Trajectory planning method and device for unmanned vehicle and unmanned vehicle

Abstract

The present disclosure provides a trajectory planning method, device and unmanned vehicle for an unmanned vehicle, which can be applied to the fields of unmanned driving, automatic driving and unmanned vehicle technology. The trajectory planning method for the unmanned vehicle includes: determining a planned driving trajectory of the unmanned vehicle in response to a trajectory planning instruction, wherein the trajectory planning instruction is used to instruct to update the destination of a first planned trajectory from a first destination to a second destination, and the planned driving trajectory represents a first trajectory segment in which the unmanned vehicle continues to travel along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction; determining a second trajectory segment according to the trajectory planning instruction, wherein the second trajectory segment represents a trajectory segment obtained by trajectory planning with the second destination as the destination based on the trajectory planning instruction; and generating a target planned trajectory according to the planned driving trajectory and the second trajectory segment.

Description

Technical Field

[0001] The present disclosure relates to the technical fields of unmanned driving, automatic driving and unmanned vehicles, and more specifically, to a trajectory planning method and device for an unmanned vehicle and an unmanned vehicle. Background Art

[0002] With the development of artificial intelligence technology, unmanned vehicles have emerged to improve the autonomy of vehicle operation.

[0003] When a driverless car receives a signal indicating a destination change during driving, it must perform a new round of global planning while driving to determine a new global trajectory. Therefore, effectively ensuring the accuracy of global planning is an urgent issue. Summary of the Invention

[0004] The present disclosure provides a trajectory planning method and device for an unmanned vehicle, and an unmanned vehicle.

[0005] According to one aspect of the present disclosure, a trajectory planning method for an unmanned vehicle is provided, comprising: determining a planned driving trajectory of the unmanned vehicle in response to a trajectory planning instruction, wherein the trajectory planning instruction is used to instruct that a destination of a first planned trajectory be updated from a first destination to a second destination, and the planned driving trajectory represents a first trajectory segment in which the unmanned vehicle continues to travel along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction; determining a second trajectory segment according to the trajectory planning instruction, wherein the second trajectory segment represents a trajectory segment obtained by trajectory planning with the second destination as the destination based on the trajectory planning instruction; and generating a target planned trajectory according to the planned driving trajectory and the second trajectory segment.

[0006] According to another aspect of the present disclosure, a trajectory planning device for an unmanned vehicle is provided, comprising: a first determination module, configured to determine a planned driving trajectory of the unmanned vehicle in response to a trajectory planning instruction, wherein the trajectory planning instruction is configured to instruct that the destination of the first planned trajectory be updated from the first destination to the second destination, and the planned driving trajectory represents a first trajectory segment in which the unmanned vehicle continues to travel along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction; a second determination module, configured to determine a second trajectory segment according to the trajectory planning instruction, wherein the second trajectory segment represents a trajectory segment obtained by trajectory planning with the second destination as the destination based on the trajectory planning instruction; and a generation module, configured to generate a target planned trajectory based on the planned driving trajectory and the second trajectory segment.

[0007] According to another aspect of the present disclosure, an unmanned vehicle is provided, comprising: a trajectory planning device for the unmanned vehicle.

[0008] According to another aspect of the present disclosure, an electronic device is provided, comprising: at least one processor and at least one memory; the memory stores executable instructions of the processor; and the processor is configured to execute any of the above methods.

[0009] According to another aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a processor, the steps of any of the above methods are implemented.

[0010] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The above and other objects, features and advantages of the present disclosure will become more apparent through the following description of the embodiments of the present disclosure with reference to the accompanying drawings, in which:

[0012] Figure 1 The system architecture of the trajectory planning method for an unmanned vehicle according to an embodiment of the present disclosure is schematically shown;

[0013] Figure 2 The following schematically shows a flow chart of a trajectory planning method for an unmanned vehicle according to an embodiment of the present disclosure;

[0014] Figure 3 Schematically illustrates an example of a process for determining a planned driving trajectory of an unmanned vehicle based on an expected planning time according to an embodiment of the present disclosure;

[0015] Figure 4 The following schematically illustrates an example process of determining a planned driving trajectory of an unmanned vehicle based on an expected planned distance according to an embodiment of the present disclosure;

[0016] Figure 5A An example schematic diagram of a trajectory planning process in related technology according to an embodiment of the present disclosure is schematically shown;

[0017] Figure 5B An example diagram of a trajectory planning process according to an embodiment of the present disclosure is schematically shown;

[0018] Figure 5C An example schematic diagram of a trajectory planning process in related art according to another embodiment of the present disclosure is schematically shown;

[0019] Figure 5DAn example diagram of a trajectory planning process according to another embodiment of the present disclosure is schematically shown;

[0020] Figure 6 An example schematic diagram of a trajectory planning process for an unmanned vehicle according to an embodiment of the present disclosure is schematically shown;

[0021] Figure 7 A block diagram schematically illustrates a trajectory planning device for an unmanned vehicle according to an embodiment of the present disclosure; and

[0022] Figure 8 A block diagram of an electronic device suitable for implementing a trajectory planning method for an unmanned vehicle according to an embodiment of the present disclosure is schematically shown. DETAILED DESCRIPTION

[0023] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the detailed description below, for ease of explanation, many specific details are set forth to provide a comprehensive understanding of the embodiments of the present disclosure. However, it is apparent that one or more embodiments may also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessary confusion of the concepts of the present disclosure.

[0024] The terms used herein are only for describing specific embodiments and are not intended to limit the present disclosure. The terms "comprise," "include," etc. used herein indicate the presence of the features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0025] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0026] When expressions such as "at least one of A, B and C, etc." are used, they should generally be interpreted in accordance with the meaning of the expression commonly understood by those skilled in the art (for example, "a system having at least one of A, B and C" should include but is not limited to a system having A alone, B alone, C alone, A and B, A and C, B and C, and / or A, B, C, etc.).

[0027] When global planning needs to be re-performed during the driving process of an unmanned vehicle, the old global trajectory is an important reference line that the global planning relies on. That is, appropriate offsets can be made in real time based on the old global trajectory to obtain a new global trajectory, and the driving task can be completed based on the new global trajectory.

[0028] While the aforementioned global planning method ensures the vehicle's operational efficiency, the global planning process takes approximately five seconds, during which the vehicle travels some distance along the old global trajectory. As a result, by the time planning for the new global trajectory is complete, the vehicle may have already moved away from the new global trajectory, resulting in a lack of a reference line for global planning. Furthermore, even if the vehicle remains near the new global trajectory at the time global planning is complete, it may still deviate from the new global trajectory due to random lateral deviations required for uniform rolling and positioning errors, similarly preventing the global trajectory from being used properly.

[0029] To this end, the disclosed embodiments propose a trajectory planning scheme for an unmanned vehicle. For example, in response to a trajectory planning instruction, a planned driving trajectory of the unmanned vehicle is determined, wherein the trajectory planning instruction is used to instruct the destination of a first planned trajectory to be updated from the first destination to the second destination, and the planned driving trajectory represents a first trajectory segment in which the unmanned vehicle continues to travel along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction; based on the trajectory planning instruction, a second trajectory segment is determined, wherein the second trajectory segment represents a trajectory segment obtained by trajectory planning with the second destination as the destination based on the trajectory planning instruction; and, based on the planned driving trajectory and the second trajectory segment, a target planned trajectory is generated.

[0030] According to an embodiment of the present disclosure, in the process of dynamically adjusting the driving trajectory, by determining the first trajectory segment that continues to travel along the original first planned trajectory after receiving the trajectory planning instruction, and planning the second trajectory segment from the current position to the new second destination, and then integrating them into the target planned trajectory, the splicing point of the first trajectory segment and the second trajectory segment can be accurately calculated, ensuring that the position of the unmanned vehicle is in the overlapping part of the old global trajectory and the new global trajectory when the global planning is completed, thereby ensuring the smoothness and continuity of the trajectory conversion, effectively reducing unnecessary driving path adjustments, avoiding the problems of the new global trajectory being far away from the unmanned vehicle and the instantaneous excessive lateral deviation, improving the flexibility and adaptability of the unmanned vehicle when facing temporary changes in destination, and ensuring that the unmanned vehicle can quickly and accurately drive according to the new instructions, thereby optimizing driving efficiency.

[0031] In the technical solution of the present invention, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0032] In the technical solution of the present invention, the user's authorization or consent is obtained before obtaining or collecting the user's personal information.

[0033] Figure 1The system architecture of the trajectory planning method for an unmanned vehicle according to an embodiment of the present disclosure is schematically shown. Figure 1 The examples shown are merely examples of system architectures to which the embodiments of the present disclosure may be applied, to help those skilled in the art understand the technical content of the present disclosure, but do not mean that the embodiments of the present disclosure may not be used in other devices, systems, environments or scenarios.

[0034] In one example, the system architecture 100 according to this embodiment may include a vehicle 101 and a control device 102 deployed on the vehicle 101 .

[0035] The control device 102 can be used to determine the planned driving trajectory of the unmanned vehicle in response to the trajectory planning instruction; determine the second trajectory segment according to the trajectory planning instruction; and generate a target planned trajectory according to the planned driving trajectory and the second trajectory segment.

[0036] In this case, the trajectory planning method for the unmanned vehicle provided in the embodiment of the present disclosure can generally be executed by the control device 102 of the vehicle 101. Accordingly, the trajectory planning device for the unmanned vehicle provided in the embodiment of the present disclosure can generally be set in the control device 102 of the vehicle 101.

[0037] In another example, the system architecture 100 according to this embodiment may include a vehicle 101, a network 103, and a server 104. The network 103 is a medium for providing a communication link between the vehicle 101 and the server 104. The network 103 may include a wired communication network and a wireless communication network.

[0038] In this case, the trajectory planning method for the unmanned vehicle provided in the embodiments of the present disclosure may also be executed by a server 104 or a server cluster that is different from the control device 102 of the vehicle 101 and that is capable of communicating with the vehicle 101. Accordingly, the trajectory planning device for the unmanned vehicle provided in the embodiments of the present disclosure may also be provided in a server 104 or a server cluster that is different from the control device 102 of the vehicle 101 and that is capable of communicating with the vehicle 101.

[0039] It should be understood that Figure 1 The number of vehicles, control devices, networks, and servers shown in the figure is merely illustrative. Any number of vehicles, control devices, networks, and servers may be used as needed.

[0040] It should be noted that the sequence numbers of the operations in the following method are only used to indicate the operation for the purpose of description, and should not be regarded as indicating the order in which the operations should be performed. Unless explicitly stated, the method does not need to be performed in the order shown.

[0041] Figure 2 The flowchart of the trajectory planning method of the unmanned vehicle according to the embodiment of the present disclosure is schematically shown.

[0042] like Figure 2 As shown, the unmanned vehicle trajectory planning method 200 includes operations S210 to S230.

[0043] In operation S210, in response to a trajectory planning instruction, a planned driving trajectory of the unmanned vehicle is determined, wherein the trajectory planning instruction is used to instruct to update the destination of the first planned trajectory from the first destination to the second destination, and the planned driving trajectory represents a first trajectory segment in which the unmanned vehicle continues to travel along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction.

[0044] In operation S220 , a second trajectory segment is determined according to the trajectory planning instruction, wherein the second trajectory segment represents a trajectory segment obtained by performing trajectory planning with the second destination as the destination based on the trajectory planning instruction.

[0045] In operation S230 , a target planned trajectory is generated according to the planned driving trajectory and the second trajectory segment.

[0046] The trajectory planning method provided by the present disclosure can be applied to regenerate a planned trajectory for an unmanned vehicle when the destination changes. The triggering method of the trajectory planning instruction can be configured according to actual business needs and is not limited here. For example, the trajectory planning instruction can be triggered when it is determined that the unmanned vehicle meets predetermined conditions. The trajectory planning instruction may refer to an instruction for instructing that the destination of the first planned trajectory is updated from the first destination to the second destination. The instruction may include direct instructions and indirect instructions. A direct instruction may refer to a method in which an operand is directly given in the instruction, that is, the execution of the direct instruction does not require additional steps or calculations to determine the operand. An indirect instruction may refer to a method in which an operand is not directly given, that is, the execution of the indirect instruction requires additional steps or calculations to determine the operand.

[0047] The first planned trajectory may refer to the current trajectory of the unmanned vehicle, and the end point of the first planned trajectory is the first destination, that is, the first destination may be understood as the original destination. The second destination may refer to the new destination.

[0048] After receiving the trajectory planning instruction, the current operating state of the unmanned vehicle can be determined. The current operating state may include at least one of the following: parking, forward movement, and backward movement. On this basis, whether trajectory splicing is required and the type of trajectory splicing can be determined according to the current operating state of the unmanned vehicle. The specific correspondence between the current operating state and the trajectory splicing type can be configured according to actual business needs and is not limited here. For example, when the current operating state of the unmanned vehicle is parking, it can be determined that trajectory splicing is not required. Alternatively, when the current operating state of the unmanned vehicle is forward movement, it can be determined that trajectory splicing is required and the trajectory splicing type is forward splicing. Alternatively, when the current operating state of the unmanned vehicle is backward movement, it can be determined that trajectory splicing is required and the trajectory splicing type is backward splicing.

[0049] After receiving the trajectory planning instruction, the planned driving trajectory can be determined. The determination method can be configured according to actual business needs and is not limited here. For example, sensors can be used to obtain surrounding environmental information in real time, path planning can be performed based on the environmental information, and the planned driving trajectory can be obtained based on the environmental information and the path planning results. The planned driving trajectory can represent the first trajectory segment that continues to follow the first planned trajectory with the first destination as the destination after the unmanned vehicle receives the trajectory planning instruction. That is, the starting point of the first trajectory segment is the position of the unmanned vehicle when it receives the trajectory planning instruction, the end point of the first trajectory segment is the first destination, and the first trajectory segment is a partial trajectory in the first planned trajectory.

[0050] After receiving the trajectory planning instruction, a second trajectory segment can be determined based on the trajectory planning instruction. The second trajectory segment can represent a trajectory segment obtained by performing trajectory planning based on the trajectory planning instruction with the second destination as the destination. That is, the starting point of the second trajectory segment is the end point of the planned driving trajectory, and the end point of the second trajectory segment is the second destination.

[0051] After obtaining the planned driving trajectory and the second trajectory segment, a target planned trajectory can be generated based on the planned driving trajectory and the second trajectory segment. The generation method can be configured based on actual business needs and is not limited here. For example, the generation method may include at least one of the following: an optimization-based trajectory generation method, an interpolation-based trajectory generation method, a sampling and search algorithm-based trajectory generation method, and a model predictive control-based trajectory generation method.

[0052] Optimization-based trajectory generation methods may refer to methods that describe the trajectory planning problem as an optimal control problem and use optimization methods to find the optimal motion trajectory. Interpolation-based trajectory generation methods may refer to methods that generate smooth trajectories by inserting a series of intermediate points between known path points. Sampling and search algorithm-based trajectory generation methods may refer to methods that use sampling and search algorithms to find trajectories that meet conditions in state space or configuration space. Model predictive control-based trajectory generation methods may refer to methods that optimize the vehicle's driving trajectory based on the vehicle model and predicted future environmental information.

[0053] In one example, the end point of the trajectory segment in which the unmanned vehicle continues to travel along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction can be used as the first reference point, and the starting point of the trajectory segment obtained by trajectory planning with the second destination as the destination can be used as the second reference point. Based on the first reference point and the second reference point, the first trajectory segment and the second trajectory segment are spliced ​​to obtain the target planned trajectory.

[0054] According to the embodiments of the present disclosure, in the process of dynamically adjusting the driving trajectory, by determining the first trajectory segment that continues to travel along the original first planned trajectory after receiving the trajectory planning instruction, and planning the second trajectory segment from the current position to the new second destination, and then integrating them into the target planned trajectory, the splicing point of the first trajectory segment and the second trajectory segment can be accurately calculated, thereby ensuring that the position of the unmanned vehicle is in the overlapping part of the old global trajectory and the new global trajectory when the global planning is completed, thereby ensuring the smoothness and continuity of the trajectory conversion, effectively reducing unnecessary driving path adjustments, avoiding the problems of the new global trajectory being away from the unmanned vehicle and the instantaneous excessive lateral deviation, improving the flexibility and adaptability of the unmanned vehicle when facing temporary changes in destination, ensuring that the unmanned vehicle can travel quickly and accurately according to the new instructions, thereby optimizing driving efficiency and improving the accuracy of the target planned trajectory.

[0055] In one example, trajectory planning instructions can be triggered when the unmanned vehicle meets predetermined conditions. These conditions can be configured based on actual business needs and are not limited here. They can be used to limit the timing of triggering a new global plan.

[0056] For example, the predetermined condition may be receiving a target instruction. The target instruction may be obtained through the unmanned vehicle's own monitoring or received from upstream. Target instructions may include a return instruction or a refueling instruction. A return instruction may be a command instructing the unmanned vehicle to stop its current mission and return to a designated location. For example, a return instruction may include destination information and time information. A refueling instruction may be a command instructing the unmanned vehicle to refuel at a designated location. For example, a refueling instruction may include the refueling location and amount of fuel to be refueled.

[0057] Alternatively, the predetermined condition may be that the distance between the current location of the unmanned vehicle and the location of the first destination is less than a preset distance threshold. The preset distance threshold can be configured based on actual business needs and is not limited here. For example, the preset distance threshold may be 2 meters. If the distance between the current location of the unmanned vehicle and the location of the first destination is less than 2 meters, the unmanned vehicle may be determined to have met the predetermined condition, thereby triggering a new global trajectory planning.

[0058] According to the embodiments of the present disclosure, by setting predetermined conditions to trigger trajectory planning instructions, the unmanned vehicle can plan its driving trajectory at the appropriate time, thereby avoiding unnecessary driving and waiting time and improving driving efficiency. At the same time, this flexible instruction triggering mechanism also enables the unmanned vehicle to better adapt to the needs of different scenarios.

[0059] In one example, the planned driving trajectory of the autonomous vehicle can be determined based on the expected planning time, current speed, maximum acceleration, and maximum speed. In this case, the planned driving trajectory of the autonomous vehicle can represent the maximum distance the autonomous vehicle travels during the expected planning time, accelerating at maximum acceleration to maximum speed, and using this distance as the length of the retained old global trajectory.

[0060] The expected planning time can be configured according to actual business needs and is not limited here. For example, the expected planning time can be a fixed value configured in advance or a dynamically changing value. For example, in the case where the expected planning time is a fixed value, the expected planning time can be obtained first, and the planned driving trajectory of the unmanned vehicle can be determined directly based on the expected planning time, maximum acceleration and maximum speed. Alternatively, in the case where the expected planning time is a dynamically changing value, the expected planning distance can be determined first, and the dynamically changing expected planning time can be determined based on the expected planning distance, and then the planned driving trajectory can be determined based on the expected planning time. The expected planning distance is the distance between the current position of the unmanned vehicle and the position of the second destination.

[0061] According to the embodiments of the present disclosure, by setting the expected planning time or the expected planning distance, the unmanned vehicle can automatically determine the optimal planned driving trajectory in a more targeted manner, thereby improving the flexibility of the planned driving trajectory, optimizing driving efficiency and energy consumption, and being able to adapt to the needs of different scenarios.

[0062] In the embodiment of the present disclosure, the planned driving trajectory can be determined according to the expected planning time. Figure 3 The process of determining the planned driving trajectory of the unmanned vehicle based on the expected planning time is further explained.

[0063] Figure 3 An example diagram of a process for determining a planned driving trajectory of an unmanned vehicle based on an expected planning time according to an embodiment of the present disclosure is schematically shown.

[0064] like Figure 3 As shown, in step 300, the method for determining the planned driving trajectory 310 can be as shown in the following equation (1). That is, the intermediate speed 301 can be determined based on the current speed, maximum acceleration, and expected planning time of the unmanned vehicle. The relationship between the intermediate speed 301 and the maximum speed 302 is determined. In operation S310, it is determined whether the intermediate speed is greater than the maximum speed.

[0065] If not, the planned driving trajectory 310 may be determined based on the first preset method.

[0066] If so, the planned driving trajectory 310 may be determined based on the second preset method.

[0067]

[0068] Among them, v represents the current speed of the unmanned vehicle, a max represents the maximum acceleration, t represents the expected planning time, v+a max t represents the intermediate speed, v max Indicates the maximum speed.

[0069] According to the embodiments of the present disclosure, the intermediate speed is calculated by considering the current speed, maximum acceleration and expected planning time of the unmanned vehicle, and the specific method of planning the driving trajectory is selected based on the relationship between the intermediate speed and the maximum speed of the unmanned vehicle. In this way, the planning method can be flexibly adjusted according to the speed conditions, which helps to optimize the driving trajectory and enable the unmanned vehicle to better cope with various complex and changeable driving environments, thereby improving driving efficiency.

[0070] The first preset method and the second preset method can be configured according to actual business needs and are not limited here. For example, the first preset method and the second preset method can include at least one of the following: a method based on Euclidean distance, a method based on Manhattan distance, a method based on Chebyshev distance, a method based on Mahalanobis distance, and a method based on angle cosine.

[0071] In a specific example, based on the first preset method, the method for determining the planned driving trajectory can be shown in the following formula (2).

[0072] That is, the product of the current speed v and the expected planning time α can be obtained to obtain the first distance 303. maxThe product of the first distance 303 and the expected planning time is used to obtain the second distance 304. The predetermined coefficient can be configured according to actual business needs and is not limited here. For example, the predetermined coefficient can be 1 / 2. Based on this, the sum of the first distance 303 and the second distance 304 is obtained as the first target distance 305, and the planned driving trajectory 310 is generated based on the first target distance 305.

[0073]

[0074] Among them, vt represents the first distance, represents the second distance, and s1 represents the first target distance.

[0075] In another specific example, based on the second preset method, the planned driving trajectory can be determined as shown in the following equations (3) and (4).

[0076] That is, the product of the first time Δt and the current speed v can be obtained to obtain the third distance 306. The first time Δt can be based on the maximum speed v max The difference between the current velocity v and the maximum acceleration a max Determined. Get the predetermined coefficient and maximum acceleration a max The product of the first time Δt and the fourth distance 307 is obtained. The maximum speed v is obtained max The product of the third distance 306, the fourth distance 307, and the fifth distance 308 is obtained by multiplying the distance by the second time (t-Δt), which is determined based on the difference between the expected planned time and the first time Δt. Based on this, the sum of the third distance 306, the fourth distance 307, and the fifth distance 308 is obtained as the second target distance 309, and the planned driving trajectory 310 is generated based on the second target distance 309.

[0077]

[0078]

[0079] Among them, △t represents the first time, vΔt represents the third distance, represents the fourth distance, (t-△t) represents the second time, v max (t-Δt) represents the fifth distance, and s2 represents the second target distance.

[0080] According to an embodiment of the present disclosure, when the intermediate speed is less than or equal to the maximum speed, and when the intermediate speed is greater than the maximum speed, multiple factors such as the current speed, expected planning time, maximum acceleration and predetermined coefficient are comprehensively considered based on different preset methods to calculate the respective target distances, and a planned driving trajectory is generated based on this. This ensures that the planned driving trajectory not only meets the physical performance limitations of the unmanned vehicle, but also is as close as possible to the user's expectations, thereby improving the rationality of the driving trajectory.

[0081] In the embodiment of the present disclosure, the planned driving trajectory can also be determined based on the expected planned distance. Figure 4 The process of determining the planned driving trajectory of the unmanned vehicle based on the expected planned distance is further explained.

[0082] Figure 4 An example diagram of a process for determining a planned driving trajectory of an unmanned vehicle based on an expected planned distance according to an embodiment of the present disclosure is schematically shown.

[0083] like Figure 4 As shown in Figure 400, an expected planned distance 404 can be determined based on the current location 402 of the unmanned vehicle and the location 403 of the second destination. Based on this, a planned driving trajectory 407 is determined based on the expected planned distance 404 and the complexity 405 of the road on which the unmanned vehicle is traveling. Road complexity 405 can represent the complexity of the road system and the difficulty of traffic operation. For example, factors influencing road complexity 405 may include at least one of the following: road geometry, traffic flow, intersection design, pedestrian and non-motorized vehicle traffic flow, and road facilities.

[0084] In one example, the method for obtaining road complexity can be configured based on actual business needs and is not limited here. For example, traffic flow monitoring data, traffic accident data, traffic violation data, etc. can be used to quantitatively analyze road operating conditions and thus assess road complexity. Alternatively, traffic flow models or traffic simulation models can be established to simulate and predict road operating conditions to assess road complexity.

[0085] According to the embodiments of the present disclosure, by determining the expected planning distance based on the current position of the unmanned vehicle and the position of the second destination, and obtaining the planned driving trajectory by comprehensively considering the expected planning distance and road complexity, potential risk points can be identified and avoided in advance, thereby effectively improving the targetedness of the driving trajectory and enhancing driving safety and reliability.

[0086] In one example, a preset algorithm can be used to process the expected planning distance 404 and the road complexity 405 to obtain a planned driving trajectory 407. The preset algorithm can be configured according to actual business needs and is not limited here.

[0087] In another example, the mapping relationship 401 may be pre-configured based on historical data. The mapping relationship 401 may include a one-to-one correspondence between candidate planning distances 4011 , candidate road complexities 4012 , and candidate planning times 4013 .

[0088] After obtaining the expected planning distance 404 and the road complexity 405 of the unmanned vehicle, the expected planning distance 404 can be matched with the multiple candidate planning distances 4011 in the mapping relationship 401 to determine a first number of target planning distances from the multiple candidate planning distances 4011. The road complexity 405 can be matched with the first number of candidate road complexities 4012 corresponding to the first number of target planning distances in the mapping relationship 401 to obtain a second number of target road complexities. On this basis, the candidate planning time corresponding to the second number of target road complexities in the mapping relationship 401 can be determined as the expected planning time 406. On this basis, the planned driving trajectory 407 of the unmanned vehicle can be further determined based on the expected planning time 406.

[0089] According to the embodiments of the present disclosure, by utilizing a preconfigured mapping relationship to derive an expected planning time based on the expected planning distance and road complexity, it is possible to more accurately reflect actual driving conditions and improve the accuracy of the expected planning time. Furthermore, by planning the unmanned vehicle's planned driving trajectory based on the expected planning time, it is possible to simultaneously consider the distance factor as well as the road complexity and time requirements, thereby improving the accuracy of the planned driving trajectory.

[0090] The above describes the process of determining the planned driving trajectory of the unmanned vehicle based on the expected planning time or the expected planning distance. Figures 5A to 5D A comparison is made between the trajectory planning process of the related art and the trajectory planning process implemented based on the trajectory planning method provided by the present disclosure.

[0091] Figure 5A and Figure 5B The diagram schematically shows a scenario in which the first planned trajectory (ie, the old global trajectory) is a straight line and the second trajectory segment (ie, the new global trajectory) is a turn.

[0092] Figure 5A An example schematic diagram of a trajectory planning process in related technology according to an embodiment of the present disclosure is schematically shown; Figure 5B An example schematic diagram of a trajectory planning process according to an embodiment of the present disclosure is schematically shown.

[0093] like Figure 5AAs shown, position 501 represents the position of the unmanned vehicle when the trajectory planning instruction is received, that is, the position of the unmanned vehicle when a new round of global planning begins. Position 502 represents the position of the unmanned vehicle when the new round of global planning is completed.

[0094] Dashed line 503 represents the first planned trajectory, i.e., the trajectory that the unmanned vehicle traveled to the first destination before receiving the trajectory planning instruction. Dashed line 504 represents the second trajectory segment, i.e., the trajectory segment obtained by trajectory planning based on the trajectory planning instruction and the second destination.

[0095] It can be seen from the dotted line 503 and the dotted line 504 that in the trajectory planning process provided by the relevant technology, the unmanned vehicle has moved away from the new round of global trajectory.

[0096] like Figure 5B As shown, position 505 represents the position of the unmanned vehicle when the trajectory planning instruction is received, that is, the position of the unmanned vehicle when a new round of global planning begins. Position 506 represents the position of the unmanned vehicle when the new round of global planning is completed.

[0097] Dashed line 507 represents the first planned trajectory, i.e., the trajectory that the unmanned vehicle traveled towards the first destination before receiving the trajectory planning instruction. d1 represents the planned driving trajectory, i.e., the first trajectory segment that the unmanned vehicle continues to travel along after receiving the trajectory planning instruction. Dashed line 508 represents the second trajectory segment, i.e., the trajectory segment that is obtained by planning the trajectory towards the second destination based on the trajectory planning instruction.

[0098] As can be seen from the dotted line 507 and the dotted line 508, in the trajectory planning process provided by the present disclosure, the position of the unmanned vehicle is still on the new global trajectory when a new round of global planning is completed.

[0099] Figure 5C and Figure 5D The diagram schematically illustrates a scenario in which the second trajectory segment (ie, the new global trajectory) has a certain lateral deviation compared to the first planned trajectory (ie, the old global trajectory).

[0100] Figure 5C An example schematic diagram of a trajectory planning process in related art according to another embodiment of the present disclosure is schematically shown; Figure 5D An example schematic diagram of a trajectory planning process according to another embodiment of the present disclosure is schematically shown.

[0101] like Figure 5C As shown, position 509 represents the position of the unmanned vehicle when the trajectory planning instruction is received, that is, the position of the unmanned vehicle when a new round of global planning begins. Position 510 represents the position of the unmanned vehicle when the new round of global planning is completed.

[0102] Dashed line 511 represents the first planned trajectory, i.e., the trajectory that the unmanned vehicle traveled towards the first destination before receiving the trajectory planning instruction. Dashed line 512 represents the second trajectory segment, i.e., the trajectory segment obtained by trajectory planning based on the trajectory planning instruction and the second destination.

[0103] It can be seen from the dotted lines 511 and 512 that in the trajectory planning process provided by the related art, there is a large instantaneous lateral deviation between the position of the unmanned vehicle when a new round of global planning is completed and the new round of global trajectory.

[0104] like Figure 5D As shown, position 513 represents the position of the unmanned vehicle when the trajectory planning instruction is received, that is, the position of the unmanned vehicle when a new round of global planning begins. Position 514 represents the position of the unmanned vehicle when the new round of global planning is completed.

[0105] Dashed line 515 represents the first planned trajectory, i.e., the trajectory that the unmanned vehicle traveled towards the first destination before receiving the trajectory planning instruction. d2 represents the planned driving trajectory, i.e., the first trajectory segment that the unmanned vehicle continues to travel along after receiving the trajectory planning instruction. Dashed line 516 represents the second trajectory segment, i.e., the trajectory segment obtained by planning the trajectory towards the second destination based on the trajectory planning instruction.

[0106] As can be seen from the dotted line 515 and the dotted line 516, in the trajectory planning process provided by the present disclosure, the position of the unmanned vehicle is still on the new global trajectory when a new round of global planning is completed.

[0107] The above describes the trajectory planning process of the related art and the trajectory planning process implemented by the trajectory planning method provided by the present disclosure. Figure 6 The trajectory planning process provided by the present disclosure is further explained.

[0108] Figure 6 An example schematic diagram of the trajectory planning process of an unmanned vehicle according to an embodiment of the present disclosure is schematically shown.

[0109] like Figure 6 As shown, in 600, in response to a trajectory planning instruction 601, a current operating state 602 of the unmanned vehicle can be determined. Based on the current operating state 602 of the unmanned vehicle, a trajectory splicing type 603 is determined. For example, the current operating state 602 can include at least one of the following: parking, forward motion, and backward motion. The trajectory splicing type 603 can include at least one of the following: no trajectory splicing required for parking, forward splicing for forward motion, and backward splicing for backward motion.

[0110] After obtaining the track splicing type 603, operation S610 may be performed. In operation S610, it is determined whether the track splicing type 603 indicates that track splicing is required. If not, the process may be terminated. If so, a planned driving track 604 may be determined.

[0111] According to the embodiments of the present disclosure, by determining whether trajectory splicing is necessary based on the current operating state of the unmanned vehicle, the continuity and stability of the driving trajectory are ensured. Furthermore, when splicing is necessary, the two segments are seamlessly connected, avoiding abruptness and instability during driving and improving the accuracy of the planned driving trajectory.

[0112] After obtaining the planned driving trajectory 604, a target starting position 605 can be determined based on the end point of the planned driving trajectory 604. Using the target starting position 605 as the planning starting point, a second trajectory segment 606 is determined according to the trajectory planning instruction 601. The method for determining the second trajectory segment 606 can be configured based on actual business needs and is not limited here.

[0113] After the planned driving trajectory 604 and the second trajectory segment 606 are obtained, a target planned trajectory 607 may be generated according to the planned driving trajectory 604 and the second trajectory segment 606 .

[0114] According to the embodiments of the present disclosure, by taking the end point of the planned driving trajectory as the new target starting position and the target starting position as the new planning starting point, the unmanned vehicle can accurately plan a new driving path starting from the end point of the current driving state, ensuring seamless connection between the first trajectory segment and the second trajectory segment, and helping to maintain the continuity and smoothness of the driving process.

[0115] The above are merely exemplary embodiments, but are not limited thereto. Other unmanned vehicle trajectory planning methods known in the art may also be included, as long as they can improve the accuracy of the target planned trajectory.

[0116] Figure 7 A block diagram of a trajectory planning device for an unmanned vehicle according to an embodiment of the present disclosure is schematically shown.

[0117] like Figure 7 As shown, the trajectory planning device 700 of the unmanned vehicle may include a first determination module 710 , a second determination module 720 and a generation module 730 .

[0118] The first determination module 710 is used to determine the planned driving trajectory of the unmanned vehicle in response to the trajectory planning instruction, wherein the trajectory planning instruction is used to instruct to update the destination of the first planned trajectory from the first destination to the second destination, and the planned driving trajectory represents the first trajectory segment in which the unmanned vehicle continues to travel along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction.

[0119] The second determining module 720 is configured to determine a second trajectory segment according to the trajectory planning instruction, wherein the second trajectory segment represents a trajectory segment obtained by performing trajectory planning with the second destination as the destination based on the trajectory planning instruction.

[0120] The generating module 730 is configured to generate a target planned trajectory according to the planned driving trajectory and the second trajectory segment.

[0121] According to an embodiment of the present disclosure, the second determining module 720 may include a first determining submodule and a second determining submodule.

[0122] The first determination submodule is configured to determine a target starting position based on an end point of the planned driving trajectory.

[0123] The second determining submodule is configured to determine a second trajectory segment based on the trajectory planning instruction using the target starting position as the planning starting point.

[0124] According to an embodiment of the present disclosure, the first determining module 710 may include a third determining submodule and a fourth determining submodule.

[0125] The third determination submodule is used to determine the trajectory splicing type in response to the trajectory planning instruction and according to the current operating state of the unmanned vehicle.

[0126] The fourth determining submodule is configured to determine the planned driving trajectory when the trajectory splicing type indicates that trajectory splicing is required.

[0127] According to an embodiment of the present disclosure, the first determining module 710 may include a fifth determining submodule or a sixth determining submodule.

[0128] The fifth determination submodule is used to obtain the expected planning time and determine the planned driving trajectory of the unmanned vehicle based on the expected planning time.

[0129] The sixth determination submodule is used to obtain the expected planning distance and determine the planned driving trajectory of the unmanned vehicle based on the expected planning distance.

[0130] According to an embodiment of the present disclosure, the fifth determining submodule may include a first determining unit, a second determining unit, a third determining unit, and a fourth determining unit.

[0131] The first determination unit is used to determine the intermediate speed according to the current speed, maximum acceleration and expected planning time of the unmanned vehicle.

[0132] The second determining unit is used to determine the magnitude relationship between the intermediate speed and the maximum speed.

[0133] The third determining unit is configured to determine the planned driving trajectory based on a first preset method when the intermediate speed is less than or equal to the maximum speed.

[0134] The fourth determining unit is configured to determine the planned driving trajectory based on a second preset method when the intermediate speed is greater than the maximum speed.

[0135] According to an embodiment of the present disclosure, the third determining unit may include a first obtaining subunit, a second obtaining subunit, and a first generating subunit.

[0136] The first obtaining subunit is used to obtain the product of the current speed and the expected planning time to obtain a first distance.

[0137] The second obtaining subunit is used to obtain the product of the predetermined coefficient, the maximum acceleration and the expected planning time to obtain a second distance.

[0138] The first generating subunit is configured to obtain the sum of the first distance and the second distance as a first target distance, and generate a planned driving trajectory according to the first target distance.

[0139] According to an embodiment of the present disclosure, the fourth determining unit may include a third obtaining subunit, a fourth obtaining subunit, a fifth obtaining subunit, and a second generating subunit.

[0140] The third obtaining subunit is configured to obtain a product of a first time and a current speed to obtain a third distance, wherein the first time is determined based on a difference between a maximum speed and a current speed and a maximum acceleration.

[0141] The fourth obtaining subunit is configured to obtain a product of a predetermined coefficient, the maximum acceleration, and the first time to obtain a fourth distance.

[0142] The fifth obtaining subunit is configured to obtain a product of the maximum speed and a second time to obtain a fifth distance, wherein the second time is determined based on a difference between the expected planning time and the first time.

[0143] The second generating subunit is configured to obtain the sum of the third distance, the fourth distance, and the fifth distance as a second target distance, and generate a planned driving trajectory according to the second target distance.

[0144] According to an embodiment of the present disclosure, the sixth determining submodule may include a fifth determining unit and a sixth determining unit.

[0145] The fifth determining unit is used to determine the expected planning distance according to the current position of the unmanned vehicle and the position of the second destination.

[0146] The sixth determination unit is used to determine the planned driving trajectory based on the expected planning distance and the complexity of the road on which the unmanned vehicle is traveling.

[0147] According to an embodiment of the present disclosure, the sixth determining unit may include a matching subunit and a determining subunit.

[0148] The matching subunit is used to match the expected planning distance and road complexity in a pre-configured mapping relationship to obtain the expected planning time, wherein the mapping relationship includes the correspondence between the candidate planning distance, candidate road complexity and candidate planning time determined based on historical data.

[0149] The determination subunit is used to determine the planned driving trajectory of the unmanned vehicle based on the expected planning time.

[0150] According to an embodiment of the present disclosure, the trajectory planning instruction is triggered when it is determined that the unmanned vehicle meets predetermined conditions. The predetermined conditions include at least one of the following: receiving a target instruction, where the target instruction includes a vehicle stop instruction or a refueling instruction; and the distance between the unmanned vehicle's current location and the location of the first destination is less than a preset distance threshold.

[0151] According to one embodiment of the present disclosure, an unmanned vehicle is provided, which includes the trajectory planning device 700 of any of the above-mentioned unmanned vehicles.

[0152] Figure 8 A block diagram of an electronic device suitable for implementing a trajectory planning method for an unmanned vehicle according to an embodiment of the present disclosure is schematically shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0153] like Figure 8 As shown, the device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. Various programs and data required for the operation of the device 800 can also be stored in the RAM 803. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0154] Various components in device 800 are connected to I / O interface 805, including an input unit 806, such as a keyboard, mouse, etc.; an output unit 807, such as various types of displays, speakers, etc.; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, etc. The communication unit 809 allows device 800 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0155] The computing unit 801 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as the trajectory planning method for the unmanned vehicle. For example, in some embodiments, the trajectory planning method for the unmanned vehicle can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 800 via the ROM 802 and / or the communication unit 809. When the computer program is loaded into the RAM 803 and executed by the computing unit 801, one or more steps of the trajectory planning method for the unmanned vehicle described above can be performed. Alternatively, in other embodiments, the computing unit 801 can be configured to perform the trajectory planning method for the unmanned vehicle by any other appropriate means (e.g., by means of firmware).

[0156] Various embodiments of the systems and techniques described above can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0157] The program code for implementing the method of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device so that when the program code is executed by the processor or controller, the functions / operations specified in the flow chart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0158] In the context of the present disclosure, a machine-readable medium can be a tangible medium that can contain or store a program for use by or in conjunction with an instruction execution system, device or equipment. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0159] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0160] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), and the Internet.

[0161] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact through a communication network. The client-server relationship arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0162] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not a limitation herein.

[0163] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A trajectory planning method for an unmanned vehicle, comprising: Determining a planned driving trajectory of the unmanned vehicle in response to a trajectory planning instruction, wherein the trajectory planning instruction is used to instruct that a destination of a first planned trajectory be updated from a first destination to a second destination, the planned driving trajectory is determined based on an expected planning time, the expected planning time is obtained by matching an expected planning distance and a road complexity in a preconfigured mapping relationship, the expected planning distance is determined based on a current position of the unmanned vehicle and a position of the second destination, the mapping relationship includes a correspondence between candidate planning distances, candidate road complexities, and candidate planning times determined based on historical data, the planned driving trajectory representing a first trajectory segment for the unmanned vehicle to continue traveling along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction; determining a second trajectory segment based on the trajectory planning instruction, wherein the second trajectory segment represents a trajectory segment obtained by performing trajectory planning based on the trajectory planning instruction with the second destination as the destination, and the second trajectory segment has a target starting position determined based on the end point of the planned driving trajectory as a planning starting point; and A target planned trajectory is generated according to the planned driving trajectory and the second trajectory segment.

2. The method according to claim 1, wherein Determining the planned driving trajectory of the unmanned vehicle includes: In response to the trajectory planning instruction, determining a trajectory splicing type according to a current operating state of the unmanned vehicle; When the trajectory splicing type indicates that trajectory splicing is required, the planned driving trajectory is determined.

3. The method according to claim 1 or 2, wherein: The trajectory planning instruction is triggered when it is determined that the unmanned vehicle meets predetermined conditions; The predetermined condition includes at least one of the following: receiving a target instruction, wherein the target instruction includes a vehicle collection instruction or a refueling instruction; and The distance between the current position of the unmanned vehicle and the position of the first destination is less than a preset distance threshold.

4. A trajectory planning device for an unmanned vehicle, comprising: a first determination module, configured to determine a planned driving trajectory of the unmanned vehicle in response to a trajectory planning instruction, wherein the trajectory planning instruction is used to instruct to update the destination of the first planned trajectory from the first destination to the second destination, the planned driving trajectory is determined based on an expected planning time, the expected planning time is obtained by matching an expected planning distance and a road complexity in a preconfigured mapping relationship, the expected planning distance is determined based on a current position of the unmanned vehicle and a position of the second destination, the mapping relationship includes a correspondence between candidate planning distances, candidate road complexities, and candidate planning times determined based on historical data, the planned driving trajectory representing a first trajectory segment in which the unmanned vehicle continues to travel along the first planned trajectory with the first destination as the destination after receiving the trajectory planning instruction; a second determining module, configured to determine a second trajectory segment based on the trajectory planning instruction, wherein the second trajectory segment represents a trajectory segment obtained by performing trajectory planning based on the trajectory planning instruction with the second destination as the destination, and the second trajectory segment has a target starting position determined based on an end point of the planned driving trajectory as a planning starting point; and A generation module is used to generate a target planned trajectory based on the planned driving trajectory and the second trajectory segment.

5. An unmanned vehicle, characterized in that: The unmanned vehicle includes: the unmanned vehicle trajectory planning device according to claim 4.

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