Method for determining trajectory, control device and motor vehicle

Abstract

The invention relates to a method for ascertaining a trajectory of a vehicle, in which a target point, a time interval and a further time interval are ascertained, a trajectory of the vehicle and a further vehicle and a further trajectory are ascertained, a tuple is formed therefrom, and the tuple is evaluated by means of a cost function. The invention also relates to an associated control device and to an associated motor vehicle.

Description

[0001] The invention relates to a method for determining a vehicle trajectory, an associated control device and an associated motor vehicle.

[0002] Until now, vehicles in traffic have been controlled mainly manually. In the future, the degree of automation will continue to increase, which can also be understood as the fact that the motor vehicle driver no longer has to control the vehicle at least sometimes, that is, the vehicle is controlled by electronic components, or the vehicle can also participate in traffic completely independently. This requires the rational planning of the route that the motor vehicle should travel. Such a route in combination with the associated time information is often also called a trajectory.

[0003] The object of the present invention is to provide a method for determining a vehicle trajectory which is implemented in an alternative manner or in an improved manner compared to known embodiments. In addition, the object of the present invention is to provide a related control device and a related motor vehicle.

[0004] According to the invention, this object is achieved by a method, a control device and a motor vehicle according to the respective main claims. Advantageous embodiments can be derived, for example, from the respective dependent claims. The content of the claims is given by explicit reference to the content of the description.

[0005] The present invention relates to a method for obtaining a vehicle trajectory. The method comprises the following steps:

[0006] - Confirm the target point,

[0007] - confirming a time interval and a further time interval, wherein the further time interval overlaps with the time interval,

[0008] - determine multiple vehicle trajectories that the vehicle can be expected to reach the target point within the time interval,

[0009] - detect at least one other vehicle,

[0010] - determining one or more further vehicle trajectories of the or each further vehicle with which the further vehicle can be expected to reach the target point within the further time interval,

[0011] - forming tuples, wherein each tuple contains the trajectory (T1) of the vehicle (10) and the further trajectory (T2) of the further vehicle (20) or each trajectory (T2) of each further vehicle (20),

[0012] - evaluate the tuple with the help of a cost function,

[0013] -find the tuple in which the cost function has an extreme value as the final tuple, and

[0014] -Confirm vehicle trajectories included in the final tuple.

[0015] This method makes it possible to determine a vehicle trajectory in a rational and efficient manner, with which the vehicle can, for example, pass through a predefined area, such as an intersection. In particular, other vehicles that could potentially collide with the vehicle are also taken into account. This allows for collaborative planning of trajectories, so that the vehicle can better fit into the traffic flow.

[0016] The method is usually carried out in such a way that trajectories and / or further trajectories are not exchanged or coordinated with other vehicles. The further trajectory of the final tuple is usually not communicated to other vehicles either. The method is therefore a method for finding the own trajectory by the vehicle itself and not for determining further trajectories for other vehicles. The calculation of the further trajectory is especially intended to take into account possible behaviors of other vehicles when calculating the own trajectory.

[0017] The trajectory determined in the last-mentioned step can in particular be a trajectory ascertained within the scope of the method.

[0018] In particular, the destination point can be selected such that the vehicle has travelled a certain distance and / or has passed a certain area, in particular a critical area (such as, for example, a junction or a road junction) by the time it reaches the destination point, and / or such that the destination point is at the beginning of a critical area. In particular, the vehicle can then pass through an area that is usually also passed by other vehicles, with which it can collide. This is the case, for example, at a junction or a road junction.

[0019] In particular, the trajectory can be determined in such a way that the vehicle can reach the target point while taking into account conditions such as lane boundaries or obstacles. The same applies to further trajectories. In particular, all possible trajectories or further trajectories can be determined here.

[0020] The other vehicle or the other vehicles usually at least approximately pass through the same area as the vehicle. With regard to the trajectory and the other trajectory, all possible trajectories can be determined respectively, especially taking into account a preset resolution and / or a preset minimum distance between the trajectories. In particular, the extreme value of the cost function can be a high point or a low point. In particular, the cost function can map the costs of the corresponding trajectory and the other trajectory in the tuple, for example, related to time consumption and / or energy consumption and other obstructions or dangers that may be caused to other traffic participants. In particular, the less time consumption and / or energy consumption required for the trajectory and the other trajectory in a specific tuple, and the less danger or obstruction caused to other traffic participants, the greater the value of the cost function may be, at least in terms of magnitude.

[0021] In principle, it should be mentioned that a tuple contains at least one track and at least one further track, wherein a tuple may also contain a plurality of further tracks. Unless otherwise stated, variants which differ only in the number of further tracks are considered equivalent in principle here, even if they are not mentioned separately in each case.

[0022] In particular, the final tuple can be used to use the trajectory contained therein for automated control of the vehicle. Typically, the further trajectories contained therein are discarded after execution of the method.

[0023] Preferably, the earliest limit of the time interval is calculated in such a way that the vehicle can reach the destination point as early as possible. This can mean, in particular, that first it is determined how long it takes for the vehicle to reach the destination point at least, that is, how long it takes to reach the destination point in the best conceivable case consistent with given framework conditions (such as, for example, lane boundaries or speed limits). Subsequently, the destination point thus determined can be used as the earliest limit of the time interval. In particular, the earliest limit of the other time interval can be calculated in such a way that the earliest limit of the other time interval is earlier than the earliest limit of the time interval. In particular, a predefined distance can be applied between the earliest limit of the time interval and the earliest limit of the other time interval. The predefined distance can also be variable, wherein the predefined distance can, for example, depend on the duration of the time interval and / or other conditions.

[0024] The time interval can in particular have a predefined duration. The duration can be the same for all executions of the method or it can also depend on certain other conditions, such as, for example, a speed limit or traffic flow.

[0025] Advantageously, the further time interval can have the duration of the time interval, wherein preferably, at the limits of the further time interval, it is additionally extended by a tolerance value, which corresponds, for example, to a pre-set, temporal safety distance. The safety distance can be the same at both limits, or different safety distances can be pre-set at the limits.

[0026] Preferably, all vehicle trajectories that the vehicle can expect to reach the target point within the time interval are determined. Thus, as many trajectories as possible can be used, so that the best possible trajectory can also be selected from these trajectories. Alternatively, only a part of these trajectories can also be determined.

[0027] In particular, one or more further vehicles can be detected by environmental sensors and / or by vehicle-to-X communication. This allows for simple and reliable detection of further vehicles using known techniques. For example, the environmental sensors can be cameras, radar devices or ultrasonic sensors.

[0028] Preferably, all further trajectories of each further vehicle are determined, with which further vehicles can be expected to arrive at the target point within further time intervals. The embodiments already given above for the trajectories apply here. Alternatively, only a portion of these further trajectories can also be determined for each further vehicle or a portion of a plurality of further vehicles.

[0029] Advantageously, a tuple can be formed for all trajectories of all vehicles or for each combination of all further trajectories. Thus, the number of tuples formed from existing trajectories and further trajectories is maximized, so that the best possible trajectory can be expected.

[0030] As a rule, further trajectories of further vehicles contained in the final tuple are discarded and are generally used only to optimize the ascertainment process within the framework of calculating the trajectory of the vehicle carrying out the method.

[0031] In particular, the trajectories can be calculated with a predefined spatial resolution and / or a predefined temporal resolution. In particular, it can be predefined how far two trajectories must differ in space and / or time in order to be considered separately. In particular, this can also apply to further trajectories. The predefined spatial resolution specifies in particular how far the two trajectories must be apart from one another in space, in particular at at least one point along the respective trajectory and / or along at least one predefined route. The temporal resolution can specify in particular how far the two trajectories must differ in terms of reaching a target point in order to be considered as a separate trajectory or as a further trajectory.

[0032] In particular, the target point can be confirmed based on the identified situation. The identified situation can, in particular, indicate what kind of environment the vehicle is in. For example, a crossroads, junctions, straight roads, curves or other environmental variants can be distinguished at this time. In particular, the situation can be identified based on a path, a map and / or one or more dynamic objects. For example, the path can be confirmed in that the navigation system predetermines the desired destination to which the vehicle should go during the journey. For example, the map can be an electronic map present in the vehicle. For example, the dynamic objects can be other vehicles, or other traffic participants (such as pedestrians) or temporarily appearing obstacles. Dynamic objects can be taken into account when selecting the situation. In particular, the situation can be identified as one of a plurality of predefined situations. For example, the predefined situations can be stored in the vehicle.

[0033] Advantageously, the tuple is formed in a collision-free manner. This means, in particular, that the trajectory recorded in the tuple and the further trajectories have been selected such that no collision can be expected when the vehicle travels over the trajectory and each further vehicle travels over the respective further trajectory in the tuple. This also applies to the case where only one further vehicle and, respectively, only one further trajectory are present.

[0034] According to one embodiment, the cost function has values ​​that can lead to extreme values ​​only in conflict-free tuples. Conflict-free tuples are in particular the tuples just mentioned, that is, in which no collision between the vehicle and one or more further vehicles can be expected when driving over the respective trajectory and the respective further trajectory. This excludes the declaration of tuples in which a collision is expected as final tuples. In particular, the cost function can be defined accordingly. Thus, the formation of conflict-free tuples can be omitted.

[0035] The invention further relates to a control device which is configured to carry out the method described herein. With regard to the method, all described embodiments and variants can be used. The control device can in particular have a storage device and a processor device, wherein the storage device contains a program code, wherein the processor device carries out the method described herein when executing the program code. The invention further relates to a non-volatile machine-readable storage medium, on which a program code is contained, wherein when the program code is executed by processing, the processor carries out the method according to the invention. With regard to the method, all described embodiments and variants can be used.

[0036] The invention further relates to a motor vehicle having a control device described here. With regard to the described control device, in particular with regard to the method implemented therein, all embodiments and variants described here can be used.

[0037] In general, it should be mentioned that there are a number of different maneuver planning algorithms These maneuver planning algorithms are based, for example, on brute force solutions, on intelligent path planning or on scenario analysis. However, the process described here makes it possible to carry out collaborative maneuver planning, in which not only the variables of the own route are calculated, but also the variables of the vehicles that can cooperate, so that an acceptable maneuver solution is calculated for all participating vehicles in a short time, even if other vehicles must deviate from their optimal trajectory in order to achieve a better maneuver result. In particular, the process described here can be implemented with a clear computational effort, since it is avoided that a plurality of impossible trajectories have to be considered and evaluated again and again.

[0038] In principle, in particular, trajectories or plans can be generated first and then evaluated and selected. A characteristic of cooperative maneuver planners is that the plan selection offers a variety of plans, depending on whether the aspect of seeking cooperation, the aspect of executing cooperation or the aspect of providing cooperation is taken into account. A disadvantage of the known embodiments is that the first step, i.e. the generation of plans or trajectories, does not take the specific situation into account, but rather all physically possible trajectories of all participating vehicles are determined by a brute force approach and plans are generated for them. Even with parallel computing, the variants of the generated trajectories are necessarily limited to such an extent that it is currently almost impossible to obtain a good trajectory for a given situation in the case of long planning cycles, such as, for example, a reasonable stop at a stop line halfway in the case of an intersection.

[0039] This is where the process described here begins. Plan generation provides better trajectories while reducing the generated plans. In particular, scenarios can be derived from paths, maps and / or dynamic objects. It can be checked whether this scenario and the route you want exist as templates. For example, possible predefined trajectories can be calculated for your own vehicle and third-party vehicles that may be involved in the collaboration by means of different speed profiles for this scenario. In this case, the requirements for collaboration can be taken into account in particular. Collaboration with other vehicles means, for example, that the arrival time at the conflict point must be changed. In particular, it can be decided later which combination of arrival times is ultimately suitable. For each vehicle considered, this is done in particular as follows:

[0040] - Find the shortest arrival time Tk at the conflict point (e.g., at the overlapping area of ​​an intersection or at the end of a highway entrance),

[0041] - Define an arrival time interval [Tk, ..., Tk+Offset], where Offset may comprise 10s, for example. For example, the interval is sampled in increments of 0.5s. In other vehicles, this may also be done more coarsely.

[0042] - The best trajectory can be calculated for each arrival time within this interval. This can be done physically accurately using situation knowledge (speed profiles) or also by other methods in the literature.

[0043] - The inter-arrival time can also be used to determine the planning period. It is usually easier to evaluate plans if the trajectories have the same length in time.

[0044] The resulting trajectory can then form the result of the first step. In particular, the first step can be to generate a plan. Some of the brute force approach of the known methods can be retained with a significantly reduced resolution, since this can cover special cases that may not have been taken into account in the situation analysis. It is also possible to use a general maneuver plan that generates a trajectory for the host vehicle in all cases and thus ensures that the cooperative planner always provides a result for the host vehicle as well. All of this can then fill the result pool of the first step.

[0045] In particular, the procedure described here can significantly simplify maneuver planning and require less computing time.

[0046] A person skilled in the art will derive additional features and advantages from the embodiments described below with reference to the accompanying drawings. In the drawings:

[0047] Figure 1 : Shows an intersection with two vehicles.

[0048] Figure 1 The intersection K is shown, at which the first road S1 and the second road S2 intersect. A vehicle 10 is traveling on the first road S1. Another vehicle 20 is traveling on the second road S2. Both the vehicle 10 and the other vehicle 20 are moving toward the intersection K and want to pass through it. Here, the vehicle 10 is to turn left and thereby merge into the second road S2, while the other vehicle 20 is to remain on the second road S2 even after it has passed the intersection K.

[0049] The vehicle 10 has a control device 15 which is configured to carry out the method described here according to a possible implementation. The vehicle also has a camera 12 , shown as an example, which is a surroundings sensor and can detect other vehicles 20 for this purpose.

[0050] In a possible implementation of the method, a target point ZP is first determined, which is selected so that the two vehicles 10, 20 reach the target point after passing the intersection K along their desired routes, and the target point corresponds to, for example, the point where the routes of the vehicles 10, 20 first meet. Next, the time interval and the further time interval are calculated, wherein the earliest limit of the time interval is calculated so that the vehicle 10 can arrive as early as possible. Here, for example, the geometric conditions of the intersection K and the two roads S1, S2 and other conditions such as speed limits can be taken into account. The duration of the time interval is determined according to a predefined value. The further time interval is determined so that the earliest limit of the further time interval is earlier than the earliest limit of the time interval by a predefined distance.

[0051] Now, taking into account the given spatial resolution and time resolution, all possible trajectories T1 of the vehicle 10 that can reach the target point ZP within the time interval are calculated. Similarly, all other trajectories T2 of other vehicles 20 that can reach the target point ZP within the other time interval are calculated. Tuples are formed by the trajectory T1 and the other trajectory T2, wherein all combinations of trajectories produce tuples respectively. The tuples are evaluated with the help of a pre-set cost function. The faster the two vehicles 10, 20 pass through the intersection K, and the smaller the danger or obstruction caused to other traffic participants at this time, the greater the value of the cost function. The cost function is also implemented as follows: if there is a risk of collision between the two vehicles 10, 20, the value of the cost function is zero. After the value of the cost function is obtained for each tuple in the tuple, it is found that in which tuple the value of the cost function is the largest. Subsequently, the tuple is used as the final tuple, wherein the trajectory of the vehicle 10 contained therein is used to control the vehicle 10. The other trajectory T2 contained therein is discarded.

[0052] The described procedure makes it possible to implement a collaborative trajectory planning which requires significantly less computing power than known approaches, such as, for example, brute force approaches.

[0053] In general, it should be noted that vehicle-to-X communication is particularly understood as direct communication between vehicles and / or between vehicles and infrastructure. That is, for example, vehicle-to-X communication can be vehicle-to-infrastructure communication or vehicle-to-infrastructure communication. If communication between vehicles is mentioned within the framework of this application, the communication can in principle be carried out within the framework of such vehicle-to-infrastructure communication, that is, the vehicle-to-infrastructure communication is usually carried out without the aid of a mobile wireless network or a similar external infrastructure, and the vehicle-to-infrastructure communication can therefore be distinguished from other solutions based on, for example, a mobile wireless network. For example, vehicle-to-X communication can be carried out using the IEEE 802.11p or IEEE 1609.4 standards. Other examples of communication technologies include LTE-V2X, 5G-V2X, C-V2X, WLAN, WiMax, UWB or Bluetooth. Vehicle-to-X communication can also be referred to as C2X communication. The subfield can be referred to as C2C (vehicle-to-car communication) or C2I (vehicle-to-infrastructure communication). However, the present invention explicitly does not exclude vehicle-to-X communication, for example, with the aid of a mobile wireless network.

[0054] The above-mentioned steps of the method according to the invention can be carried out in the order given. However, as long as it is technically reasonable, the steps can also be carried out in a different order. The method according to the invention can be carried out in one of its embodiments, for example using a specific combination of steps, so that no further steps are carried out. However, in principle, further steps can also be carried out, even those steps that are not mentioned.

[0055] It should be noted that in the claims and the description, features may be described in combination, for example to facilitate understanding, even though these features can also be used separately from each other. Those skilled in the art recognize that such features can also be combined with other features or feature combinations independently of each other.

[0056] The references in the dependent claims may characterize preferred combinations of individual features, but do not exclude other combinations of features.

[0057] List of Reference Numerals

[0058] K Junction

[0059] S1 First Road

[0060] S2 Second Road

[0061] ZP Target Point

[0062] T1 trajectory

[0063] T2 Another track

[0064] 10 Vehicles

[0065] 12 Camera

[0066] 15 Control Device

[0067] 20 Other vehicles

Claims

1. A method for determining a trajectory (T1) of a vehicle (10), wherein the method comprises the following steps: - confirm the target point (ZP), - confirming a time interval and a further time interval, wherein said further time interval overlaps with said time interval, - determining a plurality of trajectories (T1) of the vehicle (10) with which the vehicle (10) can be expected to reach the target point (ZP) within the time interval, - detecting at least one further vehicle (20), - determining one or more further trajectories (T2) of the or each further vehicle (10) with which the further vehicle (10) can be expected to reach the target point (ZP) within the further time interval, - forming tuples, each tuple containing the trajectory (T1) of the vehicle (10) and the further trajectory (T2) of the further vehicle (20) or each trajectory (T2) of each further vehicle (20), - evaluating said tuples by means of a cost function, - obtaining a tuple in which the cost function has an extreme value as a final tuple, and - confirming the trajectory (T1) of the vehicle (10) contained in the final tuple.

2. The method according to claim 1, - wherein the earliest limit of the time interval is calculated in such a way that the vehicle (10) can reach the destination point (ZP) as early as possible, and / or wherein the earliest limit of the further time interval is calculated in such a way that the earliest limit of the further time interval is earlier than the earliest limit of the time interval.

3. The method according to any one of the preceding claims, - wherein said time interval has a predetermined duration.

4. The method according to any one of the preceding claims, - wherein all trajectories (T1) of the vehicle (10) are determined with which the vehicle (10) can be expected to reach the target point (ZP) within the time interval.

5. The method according to any one of the preceding claims, - wherein one or more further vehicles (20) are detected via environmental sensors (12) and / or via vehicle-to-X communication.

6. The method according to any one of the preceding claims, - all further trajectories (T2) of each further vehicle (20) are determined, with which the further vehicle (20) can be expected to reach the destination point (ZP) within the further time interval.

7. The method according to any one of the preceding claims, - where each combination of all trajectories (T1, T2) for all vehicles (10, 20) forms a tuple.

8. The method according to any one of the preceding claims, - wherein the further trajectory (T2) of the further vehicle (20) contained in the final tuple is discarded.

9. The method according to any one of the preceding claims, wherein the trajectory (T1, T2) is calculated with a predetermined spatial resolution and / or a predetermined temporal resolution.

10. The method according to any one of the preceding claims, - wherein the target point (ZP) is determined based on the identified situation.

11. The method according to claim 10, - wherein the situation is identified based on a path, a map and / or one or more dynamic objects.

12. The method according to claim 10 or 11, - wherein the situation is identified as one of a plurality of pre-defined situations.

13. The method according to any one of the preceding claims, - wherein said tuples are formed in a conflict-free manner, and / or - wherein the cost function has values ​​that can lead to extreme values ​​only in conflict-free tuples.

14. A control device (15) configured to carry out the method according to one of the preceding claims.

15. A motor vehicle (10) having a control device (15) according to claim 14.

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