Method and apparatus for determining a functional road class and a most likely route

Abstract

The invention relates to a method for providing functional road classes for individual route sections of a digital map, having the following steps: periodically or continuously providing current geographical positions of a plurality of vehicles of a vehicle fleet in a central unit; determining, from the geographical positions of the plurality of vehicles, an open attribute for all route sections of the digital map; assigning a functional road class to each route section, respectively, depending on the open attribute. Furthermore, a most probable route can be determined by concatenating route sections with one another, wherein, starting from a route section initially corresponding to the geographical position of a vehicle, which is considered accordingly, an extension is made by adding another route section depending on the functional road class.

Description

Technical Field

[0001] This invention relates to vehicle functions that utilize the most likely routes of a motor vehicle, particularly for optimizing vehicle operation. Background Technology

[0002] To predict vehicle operating parameters, it is typically necessary to envision the most probable route ahead. The direction of this most probable route can be used, for example, for energy management and other functions in hybrid vehicles. In particular, determining the most probable route enables the determination of cumulative operating parameters, such as energy consumption within prediction limits.

[0003] For example, the most probable route can be determined along each segment of the digital map based on the segment attributes in the digital map and the angle of directional change from one segment to the next, wherein the segment with the highest road grade value is selected at each intersection or fork in the road. If all considered segments have the same road grade value, the segment with the smallest change angle of travel direction is preferred.

[0004] However, calculating the most probable route based on road classifications of road segments in digital maps has the following drawbacks: the road classifications stored in the digital maps have already been optimized for navigation applications, particularly for calculating the shortest and fastest paths. However, the accuracy of predictions based on the most probable route may not be sufficient for other predictions of vehicle operating parameters. Summary of the Invention

[0005] Therefore, there is a task to provide driver assistance functions or vehicle functions with the possibility of improving the determination of the most likely route independently of the application, so as to improve the prediction of vehicle operating parameters, especially on the prediction limits.

[0006] The task is addressed by methods for determining functional road classifications and by methods and corresponding devices for determining the most probable routes.

[0007] According to the first aspect, a method is provided for providing functional road classifications for route segments in a digital map, the method comprising the following steps:

[0008] - Provide the current geographic location of multiple vehicles in the fleet periodically or continuously in the central unit;

[0009] - Determine the traffic access attributes for all road segments in the digital map based on the geographical locations of the multiple vehicles;

[0010] - Based on the aforementioned traffic attributes, functional road grades are assigned to each route segment.

[0011] The most probable routes are determined using a digital map, which represents a network of interconnected route segments. Each route segment is assigned a segment attribute. One of these segment attributes may correspond to a functional road level, which indicates the general availability of the route segment in question.

[0012] Current classifications of functional road grades are typically based on road type (e.g., highways, residential roads, arterial roads, etc.). These functional road grades use road grade values ​​to distinguish priorities, with the highest road grade value corresponding to highways and the lowest to residential roads. To determine the most probable route in the absence of prior knowledge of the destination, the road segment with the highest road grade value at each intersection or junction is selected as the next road segment.

[0013] However, in reality, the allocation of functional road grades and route segments may deviate from the actual availability of the corresponding route segments. If a highway route segment is cumbersome to navigate and, for example, because the entry and exit points have proven unfavorable for many destinations, then that highway route segment may actually be used less than a local road segment. Currently, when branching into highway-route segments and local road-route segments, unless otherwise specified, such as regarding the destination, the highway route is assumed to be the next route segment of the most probable route based on the higher road grade value.

[0014] Therefore, the above method specifies obtaining the actual usage of each route segment on the digital map and deriving road grade values ​​from the actual usage. This is achieved by evaluating fleet data, thereby obtaining the traffic attributes of the route segment over a longer time period, such as multiple days or months. The road grade value is then determined based on the traffic attributes, such as traffic frequency or average speed.

[0015] By using collected fleet data, the actual use and availability of route segments can be determined, allowing for the assignment of appropriate road class values ​​to these segments. Therefore, when using functional road classes, the most probable routes are envisioned not only based on the intentional use of the route segments involved (e.g., highways, residential roads, arterial roads, local roads, etc.) but also on the actual use by traffic participants. This enables: improved prediction of the most probable routes. Furthermore, it allows for: maintaining a simple determination algorithm for identifying the most probable routes based on road classes.

[0016] It can be specified that the traffic access attribute gives the traffic frequency of the route segment within a predetermined time period and, in particular, the average speed of each vehicle on the route segment.

[0017] Furthermore, the functional road level can be classified by road level value, which is assigned to one or more traffic attributes of a route segment as a percentage of the sum of the one or more traffic attributes of all route segments in the digital map.

[0018] The use of convoy data can be performed in a central unit that is communicatively connected to multiple vehicles in the convoy. At the central unit, position data of the vehicles in the convoy is received at regular time intervals, such as time periods between 0.5 and 10 seconds, or for example, per second, and optionally, speed data. This data can be collected in the central unit, and the traffic frequency (i.e., the number of times each traffic segment is traversed per unit time interval) and optionally, the average traffic speed can be determined accordingly for each road segment of the digital map. The traffic frequency and, if necessary, the average speed are then assigned to a functional road class, particularly to a given road class value.

[0019] For example, the functional road classification can be determined by the proportion of traffic frequency within the total number of traffic passages in each route segment of a digital map. Thus, the highest road classification value can be assigned to route segments where the proportion of times the route segment has been driven exceeds a predetermined proportion of the total number of traffic passages. Other road classifications can be determined using such determined proportion ranges.

[0020] In particular, the functional road grade can also be assigned to the route segment in a time-related manner, especially in relation to the time of day.

[0021] Alternative or additional locations can be configured with the functional road class down to the lane level for multi-lane road segments. Obtaining traffic attributes down to the lane level allows for improved prediction of turns at the next intersection or junction and improved lane change prediction on multi-lane routes. In particular, configuring functional road classes down to the lane level can be applied in lane change assistance and in automatic lateral guidance when using the most probable route generated as a result.

[0022] According to another aspect, a method is provided for determining the most probable route for use in vehicle functions of a fleet of vehicles, wherein a digital map is provided having route segments and functional road grades corresponding to the route segments, wherein the functional road grades are determined according to the above method, wherein the most probable route is determined by connecting the route segments to each other, wherein the connecting route segments are expanded by adding another route segment according to the functional road grades, starting from a route segment initially considered corresponding to the geographical location of the vehicle, wherein vehicle functions are implemented according to the most probable route.

[0023] According to another aspect, a device, particularly a central unit, is specified for providing functional road classifications for route segments in a digital map, wherein the device is configured to:

[0024] - Receive the current geographical location of multiple vehicles in the fleet periodically or continuously in the central unit;

[0025] - Determine the traffic access attributes for all road segments in the digital map based on the geographical locations of the multiple vehicles;

[0026] - Based on the aforementioned traffic attributes, functional road grades will be assigned to each route segment; and

[0027] - Provides digital maps with functional road classifications for each route segment.

[0028] According to another aspect, a device is provided for determining the most probable route for use in vehicle functions, wherein the device is configured to:

[0029] - Receive digital maps with route segments and functional road classifications associated with each route segment;

[0030] - Determine the most likely route by connecting the segments of the journey together;

[0031] - Starting from the initial route segment corresponding to the vehicle's geographical location and taking into account the corresponding route segment, the interconnected route segments are expanded by adding another route segment according to the functional road level.

[0032] - Implement vehicle functions based on the most likely route. Attached Figure Description

[0033] The various embodiments are described in more detail below with the aid of the accompanying drawings. In the drawings:

[0034] Figure 1A schematic diagram of a system with a fleet and a central unit is shown, the central unit being used to determine the functional road class of each route segment of the digital map;

[0035] Figure 2 A flowchart illustrating the methods used to provide functional road classification is shown;

[0036] Figure 3 A flowchart is shown for the use of functional road classifications from a central unit in motor vehicles; and

[0037] Figure 4 A graphical view of a digital map showing functional road classifications that correspond to road segments. Detailed Implementation

[0038] Figure 1 A schematic diagram of a system 1 with a fleet 3 and a central unit 2 is shown, the fleet consisting of multiple vehicles 4. Each vehicle 4 is communicatively connected to the central unit 2.

[0039] In addition, each of the vehicles 4 has a control unit 41, which is in communication connection with the central unit 2 via a communication device 42.

[0040] The following is combined Figure 2 The flowchart describes a method for providing traffic attributes for various road segments in a digital map.

[0041] In step S1, vehicle 4 obtains its own geographical location and instantaneous speed by means of a suitable geolocation system, such as GPS or GLONASS, and in step S2, it transmits the geographical location and instantaneous speed to the central unit 2 at regular time intervals, such as time intervals between 0.5 and 10 seconds, particularly every second.

[0042] In central unit 2, in step S3, the vehicle's geographical location is assigned to the corresponding route segment of a digital map, which represents a real-world road network. Figure 4 An exemplary digital map is shown. A road network 10 consisting of interconnected road segments 11 can be seen.

[0043] In step S4, corresponding traffic attributes are calculated for each route segment 11 of the digital map. Specifically, the traffic frequency within a time period, such as within a day or a week, is determined by accumulating the traffic of a route segment 11, and the average speed is assigned to the corresponding route segment 11. This selection of the time period ensures that the traffic frequency has sufficient statistical significance and relevance. Road grade values ​​are then assigned to each route segment 11 of the digital map based on the traffic frequency and, if necessary, the average speed.

[0044] This determines the road grade value (in Figure 4 (The values ​​shown in each circle) can be transmitted back to each vehicle 4 in the convoy 3 in step S5 in the manner assigned to each route segment of the digital data map, so that the most probable route can be determined at the vehicle.

[0045] Road class values ​​can be assigned to traffic frequencies based on the proportion of vehicular traffic occurring in a single route segment relative to the total number of vehicular traffic occurring in all route segments across the entire geographic area covered by the digital map.

[0046] Thus, for example, the highest road class (given by, for example, road class value 5) can be assigned to a route segment where a certain percentage, for example, more than all travels occur, on that route segment. Correspondingly, other road classes can be assigned to each route segment where a share of a predetermined range for the relevant road class is generated. For example, another road class (given by, for example, road class value 4) can be assigned to, for example, a share range of 0.03% to 0.1%.

[0047] Furthermore, road grades can be assigned according to the time of day, that is, different road grades can be assigned to different segments of the journey based on the determination of the most likely route within a certain time frame of the day. Thus, for example (especially for weekdays), different road grade values ​​can be envisioned for the time period between 6:00 and 9:00 in the morning and between 16:00 and 20:00 in the evening, because a large amount of traffic travels in the morning towards the workplace and a large amount of traffic travels in the evening towards the residence.

[0048] Furthermore, road classification can be determined precisely down to the lane level for each route segment, allowing for the assignment of different road classifications to different lanes when necessary. In particular, lane-level precise allocation of functional road classifications can be used in lane change assist and automatic lateral guidance when employing the most probable route generated as a result.

[0049] exist Figure 3 The flowchart details the method used to determine the most probable route. This method can be applied to each of the vehicles 4 in convoy 3.

[0050] In step S11, the instantaneous geographical location of vehicle 4 is determined.

[0051] In step S12, the instantaneous position of vehicle 4 is assigned to a route segment on the digital map using map data.

[0052] Now, in step S13, starting from the current or last considered route segment, the most likely route is determined based on road classification. To do this, the most likely route is constructed segment by segment. Starting from the current or considered route segment 11, the next intersection or fork in the road is searched along the direction of travel.

[0053] The road segment 11 connecting the intersection or the fork in the road is examined in terms of its functional road grade value. In step S14, the road segment with the highest road grade value is selected.

[0054] In step S15, it is checked whether there are multiple road segments with the highest road grade value among the connecting road segments originating from the current intersection / intersection. If so (option: Yes), in step S16, a road segment is selected that causes a small angle of change in vehicle direction with respect to the current or previous road segment, and the most probable route is extended to the road segment determined in this way. Otherwise (option: No), in step S17, the most probable route is directly extended to the road segment determined in step S14.

[0055] In step S18, it is checked whether the most likely route has sufficient length, that is, whether the most likely route extends across the entire prediction boundary, which can match the predetermined distance length or predetermined duration. If the condition is yes (option: Yes), then in step S19, the most likely route is signaled to the lower-level vehicle function; otherwise (option: No), the method continues to step S13.

[0056] As a possible vehicle function for using the most likely route, energy management or destination guidance or direction without explicit destination instructions can be implemented.

[0057] List of reference numerals

[0058] 1 system

[0059] 2 Central Unit

[0060] Team 3

[0061] 4 vehicles

[0062] 41 Control Unit

[0063] 42 communication devices

[0064] 10 road network

[0065] 11-kilometer section

Claims

1. A computer-implemented method for determining the functional road class of a route segment (11) in a digital map, the method comprising the following steps: - Provide fleet data (S1) of the fleet (3) periodically or continuously in the central unit (2), the fleet data including the current geographical locations of at least a number of vehicles (4); - Based on the geographical locations of the multiple vehicles (4), determine the traffic access attributes (S4) for all road segments (11) of the digital map, wherein, The actual usage of each route segment of the digital map is obtained by evaluating the fleet data, thereby obtaining the traffic attributes of the route segment over a longer period of time. - Based on the aforementioned traffic attributes, the functional road grades are assigned to each route segment (11). The functional road level is classified by the following road level value, which is assigned to one or more traffic attributes of each route segment (11) in proportion to the sum of the one or more traffic attributes of all route segments (11) of the digital map.

2. The method according to claim 1, wherein, The traffic access attribute gives the traffic frequency of each route segment (11) within a predetermined time period.

3. The method according to claim 2, wherein, The traffic attribute gives the average speed of the vehicle (4) on each road segment (11) within a predetermined time period.

4. The method according to claim 1, wherein, The functional road grades are also assigned to each route section (11) in a time-related manner.

5. The method according to claim 4, wherein, The functional road grades are assigned to each route segment (11) in relation to the time of day.

6. The method according to claim 1, wherein, The functional road class is assigned to multi-lane road sections with precise lane assignment.

7. A computer-implemented method for determining the most probable route for use in the vehicle functions of vehicles in a convoy (3), wherein, A digital map is provided having route segments (11) and functional road levels corresponding to each route segment (11), wherein the functional road levels are determined according to the method of any one of claims 1 to 6, wherein the most probable route is determined by connecting the route segments (11) to each other, wherein the connected route segments are expanded (S17) by adding another route segment (11) according to the functional road level, starting from the route segment (11) initially considered corresponding to the geographical location of the vehicle (4), wherein vehicle functions are implemented (S19) according to the most probable route.

8. A central unit (2) for determining the functional road class of each route segment (11) in the digital map, wherein, The central unit is configured for: - The fleet data of the fleet (3) is received periodically or continuously in the central unit (2), the fleet data including the current geographical locations of at least a number of vehicles (4); - Based on the geographical locations of the multiple vehicles (4), determine (S4) traffic attributes for all road segments (11) of the digital map, wherein the actual usage of each road segment of the digital map is obtained, which is achieved by evaluating the fleet data, thereby enabling the traffic attributes of the road segments over a longer period of time to be obtained. - Based on the aforementioned traffic attributes, functional road grades are assigned to each route segment (11); and - Provide digital maps with functional road classifications for each route segment (11), The functional road level is classified by the following road level value, which is assigned to one or more traffic attributes of each route segment (11) in proportion to the sum of the one or more traffic attributes of all route segments (11) of the digital map.

9. A device for determining the most probable route for use in the vehicle function of vehicle (4), wherein, The device is configured to: - Receive a digital map having route segments (11) and having functional road grades corresponding to each route segment (11), wherein the functional road grades are determined according to the method of any one of claims 1 to 6; - Determine the most likely route by connecting the segments (11) together; - Starting from the route segment (11) that is initially considered in accordance with the geographical location of the vehicle (4), the route segments (11) that are connected to each other are extended by adding another route segment (11) according to the functional road level. - Implement vehicle functions based on the most likely route.

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