Method for determining a navigation route for automated driving operation of a vehicle - Patents.com

Abstract

The present invention relates to a method for determining a navigation route (NR) for an automated driving operation of a vehicle, which comprises the steps of: - based on a digital attribute-based road map (AK), a node list is created which represents a number of geographic coordinates (KP1-KP7) of a route (R) determined by a navigation system; - the node list is transferred to a digital sensor-based road map (SK) which includes a representation of the environment detected by sensors; - based on the node list, precise lane-by-lane candidates are determined in the digital sensor-based road map (SK); - irrelevant candidate lane-by-lane candidates are identified and eliminated; - the route (R) is divided into a subgraph (T1) which includes only one possible lane and a subgraph (T2) which includes a number of possible lanes; - optimal lanes (oF) are identified; - combining the optimal lanes (oF) into a desired lane-by-lane precise navigation route (NR), thereby determining a lane-by-lane precise navigation route (NR) for an automated driving operation, in particular a highly automated or autonomous driving operation.

Description

[Technical Field]

[0001] The present invention relates to a method for determining a navigation route for automated driving operation of a vehicle. [Background technology]

[0002] It is generally known in the prior art to specify a destination in a navigation system of a vehicle capable of autonomous operation, where the navigation system determines a navigation route to the specified destination, and where possible, the vehicle travels at least partially autonomously along the determined navigation route.

[0003] U.S. Patent No. 10,969,232 describes the arrangement of standard definition (SD) road maps and high definition (HD) road maps from various sources. In response to input of a destination, a route to the destination is defined, and waypoints in the SD road map are generated from the defined route. A graph is created from the HD road map. Waypoints correspond to nodes and edges in the graph. One or more edges form segments in the HD road map. Multiple segments are identified to correspond to the route.

[0004] German patent application No. 102021006166.7 describes a method for transferring data between two digital road maps, where the data transfer is performed between a digital sensor-based road map and a digital attribute-based road map, where the sensor-based road map is divided into intersections and route segments, attributes for automated driving are assigned to the route segments, attributes for automated driving are assigned to intersection segments, and it is checked whether the attribute values ​​for automated crossing in the intersection areas can comply with the turn restrictions in the attribute-based road map.

[0005] From US 2022 / 0032907 A1, an apparatus and a method for managing a vehicle traveling on a road along a route are known, in which the surrounding environment of the road on which the vehicle is traveling is detected by sensors arranged in the surrounding environment, map information relating to the road being traveled is detected, and based on the detected surrounding environment and the detected map information, information relating to the traveling speed of the vehicle on the route or the passability of the route is determined and transmitted to the vehicle.

[0006] US Patent Application Publication No. 2020 / 0200547 discloses a method for generating a drivable boundary of a road, which is stored as a three-dimensional representation in a high-resolution three-dimensional map. In this method, a two-dimensional projection of the road is created, which includes a number of points along the road. Each of these points is evaluated in terms of its drivability, and based on this, a drivable area and its boundaries are determined in the projection of the road. The determined drivable area and its boundaries are then converted into a three-dimensional representation and used to update the three-dimensional map. Summary of the Invention [Problem to be solved by the invention]

[0007] It is an object of the present invention to provide a method for determining a navigation route for automated driving operation of a vehicle that is an improvement over the prior art. [Means for solving the problem]

[0008] This object is achieved according to the invention by a method for determining a navigation route for automated driving operation of a vehicle having the features of claim 1.

[0009] Advantageous embodiments of the invention are the subject matter of the dependent claims.

[0010] The method for determining a navigation route for automated driving operation of a vehicle according to the present invention is intended to determine an accurate navigation route on a lane-by-lane (lane-level accuracy) basis for automated driving operation, particularly for highly automated or autonomous driving operation. This is performed as follows.

[0011] The implementation system is A node list representing the geographic coordinates of a route determined by a navigation system based on a digital attribute-based road map. of create do . The implementation system is Node List of The information detected by the sensors is transferred to a digital sensor-based road map containing a representation of the environment (description of the environment). The implementation system is before In the section of the navigation route (NR) where the deviation between the digital attribute-based road map (AK) and the digital sensor-based road map (SK) at the geographic coordinates (KP1 to KP7) is within a tolerance range (TB), Based on the node list, accurate lane-level driving section candidates are identified on a digital sensor-based road map. of decision On the other hand, in a section of the navigation route (NR) where the deviation is not within the tolerance range (TB), an accurate driving section candidate is not determined for each lane. Unrelated driving section candidates of Identify and eliminate. The implementation system is root of, Subgraphs containing only one possible lane and subgraphs containing multiple possible lanes and In a subgraph that contains only one possible lane, the exact candidate lane segment for that lane exists in only one lane. In a subgraph that contains multiple possible lanes, the exact candidate lane segment for that lane exists in multiple lanes. The implementation system is Optimal multiple lanes of In a subgraph that contains only one possible lane, this lane is identified as the optimal lane. In a subgraph that contains multiple possible lanes, one of the multiple lanes is selected as the optimal lane. The implementation system is Combine optimal lanes to accurately navigate the desired lane-by-lane route of formation By executing the above process, the implementation system determines the accurate navigation route (NR) for the automated driving operation, particularly for highly automated or autonomous driving operation, on a lane-by-lane basis. In sections of the navigation route (NR) for which the accurate driving section candidate for each lane has not been determined, the implementation system executes a process to block the automated driving operation.

[0012] The method according to the invention uses a route determined by a navigation system, in particular an edge-based and / or node-based system, to determine an optimal, lane-precise, i.e., lane-defined, as well as possible, automatically drivable driving path from the perspective of a digital sensor-based road map in the form of a lane-precise navigation route that can be optimally followed by a vehicle in an automated driving operation. This driving path, i.e., the lane-precise navigation route in the digital sensor-based road map, corresponds as well as possible to the driving path, i.e., route, from a digital attribute-based road map. Using this driving path, i.e., the lane-precise navigation route, the vehicle can be driven automatically at least along automatically drivable sections of the navigation route, i.e., along sections of the navigation route for which lane-precise driving section candidates have been determined, and can initiate a timely handover of vehicle control to or from the driver, and can enable manual driving, particularly based on driving recommendations by the navigation system, for sections of the navigation route that are not automatically drivable, i.e., sections of the navigation route for which lane-precise driving section candidates have not been determined.

[0013] In one possible embodiment, automated driving operations are blocked (prevented, prohibited) in sections of the navigation route where accurate lane-level candidate driving segments have not been determined. In one possible embodiment, automated driving operations are permitted (released) in sections of the navigation route where accurate lane-level candidate driving segments have been determined. In one possible embodiment, highly automated or autonomous driving operations are permitted only in permitted sections. In this way, it is ensured that automated driving operations, in particular highly automated or autonomous driving operations, are performed only in sections of the navigation route that are suitable for them.

[0014] In one possible embodiment, during highly automated or autonomous driving operations, a takeover request is issued to the driver of the vehicle before reaching the blocked section, allowing the driver to take over vehicle control in a timely manner, thereby enabling uninterrupted driving operations and avoiding unsafe situations.

[0015] In one possible embodiment, if the determined (found) lane-by-lane precise navigation route includes one or more blocked sections, an alternative lane-by-lane precise navigation route is searched for that does not have any sections where the automated driving operation is blocked or has fewer sections where the automated driving operation is blocked, thereby finding a navigation route that does not have any sections where the automated driving operation is blocked or has at least the fewest such sections, thereby enabling the automated driving operation to travel from the starting point to the destination, or at least the longest possible section.

[0016] In one possible embodiment, for each subgraph containing multiple possible lanes, the optimal lane is identified using a cost function. In particular, in this case, each lane in each subgraph containing multiple possible lanes is assigned a cost, the magnitude of which depends, in particular, on the suitability of the respective lane for automated driving operation, and in particular on the degree of automated driving operation, i.e., on the SAE level (SAE-Level), i.e., the automation level of the driving operation. The lower the SAE level, the higher the assigned cost. Alternatively or additionally, required lane changes can also be included in this evaluation. Therefore, the optimal lane is identified by minimizing these costs. Instead of this cost function, the optimal lane can also be identified using an inverse approach, i.e., a bonus function. In this case, the bonus assignment is performed inversely to the cost assignment. In this case, in the example of the SAE level, the higher the SAE level, the higher the assigned bonus. Alternatively or additionally, required lane changes can also be included in this evaluation. In this case, the optimal lane is identified by maximizing the bonus.

[0017] For example, the evaluation can be weighted using costs or bonuses. Alternatively, machine learning can be performed using, for example, training data, and then the evaluation can be performed using, for example, a neural network.

[0018] In one possible embodiment, based on the lane-by-lane precise navigation route, lane change recommendations are generated for performing automatic or manual lane changes to follow the lane-by-lane precise navigation route.

[0019] Preferably, lane change recommendations for performing automatic lane changes are generated during highly automated or autonomous driving operations and output to a device for performing highly automated or autonomous driving operations.

[0020] Lane change recommendations for performing manual lane changes are generated and issued to a vehicle driver, particularly for vehicles not designed to perform automatic lane changes. Thus, the recommendations may be issued to a driver during highly automated or autonomous driving operations or manual driving operations. Issuing recommendations during manual driving operations is effective because they guide the driver to the optimal lane of a lane-accurate navigation route in preparation for a later highly automated or autonomous driving operation.

[0021] In this way, the driving experience of highly automated or autonomous driving can be improved by pre-planning a lane change to an optimal lane and automatically executing or recommending it to the driver, thereby minimizing unexpected interruptions of highly automated or autonomous driving operations because the lane section ahead is not suitable for this driving operation.

[0022] The features described in the method for determining a navigation route for automated driving operation of a vehicle may be, individually or in combination with each other, part of a method for performing driving operation of a vehicle, in particular for performing highly automated or autonomous driving operation of a vehicle.

[0023] In the following, an embodiment of the invention will be described in detail with reference to the drawings. [Brief explanation of the drawings]

[0024] [Figure 1] 1 shows a schematic diagram of a digital sensor-based road map. [Figure 2] 1 shows a schematic diagram of a digital attribute-based road map. [Figure 3] 3 shows a schematic representation of the digital sensor-based road map of FIG. 1 including geographic coordinates transferred from the digital attribute-based road map according to FIG. 2; [Figure 4] FIG. 1 shows a schematic representation of a digital sensor-based road map together with possible driving trajectories resulting from the determined relevant candidate driving segments. [Figure 5] Schematic showing the partitioning into subgraphs and the determination of accurate lane-by-lane navigation routes. DETAILED DESCRIPTION OF THE INVENTION

[0025] In all the drawings, the same reference numerals are used to designate corresponding parts.

[0026] With reference to FIGS. 1 to 5, a method for accurately determining a navigation route for automatic driving operation (automated driving operation) of a vehicle, particularly for highly automated or autonomous driving operation, on a lane-by-lane basis will be described below.

[0027] In the prior art, autonomous driving has been further developed, particularly using a data-driven approach. Vehicles are equipped with a wide range of sensors that inspect various aspects of the vehicle's surroundings and provide environmental data. The sensor technologies used include, for example, radar, lidar, camera, and / or ultrasonic and / or other sensor technologies suitable for detecting the surrounding environment. Using this environmental data, a model of the environment is created, particularly using machine learning techniques. The autonomous vehicle is introduced into this model, and hypotheses are generated by predicting future movements, from which possible courses of action for autonomous vehicle control are determined and executed.

[0028] To validate the position estimates of an autonomous vehicle, to validate the modeling and ultimately to validate possible courses of action, prior art techniques not only rely on current sensor data from the vehicle itself, but also use historical sensor data for safety reasons.

[0029] As illustrated in Figure 1, these sensor data from past trips, ideally from multiple different vehicles, are abstracted, particularly using machine learning techniques, and stored in a digital sensor-based road map SK. This sensor-based road map SK shows the exact lane-by-lane route segments recorded in the past, as well as an image of the surrounding environment as detected (discovered) in terms of the stored sensor data. It is therefore a sensor-based road map SK that has been learned by the vehicle using sensor technology.

[0030] On the other hand, a navigation system requires a digital attribute-based road map AK, as exemplified in Figure 2. This attribute-based road map AK is used for a purpose completely different from that of the sensor-based road map SK. The attribute-based road map AK is primarily used to calculate a route from the current position of the vehicle to a specified destination. Therefore, the attribute-based road map AK has fundamentally different characteristics from the sensor-based road map SK.

[0031] The attribute-based road map AK is quite abstract; otherwise, a route cannot be calculated efficiently by a navigation system built into a vehicle. Additionally, the attribute-based road map AK consists of at least edges, also called links or road segments, nodes, also called junctions or intersections between links, and relations. Relations are relationships and conditions between edges, such as turn restrictions and AV restrictions.

[0032] Attribute-based road maps AK have many attributes that cannot be or are difficult to automatically detect by sensors, such as road names, road types, predetermined points, street addresses, or specific restrictions at complex intersections (e.g., vehicle-based and / or time-based turn restrictions AV).

[0033] In particular, the attribute-based road map AK and the sensor-based road map SK differ in three main respects, which are explained below.

[0034] Unlike attribute-based road maps AK, which depict the road network according to a predefined set of rules optimized to enable efficient navigation functions, sensor-based road maps SK depict the surrounding environment in the way it is observed and measured by sensors.

[0035] As shown in Figure 2, the attribute-based road map AK shows road-level geometry, i.e., edge-based geometry. In contrast, the sensor-based road map SK shows lane-level geometry, i.e., lane-based geometry, as shown in Figure 1, where the sensor-based road map SK is shown with lanes. As can be seen by comparing Figures 1 and 2, these differences are particularly apparent at complex intersections.

[0036] Unlike the attribute-based road map AK, which shows turn-restricted AVs, the sensor-based road map SK models turn options through the presence of dedicated turn lanes.

[0037] The sensor-based road map SK in Figure 1 shows all turn and driving options AF at intersections. The attribute-based road map AK in Figure 2 shows turn restrictions AV at intersections with dashed arrows. The difference in abstraction level is clear.

[0038] The sensor-based road map SK contains information for automated driving operations, while the attribute-based road map AK enables route calculation and route guidance in, for example, a navigation system.

[0039] Accordingly, German Patent Application No. 102021006166.7 describes a matching technique that allows information from a sensor-based road map to be mapped onto a much more abstract attribute-based road map, in particular on the basis of Figures 1 to 5 and the associated drawing description. German Patent Application No. 102021006166.7, in particular Figures 1 to 5 and the drawing description therein, are incorporated herein by reference.

[0040] The method described below enables in a particularly optimal manner the mapping of a route R from a start point SP to a destination point ZP determined by a navigation system on the basis of a digital attribute-based road map AK, which is particularly suitable for performing automated driving operations, in particular highly automated or autonomous driving operations, onto a digital sensor-based road map SK, thereby enabling a vehicle to drive the route R in a particularly automated, in particular highly automated or autonomous manner.

[0041] The overall system in which this mapping is implemented consists in particular of a vehicle equipped with a device for performing automated driving operations, in particular highly automated or autonomous driving operations, based on a digital sensor-based road map SK, and a navigation system with a digital attribute-based road map AK. The navigation system can be implemented in the vehicle using a local database, or can be provided as a back-end-based online solution, i.e. with a data connection between the vehicle and a server external to the vehicle, or both of these embodiments can be combined.

[0042] In the method described herein, particularly in step I, a node list is created based on the digital attribute-based road map AK, representing the geographic coordinates KP1 to KP7 of the route R determined by the navigation system. These geographic coordinates KP1 to KP7 are also called nodes.

[0043] First, a route R is calculated in the navigation system. This route R or a desired, in particular predicted, driving path is advantageously transferred to a device for performing automated driving operations, in particular highly automated or autonomous driving operations, in order to enable the vehicle to follow the route R or the driving path with the highest possible degree of automation (SAE level). In the following, the method will be described using the route R determined by the navigation system, although a driving path can also be used instead of the route R.

[0044] In particular, the route R determined by the navigation system is initially available within the navigation system as a directed list (sequence) of edges and / or nodes in the map graph of the attribute-based road map AK. This list typically consists of alphanumeric or numeric IDs, i.e., identification codes (codes) for edges / nodes in the map graph. All edges and / or nodes are completely included.

[0045] In particular, in substep I.1, the geometric course of the route R is completely and unambiguously contained in the complete node list, so that a mapping to a list containing only nodes is performed. This is the information available for mapping to the sensor-based road map SK.

[0046] If the route list of a route R determined by the navigation system consists of edges and nodes, then in particular, all edges shall be removed from the route list, so that the route list contains a complete list of only the nodes along the route R.

[0047] If the route list of a route R determined by the navigation system consists of edges only, then in particular, the respective start and end nodes of each edge shall be searched in the map graph. The complete list of edges shall be replaced by the complete list of nodes along the route R.

[0048] In particular, in sub-step I.2, the node list along the route R is looked up using the IDs mentioned above, so that the node list is mapped to a common exchange format, although these IDs are unknown to the sensor-based road map SK, and so a mapping to a common exchange format known to both is performed.

[0049] The IDs of the complete list of nodes along the route R are transposed into a dataset (data record) in an exchange format known equally to both road maps AK, SK. For example, the geographic coordinates may be in WGS84 format and may optionally be enriched with additional data, for example elevation information or road names or numbers.

[0050] Furthermore, in the method described herein, particularly in step II, the node list is transferred to a digital sensor-based road map SK, which includes a representation (description) of the environment (surroundings) detected by the sensors, particularly in a common exchange format.

[0051] For example, the node list of the route R is transferred to the computation unit in a common exchange format, which accesses the sensor-based road map SK and performs mapping, ie, matching, to the sensor-based road map SK.

[0052] FIG. 2 shows a complete node list of a route R including nodes, ie, multiple geographic coordinates KP1-KP7 in an attribute-based road map AK.

[0053] Figure 3 shows the complete node list of route R mapped to the sensor-based road map SK in a common exchange format. Due to the generalized characteristics of the attribute-based road map AK, the geographic coordinates KP1-KP7 may deviate significantly from the actual lane-by-lane accurate driving path on the sensor-based road map SK, and therefore from the possible lane-by-lane accurate navigation route NR.

[0054] Furthermore, in the method described here, particularly in step III, accurate driving section candidates are determined on a lane-by-lane basis in the digital sensor-based road map SK based on the node list.

[0055] Due to the large deviations in the geometric representations of the two road maps AK and SK, which are evident from a comparison of Figures 2 and 3, tolerances TB are provided, particularly for the nodes, i.e., the geographic coordinates KP1 to KP7, imported from the attribute-based road map AK into the sensor-based road map SK. Each tolerance TB can be, for example, larger in the lateral direction, i.e., in the direction perpendicular to the vehicle's direction of travel (transverse direction), than in the direction of travel.

[0056] All lane-level accurate driving sections within the respective tolerance range TB are the driving section candidates to be used, i.e., lane-level accurate driving section candidates. If lane-level accurate driving sections do not exist in the sensor-based road map SK for several consecutive nodes, i.e., geographic coordinates KP1-KP7, within the tolerance range TB, it can be assumed that there is a small discrepancy between the two road maps AK, SK. In this case, these several individual nodes, i.e., geographic coordinates KP1-KP7, can be skipped. If lane-level accurate driving sections do not exist in the sensor-based map SK for a sufficient number of consecutive nodes, i.e., geographic coordinates KP1-KP7, within the tolerance range TB, it can be assumed that automated driving operation is not possible within this section along the nodes, i.e., geographic coordinates KP1-KP7. Further procedures in this case are described in step V below.

[0057] Furthermore, in the method described herein, particularly in step IV, irrelevant or invalid travel segment candidates are identified and eliminated or discarded (deleted), thereby leaving relevant travel segment candidates RF.

[0058] In particular, in sub-step IV.1, candidate segments that have a direction of travel opposite to the path of route R are discarded, i.e., excluded. Candidate segments that move further away from route R, in particular candidate segments that eventually depart from route R, are discarded, i.e., excluded. Candidate segments that join route R from outside, i.e., from the other side of route R, are discarded, i.e., excluded. Candidate segments that are not part of route R, but for example only pass nearby, are also discarded, i.e., excluded.

[0059] In particular, in substep IV.1, dead paths are eliminated. Candidate stretches that correspond to the travel course, i.e., route R, but whose further course makes it impossible to follow route R, are eliminated retroactively until a point is reached that allows a lane change to an alternative stretch further along route R. This is, for example, the next point reached retroactively, which has a dashed road marking indicating that a lane change is possible. Similarly, the reverse, or mirroring, procedure also applies to candidate stretches that run parallel based on history but can only be reached by changing lanes along route R from a specific point in the direction of travel. In the illustrated example, a lane change from the left lane in the direction of travel along route R to the right lane in the direction of travel along route R is only possible up to the third geographic coordinate KP3. Therefore, from the third geographic coordinate KP3 onwards, the stretches in the left lane in the direction of travel along route R are not relevant candidate stretches RF.

[0060] As shown in FIG. 4, the remaining relevant candidate driving segments RF allow one or more driving trajectories FT (illustrated by dashed lines) of the vehicle in either the case of driving directly in the right lane, or in the case of driving first in the left lane and then changing lanes to the right lane, as permitted, as illustrated by dashed lines.

[0061] Furthermore, in the method described herein, and particularly in step V, the route R is divided (segmented) into a subgraph T1 containing only one possible lane and a subgraph T2 containing multiple possible lanes. This is illustrated in Figure 5 for the entire route R from the start point SP to the destination point ZP, where the different segments S are indicated by boxes.

[0062] As shown in the figure, there may be a segment So without a possible subgraph. The desired lane-by-lane precise navigation route NR for automated driving, particularly highly automated or autonomous driving, has gaps (gap) in such a segment So. Automated driving is not possible in this segment So. For sections of the navigation route in such a segment So where there is no possible subgraph and no precise lane-by-lane candidate driving section can be determined, automated driving operations, for example, at SAE levels 3, 4, and 5, are blocked, i.e., at least highly automated and autonomous driving operations are not permitted (released). Therefore, for highly automated or autonomous driving, it is necessary to hand over vehicle guidance to the vehicle driver before reaching the blocked section. Therefore, a handover request is issued to the vehicle driver before reaching the blocked section.

[0063] In the illustrated example, fully automated or autonomous driving is not possible along the entire route R, because in a fully automated or autonomous driving scenario, the vehicle would need to reach the destination, i.e., the destination point ZP of the route R, without a driver, which would not be possible if the driving task needed to be handed over to a driver during that time. If the vehicle were to drive in a fully automated or autonomous driving scenario, the route R would be discarded and an alternative route R would be found that does not include a segment So without a possible subgraph.

[0064] In the partitioning (segmentation) step, as described above, particularly in step V, the graph containing all possible trajectories along the route R is partitioned into a subgraph T1 with only one possible lane, i.e., a subgraph T1 in which the relevant lane-by-lane exact driving segment candidate RF exists in only one lane, and a subgraph T2 with multiple possible lanes, i.e., a subgraph T2 in which the relevant lane-by-lane exact driving segment candidate RF exists in multiple lanes.

[0065] Sections of the route R that cannot be mapped to the sensor-based road map SK, i.e., sections where automated driving operation is not possible, are treated separately as described above in step III. As already mentioned, automated driving operation is not possible here, so driving is handed over to the driver before reaching that section. After such a section is completed, automated driving can be restored, i.e., automated driving operation can be resumed. Alternatively, a method for performing automated driving operation without a sensor-based road map SK can be used, in which automated driving operation is possible without handing over to the driver in those segments So where there are no possible subgraphs.

[0066] Furthermore, in the method described herein, optimal lanes oF are identified, particularly in step VI. In a subgraph T1 that includes only one possible lane, the only possible lane is the optimal lane oF because there is no alternative way to perform automated driving operations. The vehicle must stay in this lane. In a subgraph T2 that includes multiple possible lanes, one of the lanes must be selected as the optimal lane oF for creating an accurate lane-by-lane navigation route NR. For example, the selection takes into account (determines) which of the possible lanes allows driving at the highest possible automation level, i.e., the highest possible SAE level, with as few lane changes as possible, and with as few and / or as few obstacles as possible, requiring as few and / or as few speed adjustments as possible.

[0067] Therefore, the selection of the optimal lane oF in the subgraph T2 containing multiple possible lanes is performed by considering (determining) the results of the evaluation, for example using a cost function. The optimal lane, i.e., the lane with the smallest cost, is selected as the optimal lane oF and is thus used to enable the vehicle to drive automatically along the subgraph T2. For example, lane changes along the subgraph T2 are also taken into account if corresponding data from the sensor-based road map SK allows lane changes. The cost function may include one or more or all of the following parameters: These parameters are included in the cost function, for example, proportionally to the length along the route R (according to the proportion of the length):

[0068] Costs decrease as the level of automation (SAE level) of autonomous driving increases. The level of automation is an attribute of the sensor-based road map SK, and individual sections can have different automation levels. -Increasing the proportion of autonomous driving operations reduces costs. Costs increase with the number of handovers required between the driver and the autonomous driving operation. Conversely, costs decrease as the length of each autonomous driving operation segment increases. Costs increase with the number of lane changes performed. Costs increase depending on the presence of current traffic delays, determined for example from accurate lane-level traffic information. Costs decrease depending on the exact driving speed per lane, which can be determined in particular by the sensor system.

[0069] The individual cost parameters can, for example, be weighted relative to one another depending on the particular system design and translated into a common overall cost function that takes all partial parameters (sub-parameters) into account accordingly.

[0070] Instead of this cost model of evaluation, in which adverse costs are summed up and minimized overall, a bonus value can also be calculated and maximized in the opposite way. At points where said costs decrease, the bonus value increases, and at points where said costs increase, the bonus value decreases. Here again, for example, the individual bonus parameters can be weighted relative to one another, depending in particular on the system design, and translated accordingly into a common overall bonus function that takes all partial parameters into account. The difference is that the cost function minimizes the cost, while the bonus function maximizes the bonus value.

[0071] Instead of a weighted evaluation of the individual arguments, in particular the above-mentioned parameters, machine learning can be carried out, for example on the basis of training data, in which case the evaluation is carried out, for example, using a neural network.

[0072] Furthermore, in the method described herein, particularly in step VII, the determined optimal lanes oF are combined to form a determined lane-by-lane precise navigation route NR. That is, a reconstructed, autonomously drivable, and navigable optimal driving path oF is formed in the sensor-based road map SK. The lane-by-lane precise navigation route NR is shown by a dashed line in FIG. 5. The lanes of the subgraph T1 containing only one possible lane, i.e., the optimal lane oF, are combined with the subgraphs containing the respective optimal lanes oF identified above in the subgraph T2 containing multiple possible lanes to form a global graph. If the global graph has one or more gaps, such as the sixth and ninth segments S in the driving direction in the illustrated example, autonomous driving operation based on the data of the sensor-based road map SK is not possible. In this case, a handover to the driver must be performed or an alternative route R must be requested in the navigation system. That is, an alternative lane-by-lane precise navigation route NR with no or fewer sections where autonomous driving operation is blocked can be searched for.

[0073] Because the lane-by-lane precise navigation route NR is composed of a series of optimal lanes oF, it is possible to generate lane change recommendations that will guide the vehicle to the respective optimal lanes oF when following the lane-by-lane precise navigation route NR. The lane recommendations can be output to a device for performing an automated or highly automated driving operation in an automated or highly automated driving operation as a control instruction to initiate the execution of an automated lane change. The lane recommendations can also be execution instructions issued to a driver to instruct the driver to manually execute a lane change. This is particularly useful for vehicles that are not designed to execute automated lane changes. In this case, issuing lane change recommendations to the driver can be performed in a highly automated or autonomous driving operation, but can also be performed in a manual driving operation. In this case, issuing lane change recommendations in a manual driving operation is effective because it guides the driver to the optimal lane oF from the beginning, in preparation for a subsequent highly automated or autonomous driving operation that may be performed. [Prior art documents] [Patent documents]

[0074] [Patent Document 1] U.S. Patent No. 10,969,232 [Patent Document 2] German Patent Application No. 102021006166.7 [Patent Document 3] US Patent Application Publication No. 2022 / 0032907 [Patent Document 4] U.S. Patent Application Publication No. 2020 / 0200547

Claims

1. 1. A method for determining a navigation route (NR) for automated driving operation of a vehicle, comprising: - creating a node list representing a number of geographic coordinates (KP1 to KP7) of the route (R) determined by the navigation system based on a digital attribute-based road map (AK); - transferring said node list to a digital sensor-based road map (SK) containing a representation of the environment detected by the sensors; - In the section of the navigation route (NR) where the deviation between the digital attribute-based road map (AK) and the digital sensor-based road map (SK) at the geographical coordinates (KP1 to KP7) is within a tolerance range (TB), accurate driving section candidates in lane units are determined in the digital sensor-based road map (SK) based on the node list, while in the section of the navigation route (NR) where the deviation is not within the tolerance range (TB), accurate driving section candidates in lane units are not determined; - Identifying and eliminating irrelevant candidate driving segments; - dividing the route (R) into a subgraph (T1) containing only one possible lane and a subgraph (T2) containing several possible lanes, - Identifying optimal lanes (oF), - Combining the optimal lanes (oF) to form a precise lane-by-lane navigation route (NR); The implementation system executes the process, determining, by the on-board system, a precise navigation route (NR) for the automated driving operation, in particular for a highly automated or autonomous driving operation, on a lane-by-lane basis; A method characterized in that the implemented system executes a process of blocking automated driving operations in sections of the navigation route (NR) where accurate lane-by-lane driving section candidates have not been determined.

2. The automatic driving operation is permitted in the section of the navigation route (NR) where the accurate driving section candidate for each lane has been determined. The process is performed by the implementation system. The method of claim 1.

3. Allowing such highly automated or autonomous driving operations only in authorized sections The process is performed by the implementation system. The method of claim 2.

4. issuing a takeover request to a driver of the vehicle before the highly automated or autonomous driving operation reaches a blocked section. The process is performed by the implementation system. The method of claim 1.

5. If the determined lane-by-lane precise navigation route (NR) includes one or more sections where the automated driving operation is blocked because the lane-by-lane precise driving section candidate has not been determined, an alternative lane-by-lane precise navigation route (NR) is searched for that does not include the section where the automated driving operation is blocked because the lane-by-lane precise driving section candidate has not been determined, or that has fewer blocked sections. The process is performed by the implementation system. The method of claim 1.

6. For each of the subgraphs (T2) containing multiple possible lanes, a cost function is used to identify the optimal lane (oF). The process is performed by the implementation system. The method of claim 1.

7. generating lane change recommendations based on the lane-by-lane precise navigation route (NR) for performing manual or automatic lane changes to follow the lane-by-lane precise navigation route (NR); The process is performed by the implementation system. The method of claim 1.

Images