An intelligent networked vehicle trajectory planning method, device, equipment and medium

Abstract

The application discloses a kind of intelligent network connection vehicle trajectory planning method, device, equipment and medium.The method comprises: obtaining the vehicle driving data of at least one vehicle to be controlled in target control area in current time slice;Vehicle driving data and associated data associated with target control area are processed to obtain the comprehensive driving data of all vehicles to be controlled in target control area corresponding to current time slice and auxiliary planning period;With the minimum comprehensive driving data as the goal, adjust the driving information of the vehicle to be controlled at each planning time, to obtain the driving trajectory data of the vehicle to be controlled in current time slice, so that the vehicle to be controlled follows corresponding driving trajectory data and drives.Solve the problem that the trajectory planning is carried out using the way of avoiding obstacles in the prior art, resulting in low planning trajectory accuracy and poor safety, improve the accuracy and smoothness of the planning trajectory, improve the efficiency and safety of vehicle driving.

Description

Technical Field

[0001] This invention relates to the field of computer processing technology, and in particular to a method, apparatus, device, and medium for intelligent connected vehicle trajectory planning. Background Technology

[0002] With the deepening of urbanization, the surge in urban vehicles has further exacerbated traffic accidents and congestion. Autonomous driving has shown great potential in reducing traffic accidents and alleviating congestion, and has become a hot research topic.

[0003] In the process of autonomous driving, it is usually necessary to plan the vehicle's trajectory based on traffic information around the vehicle. Current trajectory planning methods are usually obstacle avoidance-based trajectory planning for a single vehicle, planning the vehicle's driving trajectory by avoiding obstacles. This not only fails to accurately plan the vehicle's driving trajectory before intersections, but also suffers from low trajectory planning safety. Summary of the Invention

[0004] This invention provides a vehicle trajectory planning method, apparatus, device, and medium to improve the accuracy and smoothness of the planned trajectory, thereby enhancing vehicle driving efficiency and safety.

[0005] According to one aspect of the present invention, a vehicle trajectory planning method is provided, which is applied to intelligent connected vehicles, the method comprising:

[0006] Acquire vehicle driving data for at least one vehicle to be controlled within the target control area in the current time slice; wherein, the vehicle driving data includes initial position, initial speed, initial lane and / or target driving lane;

[0007] The vehicle driving data and associated data related to the target control area are processed to obtain comprehensive driving data for all vehicles to be controlled within the target control area, corresponding to the current time slice and the auxiliary planning cycle. The associated data includes traffic signal data, intersection location information, and lane-change prohibition zone location information. The start time of the auxiliary planning cycle is later than the start time of the current time slice. The comprehensive driving data includes driving duration data, acceleration data, and lane-change frequency data.

[0008] With the goal of minimizing the comprehensive driving data, the driving information of the vehicle to be controlled at each planned time is adjusted to obtain the driving trajectory data of the vehicle to be controlled within the current time slice;

[0009] The driving trajectory data is sent to the corresponding vehicle to be controlled, so that the vehicle to be controlled follows the corresponding driving trajectory data.

[0010] According to another aspect of the present invention, a vehicle trajectory planning device is provided, the device being configured in an intelligent connected vehicle system, the device comprising:

[0011] The data acquisition module is used to acquire vehicle driving data of at least one vehicle to be controlled in the target control area within the current time slice; wherein, the vehicle driving data includes initial position, initial speed, initial lane and / or target driving lane;

[0012] The driving data determination module is used to process the vehicle driving data and the associated data related to the target control area to obtain comprehensive driving data of all vehicles to be controlled in the target control area corresponding to the current time slice and the auxiliary planning cycle; wherein, the associated data includes traffic signal data, intersection location information and lane-changing prohibition zone location information; the start time of the auxiliary planning cycle is later than the start time of the current time slice; the comprehensive driving data includes driving duration data, acceleration data and lane-changing frequency data;

[0013] The driving trajectory determination module is used to adjust the driving information of the vehicle to be controlled at each planned time with the goal of minimizing the comprehensive driving data, so as to obtain the driving trajectory data of the vehicle to be controlled within the current time slice;

[0014] The trajectory sending module is used to send the driving trajectory data to the corresponding vehicle to be controlled, so that the vehicle to be controlled can follow the corresponding driving trajectory data.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] At least one processor; and

[0017] A memory communicatively connected to the at least one processor; wherein,

[0018] The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle trajectory planning method according to any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the vehicle trajectory planning method according to any embodiment of the present invention.

[0020] The technical solution of this invention obtains vehicle driving data of at least one vehicle to be controlled within the target control area in the current time slice; processes the vehicle driving data and associated data related to the target control area to obtain comprehensive driving data of all vehicles to be controlled within the target control area corresponding to the current time slice and the auxiliary planning cycle; and adjusts the driving information of the vehicles to be controlled at each planning moment with the goal of minimizing the comprehensive driving data to obtain the driving trajectory data of the vehicles to be controlled within the current time slice, so that the vehicles to be controlled follow the corresponding driving trajectory data. This solves the problem of low accuracy and poor safety of trajectory planning caused by using obstacle avoidance in the prior art. It realizes the determination of comprehensive driving data such as driving time data, acceleration data, and lane change frequency data of the vehicles to be controlled by setting a target control area, considering the vehicle driving data of all vehicles within the target control area, and the associated data related to the target control area. This not only comprehensively considers the driving information of each vehicle to be controlled, making the driving data of each vehicle to be controlled the same and coordinated, improving the accuracy of driving data determination and thus improving driving safety, but also combines the driving data of the auxiliary planning cycle to plan the driving trajectory within the current time slice, improving the accuracy of trajectory planning. Furthermore, by minimizing the comprehensive driving data, the driving information of the vehicle to be controlled after adjustment at each planned time is safe and smooth, thereby achieving the technical effect of improving vehicle driving efficiency and safety.

[0021] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart of a vehicle trajectory planning method provided in Embodiment 1 of the present invention;

[0024] Figure 2 This is a schematic diagram of the target control area provided in Embodiment 2 of the present invention;

[0025] Figure 3 This is a schematic diagram of the structure of a vehicle trajectory planning device according to Embodiment 3 of the present invention;

[0026] Figure 4This is a schematic diagram of the structure of an electronic device that implements the vehicle trajectory planning method of this invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0029] Example 1

[0030] Figure 1 This is a flowchart of a vehicle trajectory planning method according to Embodiment 1 of the present invention. This embodiment is applicable to the planning of vehicle driving trajectories. The method can be executed by a vehicle trajectory planning device, which can be implemented in hardware and / or software. The vehicle trajectory planning device can be configured in a computing device in an intelligent connected environment. Figure 1 As shown, the method includes:

[0031] S110. Obtain vehicle driving data of at least one vehicle to be controlled in the target control area within the current time slice.

[0032] The target control area can be understood as the area controlling the autonomous driving of the vehicle. A time slice can be understood as a planning period, with a duration of 30 seconds, 60 seconds, or determined based on traffic signal data, the number of lanes, and traffic phases (such as three-way intersections and four-way intersections). The method for planning the vehicle's trajectory data within each time slice is the same; any time slice can be used as the current time slice for explanation. Vehicle driving data includes initial position, initial speed, initial lane, and / or target lane.

[0033] In this embodiment, when planning the current time slice, all vehicles currently located in the target control area can be considered as vehicles to be controlled. Vehicle driving data for each vehicle to be controlled is acquired, and centralized driving trajectory planning is performed for all vehicles based on this data.

[0034] S120. Process the vehicle driving data and the associated data related to the target control area to obtain the comprehensive driving data of all vehicles to be controlled in the target control area corresponding to the current time slice and the auxiliary planning cycle.

[0035] The associated data includes traffic signal data, lane numbers, intersection location information, and lane-change-prohibited zone location information corresponding to the target control area. Traffic signal data can include intersection light display information, such as the duration of the green light. The start time of the auxiliary planning cycle is later than the start time of the current time slice. There can be one or more auxiliary planning cycles; for example, if the current time slice is 0-60s, the auxiliary planning cycle could be 60s-120s. Both the current time slice and the auxiliary planning cycle can include multiple planning times, and the time step between adjacent planning times can be 1 second or 0.5 seconds. The comprehensive driving data includes driving duration data, acceleration data, and lane-change frequency data.

[0036] It should be noted that in practical applications, considering that vehicles may continuously enter the first control zone, during trajectory planning, the vehicle trajectories for subsequent auxiliary planning periods can be planned simultaneously with the trajectories for the current time slice. This allows for trajectory planning based on the vehicle trajectories within the auxiliary planning periods, improving the accuracy of the current time slice. Trajectories planned in subsequent auxiliary planning periods are not output; only the vehicle trajectories planned within the current time slice are output.

[0037] In this embodiment, the driving trajectory information of each vehicle to be controlled can be planned based on its driving data and associated data. During the planning of the vehicle's driving trajectory, firstly, the driving time of all vehicles within the target control area in the current time slice and auxiliary planning period can be calculated, and the total driving time information can be used as driving time data. Secondly, the acceleration of all vehicles at each planning moment can be calculated, and the total acceleration information can be used as acceleration data. Thirdly, the number of lane changes performed by all vehicles within the target control area can be calculated, and the total number of lane changes can be used as lane change frequency data. Furthermore, the driving time data, acceleration data, and lane change frequency data can be combined into comprehensive driving data to obtain the driving trajectory of all vehicles while minimizing the comprehensive driving data.

[0038] To improve the efficiency and accuracy of trajectory planning, a trajectory planning model with the goal of minimizing comprehensive driving data can be pre-constructed. Then, the model can be solved using vehicle driving data and related data to obtain comprehensive driving data.

[0039] In this embodiment, vehicle driving data and associated data related to the target control area are processed to obtain comprehensive driving data of all vehicles to be controlled in the target control area corresponding to the current time slice and auxiliary planning cycle. This includes: determining a trajectory planning model with the goal of minimizing comprehensive driving data; inputting vehicle driving data and associated data into the trajectory planning model to obtain driving time data, acceleration data, and lane change frequency data; and obtaining comprehensive driving data based on driving time data, acceleration data, lane change frequency data, and corresponding weights.

[0040] The trajectory planning model is used to plan the vehicle's driving trajectory, with the goal of minimizing the overall driving data. Appropriate weights can be configured for driving time data, acceleration data, and lane change frequency data, such as first weight, second weight, and third weight. The weight settings can be determined by technical personnel based on actual working conditions.

[0041] In practical applications, vehicle driving data and related data can be input into the trajectory planning model. After processing by the trajectory planning model, driving time data, acceleration data, and lane change frequency data can be obtained. Furthermore, the driving time data, acceleration data, and lane change frequency data can be weighted according to their respective weights to obtain comprehensive driving data.

[0042] In this embodiment, during the process of inputting vehicle driving data and related data into the trajectory planning model to obtain driving time data, acceleration data, and lane change frequency data, the vehicle driving data and related data can be processed based on the trajectory planning model to obtain the driving sub-duration and lane change number of each vehicle to be controlled in the target control area, as well as the square of acceleration at each planning time. Based on each driving sub-duration, driving time data is obtained; based on each lane change number, lane change frequency data is obtained; and based on each square of acceleration, acceleration data is obtained.

[0043] Specifically, in the trajectory planning process based on the trajectory planning model, the trajectory planning model can be solved using vehicle driving data and associated data to obtain the driving sub-duration and lane change number of each vehicle to be controlled in the target control area, as well as the square of the acceleration at each planning time. The sum of each driving sub-duration can be calculated as the driving duration data. The sum of each lane change number can be used as the lane change frequency data. The sum of each acceleration square can be used as the acceleration data.

[0044] For example, the objective function of a trajectory planning model can be:

[0045]

[0046] in, v k (t)=v k (t-1)+a k (t)·Δt,x k (t)=x k (t-1)+v k (t)·Δt, where t represents the planning time and k can be represented as the total number of vehicles. τ k Let ρ1 and ρ2 represent the travel time of vehicle k. The value is 1 if vehicle k is in lane l at time t; otherwise, it is 0. Characterize if vehicle k is at the time boundary If the path passes through the intersection, the value is 1; otherwise, it is 0. Δt can be represented as the time step for trajectory planning. For example, assuming Δt is 0.5S, if t = 1S, then t-1 = 0.5S, t+1 = 1.5S. k (t) represents the longitudinal acceleration of vehicle k at time t. k (t) represents the longitudinal velocity of vehicle k at time t. k (t) represents the longitudinal position of vehicle k at time t. Longitudinal refers to the connecting traction direction between the front and rear of the vehicle. The trajectory planning model can be input with the vehicle driving data of all vehicles, the target driving lane at the intersection, traffic signal data, etc., to minimize the overall driving data and solve for the optimal trajectory of all vehicles.

[0047] S130. With the goal of minimizing the comprehensive driving data, adjust the driving information of the vehicle to be controlled at each planned time to obtain the driving trajectory data of the vehicle to be controlled within the current time slice.

[0048] In practical applications, the overall driving data can be minimized by adjusting the vehicle's position, speed, acceleration, driving lane, lane-changing timing, and lane-changing direction at each planned moment. The advantage of this setting is that it considers both the initial and target driving lanes of the vehicle, ensuring that the trajectory includes not only longitudinal information such as acceleration, deceleration, speed, and position, but also lateral information such as lane-changing timing and direction, thus improving trajectory planning accuracy.

[0049] In this embodiment, with the goal of minimizing the overall driving data, the driving information of the vehicle to be controlled at each planning time is adjusted to obtain the driving trajectory data of the vehicle to be controlled within the current time slice. This includes: obtaining the constraints of the trajectory planning model; and when adjusting the driving information of the vehicle to be controlled at each planning time with the goal of minimizing the overall driving data, the driving information is constrained based on the constraints to obtain the driving trajectory data of each vehicle to be controlled within the current time slice.

[0050] The constraints of the trajectory planning model include the relationship constraints between acceleration, velocity and position, the range constraints of velocity and acceleration, driving behavior constraints, inter-vehicle distance constraints, lane changing constraints, no-lane-changing zone constraints, variable consistency constraints, initial parameter constraints, variable definition constraints, planning cycle constraints, and intersection traffic constraints; intersection traffic constraints include traffic speed constraints, traffic lane constraints, traffic light constraints, and cycle boundary constraints.

[0051] Relational constraints can be: x k (t)=x k (t-1)+v k (t)·Δt and v k (t)=v k (t-1)+a k (t)·Δt.

[0052] The range constraint can be: v min ≤v k (t)≤v max and a min ≤a k (t)≤a max ; where v min Represented as the lower limit of velocity, v max This represents the upper limit of speed, a. min This is represented as the lower limit of acceleration, a max This represents the upper limit of acceleration.

[0053] Driving behavior constraints can be: Its characteristic is that vehicles can only be in at most one lane at a time.

[0054] Workshop distance constraints can be:

[0055]

[0056] in, B k,k′ (t) indicates that if vehicle k is ahead of vehicle k′ at time t, the value is 1; otherwise, it is 0. N is a sufficiently large constant. Let be the target lane for vehicle k. Let g be the safe distance between adjacent vehicles. A vehicle-to-vehicle distance constraint is used to ensure that the distance between two adjacent vehicles is at least g.

[0057] Lane changing constraints can be: Lane change constraints are used to limit a vehicle to changing lanes only to the adjacent lane at a time.

[0058] No-lane-changing zone constraints can be: Among them, D n These are the coordinates of the no-lane-changing zone. The no-lane-changing zone constraint is used to prevent vehicles from changing lanes within the zone.

[0059] Variable consistency constraints can be: and It restricts the relationships between different related decision variables, preventing their definitions and values ​​from contradicting each other.

[0060] Initial parameter constraints can be: It constrains the initial position of the vehicle (denoted as x). k Initial velocity (represented as v) k ) and the initial lane (represented as ).in, These are all input parameters for the trajectory planning model.

[0061] Variable definition constraints can be: (-ζ) k (t))×N≤D all -x k (t), (1-ζ k (t))×N≥D all -x k (t) and ζ k (t+1)≥ζ k (t); where D all Represented as the coordinates of the stop line at the intersection, the variable is defined with constraint on ζ. k The definition of ζ(t) represents the state of ζ after vehicle k has passed the stop line at the intersection. k (t) = 1, otherwise 0.

[0062] Planning period constraints can be: in, This means that if vehicle k is assigned to the c-th cycle and passes through the intersection, the value is 1; otherwise, it is 0. The planning cycle constraint ensures that a vehicle can pass through the intersection in at most one planning cycle.

[0063] Traffic speed constraints can be: and in, This represents the lower speed limit for vehicles leaving the intersection. This represents the maximum speed limit for vehicles leaving the intersection.

[0064] Traffic lane constraints can be: This indicates the lane the vehicle was in when it left the intersection.

[0065] Traffic light constraints can be: and in, This can be represented as the start time of the green light in the c-th planning cycle. It can be represented as the end time of the green light in the c-th planning cycle.

[0066] Periodic boundary constraints can be: It stipulates that if a vehicle cannot leave the intersection within C cycles of the cycle boundary, it will leave the intersection in a subsequent cycle, and its travel time is set as the cycle boundary.

[0067] Specifically, the purpose of the trajectory planning model is to minimize the overall driving data under the condition that the vehicle to be controlled changes to the target driving lane, and to determine the driving information of each vehicle to be controlled at each planning time. The driving trajectory data of each vehicle to be controlled in the current time slice can be selected as the output.

[0068] S140. Send the driving trajectory data to the corresponding vehicle to be controlled so that the vehicle to be controlled can follow the corresponding driving trajectory data.

[0069] Specifically, the driving trajectory data of each vehicle to be controlled can be sent to the corresponding vehicle to be controlled, so that the vehicle to be controlled can follow the corresponding driving trajectory data.

[0070] The technical solution of this embodiment obtains the vehicle driving data of at least one vehicle to be controlled in the target control area within the current time slice; processes the vehicle driving data and the associated data related to the target control area to obtain the comprehensive driving data of all vehicles to be controlled in the target control area corresponding to the current time slice and the auxiliary planning cycle; with the goal of minimizing the comprehensive driving data, the driving information of the vehicles to be controlled at each planning time is adjusted to obtain the driving trajectory data of the vehicles to be controlled in the current time slice, so that the vehicles to be controlled follow the corresponding driving trajectory data. This solves the problem of low accuracy and poor safety of the planned trajectory caused by the use of obstacle avoidance in the prior art. It realizes the determination of comprehensive driving data such as driving time data, acceleration data, and lane change frequency data of the vehicles to be controlled by setting a target control area, considering the vehicle driving data of all vehicles in the target control area, and the associated data related to the target control area. This not only comprehensively considers the driving information of each vehicle to be controlled, making the driving data of each vehicle to be controlled the same and coordinated, improving the accuracy of driving data determination and thus improving driving safety, but also combines the driving data of the auxiliary planning cycle to plan the driving trajectory in the current time slice, improving the accuracy of trajectory planning. Furthermore, by minimizing the comprehensive driving data, the driving information of the vehicle to be controlled after adjustment at each planned time is safe and smooth, thereby achieving the technical effect of improving vehicle driving efficiency and safety.

[0071] Example 2

[0072] As an optional embodiment of the above embodiments, specific application scenario examples are provided to enable those skilled in the art to further understand the technical solutions of the embodiments of the present invention. Specifically, please refer to the following detailed content.

[0073] See Figure 2 The system allows for centralized trajectory planning for all vehicles (i.e., vehicles to be controlled) within the target control area. Trajectory planning includes both longitudinal trajectory (speed, acceleration) and lateral trajectory (lane-changing timing and direction). For example, suppose the target control area for vehicles is a distance before a single intersection. Vehicles may have mandatory lane-changing needs; that is, each vehicle may have its own target lane, but may not initially be in its target lane and need to change lanes. During the trajectory planning process, the control center can receive the traffic light timings (i.e., traffic signal data) corresponding to the target control area. Based on the traffic signal data and the vehicles' current driving data, the system plans the trajectories for all vehicles and sends the planned trajectories to the vehicles. Vehicles can then follow the received trajectories.

[0074] The technical solution of this embodiment plans the trajectory of an autonomous vehicle before an intersection in a networked environment. The trajectory includes not only longitudinal information such as acceleration / deceleration, speed, and position, but also lateral information such as lane-changing timing and direction. During this process, when a lane-changing requirement may arise, the vehicle changes lanes to the necessary lane before passing through the intersection, ensuring a safe and collision-free trajectory and allowing it to pass through the intersection during the green light period. This improves both vehicle safety and driving efficiency.

[0075] Example 3

[0076] Figure 3 This is a structural schematic diagram of a vehicle trajectory planning device according to Embodiment 3 of the present invention. Figure 3 As shown, the device includes: a data acquisition module 310, a driving data determination module 320, a driving trajectory determination module 330, and a trajectory transmission module 340.

[0077] The system includes a data acquisition module 310, which acquires vehicle driving data for at least one vehicle to be controlled within the target control area in the current time slice. The vehicle driving data includes initial position, initial speed, initial lane, and / or target driving lane. A driving data determination module 320 processes the vehicle driving data and associated data related to the target control area to obtain comprehensive driving data for all vehicles to be controlled within the target control area, corresponding to the current time slice and the auxiliary planning cycle. The associated data includes traffic signal data, intersection location information, and lane-changing zone location information. The start time of the auxiliary planning cycle is later than the start time of the current time slice. The comprehensive driving data includes driving duration data, acceleration data, and lane-changing frequency data. A driving trajectory determination module 330 adjusts the driving information of the vehicles to be controlled at each planning time with the goal of minimizing the comprehensive driving data, to obtain the driving trajectory data of the vehicles to be controlled within the current time slice. A trajectory sending module 340 sends the driving trajectory data to the corresponding vehicles to be controlled, so that the vehicles to be controlled follow the corresponding driving trajectory data.

[0078] The technical solution of this embodiment obtains the vehicle driving data of at least one vehicle to be controlled in the target control area within the current time slice; processes the vehicle driving data and the associated data related to the target control area to obtain the comprehensive driving data of all vehicles to be controlled in the target control area corresponding to the current time slice and the auxiliary planning cycle; with the goal of minimizing the comprehensive driving data, the driving information of the vehicles to be controlled at each planning time is adjusted to obtain the driving trajectory data of the vehicles to be controlled in the current time slice, so that the vehicles to be controlled follow the corresponding driving trajectory data. This solves the problem of low accuracy and poor safety of the planned trajectory caused by the use of obstacle avoidance in the prior art. It realizes the determination of comprehensive driving data such as driving time data, acceleration data, and lane change frequency data of the vehicles to be controlled by setting a target control area, considering the vehicle driving data of all vehicles in the target control area, and the associated data related to the target control area. This not only comprehensively considers the driving information of each vehicle to be controlled, making the driving data of each vehicle to be controlled the same and coordinated, improving the accuracy of driving data determination and thus improving driving safety, but also combines the driving data of the auxiliary planning cycle to plan the driving trajectory in the current time slice, improving the accuracy of trajectory planning. Furthermore, by minimizing the comprehensive driving data, the driving information of the vehicle to be controlled after adjustment at each planned time is safe and smooth, thereby achieving the technical effect of improving vehicle driving efficiency and safety.

[0079] Optionally, based on the above-mentioned device, the driving data determination module 320 includes a trajectory planning model determination unit, a model processing unit, and a driving data determination unit.

[0080] A trajectory planning model determination unit is used to determine a trajectory planning model with the objective of minimizing the comprehensive driving data;

[0081] The model processing unit is used to input the vehicle driving data and the associated data into the trajectory planning model to obtain driving time data, acceleration data and lane change frequency data;

[0082] The driving data determination unit is used to obtain the comprehensive driving data based on the driving time data, acceleration data, lane change frequency data, and corresponding weights.

[0083] Based on the above-mentioned device, optionally, the model processing unit includes a data processing subunit, a driving time data determination subunit, a lane change frequency data determination subunit, and an acceleration data determination subunit.

[0084] The data processing subunit is used to process the vehicle driving data and the associated data based on the trajectory planning model to obtain the driving time and lane change times of each vehicle to be controlled in the target control area, as well as the square of the acceleration at each planning time.

[0085] The driving time data determination subunit is used to obtain the driving time data based on each of the driving sub-times;

[0086] The lane change frequency data determination subunit is used to obtain the lane change frequency data based on each lane change number;

[0087] An acceleration data determination subunit is used to obtain the acceleration data based on the square of each acceleration.

[0088] Based on the above-mentioned device, optionally, the driving trajectory determination module 330 includes: a constraint condition determination unit and a driving trajectory determination unit.

[0089] The constraint determination unit is used to obtain the constraints of the trajectory planning model;

[0090] The driving trajectory determination unit is used to adjust the driving information of the vehicle to be controlled at each planned time with the goal of minimizing the comprehensive driving data, and constrain the driving information based on the constraint conditions to obtain the driving trajectory data of each vehicle to be controlled within the current time slice.

[0091] Based on the above-mentioned device, optionally, the constraints include constraints on the relationship between acceleration, velocity and position, range constraints on velocity and acceleration, driving behavior constraints, inter-vehicle distance constraints, lane changing constraints, lane-change prohibited zone constraints, variable consistency constraints, initial parameter constraints, variable definition constraints, planning period constraints, and intersection traffic constraints.

[0092] Based on the above-mentioned device, optionally, the intersection traffic constraints include traffic speed constraints, traffic lane constraints, traffic light constraints, and periodic boundary constraints.

[0093] The vehicle trajectory planning device provided in this embodiment of the invention can execute the vehicle trajectory planning method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0094] Example 4

[0095] Figure 4A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is 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 can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0096] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0097] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0098] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as vehicle trajectory planning methods.

[0099] In some embodiments, the vehicle trajectory planning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle trajectory planning method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the vehicle trajectory planning method by any other suitable means (e.g., by means of firmware).

[0100] Various embodiments of the systems and techniques described above herein 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), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0101] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0102] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0103] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device 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 provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, 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 sound input, voice input, or tactile input).

[0104] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0105] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0106] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0107] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A vehicle trajectory planning method, characterized in that, Applications in intelligent connected vehicles include: Acquire vehicle driving data for at least one vehicle to be controlled within the target control area in the current time slice; wherein, the vehicle driving data includes initial position, initial speed, initial lane and / or target driving lane; The vehicle driving data and associated data related to the target control area are processed to obtain comprehensive driving data for all vehicles to be controlled within the target control area, corresponding to the current time slice and the auxiliary planning cycle. The associated data includes traffic signal data, intersection location information, and lane-change prohibition zone location information. The start time of the auxiliary planning cycle is later than the start time of the current time slice. The comprehensive driving data includes driving duration data, acceleration data, and lane-change frequency data. With the goal of minimizing the comprehensive driving data, the driving information of the vehicle to be controlled at each planned time is adjusted to obtain the driving trajectory data of the vehicle to be controlled within the current time slice; The driving trajectory data is sent to the corresponding vehicle to be controlled, so that the vehicle to be controlled follows the corresponding driving trajectory data. The step of adjusting the driving information of the vehicle to be controlled at each planned time point with the goal of minimizing the comprehensive driving data, in order to obtain the driving trajectory data of the vehicle to be controlled within the current time slice, includes: Obtain the constraints of the trajectory planning model; With the goal of minimizing the comprehensive driving data, when adjusting the driving information of the vehicle to be controlled at each planned time, the driving information is constrained based on the constraint conditions to obtain the driving trajectory data of each vehicle to be controlled within the current time slice; The constraints include constraints on the relationship between acceleration, velocity and position, range constraints on velocity and acceleration, driving behavior constraints, inter-vehicle distance constraints, lane changing constraints, no-lane-changing zone constraints, variable consistency constraints, initial parameter constraints, variable definition constraints, planning cycle constraints, and intersection traffic constraints. Among these, the intersection traffic constraints include traffic speed constraints, traffic lane constraints, traffic light constraints, and cycle boundary constraints.

2. The method according to claim 1, characterized in that, The process of processing the vehicle driving data and the associated data related to the target control area yields comprehensive driving data for all vehicles to be controlled within the target control area, corresponding to the current time slice and the auxiliary planning cycle. This includes: Determine a trajectory planning model with the objective of minimizing the comprehensive driving data; The vehicle driving data and the associated data are input into the trajectory planning model to obtain driving time data, acceleration data and lane change frequency data; The comprehensive driving data is obtained based on the driving time data, acceleration data, lane change frequency data, and corresponding weights.

3. The method according to claim 2, characterized in that, The step of inputting the vehicle driving data and the associated data into the trajectory planning model to obtain driving time data, acceleration data, and lane change frequency data includes: Based on the trajectory planning model, the vehicle driving data and the associated data are processed to obtain the driving sub-duration and lane change number of each vehicle to be controlled in the target control area, as well as the square of the acceleration at each planning time. The driving time data is obtained based on each of the driving sub-durations; Based on the number of lane changes, the lane change frequency data is obtained; The acceleration data is obtained based on the square of each acceleration.

4. The method according to claim 1, characterized in that, The intersection traffic constraints include traffic speed constraints, traffic lane constraints, traffic light constraints, and periodic boundary constraints.

5. A vehicle trajectory planning device, characterized in that, Configured for intelligent connected vehicles, including: The data acquisition module is used to acquire vehicle driving data of at least one vehicle to be controlled in the target control area within the current time slice; wherein, the vehicle driving data includes initial position, initial speed, initial lane and / or target driving lane; The driving data determination module is used to process the vehicle driving data and the associated data related to the target control area to obtain comprehensive driving data of all vehicles to be controlled in the target control area corresponding to the current time slice and the auxiliary planning cycle; wherein, the associated data includes traffic signal data, intersection location information and lane-changing prohibition zone location information; the start time of the auxiliary planning cycle is later than the start time of the current time slice; the comprehensive driving data includes driving duration data, acceleration data and lane-changing frequency data; The driving trajectory determination module is used to adjust the driving information of the vehicle to be controlled at each planned time with the goal of minimizing the comprehensive driving data, so as to obtain the driving trajectory data of the vehicle to be controlled within the current time slice; The trajectory sending module is used to send the driving trajectory data to the corresponding vehicle to be controlled, so that the vehicle to be controlled follows the corresponding driving trajectory data. The driving trajectory determination module includes: a constraint condition determination unit and a driving trajectory determination unit; The constraint determination unit is used to obtain the constraint conditions of the trajectory planning model; The driving trajectory determination unit is used to adjust the driving information of the vehicle to be controlled at each planned time with the goal of minimizing the comprehensive driving data, and constrain the driving information based on the constraint conditions to obtain the driving trajectory data of each vehicle to be controlled within the current time slice. The constraints include constraints on the relationship between acceleration, velocity and position, range constraints on velocity and acceleration, driving behavior constraints, inter-vehicle distance constraints, lane changing constraints, lane-change prohibited zone constraints, variable consistency constraints, initial parameter constraints, variable definition constraints, planning cycle constraints, and intersection traffic constraints. The intersection traffic constraints include traffic speed constraints, traffic lane constraints, traffic light constraints, and cycle boundary constraints.

6. The apparatus according to claim 5, characterized in that, The driving data determination module includes: A trajectory planning model determination unit is used to determine a trajectory planning model with the objective of minimizing the comprehensive driving data; The model processing unit is used to input the vehicle driving data and the associated data into the trajectory planning model to obtain driving time data, acceleration data and lane change frequency data; The driving data determination unit is used to obtain the comprehensive driving data based on the driving time data, acceleration data, lane change frequency data, and corresponding weights.

7. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the vehicle trajectory planning method according to any one of claims 1-4.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the vehicle trajectory planning method according to any one of claims 1-4.

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