Control system and method for a hybrid approach to determining possible driving paths for an automotive vehicle

Abstract

The control system 10 is suitable for use with an automobile vehicle 12 and is configured and intended to monitor the current driving situation of the automobile vehicle based on ambient data of the automobile vehicle obtained from at least one ambient sensor disposed on the automobile vehicle 12 during a current driving situation. The control system is configured and intended to determine information related to the current driving situation of the automobile vehicle 12 based on the provided ambient data, determine information related to the current driving situation of the automobile vehicle 12 based on the provided ambient data, and determine components of a future driving maneuver for the automobile vehicle 12 based on the information related to the current driving situation of the automobile vehicle 12. The control system 10 is further configured and intended to determine a multiplicity of model driving paths for the automobile vehicle 12 based on the determined components of the future driving maneuver for the automobile vehicle 12, and determine a driving path for the automobile vehicle 12 to follow during a further course of its travel from the multiplicity of model driving paths for the automobile vehicle 12. Finally, the control system (10) is configured and intended to update information related to the current driving situation of the motor vehicle (12) and / or the supplied ambient data, and adapt a driving path for the motor vehicle (12) based on the objective function and based on the updated supplied ambient data and / or based on updated information related to the current driving situation of the motor vehicle (12).

Description

[Background technology]

[0001] A control system and a control method are described below that determine a driving path that a vehicle is intended to follow as the best possible response to the current driving system. The control system and the control method are specifically based on an environmental sensor system within the vehicle and assist the driver or the autonomously driven vehicle. In particular, in the case of autonomous and autonomously controlled vehicles, it helps to improve the safety and driving comfort of the vehicle occupants by using an efficient and robust planning and optimization approach in which a driving path is determined.

[0002] The increasing variety of capabilities of autonomously driven automobile vehicles increases the need for reliable and fast control systems and algorithms for planning the movements of automobile vehicles operating in at least a partially autonomous manner (route planning). The increasing number of road users in an increasingly complex and dynamically changing vehicular environment presents a particular challenge. In control system architectures for automobile vehicles operating in a partially autonomous or autonomous manner, in addition to detecting and controlling specific traffic situations, decision-making and movement planning significantly impact the overall performance of the corresponding automobile vehicle.

[0003] The control systems and algorithms that have been increasingly developed in recent years to plan the driving paths of (partially) autonomous automobile vehicles attest to the complexity of automated driving. In general, the challenges associated with trip planning involve determining a convenient, collision-free driving path based on a robust computational model in which static and dynamic objects in the environment of these automobile vehicles are taken into account. It is also desirable to calculate this driving path as quickly as possible and to optimize it online or adapt it to the constantly changing vehicle environment, thereby ensuring the real-time performance of the automobile vehicle's (partially) autonomous system in a dynamically changing environment.

[0004] In the past, various approaches to route planning have been developed, such as motion planning algorithms or sampling-based route planning techniques. Within these approaches, for example, (locally) continuous or (globally) discrete optimization techniques are used to calculate and optimize route plans.

[0005] Although discrete planning and optimization techniques offer the possibility of good decision-making, they also have the drawback of being time-consuming due to the variety of calculations performed, which prevents online or real-time calculations to quickly react to current operating conditions.

[0006] In general, local sequential planning and optimization techniques provide fast optimized solutions, but must be initialized in an appropriate manner to handle the combinatorial tasks that arise in a dynamic vehicular environment.

[0007] In modern motor vehicles, driver assistance systems (ADAS - Advanced Driver Assistance Systems) provide a variety of monitoring and instruction functions to make driving the motor vehicle safer. In this case, the environment of the motor vehicle is monitored with respect to the course of the motor vehicle's journey based on environmental data obtained from one or more environmental sensors on the motor vehicle.

[0008] Known driver assistance systems, for example, determine whether a motor vehicle is in a lane and whether the driver is unintentionally drifting to one side of the lane or about to leave the lane. These driver assistance systems generate an "image" of the road, and in particular the lane, from acquired environmental data. In this case, objects such as curbs, lane boundary lines, lane markings, and direction arrows are detected and tracked during driving. Moving objects, such as other motor vehicles, are detected and tracked during driving (tracking).

[0009] Additionally, modern driver assistance systems include so-called "blind spot monitors," which determine, for example, by radar, lidar, video, etc., whether another motor vehicle, road user, or object is to the side and / or behind the motor vehicle, such that a lane change or turn of the subject motor vehicle could result in a collision with it.

[0010] Furthermore, in so-called ACC systems (adaptive cruise control), the automatic speed control of a motor vehicle is adapted to the speed of the motor vehicle driving in front. In this case, a certain distance to the motor vehicle driving in front must always be met. For this purpose, such systems determine the direction of movement and / or speed of the motor vehicle driving in front in order to avoid motor vehicles that cross the path of the motor vehicle driving in front so as to create critical situations. On the one hand, this is related to lane-changing or turning maneuvers, and on the other hand, related to rear-end collisions.

[0011] In human-controlled automobile vehicles, driver assistance systems typically provide an instruction function to warn the driver about critical situations or corresponding actions, or to suggest appropriate actions to the driver for the automobile vehicle. Similarly, driver assistance systems can also be used in autonomously controlled automobile vehicles to provide appropriate environmental data to the autonomous controller.

[0012] The underlying problem In road traffic, situations may arise in which the driver or the (partially) autonomous driver assistance system of the vehicle needs to perform certain maneuvers. For example, a curving lane course may already require a corresponding maneuver of the vehicle.

[0013] However, the current situation of the target vehicle is not constant, but rather continuously changes. For example, other road users may intentionally or unintentionally change lanes or speeds as a result of an accident. In addition, the current driving situation of the vehicle may already be changing due to driving behavior and / or changing lane course. Responding appropriately and timely to such changes in the current situation is a major challenge for both conventional driver assistance systems and human drivers. For this purpose, conventional driver assistance systems, for example, calculate a travel path (driving route) that the vehicle is intended to follow in the current driving situation. Today, complex, dynamically changing traffic situations and increasingly congested traffic are reflected in the increasing resource usage by these conventional driver assistance systems. In addition, limitations on the execution speed are imposed on the continuous optimization of the calculated driving route in conventional driver assistance systems. Summary of the Invention [Problem to be solved by the invention]

[0014] Therefore, the object is to provide a control system and a control method for a motor vehicle that robustly improves the driving safety and driving comfort of the motor vehicle according to the current traffic situation of the motor vehicle, in this case also with the intention of reducing the computing time compared to conventional control systems and methods. [Means for solving the problem]

[0015] This object is achieved by a control system having the features of claim 1 and by a control method having the features of claim 11.

[0016] Preferred embodiments will become apparent from the dependent claims 2 to 10 and 12 and from the following description.

[0017] One aspect relates to a control system for use in a motor vehicle configured and intended to detect lanes, road boundaries, road markings, and / or further motor vehicles within areas ahead, immediately to the sides, and / or behind the motor vehicle based on environmental data obtained from at least one environmental sensor disposed in the motor vehicle, the at least one environmental sensor configured to provide environmental data representative of the areas ahead, immediately to the sides, and / or behind the motor vehicle to an electronic controller of the control system. The control system is at least configured and intended to: determine information related to a current driving situation of the motor vehicle based on the provided environmental data; and determine at least one component of a future driving maneuver for the motor vehicle based on the information related to the current driving situation of the motor vehicle. The control system is also at least configured and intended to: determine a plurality of model driving paths for the motor vehicle based on the determined components of the future driving maneuver for the motor vehicle; and determine from the plurality of model driving paths for the motor vehicle a driving path for the motor vehicle that the motor vehicle is intended to follow during a further part of its journey. Finally, the control system is at least configured and intended to update information related to the current driving situation of the motor vehicle and / or the given environmental data, and adapt a driving path for the motor vehicle using the objective function and based on the given updated environmental data and / or based on updated information related to the current driving situation of the motor vehicle.

[0018] The component of the future maneuver can be a lateral component of the future maneuver. Additionally, it can be a longitudinal component. The component of the future maneuver can also include a combination of a lateral component and a longitudinal component.

[0019] The lateral components may include, for example, lane keeping, lane changes to the left, and / or lane changes to the right, each originating from the lane currently being used by the motor vehicle.

[0020] The longitudinal component may include, for example, a longitudinal speed and / or a longitudinal acceleration of the motor vehicle. Alternatively or additionally, the longitudinal component may include data and / or control signals for one or more electronic control systems of the motor vehicle that may perform or at least initiate adaptive cruise control (ACC), adaptive chassis control (DCC), and / or emergency braking in accordance with corresponding output signals.

[0021] The control system may be configured and intended to determine a model driving path based on a pre-selection of a driving maneuver and / or a selection of a driving maneuver, and further to perform a pre-selection of a driving maneuver and / or a selection of a driving maneuver to create the model driving path.

[0022] The beginning of a further stage of the journey can temporally mark the end of the current driving situation.

[0023] The adaptation of the driving path for the motor vehicle using the objective function and based on given updated environmental data and / or updated information related to the current driving situation can be performed continuously or at specific intervals, in which case a new adaptation may require reinitialization of the objective function.

[0024] The control system may be configured and intended to determine a driving path from a plurality of model driving paths using an objective function that is the same as the objective function for adapting a driving path for an automotive vehicle.

[0025] The same objective function can also be used, for example, when determining a model driving path.

[0026] Therefore, a comparison option may be provided during the reinitialization of the objective function described above.

[0027] The objective function may be, for example, a cost function.

[0028] According to certain exemplary embodiments, the information related to the current driving situation of the motor vehicle includes at least the lateral distance of the motor vehicle from its currently used lane. In these cases, the control system may also be configured and intended to determine components of future driving maneuvers based on the lateral distance of the motor vehicle from its currently used lane, such as lane keeping or lane changing.

[0029] In this case, the lateral distance may be measured relative to the longitudinal axis of the vehicle, for example. Lane changes may include left lane changes and right lane changes. Lane keeping may include left lane keeping, center lane keeping, and / or right lane keeping.

[0030] The information relating to the current driving situation of the motor vehicle may also include the lateral distance of one or more further motor vehicles (or their respective longitudinal axes) from their currently used lane in the motor vehicle's environment.

[0031] According to certain exemplary embodiments, the information related to the current driving situation of the motor vehicle may also include the motor vehicle's longitudinal distance along its currently used lane from a further motor vehicle. In these cases, the control system may be configured and intended to determine the further component of the future driving maneuver based on the determined component of the future driving maneuver and based on the motor vehicle's longitudinal distance from the further motor vehicle.

[0032] Alternatively or additionally, the information relating to the current driving situation of the motor vehicle may include a relative speed and / or a relative acceleration between the motor vehicle and the further motor vehicle, although in this case the relative acceleration may not be derived from the relative speed by the control system or a further electronic controller of the motor vehicle.

[0033] The additional components may be the fore-aft components described above, although the present disclosure is not limited thereto.

[0034] The further motor vehicle may be a stationary (parked) motor vehicle or a moving motor vehicle.

[0035] According to a particular embodiment, the control system may also be configured and intended to determine information relating to the current driving situation of the motor vehicle based on given environmental data in the form of discrete sampled values.

[0036] In this case, the control system may also be configured and intended to determine a number or all of the discrete sampled values ​​as nodes and / or edges of a graph and to determine a connected graph from the determined nodes and / or edges, which makes it possible to implement a graph-based method for determining and processing the discrete sampled values.

[0037] In this case, the control system may also be configured and intended to select nodes and / or edges of the graph as stopping points for a driving path and calculate a driving path for the automobile vehicle by spline-based interpolation between the selected stopping points.

[0038] According to certain exemplary embodiments, the control system may also be configured and intended to determine updated information in the form of continuous values ​​and / or updated environmental data.

[0039] Within the scope of the present disclosure, the continuous values ​​may be, for example, quasi-continuous values, where a measurement time is assigned to each value and the quasi-continuous values ​​are organized according to the measurement time. For reasons of efficiency, the continuous values ​​may consist of only a portion of the quasi-continuous values, for example, every second or every third quasi-continuous value.

[0040] In this case, the control system may also be configured and intended to combine updated information in the form of continuous values ​​and / or updated environmental data with information related to the current driving situation of the motor vehicle in the form of discrete sampled values ​​in order to adapt a driving path for the motor vehicle.

[0041] This makes it possible to implement a hybrid planning approach for travel paths, which combines discrete and continuous planning and optimization methods, thus compensating for the inherent disadvantages of the individual planning and optimization methods.

[0042] In this case, the combination of information related to the current driving situation of the motor vehicle in the form of discrete sampled values ​​with updated information and / or updated environmental data in the form of continuous values ​​may at least include initializing and / or reinitializing the adaptation of the driving path for the motor vehicle using an objective function.

[0043] Alternatively or additionally, the combination of information relating to the current operating situation of the motor vehicle in the form of discrete sampled values ​​with updated information and / or updated environmental data in the form of continuous values ​​may include initializing and / or reinitializing the determination of the continuous values ​​based on the updated information and / or based on the updated environmental data.

[0044] A further aspect relates to a control method for a motor vehicle for detecting lanes, road boundaries, road markings and / or further motor vehicles in areas ahead, immediately to the sides and / or behind the motor vehicle based on environmental data obtained from at least one environmental sensor arranged in the motor vehicle, the control method being particularly executed by the control system described above, comprising: determining information related to a current driving situation of the motor vehicle based on the given environmental data; determining at least one component of a future driving maneuver for the motor vehicle based on information related to a current driving situation of the motor vehicle; determining a plurality of model driving paths for the motor vehicle based on the determined components of future driving maneuvers for the motor vehicle; determining a driving route for the motor vehicle that is intended to be followed by the motor vehicle in a further part of the journey from a plurality of model driving routes for the motor vehicle; updating information related to the current driving situation of the motor vehicle and / or the given environmental data; and adapting a driving path for the motor vehicle using the objective function and based on the provided updated environmental data and / or based on updated information related to the current driving situation of the motor vehicle.

[0045] Yet another aspect relates to an automotive vehicle including the control system described above.

[0046] Compared to conventional driver assistance systems, the solution presented here improves the accurate assessment and accurate detection of the current driving situation of the motor vehicle and the further motor vehicle. In addition, a real-time planning and optimization approach for the driving path of the motor vehicle is provided, which combines discrete and continuous planning approaches to enable robust and fast determination of the best possible driving path for future driving maneuvers of the motor vehicle.

[0047] The best possible driving route can thus be determined as an appropriate response to the current traffic situation of the motor vehicle. The environmental data obtained by the at least one environmental sensor can be constantly changing and periodically updated according to the actual traffic and driving situation.

[0048] When a driving route for future driving operations of the target motor vehicle is used, this improves the driving comfort and driving safety of the motor vehicle by taking into account the driving dynamics of the motor vehicle and the dynamically changing environment when adapting the driving route.

[0049] It will be clear to those skilled in the art that the above-described aspects and features may be combined in a control system and / or a control method as required. Although some of the above-described features have been described with respect to a control system, it goes without saying that these features can also be applied to a control method. Features described with respect to a control method can likewise be applied in a corresponding manner to a control system.

[0050] Further objectives, features, advantages and possible applications emerge from the following description of exemplary embodiments, which should not be understood in a limiting manner, with reference to the associated drawings. In this case, all features described and / or illustrated in the drawings, alone or in any desired combination, represent the subject matter disclosed herein. The dimensions and proportions of the components shown in the figures are not to scale here. Components that are identical or act identically are provided with the same reference signs. [Brief explanation of the drawings]

[0051] [Figure 1] 1 is a schematic diagram illustrating an automotive vehicle having a control system and at least one environmental sensor in accordance with certain exemplary embodiments. [Figure 2] FIG. 1 is a diagram schematically comparing a discrete planning and optimization approach with a continuous planning and optimization approach. [Figure 3] FIG. 1 is a schematic diagram illustrating an architecture of a hybrid approach to planning and adapting driving paths for an automotive vehicle, in accordance with certain illustrative embodiments. [Figure 4] 10A-10C are schematic diagrams for determining components of lateral (left-hand) steering and for determining components of longitudinal (right-hand) steering, in accordance with certain exemplary embodiments; [Figure 5]1 is a schematic diagram for adapting a selected driving path for an automotive vehicle, in accordance with certain illustrative embodiments; [Figure 6] 3 is a schematic diagram illustrating the relationship of the planning approach shown in FIG. 2 within the context of a hybrid approach to planning and adapting a driving path for an automotive vehicle, in accordance with certain illustrative embodiments. [Figure 7] 1 is a diagram illustrating a model driving path and a selected driving path for an automotive vehicle in accordance with certain illustrative embodiments; [Figure 8] FIG. 1 is a schematic diagram illustrating a flow chart of a control method in accordance with certain exemplary embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0052] Within the following disclosure, some aspects are primarily described with reference to control systems. However, it should be understood that these aspects are also valid within the disclosed control methods that can be executed, for example, by a central control unit (ECU) of an automotive vehicle. This can be implemented by performing appropriate read and write accesses to memory allocated to the automotive vehicle. The control methods can be implemented within the automotive vehicle using both hardware and software, as well as combinations of hardware and software. These include digital signal processors, application-specific integrated circuits, field-programmable gate arrays, and other suitable switching and computing components.

[0053] FIG. 1 schematically illustrates an automotive vehicle 12 equipped with a control system 10. The control system 10 is coupled to at least one environmental sensor 14, 16, 18 on the automotive vehicle 12 for receiving environmental data from the at least one sensor 14, 16, 18. The control system 10 may include an electronic controller ECU (electronic control unit; not shown). For example, the control system 10 may be configured and intended to at least determine, with the aid of the ECU and / or further electronic control systems, a driving path for the automotive vehicle 12 that the automotive vehicle 12 is intended to follow in further journeys. For this purpose, the ECU receives signals from the environmental sensors 14, 16, 18, processes these signals and associated environmental data, and generates corresponding control and / or output signals.

[0054] 1 shows three environmental sensors 14, 16, 18 that send corresponding signals to the control system 10 or electronic controller ECU. In particular, at least one environmental sensor 14 is disposed on the automobile vehicle 12, facing forward in the direction of travel of the automobile vehicle 12 and capturing an area 22 in front of the automobile vehicle 12. This at least one environmental sensor 14 may be disposed, for example, in the area of ​​the front fender, front light, and / or front radiator grille of the automobile vehicle 12. As a result, the environmental sensor 14 captures an area 22 directly in front of the automobile vehicle 12.

[0055] At least one further or alternative environmental sensor 16, also facing forward in the direction of travel of the automotive vehicle 12, is shown in the region of the windshield of the automotive vehicle 12. For example, this environmental sensor 16 may be located inside the automotive vehicle 12 between the rearview mirror and its windshield. Such an environmental sensor 16 captures an area 24 in front of the automotive vehicle 12, and depending on the shape of the automotive vehicle 12, the area 24 directly in front of the automotive vehicle 12 may not be captured due to the front of the automotive vehicle 12 (or its geometric shape).

[0056] Additionally, at least one environmental sensor 18 may be located on the side and / or rear of the automotive vehicle 12. This optional environmental sensor 18 captures an area 26 to the side and / or behind the automotive vehicle 12 in the direction of travel of the automotive vehicle 12. For example, data or signals from this at least one environmental sensor 18 may be used to verify information captured by the other environmental sensors 14, 16 and / or to determine the curvature of the lane being used by the automotive vehicle 12.

[0057] The at least one environmental sensor 14, 16, 18 may be implemented in any desired manner and may include a front camera, a rear camera, a side camera, a radar sensor, a lidar sensor, an ultrasonic sensor, and / or an inertial sensor. For example, the environmental sensor 14 may be implemented in the form of a front camera, a radar sensor, a lidar sensor, or an ultrasonic sensor. In particular, a front camera is suitable for the higher environmental sensor 16, while the environmental sensor 18 located at the rear of the automotive vehicle 12 may be implemented in the form of a rear camera, a radar sensor, a lidar sensor, or an ultrasonic sensor.

[0058] The electronic controller ECU processes environmental data obtained from environmental sensors 14, 16, 18 on the motor vehicle 12 to obtain information related to the static environment (non-movable environmental objects, e.g., road boundaries, lane markings, stationary obstacles) and the dynamic environment (movable environmental objects, e.g., other moving motor vehicles or road users) of the motor vehicle 12.

[0059] Thus, the electronic controller processes environmental data obtained from the environmental sensors 14, 16, 18 on the motor vehicle 12 to capture a lane being used by the motor vehicle 12 and having first and second lateral lane boundaries in front of the motor vehicle 12. The electronic controller ECU further processes environmental data obtained from the environmental sensors 14, 16, 18 on the motor vehicle 12 to capture a lane being used by a further road user, for example another motor vehicle, that is adjacent to the lane being used by the vehicle of interest (where adjacent also means that there are one or more further lanes between the adjacent lanes), the lateral lane boundaries of which are in front of the motor vehicle 12. The other motor vehicle or further road user may in this case be stationary or may be moving in the direction of travel of the motor vehicle 12 or against it.

[0060] To this end, the environmental sensors 14, 16, 18 provide the electronic controller ECU with environmental data representative of the areas in front of, immediately to the sides of, and / or behind the vehicle. To this end, the control system 10 is connected to the at least one environmental sensor 14, 16, 18 via at least one data channel or bus (shown using dashed lines in FIG. 1). The data channel or bus may be established by cable or in a wireless manner.

[0061] Alternatively or additionally, the control system 10 or its electronic controller ECU may receive data from one or more other assistance systems 20 (hereinafter also referred to as driver assistance systems 20) of the motor vehicle 12 or another controller 20, which data may be obtained therefrom indicative of the lane being used by the motor vehicle 12 of interest and further road users, with its lateral lane boundaries. Thus, data and information already determined by other systems may be used by the control system 10.

[0062] Furthermore, the control system 10 or its electronic controller ECU determines the driving situation using environmental sensors, i.e., based on environmental data obtained with the help of at least one environmental sensor 14, 16, 18. Again, an already existing driver assistance system 20 or electronic controller 20 can alternatively or additionally provide data and / or information that defines the driving situation or from which the driving situation can be quickly derived. Then, depending on the determined driving situation, at least one possible driving route is determined, which is intended for the automotive vehicle 12 to follow on its further journey. This driving route is adapted substantially in real time to changes in the current driving situation of the automotive vehicle 12, in other words, the driving route is optimized.

[0063] The driver assistance system 20 or the electronic controller 20 may also be configured and intended to control the automotive vehicle in a (partially) autonomous manner. In this case, the control system 10 is configured and intended to output data about the autonomous driving to the driver assistance system 20 or the electronic controller 20. In particular, the control system 10 (or its ECU) may output data to the component 20 indicating the course of the determined and / or adapted driving path that the automotive vehicle 12 is intended to follow in a further course (e.g., starting immediately after the adaptation or starting from the end of the current driving situation). Likewise, the data may be transmitted in a wired manner or wirelessly via a data channel or bus.

[0064] The approach to real-time planning and adaptation of a driving path for automotive vehicle 12, as presented within this disclosure, is based on a combination of discrete (sampled) and continuous (or at least quasi-continuous) values ​​of environmental data made available to control system 10. Figure 2 compares approaches to driving path planning and optimization based on discrete (top diagram) and continuous (bottom diagram) values ​​as used in certain exemplary embodiments.

[0065] In the upper image of FIG. 2, the motor vehicle 12 is shown in the right lane of a road 36. The right lane is bounded on the left by a right lane marker 30 and by a left lane marker 32. The lane marker 32 simultaneously constitutes a right lane marker for the left lane of the road 36, which is bounded on the left by a left lane marker 34. However, the lane marker 32 may not necessarily be the imaginary centerline of the road 36. Alternatively, the lane marker 32 may actually be present on the road 36. At a certain distance ahead of the motor vehicle 12, there is another (further) motor vehicle 28 in the right lane of the road 36. In this example, the other motor vehicle 28 may be stationary or may also be moving in the direction of travel of the motor vehicle 12. For example, the motor vehicle 12 may be driving behind the other motor vehicle 28 at a constant distance based on speed control (convoy). The driver assistance system 20 may, for example, output appropriate data for this purpose.

[0066] For the convoy of motor vehicles 12 in the top diagram of FIG. 2 , the control system 10 of the motor vehicle 12 uses at least one environmental sensor 14, 16, 18 to acquire information about the other motor vehicles 28 and determine journey information associated with the motor vehicle 28. Within the scope of the present disclosure, this journey information associated with the other motor vehicles 28 may be included in information related to the current driving situation of the motor vehicle 12. The information related to the current driving situation of the motor vehicle 12 may include, for example, the current speed, and / or the current acceleration, and / or the current impact of the motor vehicle 12, which may be made available to the control system 10 in an appropriate manner by a further control system or ECU of the motor vehicle 12. The current speed may be lateral velocity and / or longitudinal velocity. The current acceleration may also be lateral acceleration and / or longitudinal acceleration. Finally, the impact may also be a lateral impact and / or a longitudinal impact.

[0067] Furthermore, the information related to the current driving situation of the automobile vehicle 12 may include the distance between the automobile vehicle 12 and the automobile vehicle 28, and / or the relative speed between the automobile vehicle 12 and the automobile vehicle 28, and / or the relative acceleration between the automobile vehicle 12 and the automobile vehicle 28. The distance, relative speed, and relative acceleration may be lateral and / or longitudinal distance, relative speed, and / or relative acceleration. To determine them, the control system 10 may determine the lateral and longitudinal distances to the other automobile vehicles 28, as well as the lateral and longitudinal speeds and accelerations of the other automobile vehicles 28, based on environmental data provided by at least one environmental sensor 14, 16, 18, for example, and relate them to the lateral and longitudinal speeds of the automobile vehicle 12.

[0068] In the top image of Figure 2, discrete values ​​(indicated by black squares) are determined by the control system 10 from environmental data provided by at least one environmental sensor 14, 16, 18. The control system 10 uses these discrete values ​​(hereinafter also referred to as sampled values) to determine the driving paths shown in the top diagram of Figure 2 (each starting in front of the automobile vehicle 12 and following the course of the current lane or a curve indicating a lane change to the left or right) and selects from these driving paths the best possible driving path 38. Therefore, a discrete (sampling-based) planning and optimization approach is involved in this case.

[0069] In this case, the selection is made, for example, using an objective function, based on specifications regarding, inter alia, the driving comfort of the motor vehicle 12 and the safety of the driver. In the example of the upper diagram of FIG. 2, where the other motor vehicle 28 has a lower speed than the motor vehicle 12 in the current driving situation and therefore must be overtaken, for example, the driving path 38 is selected. Since there are no further lanes of the road 36 to the right of the lane markings, the motor vehicle 12 must pull into the left lane of the road 36 to overtake. The driving path 38 is selected here so that this can be done in a comfortable manner for the driver of the motor vehicle 12 and at the same time a collision is avoided, which does not require any jerky driving maneuvers (changing lanes to the left), and which leaves a sufficient safety distance for the motor vehicle 28 driving in front during overtaking, thereby at least minimizing the risk of a rear-end collision.

[0070] Due to the high dimensionality of the state space, finding the best possible solution for the current driving situation can result in high computing time and inefficiency for the control system used for this purpose. Therefore, since increasing the number of discrete sampling values, increasing the number of determined model driving paths, and ultimately selecting the best possible driving path from these model driving paths increases the required computing time, it may be necessary to find a compromise between finding the (overall) best possible solution for the current driving situation of the automotive vehicle 12 and the computing time used for this purpose. On the other hand, there is a risk that the resources available in conventional control systems are too limited to actually process, or at least efficiently process, such large amounts of data. Furthermore, considering the dynamic vehicle environment and including a temporal component can increase the computing complexity and therefore the computing time.

[0071] To obtain a faster solution in this regard, for example, a continuous planning or optimization approach can be used. An illustration of such an approach is shown in the bottom diagram of FIG. 2. In this case, the driving path 38 determined with reference to the top diagram of FIG. 2 is optimized or adapted. The driving path 38 is therefore further improved in the bottom diagram of FIG. 2 based on the desired driving comfort and the required driving safety for the current driving situation. In this case, each black dot on the driving path 38 represents a (quasi-)continuous value of this driving path 38, which can be adapted by the control system 10 after selecting the driving path 38 in the lateral direction (perpendicular to the road 36) and / or the longitudinal direction (along the road 36) so that an even smoother and safer operation of the motor vehicle 12 to overtake another motor vehicle 28 can be achieved. In this example, the last quasi-continuous value corresponds to the black square in the top diagram of FIG. 2, which indicates the end of the driving path 38. The white dots surrounded by black in the upper and lower diagrams indicate starting points (in front of the motor vehicle 12) from which a driving path is determined and adapted laterally and longitudinally on the road 36, respectively. When continuous planning and optimization approaches are used, no information is lost due to discretization, but these are generally local planning and optimization approaches that require appropriate initialization and, therefore, appropriate reinitialization. For example, the starting points described above with reference to the lower diagram of FIG. 2 can represent initialization or reinitialization points for continuous planning and optimization approaches. In addition, these approaches converge toward local minima within the regions of the initialization and / or reinitialization points. Therefore, insufficiently robust initialization or reinitialization can result in increased computing complexity.

[0072] Within the scope of the present disclosure, control system 10 is configured and intended to combine the discrete planning and optimization approach described above with the continuous planning and optimization approach described above to plan and optimize a route that automotive vehicle 12 is intended to follow on future journeys. In other words, control system 10 uses a hybrid planning and optimization approach to determine the best possible route for a future journey of automotive vehicle 12 and at least substantially adapt it to the current operating conditions of automotive vehicle 12 in real time (online). The individual discrete and continuous planning and optimization approaches that are combined, in this case, are not limited to the example described above with reference to FIG. 2 . Rather, control system 10 is configured and intended to combine any suitable discrete planning and optimization approach with any suitable continuous planning and optimization approach. These include, inter alia, decision trees or random forests, deterministic discretization methods, and / or (continuous) spline-based interpolation with cubic polynomials, and / or quartic or quintic or higher order polynomials, in which case this list should not be understood as definitive.

[0073] 3 provides an overview of an approach based on a combination of continuous and discrete values ​​of environmental data made available to the control system 10 within the present disclosure, in particular on the control system 10 and control method described herein, for planning a route or routes and determining the best possible route of the planned route for the further journey of the automotive vehicle 12. It will now become apparent that an action pre-selection is made by the control system 10 primarily at the decision-making level. This action pre-selection may be made, for example, based on environmental data made available to the control system 10. Within the description of the architecture of the planning and optimization approach used by the control system 10, further reference is made at appropriate points to FIGS. 4 and 5.

[0074] For example, if, upon analyzing the current driving situation of the motor vehicle 12 by the control system 10, the motor vehicle 12 encounters a situation in which it must initiate an overtaking maneuver because another motor vehicle driving ahead of it has braked heavily, the maneuver pre-selection may include a lane change. Generally, at least lane change and lane keeping maneuvers may be included in the set of basic maneuvers from which the maneuver pre-selection is made.

[0075] FIG. 4 provides an overview of how maneuver pre-selection (which equally applies to later maneuver selection) can be performed within the scope of the present disclosure. According to this example, first, a lateral maneuver (also referred to as a lateral maneuver component) is selected based on environmental data made available to the control system 10 by at least one environmental sensor 14, 16, 18. In this case, both the static and dynamic environments of the motor vehicle 12 are considered. Alternatively, only the static or dynamic vehicle environment can be considered. During the overtaking maneuver of the motor vehicle 12 described above, a decision is made to perform a lane-change lateral maneuver because of the other motor vehicle 28 braking heavily (see FIG. 4). Because of the two-lane road 36, only overtaking maneuvers are possible in which the motor vehicle 12 overtakes the other motor vehicle 28 in the left lane of the road 36, so a lane change to the left is selected by the control system 10 at a low decision-making level. A further alternative, illustrated by way of example on the left side of Figure 4, represents lane keeping that may be used in scenarios other than those described above, for example, during a distance-controlled convoy of motor vehicles 12 behind motor vehicle 28. For example, to prepare for a subsequent driving maneuver for motor vehicle 12, lane keeping may also be divided into left lane keeping, center lane keeping, and right lane keeping (not shown in Figure 4). However, the present disclosure is not limited to the above lateral maneuver classes. Alternatively, more, fewer, or different maneuvers may be defined, thereby preselecting the maneuver or maneuver components, which may then be applied to the subsequent maneuver selection.

[0076] In this example, based on the determined lateral maneuver component, a longitudinal maneuver or a longitudinal maneuver component is then determined. However, the present disclosure is not limited thereto. Alternatively, the lateral maneuver component and the longitudinal maneuver component can be determined independently of each other by the control system 10. However, in this case, as is clear from the right diagram of FIG. 4 , the lateral maneuver component and, in addition, the static and / or dynamic environment are used by the control system 10 to determine the lateral maneuver component. The lateral maneuver component can be, for example, in the form of a specific absolute or relative distance, speed, and / or acceleration specification associated with a specific point along the road currently being used by the automotive vehicle 12, along which the driven travel path is intended to be followed. These specifications can then be implemented, for example, by the driver assistance systems 20 and / or further electronic controller 20 of the automotive vehicle 12. To this end, the driver assistance system 20 may refer to or include an adaptive cruise control assistant, and / or an adaptive chassis control assistant, and / or an emergency braking assistant.

[0077] As can be seen from Figure 3, the maneuver preselection or corresponding data (i.e., data indicative of the selected maneuver) is sent as a maneuver assumption to the route planning level of the control system 10. These data are read or captured there by a planning module (referred to as a sampling-based route planner in Figure 3), which initiates route planning from which a route is subsequently selected for further journeys of the automotive vehicle 12. In other words, the sampling-based route planner implements the discrete planning approach (also referred to as a model route) for route candidates described above with reference to the top diagram of Figure 2.

[0078] First, the sampling-based cruise path planner generates discrete sampling states, which consist of discrete longitudinal values ​​(in the direction of travel of the automotive vehicle 12) and discrete lateral values ​​(transverse to the direction of travel of the automotive vehicle 12). In other words, during this planning phase, the sampling-based cruise path planner of the control system 10 sets lateral and longitudinal states, which are then used when generating the cruise path. However, these lateral and longitudinal states may not necessarily correspond to the lateral and longitudinal maneuver components described with reference to FIG. 4.

[0079] Similarly, route processing also occurs at the route planning level, where discrete lateral and longitudinal states, or lateral and longitudinal maneuver components, or combinations of these lateral and longitudinal states or maneuver components, are used as stopping points for one or more model routes generated, for example, as part of route generation in Figure 3. For model route stopping points, control system 10 may, for example, combine lateral and longitudinal states, respectively, that have the same time instance, i.e., represent lateral and longitudinal points on the road currently being used by automotive vehicle 12 at a particular future time, through which the model route to be generated is intended to extend.

[0080] Optionally, and thus indicated by the dashed rectangle in Figure 3, a shortened optimization (pre-optimization) or adaptation of one or more of the generated model driving paths may already be performed and prompted by control system 10. This may be performed by the optimization approach described below or using another suitable optimization approach.

[0081] Finally, as part of the route selection process shown in FIG. 3, the best possible route for the current driving situation, or future journey of the automotive vehicle 12, is selected from the generated model route. In this example, this selection is made using an objective function that may correspond to a cost function. The same objective function may have already been used when determining the model route. Because this selection has so far been based solely on operational assumptions, all data and / or route generated by the sampling-based route planner is returned to a module at the decision-making level (see dashed arrow in FIG. 3 leading to "Operation Selection").

[0082] For example, the objective function includes target states related to the dynamic and static environment of the motor vehicle 12 in the current driving situation, and the driving comfort and feasibility of a model driving path and / or driving path selected therefrom. The one or more target states may be, for example, points on the current road (or in adjacent lanes) of the motor vehicle 12 in the lateral and / or longitudinal direction, possibly paired with one or more time instances.

[0083] 3, the operating assumptions, i.e., all data generated by the sampling-based route planner based on all sampled values ​​and the generated (and possibly pre-optimized) model route, as well as data related to route selection, are further made available to the route optimization module of the control system 10. Thus, optimization in the sense of adapting the route to changed driving conditions may already have been performed on the route selected based on the operating pre-selection or operating assumptions.

[0084] The resulting real-time optimization and adaptation data is appropriately combined with data provided by the sampling-based route planner and verified as part of the evaluated operating assumptions. The latter is made possible by including data obtained during online route adaptation. Each piece of data can also be made available individually to modules of the control system 10 to verify the operating assumptions.

[0085] Based on the evaluated maneuver assumptions, a maneuver selection is performed at the decision-making level, for example, by a decision-making module of the control system 10. However, here, the same maneuvers as already described above with reference to the maneuver pre-selection need not be available for selection. The selected maneuver and the data corresponding to this maneuver are then provided to a sampling-based route planner at the planning level. Additionally, data obtained as part of the maneuver pre-selection may be included in the maneuver selection here.

[0086] The sampling-based driving path planner generates sampling states including lateral and longitudinal states and / or settings of lateral and longitudinal maneuver components, and repeats the above-described operations of selecting a driving path and thereby processing the driving path including a model driving path and a shortened (pre-)optimization of the driving path selection.

[0087] Finally, the selected route (also referred to as the starting route in FIG. 3 ) is provided to the route optimization module of the control system 10, still at the route planning level. This route optimization (also referred to as route adaptation within the scope of this disclosure) is performed online, i.e., in real time or at least near real time, and can be initialized and / or reinitialized continuously and / or at specific discrete times. Then, at the control level, the optimized (adapted) route is made available to an electronic controller of the vehicle 12, such as the driver assistance system 20 or a further electronic controller 20. This operation of making the route available does not apply to the adapted route, nor to the first determined and therefore ideally pre-optimized starting route (see FIG. 3 , indicated there by the arrow with a dashed base), so that the driver assistance system 20 can have the vehicle 12 follow this starting route, for example, before or at the time of the start of the adaptation.

[0088] The data from the real-time route optimization performed in FIG. 3 is appropriately combined with the data provided by the sampling-based route planner and confirmed as part of the evaluated operating assumptions. Thus, the next planning cycle can begin, and some or all of the steps described above are performed again to provide the driver assistance system 20 with the best possible route adapted to the respective driving situation of the vehicle 12 at the control level. In particular, the selected route is repeatedly adapted to the current driving situation of the vehicle 12 in order to be able to react quickly (near real-time) and efficiently to changes in the current driving situation. Therefore, the safety and driving comfort of the driver and / or further passengers of the vehicle 12 are improved.

[0089] This optimization operation will now be described again with reference to FIGS. 5 and 6. The flowchart of FIG. 5 (which can, in more detail, be implemented according to the example described with reference to FIG. 3 for route optimization, but need not be implemented) first requires the formulation of an optimization problem. In addition to avoiding collisions with other road users and / or other objects, an important factor in route planning in a (partially) autonomous vehicle is ensuring high driving comfort for the occupants of the vehicle 12. This is achieved by preventing excessively high (negative or positive) acceleration forces from acting on the vehicle 12 while it is following the route in its future journey. For this reason, the optimization problem is formulated based on the acceleration and / or impacts of the vehicle 12 in each current driving situation. For this purpose, in this example, the time integral of impacts starting from the current time instance of the current driving situation of the vehicle 12 to a subsequent time instance (e.g., consistent with the goal of the route planning in time) is used as part of an objective function, e.g., a cost function. The relative cost of impacts should be minimized as part of this objective function to determine the best possible driving path for the current driving situation of the vehicle 12. Furthermore, in addition to impacts, deviations from the target state (e.g., the vehicle 12 intends to be centered in the lane next to the currently used lane in the event of a lane change at the end of the driving path being followed) can be included in the objective function in the form of an additional cost. This deviation can relate not only to lateral deviations from the target state but also to longitudinal deviations. Alternatively and / or in addition, the length of the interval over which the driving path is planned can be included in the objective function in the form of a cost. As a result, an overall objective function can result from a combination of lateral and longitudinal objective functions, with each or both of the individual objective functions receiving weighting. This objective function can also be used when planning a model driving path and determining the driving path from the model driving path to ensure that corresponding results can be compared if reinitialization of the optimization is required.

[0090] As is apparent from FIG. 5 , the optimization problem is solved based on the current system state of the automotive vehicle and on current environmental data made available to the control system 10. The solution to this optimization problem may represent, for example, the journey path optimization described with reference to FIG. 3 . The result of solving the optimization problem provides one or more reference journey path points for the current time instance or for the current planning period beginning at the current time instance t and continuing until time instance t+Δt. The control system 10 can generate a reference journey path from the reference journey path points. This reference journey path is intended to be satisfied, if possible, by a journey path that is determined and intended to be followed by the automotive vehicle 12 on a further journey. In particular, the control system 10 can determine deviations of individual specific points and / or all points of the journey path that is determined and intended to be followed by the automotive vehicle 12 on a further journey from the reference journey path points. These deviations can then be used when adapting a journey path for the automotive vehicle.

[0091] Vehicle state and environmental information related to the dynamic vehicle environment is then updated (see FIG. 5 ). For this purpose, environmental data made available to the control system 10 by at least one environmental sensor 14, 16, 18, from which information related to the current driving situation of the automotive vehicle 12 is obtained, is continuously updated. This may be a periodic interval-based update of the provided environmental data, or alternatively, a continuous update in real time (also referred to as an online update). The updated information related to the vehicle state (also referred to as the system state of the automotive vehicle 12) and environmental information is again returned by the control system 10 and used for the next optimization problem to be solved. For example, the updated data may be returned to a sampling-based route planner (see FIG. 3 ) and used there to adapt the determined route and / or generate a new model route. A route for further processing or adaptation may then be selected from the newly generated model route in this manner.

[0092] Because of the architecture described above, in interaction with the discrete and continuous planning and optimization approaches shown, the result is a hybrid planning and optimization approach for a driving path for the automotive vehicle 12, which combines these approaches, combines the advantages of the two approaches, and compensates for or at least reduces the disadvantages of the two approaches.

[0093] As is apparent from Figure 6, the overall best possible driving path 38' is first determined within a hybrid planning and optimization approach similar to the discrete approach described with reference to Figure 2. A continuous planning approach similar to the continuous planning approach described with reference to Figure 2 is then initialized using this determined driving path 38' and associated data characterizing the driving path 38'. In either case, the driving path 38' is then further adapted to the current operating situation of the automotive vehicle 12 with the aid of the continuous planning and optimization approach.

[0094] If a travel path other than travel path 38' is intended to be adapted to the currently prevailing driving situation with the help of the continuous optimization approach, there is also a need to initialize, or re-initialize, the continuous optimization approach based on data determined for this other travel path as part of the discrete planning approach. Re-initialization may also be required when a planning and optimization cycle associated with a particular time instance t has ended and when data related to the system state of the automotive vehicle 12 and / or environmental data has been updated. Re-initialization is then performed in a similar manner for subsequent planning and optimization cycles, for example, beginning at time t+Δt.

[0095] For example, for initialization and / or reinitialization, the start and / or end states of the updated driving path 38', possibly along with time information associated with when these start and / or end states are reached, are transmitted as data to the planning module for executing a continuous planning approach. Thus, generally, the start and / or end states are lateral and longitudinal points on the road 36 associated with time instances t (start state) and t + Δt (end state). Within the scope of optimization, i.e., adapting the driving path 38' to the current driving situation of the automotive vehicle 12, the control system 10 uses a continuous approach, which is initialized or reinitialized with specific data from the discrete approach, for example, by comparing specific (interpolated) points of the driving path 38' (see the interpolated states in the bottom diagram of FIG. 6 ) with points of a determined reference driving path (not shown), in each case at the same or consecutive time instances. For example, if a deviation along the road 36 (in the longitudinal or x-direction) or across the road 36 (in the lateral or Y-direction) is greater than a predetermined value, these individual points are adapted in the x- and / or y-direction so that the maximum predetermined deviation relative to the corresponding point of the reference driving path is at least met or undershot. Thus, adaptation does not necessarily mean that the deviating points of the driving path 38' are replaced with points of the reference driving path; it can only be a local approximation (adaptation) of the deviating points relative to the corresponding points of the reference driving path or to another suitable point of the reference driving path. Again, driving dynamics and thus comfort- or safety-related considerations play a role in determining a future driving maneuver, or a corresponding driving path, that is as smooth as possible, if possible, without high acceleration forces, which indicates the temporal and local course of this driving maneuver for the automotive vehicle 12.

[0096] A further exemplary driving situation in which the hybrid planning and optimization approach of the present disclosure is used will now be described with reference to Figure 7. The upper figure again shows the automotive vehicle 12 in a convoy behind other automotive vehicles 28. Multiple possible driving paths that can be followed by the automotive vehicle 12 in the current driving situation may also be viewed. Determination of these driving paths may be based, for example, on spline interpolation of discrete values ​​obtained by the discrete planning and optimization approach.

[0097] In an example presented herein, which should not be understood in a limiting manner, the determination of the driving path in the upper diagram of FIG. 7 is based on graph theory considerations. In this regard, nodes and / or edges for the graph are determined by the control system 10 from a specific number or from all of the discrete sampling values, and the (connected) graph itself is ultimately determined. Some, some, or all of these graphs then represent, for example, model driving paths, which are determined by the control system 10's sampling-based driving path planner (see, e.g., FIG. 3), from which the best possible driving path for the automotive vehicle 12's future journey is determined by the control system 10. The nodes / edges can also represent stopping points for spline-based interpolation of the graph or driving path. Thus, graph-based methods are used to determine and process the discrete sampling values.

[0098] The scenario performed in FIG. 7, or determining and adapting the best possible driving path for the automotive vehicle 12 in its current driving situation, or starting from its current driving situation, will now be further described with reference to the control method shown in FIG. 8.

[0099] 8 shows a flowchart of a control method for detecting, for example, lanes, road boundaries, road markings, and / or further motor vehicles of another motor vehicle 28 (see FIG. 7) in the area ahead, immediately to the side, and / or behind the motor vehicle 12 based on environmental data obtained from at least one environmental sensor 14, 16, 18 on the motor vehicle 12. The control method may be performed, for example, by the above-described control system 10 of the motor vehicle 12. All features described as part of the control system 10 can also be used here for the control method. In particular, all of the above features related to objective functions, component-based determination of future driving maneuvers, the use and combination of discrete and continuous planning and optimization approaches, and initialization and re-initialization can be applied to the control method.

[0100] In a first step S10, information relating to the current driving situation of the motor vehicle 12 is determined.

[0101] This information may be, inter alia, the lateral distance of the longitudinal axis of the motor vehicle 12 from the left lane marking 32 or the right lane marking 30, and / or the longitudinal distance and / or relative speed between the motor vehicle 12 and the other (further) motor vehicle 28.

[0102] In a second step S12, components of a future driving maneuver for motor vehicle 12 are determined based on information related to the current driving situation of motor vehicle 12. If motor vehicle 12 is approximately centered in the currently used lane (the right lane of road 36 in FIG. 7 ) and the distance between vehicles 12, 28 is relatively short (e.g., relative to the current prevailing speed of motor vehicle 12), for example, the components may involve lane keeping and / or braking to avoid a collision with motor vehicle 28.

[0103] In a third step S14, a plurality of model driving paths for the motor vehicle 12 are determined based on the determined components of future driving maneuvers for the motor vehicle 12. The plurality of determined model driving paths are shown in the upper diagram of FIG. 7 as combinations of different possible model driving paths. Accordingly, black squares represent nodes and / or stopping points of each partial driving path, each extending between two edges / nodes. In this case, the individual partial driving paths can be combined by the control system 10 in any desired manner, thus resulting in a variety of model driving paths.

[0104] In a fourth step S16, a driving path for the motor vehicle 12 is determined from a plurality of model driving paths that the motor vehicle 12 is intended to follow on its further journey. In this case, some model driving paths are excluded, for example, because dynamic and static collision checks for movable and immovable objects and / or obstacles in the environment of the motor vehicle 12 are performed by the control system 10 based on the given environmental data, and thus the best possible driving path for the motor vehicle 12 is determined. For example, it can be seen in the upper diagram of FIG. 7 that the model driving paths extend partially outside the road 36 and are therefore not possible driving paths for the further journey. In contrast, driving paths for overtaking maneuvers in the left lane of the road 36 and driving paths for convoys are still possible driving paths for the motor vehicle 12.

[0105] Finally, as can be seen in the lower diagram of FIG. 7, the bolded driving paths among the various model driving paths are determined by the control system 10 as the driving path intended for the motor vehicle 12 to follow in the further journey. In the lower diagram of FIG. 7, the control system 10 therefore determines a driving path indicating a lane change to the left as the best possible driving path in the current driving situation. Pulling out the motor vehicle 12, and thus following a different driving path, earlier would impair the driving comfort of the occupants of the motor vehicle 12, since the motor vehicle 12 would have to accelerate quickly. Pulling out too slowly is also not possible, as this could otherwise result in a collision with another motor vehicle 28. Generally, in the situation illustrated by the example of FIG. 7, the control system 10 selects a lane change to the left or a driving path for overtaking, since the other motor vehicle 28 is stationary or moving at a substantially slower speed than the motor vehicle 12. Additionally, the lateral distance of the motor vehicle 12 from the lane boundary 32 may be shorter (particularly shorter than that shown in FIG. 7, where the motor vehicle 12 is approximately centered in the lane), resulting in, for example, the motor vehicle 12 having a relatively shorter pull-off distance into the passing lane when overtaking. This selection of the driving path, like the determination of the model driving path, may be based, for example, on the objective function described above.

[0106] In a fifth step S18, information relating to the current driving situation of the motor vehicle and / or the given environmental data is updated.

[0107] In a sixth step S20, a driving path for the motor vehicle 12 is determined using the adapted objective function (e.g., the objective function described above) and based on the given updated environmental data and / or information related to the updated current driving situation of the motor vehicle 12. Based on the driving situation of FIG. 7, this means that, assuming that the motor vehicle 12 is intended to perform a speed-controlled convoy at a fixed distance behind the further motor vehicle 28, a lane-keeping driving path is first selected. This may involve, for example, combining four squares in a straight line in the right lane of the road 36. However, when updating the environmental data and / or information related to the current driving situation, it is determined that there is a possibility of a collision with another motor vehicle 28 when following this driving path (shown in the top of FIG. 7 by the course of the driving path between the third and fourth black squares through the further motor vehicle 28). The driving path may then be adapted so that it corresponds to the bolded movement path from the bottom diagram of FIG. 7. In this case, the first part of the planned driving path (between the first and second black squares in the right lane of Figure 7) can be retained, and in particular only the further course of this driving path is adapted, resulting in the motor vehicle 12 performing a lane change to the left and then performing an operation to overtake another motor vehicle 28, for example prompted by the driver assistance system 20.

[0108] The above-described planning and optimization approach may be used in particular to adapt the driving path in order to optimize at least the changing portion of the driving path and find a more efficient solution for the best possible driving path, thereby improving the driving comfort and driving safety of the occupants of the motor vehicle 12 in the current traffic situation.

[0109] Within the scope of the present disclosure, it is possible to at least reduce the inherent disadvantages of the two approaches by combining a discrete, e.g., graph-based, approach to ascertaining and determining a model route, or route for further journeys of the automotive vehicle 12, with a continuous approach to optimizing a selected route. For example, the number of discrete sampling values ​​required to determine a model route for a sampling-based route planner can be significantly reduced compared to using a purely discrete planning and optimization approach due to the subsequent continuous adaptations that are (re)initialized, resulting in one or more model routes.

[0110] The number of discrete samples mentioned can also be further reduced as a result of operational pre-selection at the decision-making level before route planning begins by the sampling-based route planner.

[0111] Thus, an efficient (as it can be executed quickly and conserves resources) and robust planning and optimization approach is provided for the entire travel path that the automotive vehicle 12 is intended to follow on its further journey.

[0112] It goes without saying that the exemplary embodiments described above are not definitive and do not limit the subject matter disclosed herein. In particular, it will be apparent to those skilled in the art that features of various embodiments may be combined with one another and / or various features of embodiments may be omitted without departing from the subject matter disclosed herein. [Appendix 1] 1. A control system (10) configured and intended for use with an automotive vehicle (12) to detect lanes, road boundaries, road markings, and / or further automotive vehicles within an area (22, 24, 26) in front of, immediately to the side of, and / or behind the automotive vehicle (12) based on environmental data obtained from at least one environmental sensor (14, 16, 18) disposed on the automotive vehicle, the at least one environmental sensor being configured to provide an electronic controller (20) of the control system (10) with the environmental data representative of the area in front of, immediately to the side of, and / or behind the automotive vehicle (12), the control system (10) comprising: determining information related to a current operating situation of the motor vehicle (12) based on the environmental data provided; determining at least one component of a future driving maneuver for the motor vehicle (12) based on the information related to the current driving situation of the motor vehicle (12); determining a plurality of model driving paths for the motor vehicle (12) based on the determined components of the future driving maneuver for the motor vehicle (12); determining a driving path for the motor vehicle (12) that is intended to be followed by the motor vehicle (12) on a further journey from the plurality of model driving paths for the motor vehicle (12); updating the information related to the current driving situation of the motor vehicle (12) and / or the given environmental data; Adapting the driving path for the motor vehicle (12) using an objective function and based on the updated environmental data provided and / or based on the updated information related to the current driving situation of the motor vehicle (12). A control system (10) at least configured and intended to: [Appendix 2] 10. The control system (10) of claim 1, configured and intended to determine the driving path from the plurality of model driving paths using an objective function that is the same as the objective function for adapting the driving path for the automotive vehicle (12). [Appendix 3] 10. The control system (10) of claim 1 or 2, wherein the information related to the current driving situation of the motor vehicle (12) includes at least a lateral distance of the motor vehicle (12) from its currently used lane, and the control system (10) is also configured and intended to determine the components of the future driving maneuver based on the lateral distance of the motor vehicle (12) from its currently used lane, such as lane keeping or lane changing. [Appendix 4] 10. The control system (10) of claim 3, wherein the information relating to the current driving situation of the motor vehicle (12) also includes a longitudinal distance of the motor vehicle (12) from a further motor vehicle (28) along its currently used lane, and the control system (10) is configured and intended to determine further components of the future driving maneuver based on the determined components of the future driving maneuver and / or based on the longitudinal distance of the motor vehicle (12) from the further motor vehicle (28). [Appendix 5] 5. The control system (10) of any one of appendices 1 to 4, further configured and intended to determine the information related to the current operating situation of the motor vehicle (12) based on the given environmental data in the form of discrete sampled values. [Appendix 6] 6. The control system (10) of claim 5, further comprising determining a plurality or all of the discrete sampled values ​​as nodes and / or edges of a graph; A control system (10) also configured and intended to create a connected graph from said determined nodes and / or edges. [Appendix 7] 7. The control system of claim 6, selecting the nodes and / or edges of the graph as stopping points for the traveled path; A control system also configured and intended to calculate the travel path for the automotive vehicle (12) by spline-based interpolation between the selected stopping points. [Appendix 8] 8. The control system (10) according to any one of appendices 1 to 7, further configured and intended to determine the updated information and / or the updated environmental data in the form of a continuous value. [Appendix 9] 9. The control system (10) of claims 5 and 8, further configured and intended to combine the updated information and / or the updated environmental data in the form of continuous values ​​with the information related to the current driving situation of the motor vehicle (12) in the form of discrete sampled values ​​in order to adapt the driving path for the motor vehicle (12). [Appendix 10] In the control system (10) described in Appendix 9, the combination of the information related to the current driving situation of the motor vehicle (12) in the form of discrete sampled values ​​with the updated information and / or the updated environmental data in the form of continuous values ​​includes at least initializing and / or reinitializing the adaptation of the driving path for the motor vehicle (12) using the objective function. [Appendix 11] 1. A control method for detecting lanes, road boundaries, road markings, and / or further motor vehicles in an area in front of, immediately to the side of, and / or behind a motor vehicle (12) based on environmental data obtained from at least one environmental sensor (14, 16, 18) arranged on the motor vehicle (12), the method being particularly performed by a control system (10) according to any one of claims 1 to 10, determining information related to a current operating situation of the motor vehicle (12) based on the environmental data provided; determining at least one component of a future driving maneuver for the motor vehicle (12) based on the information related to the current driving situation of the motor vehicle (12); determining a plurality of model driving paths for the motor vehicle (12) based on the determined components of the future driving maneuver for the motor vehicle (12); determining a driving path for the motor vehicle (12) from the plurality of model driving paths for the motor vehicle (12) that is intended to be followed by the motor vehicle (12) on a further journey; updating the information related to the current driving situation of the motor vehicle (12) and / or the given environmental data; adapting the driving path for the motor vehicle (12) using an objective function and based on the updated environmental data provided and / or based on the updated information related to the current driving situation of the motor vehicle (12); A control method comprising: [Appendix 12] An automotive vehicle (12) comprising a control system according to any one of claims 1 to 10.

Claims

1. 1. A control system (10) configured and intended for use with an automotive vehicle (12) to detect lanes, road boundaries, road markings, and / or further automotive vehicles in areas (22, 24, 26) in front of, immediately to the side of, and / or behind the automotive vehicle (12) based on environmental data obtained from at least one environmental sensor (14, 16, 18) disposed on the automotive vehicle, the at least one environmental sensor being configured to provide an electronic controller (20) of the control system (10) with the environmental data representative of the areas in front of, immediately to the side of, and / or behind the automotive vehicle (12), the control system (10) comprising: determining information related to a current operating situation of the motor vehicle (12) based on the given environmental data; determining at least one component of a future driving maneuver for the motor vehicle (12) based on the information related to the current driving situation of the motor vehicle (12); determining a plurality of model driving paths created using discrete values ​​consisting of a plurality of points in the traveling direction of the automobile vehicle (12) as stop points based on the determined components of the future driving operation of the automobile vehicle (12); determining a driving path for the motor vehicle (12) that is intended to be followed by the motor vehicle (12) on a further journey from the plurality of model driving paths for the motor vehicle (12); updating the information related to the current driving situation of the motor vehicle (12) and / or the given environmental data; Adapting the driving path for the motor vehicle (12) using an objective function and based on the given updated environmental data and / or based on the updated information related to the current driving situation of the motor vehicle (12). is at least constructed and intended to The control system (10) is also configured and intended to determine the updated information and / or the updated environmental data in the form of continuous values, and to adapt the driving path for the motor vehicle (12) by combining the updated information and / or the updated environmental data in the form of continuous values ​​with the driving path determined from the plurality of model driving paths created using discrete values ​​consisting of a plurality of points in the direction of travel of the motor vehicle (12) as stopping points, thereby adapting the driving path in the form of continuous values.

2. 2. The control system (10) of claim 1, configured and intended to determine the driving path from the plurality of model driving paths using an objective function that is the same as the objective function for adapting the driving path for the automotive vehicle (12).

3. 3. The control system (10) of claim 1 or 2, wherein the information related to the current driving situation of the motor vehicle (12) includes at least a lateral distance of the motor vehicle (12) from a left or right sign of its currently used lane, and the control system (10) is also configured and intended to determine the components of the future driving maneuver based on the lateral distance of the motor vehicle (12) from a left or right sign of its currently used lane as lane keeping or lane changing.

4. 4. The control system (10) of claim 3, wherein the information relating to the current driving situation of the motor vehicle (12) also includes the longitudinal distance of the motor vehicle (12) along its currently used lane from a further motor vehicle (28), and the control system (10) is configured and intended to determine the further components of the future driving maneuver based on the determined components of the future driving maneuver and / or based on the longitudinal distance of the motor vehicle (12) from the further motor vehicle (28).

5. 2. A control system (10) according to claim 1, comprising determining discrete sampled values ​​of a plurality or all of the nodes and / or edges of the graph; and is also configured and intended to create a connected graph from said determined nodes and / or edges. A control system (10).

6. 6. The control system of claim 5, selecting the nodes and / or edges of the graph as stopping points for the travel path; A control system also configured and intended to calculate the driving path for the automotive vehicle (12) by spline-based interpolation between the selected stopping points.

7. In the control system (10) described in any one of claims 1 to 6, the combination of the driving path determined from the plurality of model driving paths created using discrete values ​​consisting of a plurality of points in the direction of travel of the automobile vehicle (12) as stopping points with the updated information in the form of continuous values ​​and / or the updated environmental data in the form of continuous values ​​at least includes initializing and / or reinitializing the adaptation of the driving path for the automobile vehicle (12) using the objective function.

8. 8. A control method performed by a control system (10) according to any one of claims 1 to 7, in a motor vehicle (12) for detecting lanes, road boundaries, road markings and / or further motor vehicles in areas ahead, immediately to the sides and / or behind the motor vehicle (12) based on environmental data obtained from at least one environmental sensor (14, 16, 18) arranged in the motor vehicle (12), comprising: determining information related to a current driving situation of the motor vehicle (12) based on the given environmental data; determining at least one component of a future driving maneuver for the motor vehicle (12) based on the information related to the current driving situation of the motor vehicle (12); determining a plurality of model driving paths based on the determined components of the future driving maneuver of the automobile vehicle (12), the model driving paths being created as stop points at discrete values ​​consisting of a plurality of points in the traveling direction of the automobile vehicle (12); determining a driving path for the motor vehicle (12) from the plurality of model driving paths for the motor vehicle (12) that is intended to be followed by the motor vehicle (12) on a further journey; updating the information related to the current driving situation of the motor vehicle (12) and / or the given environmental data; adapting the driving path for the motor vehicle (12) using an objective function and based on the given updated environmental data and / or based on the updated information related to the current driving situation of the motor vehicle (12), the step including: adapting the driving path for the motor vehicle (12) in the form of continuous values ​​by combining the updated information and / or the updated environmental data in the form of continuous values ​​with the driving path determined from the plurality of model driving paths created using discrete values ​​consisting of a plurality of points in the direction of travel of the motor vehicle (12) as stopping points; A control method comprising:

9. An automotive vehicle (12) comprising a control system according to any one of claims 1 to 7.

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