, the microcomputer functions as multiple information processing circuits (30a, 36) equipped in the driving support device. Note that, although an example in which the multiple information processing circuits (30a, 36) equipped in the driving support device are realized by software is shown in this embodiment, it is also possible to configure the information processing circuits by preparing dedicated hardware for executing each information process. Moreover, the multiple information processing circuits may be configured by individual hardware.

[0020] The storage unit 40 is configured with an information storage device including an HDD (hard disk drive) and an SSD (solid state drive). The storage unit 40 stores map data in advance, and when map data to which information regarding lanes has been added is received from the control unit 30, the storage unit 40 updates the map data to the map data to which information regarding lanes has been added. Furthermore, the storage unit 40 stores the absolute position of the vehicle transferred from the coordinate acquisition unit (10, 20). Note that, in this embodiment, the storage unit 40 stores the map data and a plurality of absolute positions of the vehicle, but if the memory provided in the control unit 30 is capable of storing these data, it is not necessary to use the storage unit 40.

[0021] Here, the map data pre-stored in the storage unit 40 will be specifically described.

[0022] The map data does not include information on each lane of a road, but represents the road on which a vehicle travels with lines consisting of a plurality of nodes and links connecting the nodes. This map data is mainly used for car navigation systems.

[0023] A node represents a characteristic point of a road, such as an intersection, a junction with other roads, a corner, a dead end, etc. In the map data, information about the node, such as the node number, absolute position, altitude, the number of connecting links, connecting node numbers, and intersection names, is stored in association with each node.

[0024] A link indicates a road section connecting nodes and the shape of the road. The map data stores information about the link, such as the link number (the number of the start and end nodes), the absolute position of each point on the link, the road type, and the route number, in association with each link.

[0025] Next, the multiple information processing circuits included in the control unit 30 will be specifically described.

[0026] The control unit 30 includes a road estimation unit 30a and a vehicle control unit .

[0027] The road estimation unit 30a identifies a plurality of absolute positions of the vehicle on the map data (hereinafter, also referred to as "plural positions"), and identifies the link closest to each of the plurality of positions. The road estimation unit 30a generates a histogram that tallies the distances between the plurality of positions and the link closest to each of the plurality of positions, and estimates the lanes. The road estimation unit 30a adds information about the estimated lanes to the map data. As described above, the plurality of positions may be positions measured in the past for each of the plurality of vehicles, or may be the position of a single vehicle measured multiple times at different times.

[0028] The road estimation unit 30 a includes a map matching unit 31 , a distance calculation unit 32 , a histogram generation unit 33 , a lane estimation unit 34 , and a map data update unit 35 .

[0029] The map matching unit 31 identifies multiple positions of the vehicle on the map data and identifies the link closest to each of the multiple positions. Specifically, the map matching unit 31 acquires multiple absolute positions of the vehicle from the coordinate acquisition unit (10, 20) and the storage unit 40, and identifies the multiple positions on the map data and the link closest to each of the multiple positions by using a known map matching technique.

[0030] Furthermore, the map matching unit 31 identifies the positions of stop lines at intersections that exist around a plurality of positions on the vehicle's map data.

[0031] First, the map matching unit 31 identifies intersections existing around multiple positions on the map data of the vehicle. Specifically, the map matching unit 31 extracts nodes existing around multiple positions on the map data of the vehicle, and obtains the number of connecting links of the extracted nodes from the map data. The map matching unit 31 determines that a node with three or more connecting links is an intersection among the extracted nodes.

[0032] Next, the map matching unit 31 acquires the absolute positions of the stop lines present around the vehicle detected by the object detection unit 20, and identifies the position of the stop line of the intersection on the map data. Specifically, the map matching unit 31 can identify the position of the stop line of the intersection on the map data by determining that the stop line closest to the position of the node identified as the intersection is the stop line of the intersection.

[0033] The distance calculation unit 32 calculates the shortest distance between each of the multiple positions on the map data of the vehicle and the link closest to each of the multiple positions (hereinafter referred to as the "vehicle link distance"). Specifically, the distance of the perpendicular line from each of the multiple positions to the link closest to each of the multiple positions is calculated, and each calculated distance is regarded as the vehicle link distance. When viewed from the traveling direction of the vehicle, the distance calculation unit 32 calculates the vehicle link distance of a vehicle that is located to the right of the closest link by adding a minus sign, and calculates the vehicle link distance of a vehicle that is located to the left of the closest link by adding a plus sign.

[0034] The histogram generating unit 33 generates a histogram that tallies the vehicle link distances. Specifically, the histogram generating unit 33 sets an aggregation area for tallying the vehicle link distances, and tallies the vehicle link distances for each aggregation area. The aggregation area is the distance in the link direction and the distance in the direction perpendicular to the link that are set according to the following conditions, and is specified by the sum of the absolute values ​​of the minimum (negative) and maximum (positive) vehicle link distances.

[0035] First, the histogram generating unit 33 sets a counting region for counting the vehicle link distances. Specifically, the histogram generating unit 33 counts the vehicle link distances inside the stop line of the intersection. That is, the histogram generating unit 33 sets the region from the stop line of the intersection to the stop line of the next intersection as the counting region.

[0036] In addition, if the curvature of the link is equal to or greater than a predetermined curvature, or if the distance of the link is equal to or greater than a predetermined distance, the histogram generating unit 33 divides the link and aggregates the vehicle-link distances for each area corresponding to the divided link.

[0037] The predetermined curvature and the predetermined distance are determined by performing experiments or simulations in advance. Specifically, the predetermined curvature and the predetermined distance may be determined based on the error between the estimated lane and the actual lane, by changing the vehicle-link distance in a plurality of links with different curvatures and a plurality of links with different distances. More specifically, the predetermined curvature and the predetermined distance may be set within a range in which the influence of the error between the estimated lane and the actual lane on the driving assistance can be tolerated.

[0038] The histogram generating unit 33 prohibits the aggregation of the vehicle-link distance when the vehicle is changing lanes. Specifically, the histogram generating unit 33 acquires the switch state of the turn indicator of the vehicle 1, and when the turn indicator switch is ON, it determines that the vehicle 1 is changing lanes. In addition, the histogram generating unit 33 acquires information on whether or not other vehicles traveling around the vehicle 1 are changing lanes from the object detecting unit 20, and determines whether or not other vehicles traveling around the vehicle 1 are changing lanes. Specifically, the object detecting unit 20 can detect the blinking state of the turn indicator of the other vehicles from the captured image capturing the surroundings of the vehicle 1 by using a known image analysis technique, and can determine whether or not other vehicles traveling around the vehicle 1 are changing lanes. This allows the histogram generating unit 33 to determine whether or not the vehicle is changing lanes.

[0039] Next, the histogram generating unit 33 collects the vehicle link distances for each collection area and generates a histogram. Specifically, the histogram generating unit 33 classifies the vehicle link distances calculated within the collection area into corresponding collection intervals (classes) and calculates the frequency (frequency) of the vehicle link distances for each collection interval. In this way, a histogram is generated. The classes are set for each predetermined section distance, with the link closest to each of the multiple positions being set as the reference position. The predetermined section distance is, for example, 50 cm.

[0040] The histogram generating unit 33 detects the peak of the histogram and calculates the variance of the histogram. Specifically, the histogram generating unit 33 identifies the class with the highest frequency among the classes with the totalized frequencies as the peak. In addition, the histogram generating unit 33 treats adjacent classes among the classes with the totalized frequencies as one data item, and calculates the average of the squared deviations in each class for each data item as the variance.

[0041] The lane estimation unit 34 estimates lanes and lane boundaries for each counting section. Specifically, the lane estimation unit 34 estimates that a lane and lane boundaries exist when the histogram peak is equal to or greater than a predetermined frequency and the histogram variance is equal to or smaller than a predetermined variance.

[0042] The predetermined frequency is, for example, 10. The predetermined variance is determined by conducting experiments or simulations in advance. Specifically, the predetermined variance may be determined by estimating lanes by changing the vehicle-link distance in a plurality of different aggregation areas, and taking into consideration the influence of the error between the estimated lane and the actual lane on the driving assistance. More specifically, the predetermined frequency may be set within a range in which the influence of the error between the estimated lane and the actual lane on the driving assistance can be tolerated.

[0043] If the histogram peak is equal to or greater than a predetermined frequency and the histogram variance is equal to or less than a predetermined variance, the lane estimation unit 34 estimates that a lane exists within a predetermined range from a position on the map data that is a distance away from the nearest link that corresponds to the histogram peak.

[0044] The lane estimation unit 34 acquires the road type of the closest link from the map data, and determines the predetermined range based on the road type of the link. In the map data, the road types of the links are registered as expressways, urban expressways, general national roads, major regional roads, general prefectural roads, and others. In addition, the lane width is determined for each road type by the Road Structure Ordinance, which is a government ordinance that defines the general technical standards for road structures. That is, the lane estimation unit 34 can set the predetermined range according to the road type of the closest link by storing the predetermined range according to the lane width for each road type in advance based on the Road Structure Ordinance. The predetermined range is, for example, a range of ±30 cm of the lane width for each road type determined by the Road Structure Ordinance.

[0045] When the histogram peak is equal to or greater than a predetermined frequency and the variance of the histogram is equal to or less than a predetermined variance, the lane estimation unit 34 estimates that a lane boundary exists at a position on the map data that is a distance away from the closest link by a distance corresponding to the center between adjacent peaks of the histogram. Furthermore, when the distance corresponding to the distance between adjacent peaks of the histogram is equal to or greater than a first predetermined distance and less than a second predetermined distance, the lane estimation unit 34 estimates that a lane boundary exists. As in the setting of the predetermined range described above, the lane estimation unit 34 obtains the road type of the closest link from the map data and sets the first and second predetermined values ​​based on the road type of the link. The first predetermined value is, for example, the lane width for each road type determined by the Road Structure Act minus 30 cm. The second predetermined value is, for example, the lane width for each road type determined by the Road Structure Act plus 30 cm.

[0046] The lane estimation unit 34 estimates the number of lanes of the road from the estimated lanes and lane boundaries as well as the number of estimated lanes and lane boundaries.

[0047] The map data update unit 35 adds information about the lanes estimated by the lane estimation unit 34 to the map data, and updates the map data. Specifically, the map data update unit 35 adds the estimated number of lanes, the positions of the lanes, and the positions of lane boundaries to the map data, and transfers it to the storage unit 40.

[0048] The vehicle control unit 36 ​​assists the driving of the vehicle based on map data to which information about lanes has been added. Specifically, when driving a set driving route, the vehicle control unit 36 ​​assists the driving of the vehicle by displaying on a user interface a lane that is most suitable for driving the driving route, taking into consideration right and left turns at intersections and entrance routes at junctions on expressways on the driving route. Alternatively, the vehicle control unit 36 ​​assists the driving of the vehicle by controlling the steering unit so that the vehicle drives in the center of the lane based on the positions of the lane and lane boundaries, and steering the wheels.

[0049] Next, the process in which the road estimation unit 30a generates a histogram will be described typically with reference to FIGS. 2A and 2B.

[0050] First, the process of calculating the vehicle inter-link distance will be described with reference to FIG. 2A.

[0051] FIG. 2A is a schematic diagram showing a process in which the distance calculation unit 32 measures the distance between each of a plurality of positions and a link. FIG. 2A shows a road (R1, R2) with three lanes (L1, L2, L3), and the traveling direction of the road is the X direction. On the map data, the nodes (n1, n2, n3) and the links (l1, l2) are set to follow the lane L2. The roads (R1, R2) have different route numbers at the border of node n2, and node n2 indicates a junction with other roads (R1, R2). Vehicle A runs in lane L1, vehicle B runs in lane L2, and vehicle C runs in lane L3. In FIG. 2A, vehicle B is vehicle 1, and the aggregation area is the entire section of roads R1 and R2.

[0052] The map matching unit 31 identifies a plurality of positions (P1, P2, P3) on the map data of the vehicles (A, B, C). Then, the map matching unit 31 identifies the link closest to each of the plurality of positions (P1, P2, P3). The map matching unit 31 identifies the link l1 closest to the plurality of positions p1 of the vehicle A and the plurality of positions p2 of the vehicle B, and the link l1 and the link l2 closest to the plurality of positions p3 of the vehicle C.

[0053] The distance calculation unit 32 calculates the vehicle-link distances (d1, d2, d3). Specifically, the distance between each of the multiple positions (P1, P2, P3) and the link (l1, l2) closest to each of the multiple positions (P1, P2, P3) is calculated.

[0054] Next, the process of generating a histogram will be described with reference to FIG. 2B.

[0055] 2B shows a histogram generated by the histogram generating unit 33. The histogram generating unit 33 classifies the vehicle link distances (d1, d2, d3) into corresponding time intervals and calculates the frequency of the vehicle link distances for each time interval. The time intervals are divided into intervals of a predetermined distance (dr).

[0056] The calculated vehicle-to-link distance (d1, d2, d3) is the distance classified into the class with the highest frequency in the aggregation area, and is classified into the class of the peak (p1, p2, p3) of the histogram.

[0057] Next, the process of estimating lane boundaries performed by the road estimation unit 30a will be described typically with reference to FIGS. 2C and 2D.

[0058] 2C is a conceptual diagram showing the process of estimating lane boundaries by the lane estimation unit 34. The lane estimation unit 34 identifies adjacent peaks. In FIG. 2C, the adjacent peaks are p1 and p2, and p2 and p3. The lane estimation unit 34 estimates that the lane boundaries (b2, b3) are present at a position on the map data away from the closest link (l1, l2) by a distance (db2, db3) corresponding to the center (b2, b3) between the adjacent peaks (p1 and p2, p2 and p3) of the histogram.

[0059] Furthermore, the lane estimation unit 34 estimates that a lane boundary exists when the distance (d4, d5) corresponding to the distance between adjacent peaks (p1 and p2, p2 and p3) of the histogram is equal to or greater than a first predetermined distance and less than a second predetermined distance.

[0060] When the distance (d4, d5) corresponding to the distance between adjacent peaks (p1 and p2, p2 and p3) of the histogram is equal to or greater than the first predetermined distance and is less than the second predetermined distance, the lane estimation unit 34 can calculate the lane boundary in the direction in which the peaks are not adjacent, based on the distance d4 and the distance d5. Specifically, the lane estimation unit 34 can estimate that the lane boundary (b1, b4) exists at a position on the map data that is twice the distance (db1, db4) of the distance (d4, d5) corresponding to the distance between adjacent peaks (p1 and p2, p2 and p3) of the histogram from the closest link.

[0061] The estimated lane boundaries (b1, b2, b3, b4) and the distances (db1, db2, db3, db4) from the closest links (l1, l2) to the lane boundaries have the positional relationship shown in FIG. 2D on the map data.

[0062] Next, the process of estimating lanes by the lane estimation unit 34 will be described with reference to Fig. 3. The lane estimation unit 34 estimates that a lane exists within a predetermined range (p4L, p4R) from a position on the map data that is a distance d6 away from the closest link l, which corresponds to peak p4 of the histogram.

[0063] [Driving assistance method] Next, an example of the process of the driving support device shown in Fig. 1 will be described with reference to Fig. 4A. The operation of the driving support device shown in the flowchart of Fig. 4A starts at the same time that the ignition switch or the power switch of the vehicle 1 is turned ON, and ends when the ignition switch or the power switch is turned OFF.

[0064] In step S10, the control unit 30 acquires map data from the storage unit 40.

[0065] Proceeding to step S20, the map matching unit 31 acquires a plurality of absolute positions of the vehicle from the coordinate acquisition unit (10, 20) and the storage unit 40, and identifies a plurality of positions on the map data.

[0066] Proceeding to step S30, the map matching unit 31 identifies the link that is closest to each of the multiple positions.

[0067] Proceeding to step S40, the distance calculation unit 32 calculates the distance between each of the multiple positions on the map data of the vehicle and the link closest to each of the multiple positions (vehicle-link distance).

[0068] Proceeding to step S50, the histogram generating unit 33 sets a counting region for counting the vehicle-link distances. Specifically, the histogram generating unit 33 identifies the position of the stop line of the intersection, and extracts a plurality of positions that are present inside the stop line of the intersection (outside the intersection).

[0069] Proceeding to step S60, the histogram generating unit 33 identifies vehicles that are changing lanes, and excludes the positions of the vehicles that are changing lanes at the time of the lane change.

[0070] Proceeding to step S70, the histogram generating unit 33 collects the vehicle-link distances for each collection area and generates a histogram.

[0071] Proceeding to step S80, the histogram generating unit 33 detects the peak of the histogram and calculates the variance of the histogram.

[0072] Proceeding to step S90, the lane estimation unit 34 estimates the lane and lane boundaries.

[0073] Proceeding to step S100, the map data update unit 35 adds information about the lane estimated by the lane estimation unit 34 to the map data, and updates the map data.

[0074] Proceeding to step S110, the vehicle control unit 36 ​​assists the driving of the vehicle based on the map data to which the information regarding the lane has been added.

[0075] Next, an example of the process in step S90 shown in FIG. 4A will be described with reference to FIG. 4B.

[0076] In step S91, if the lane estimating unit 34 determines that the histogram peak is equal to or greater than a predetermined frequency (YES in step S91), the process proceeds to step S92.

[0077] In step S91, when it is determined that the histogram peak is not equal to or greater than the predetermined frequency (NO in step S91), the lane estimating unit 34 ends the process.

[0078] In step S92, if the lane estimating unit 34 determines that the variance of the histogram is equal to or less than a predetermined variance (YES in step S92), the process proceeds to step S93.

[0079] In step S92, if the lane estimation unit 34 determines that the variance of the histogram is not equal to or less than the predetermined variance (NO in step S92), the process ends.

[0080] In step S93, if the lane estimating unit 34 determines that a plurality of histogram peaks have been detected (YES in step S93), the process proceeds to step S94.

[0081] In step S93, if the lane estimating unit 34 determines that multiple histogram peaks have not been detected (NO in step S93), the process proceeds to step S97.

[0082] In step S94, if the lane estimating unit 34 determines that the distance between adjacent peaks in the histogram is equal to or greater than the first predetermined distance (YES in step S94), the process proceeds to step S95.

[0083] In step S94, when the lane estimating unit 34 determines that the distance corresponding to the adjacent peaks in the histogram is less than the first predetermined distance (NO in step S94), the lane estimating unit 34 ends the process.

[0084] In step S95, when the lane estimating unit 34 determines that the distance corresponding to the adjacent peaks in the histogram is equal to or greater than the second predetermined distance (NO in step S95), the lane estimating unit 34 ends the process.

[0085] In step S95, if the lane estimating unit 34 determines that the distance corresponding to the adjacent peaks in the histogram is less than the second predetermined distance (YES in step S95), the process proceeds to step S96.

[0086] In step S96, the lane estimating unit 34 estimates that a lane boundary exists at a position on the map data away from the nearest link by a distance corresponding to the center between adjacent peaks in the histogram.

[0087] In step S97, the lane estimation unit 34 estimates that a lane exists within a predetermined range from a position on the map data that is a distance away from the closest link that corresponds to the peak of the histogram.

[0088] [Effects] As described above, according to the first embodiment, the following advantageous effects can be obtained.

[0089] The driving assistance device identifies a plurality of positions on the map data of the vehicle, and identifies a link closest to each of the plurality of positions, thereby enabling the driving assistance device to calculate the distance between each of the plurality of positions on the map data of the vehicle and the link closest to each of the plurality of positions, and to generate a histogram by tallying up the calculated distances.

[0090] The driving assistance device generates a histogram that tallies the distance between each of a plurality of positions and the link, and estimates that a lane exists within a predetermined range from a position on the map data that is a distance away from the link that corresponds to the peak of the histogram. By detecting the peak of the histogram, the driving assistance device can identify the position where the most vehicles have traveled in total, and estimate that the position where the most vehicles have traveled in total is the center position of the lane. Therefore, it is possible to estimate that a lane exists within a predetermined range from the center position of the lane, and the accuracy of lane estimation can be further improved.

[0091] The driving assistance device adds information about the estimated lane to the map data, which allows the device to add information about the lane to map data that does not include information about individual lanes of the road, and can provide driving assistance using the lane information.

[0092] The driving assistance device estimates that a lane boundary exists at a position on the map data that is a distance away from the link that corresponds to the center between adjacent peaks of the histogram, and adds the estimated position of the lane boundary to the map data. By estimating that a position on the map data that is a distance away from the link that corresponds to the center between adjacent peaks of the histogram is a lane boundary, the driving assistance device can estimate the boundary position of the adjacent lane and add the boundary position of the adjacent lane to the map data.

[0093] The driving assistance device estimates that a lane boundary exists when the distance corresponding to the adjacent peaks of the histogram is equal to or greater than a first predetermined distance and less than a second predetermined distance. This makes it possible to determine whether the distance between the centers of adjacent lanes is appropriate, and to set a lane boundary when the distance between the centers of adjacent lanes is appropriate. In other words, it is possible to prevent the setting of an inappropriate lane boundary.

[0094] The driving assistance device identifies an intersection from map data, and tally up the distances between each of a plurality of positions and links inside the intersection's stop line. If the number of lanes changes after passing through an intersection, the lane into which a vehicle enters after passing through the intersection may become unclear, and the lane into which a vehicle enters after passing through the intersection may differ depending on the vehicle. Therefore, by not tallying up the distances between vehicles and links outside the intersection's stop line (inside the intersection), the driving assistance device can eliminate data within the intersection that may lead to erroneous lane estimation, and can further improve the accuracy of lane estimation.

[0095] The driving assistance device estimates that a lane exists when the histogram peaks reach a predetermined frequency or more. This makes it possible to prevent estimation of the existence of a lane in a situation where the number of collected data is small and the situation is susceptible to noise, thereby improving the accuracy of lane estimation.

[0096] The driving assistance device estimates that a lane exists when the variance of the histogram is equal to or less than a predetermined variance. This makes it possible to prevent estimation of the existence of a lane when the aggregated data has a large variance, thereby improving the accuracy of lane estimation.

[0097] When the vehicle is changing lanes, the driving assistance device prohibits the calculation of the distance between each of the multiple positions and the link. This eliminates data when the vehicle crosses lanes, and allows the calculation of only data when the vehicle is traveling within the lane, thereby improving the accuracy of lane estimation.

[0098] The driving assistance device determines when the turn signal switch is ON as the time to change lanes, and can therefore appropriately determine the timing when the vehicle is changing lanes.

[0099] Second embodiment [Configuration of driving support device] The configuration of the driving support device according to the second embodiment will be described with reference to Fig. 5. The difference from the first embodiment is that the vehicle 1 includes a transceiver unit 37 and further includes a server 2. The server 2 includes a histogram generating unit 33, a lane estimating unit 34, and a map data updating unit 35. Therefore, only the differences will be described, and descriptions of other common configurations will be omitted.

[0100] The transmitter / receiver 37 transmits the inter-vehicle link distance calculated by the distance calculation unit 32 to the server 2 and receives map data updated by the server 2.

[0101] The server 2 receives the vehicle-to-link distances from a plurality of vehicles and updates the map data. The server 2 transmits the updated map data to a plurality of vehicles that can receive the map data. The processes of the histogram generating unit 33, the lane estimating unit 34, and the map data updating unit 35 included in the server 2 are the same as those in the first embodiment.

[0102] [Effects] As described above, according to the second embodiment, the following advantageous effects can be obtained.

[0103] The driving assistance device can estimate lanes and lane boundaries based on a larger amount of data by the server 2 receiving inter-vehicle link distances from multiple vehicles. This allows the driving assistance device to estimate lanes and lane boundaries with higher accuracy and update map data more quickly.

[0104] As described above, the embodiment of the present invention has been described, but the description and drawings forming a part of this disclosure should not be understood as limiting this invention. From this disclosure, various alternative embodiments, examples and operating techniques will become apparent to those skilled in the art. [Explanation of symbols]

[0105] 1 vehicle 10 Vehicle position acquisition unit (coordinate acquisition unit) 20 Object detection unit (coordinate acquisition unit) 30 Control section 30a Road estimation section 31 Map Matching Section 32 Distance calculation unit 33 Histogram Generation Unit 34 Lane Estimation Unit 36 Vehicle control unit 40 Storage section

Claims

1. A driving assistance method for assisting driving of a vehicle by displaying, on a user interface, a lane that is most suitable for traveling along a travel route of a vehicle or by steering wheels of the vehicle, using map data that represents a road on which a vehicle travels as a plurality of nodes that represent characteristic points of the road and links that connect the nodes, and does not include information on a lane-by-lane basis divided by lane dividing lines of the road, the lane being the most suitable for traveling along a travel route of the vehicle, the method comprising: Identifying, on the map data, a plurality of positions of a vehicle whose absolute position has been measured or a vehicle whose relative position with respect to the vehicle whose absolute position has been measured, based on the absolute position or the relative position; identifying a closest link to each of the plurality of locations; aggregating vehicle link distances, which indicate the shortest distance between each of the plurality of positions on the map data and a link closest to each of the plurality of positions, for each aggregation area, which is a section set in an extension direction of a road, and generating a histogram showing a frequency distribution of the vehicle link distances, which are aggregated for each aggregation area, for each aggregation section in the direction of the shortest distance; estimating that a lane exists within a predetermined range in the direction of the shortest distance from the link, based on a position on the map data corresponding to a peak of the histogram, the position being away from the link in the direction of the shortest distance; Adding the estimated lane information to the map data. A driving assistance method comprising:

2. A lane boundary indicating a boundary position of an adjacent lane is estimated to exist at a position on the map data that is away from the link in the direction of the shortest distance and corresponds to a center between adjacent peaks of the histogram, and the estimated position of the lane boundary is added to the map data. The driving assistance method according to claim 1 .

3. A first predetermined distance and a second predetermined distance greater than the first predetermined distance are set in relation to the width of a lane for each road type, and when the distance between adjacent peaks of the histogram is equal to or greater than the first predetermined distance and less than the second predetermined distance, it is estimated that the lane boundary exists. The driving assistance method according to claim 2 .

4. A stop line existing around a vehicle is detected, and a position of a stop line of an intersection on the map data is specified from the absolute position of the stop line, and distances between the multiple positions and the link are tallied outside the intersection, on the road side of the stop line of the intersection. The driving assistance method according to claim 1 .

5. If the peak of the histogram is equal to or greater than a predetermined frequency, it is assumed that the lane exists. The driving assistance method according to claim 1 .

6. When the variance of the histogram calculated from the deviation of the frequency of each of the counting sections in each of the counting regions is equal to or less than a predetermined variance, it is estimated that the lane exists. The driving assistance method according to claim 1 .

7. When the vehicle is changing lanes, the calculation of the distances between the multiple positions and the link is prohibited. The driving assistance method according to claim 1 .

8. The lane change occurs when the turn signal switch is ON. The driving assistance method according to claim 7 .

9. A driving assistance device that uses map data that represents a road on which a vehicle travels as a plurality of nodes indicating characteristic points of the road and links connecting the nodes, and does not include information on lane units divided by lane dividing lines of the road, and that displays a lane that is most suitable for traveling along a travel route of the vehicle on a user interface or assists in driving the vehicle by steering wheels of the vehicle, a coordinate acquisition unit that identifies a plurality of positions on map data of a vehicle whose absolute position has been measured or a vehicle whose relative position with respect to the vehicle whose absolute position has been measured, based on the absolute position or the relative position; A road estimation unit, The road estimation unit a link closest to each of the plurality of positions is identified; vehicle link distances on the map data, which represent the shortest distance between each of the plurality of positions and the link closest to each of the plurality of positions, are tallied for each aggregation area, which is a section set in a road extension direction; a histogram is generated which shows a frequency distribution for each aggregation section in the shortest distance direction of the vehicle link distances tallied for each aggregation area; a lane is estimated to exist within a predetermined range in the shortest distance direction based on a position on the map data corresponding to a peak of the histogram, which is away from the link in the shortest distance direction; and information related to the estimated lane is added to the map data. A driving assistance device comprising:

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