Autonomous driving vehicle control method and device

Abstract

The present disclosure provides a method, device, storage medium, electronic device, and product for controlling an autonomous vehicle, relating to the field of autonomous driving technology, particularly planning and control technology. A specific implementation scheme comprises: determining an energy consumption cost function for the autonomous vehicle, and determining a target planning cost function based on the energy consumption cost function; calculating the vehicle speed corresponding to the minimum value of the target planning cost function; and controlling the autonomous vehicle based on the vehicle speed. This disclosure can effectively reduce the energy consumption of autonomous vehicles.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, in particular to the field of planning and control technology, and specifically designs a method, device, storage medium, electronic device and product for controlling an autonomous driving vehicle. Background Art

[0002] With the advancement of autonomous driving technology and the improvement of relevant regulations, autonomous driving will gradually be commercialized on a large scale.

[0003] During the application phase, greater consideration is given to the safety, passability, and physical experience of autonomous vehicles. As autonomous driving technology matures, driverless driving may become a reality. However, the performance of autonomous vehicles considered in related technologies is not perfect. Summary of the Invention

[0004] The present disclosure provides a method, device, storage medium, electronic device and product for controlling an autonomous driving vehicle.

[0005] According to a first aspect of the present disclosure, there is provided a method for controlling an autonomous driving vehicle, comprising:

[0006] Determine an energy consumption cost function for the autonomous vehicle, and determine a target planning cost function based on the energy consumption cost function; calculate a vehicle speed corresponding to a minimum value of the target planning cost function; and control the autonomous vehicle based on the vehicle speed.

[0007] According to a second aspect of the present disclosure, there is provided an autonomous driving vehicle control device, the device comprising:

[0008] A determination module is used to determine the energy consumption cost function of the autonomous driving vehicle and determine the target planning cost function based on the energy consumption cost function; a calculation module is used to calculate the vehicle driving speed corresponding to the minimum value of the target planning cost function; and a control module is used to control the autonomous driving vehicle based on the vehicle driving speed.

[0009] According to a third aspect of the present disclosure, there is provided an electronic device, including:

[0010] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the method described in the first aspect.

[0011] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to cause the computer to execute the method according to the first aspect.

[0012] According to a fifth aspect of the present disclosure, a computer product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method according to the first aspect.

[0013] According to a sixth aspect of the present disclosure, an autonomous driving product is provided, comprising an autonomous driving system, which, when executed by a processor, implements the method described in the first aspect.

[0014] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0016] Figure 1 A schematic diagram of an autonomous driving system in related art is shown;

[0017] Figure 2 A schematic diagram of a flow chart of an autonomous driving vehicle control method provided by an embodiment of the present disclosure is shown;

[0018] Figure 3 A flow chart of a method for determining an energy consumption cost function provided by an embodiment of the present disclosure is shown;

[0019] Figure 4 A flow chart of a method for determining a target planning cost function provided by an embodiment of the present disclosure is shown;

[0020] Figure 5 A flow chart of a method for determining a vehicle speed provided by an embodiment of the present disclosure is shown;

[0021] Figure 6 A schematic diagram showing the relationship between a segmented planned path and a vehicle driving speed provided by an embodiment of the present disclosure is shown;

[0022] Figure 7 A schematic diagram of a flow chart of a method for controlling an autonomous driving vehicle provided by an embodiment of the present disclosure is shown;

[0023] Figure 8 A schematic structural diagram of an autonomous driving vehicle control device provided by an embodiment of the present disclosure is shown;

[0024] Figure 9 A schematic block diagram of an example electronic device 900 is shown, which may be used to implement embodiments of the present disclosure. DETAILED DESCRIPTION

[0025] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0026] Figure 1 A schematic diagram of an autonomous driving system in related art is shown. Figure 1 As shown in , the core modules of the autonomous driving system include: high-precision map, positioning, perception, global navigation, prediction, planning, and control.

[0027] It should be noted that high-precision maps are used to provide high-precision map services. The positioning system is used to provide high-precision (for example, centimeter-level) positioning services. The perception system is used to provide comprehensive environmental perception services for autonomous vehicles through cameras, lidar, millimeter-wave radar, ultrasonic radar and other equipment combined with advanced obstacle detection algorithms. The prediction system is used to use the data of the upstream perception system as input, extract the historical motion parameters of the obstacle, and combine Kalman filtering, neural networks and other means to infer the future motion trajectory of the obstacle for use by the downstream planning and control module. Global navigation is used to obtain the optimal global navigation path that meets the performance evaluation indicators based on the vehicle's initial position and target position, combined with the road network topology, through a global path search algorithm. Decision planning is mainly used to provide the main vehicle with obstacle avoidance, lane change decision-making, path planning, and speed planning services. The control system is used to perform longitudinal and lateral tracking control according to the driving trajectory provided by the decision planning system.

[0028] Due to current restrictions on autonomous driving technology and regulations, autonomous vehicles can generally only be used in specific scenarios, such as closed parks and schools. Therefore, during the application process, the focus of attention on autonomous driving vehicles is on their safety, passability, and driving experience.

[0029] With the advancement of autonomous driving technology and the improvement of relevant regulations, autonomous driving is gradually being commercialized. Consequently, performance requirements for autonomous driving systems are no longer limited to driving safety, passability, and physical sensations, but also to vehicle operation. Operating costs primarily consist of drivers, energy consumption (fuel and electricity), and maintenance costs. As autonomous driving technology matures, driverless driving may become possible, reducing driver costs.

[0030] Furthermore, energy conservation and cost reduction remain key issues. For example, compared to real drivers, autonomous vehicles are less intelligent and lack the optimal driving habits of real drivers in global path selection, real-time trajectory planning, and operational control. This results in generally higher energy consumption than real drivers. Furthermore, different driving strategies of autonomous vehicles also lead to significantly different energy consumption.

[0031] To address the aforementioned energy conservation and cost reduction issues, the present disclosure proposes a method and device for controlling an autonomous vehicle. A model for controlling the energy consumption of a motor-driven autonomous vehicle can be established. This model is then applied to the self-defined driving goal planning cost function to evaluate the speed curve (i.e., speed trajectory). Combined with dynamic route planning, this allows the autonomous vehicle to control its energy consumption while satisfying the requirements of physical perception optimization, safety optimization, and versatility optimization. This optimizes energy consumption and effectively saves the vehicle's energy.

[0032] The following embodiments will illustrate the autonomous driving vehicle control method and device proposed in this disclosure in conjunction with the accompanying drawings.

[0033] Figure 2 A flow chart of a method for controlling an autonomous vehicle provided by an embodiment of the present disclosure is shown. Figure 2 As shown in , the method may include:

[0034] In step S210, an energy consumption cost function of the autonomous driving vehicle is determined, and a target planning cost function is determined based on the energy consumption cost function.

[0035] In the present disclosure, the target planning cost function of the autonomous driving vehicle is pre-constructed. The target planning cost function may include multiple sub-cost functions, for example, the sub-cost function may be a speed cost function, an energy consumption cost function, and the like.

[0036] In the disclosed embodiment, the current driving path of the autonomous vehicle is obtained. The driving path can be understood as the planned path of the autonomous vehicle to the destination. An energy consumption cost function is used to control energy consumption and optimize energy to achieve energy conservation.

[0037] In step S220, the vehicle speed corresponding to the minimum value of the target planning cost function is calculated.

[0038] In the disclosed embodiments, the planned path for the autonomous vehicle to the destination can be divided into multiple driving paths. Based on the current driving path, the speed constraint corresponding to that driving path segment can be determined. Based on the speed constraint, the vehicle speed corresponding to the minimum value of the target planning cost function can be determined.

[0039] In step S230, the autonomous driving vehicle is controlled based on the vehicle's driving speed.

[0040] In the embodiment of the present disclosure, it should be noted that the target planning cost function is a function determined based on speed. In other words, the vehicle speed can be a variable of the target planning cost function.

[0041] The target planning cost function includes an energy consumption cost function for controlling the energy consumption of the autonomous driving system. The present disclosure can control the autonomous driving vehicle based on the vehicle's speed, thereby controlling the energy consumption of the autonomous driving vehicle.

[0042] The autonomous vehicle control method provided herein adds an energy consumption cost function to the target planning cost function of the autonomous driving system of the autonomous vehicle. This allows for optimizing energy consumption while optimizing safety, physical sensation, and other indicators. By determining a speed trajectory while minimizing energy consumption, energy conservation can be achieved.

[0043] In the embodiment of the present disclosure, the energy consumption cost function may be an integral function of the vehicle's acceleration and speed. The following embodiment will illustrate how to determine the energy consumption cost function with reference to the accompanying drawings.

[0044] Figure 3 A flow chart of a method for determining an energy consumption cost function provided by an embodiment of the present disclosure is shown. Figure 3 As shown in , the method may include:

[0045] In step S310, an energy function for calculating the energy consumption of the motor of the autonomous driving vehicle is obtained.

[0046] In the present disclosure, the expression is the product between the transient current of the motor of the autonomous driving vehicle and the transient voltage of the motor of the autonomous driving vehicle.

[0047] In the embodiment of the present disclosure, the energy function used to calculate energy consumption may be an integral function of energy calculation, and its functional formula is as follows:

[0048]

[0049] Where E represents the energy consumption of the motor, i(t) represents the motor transient current passing through the motor at time t, and e(t) represents the motor transient voltage passing through the motor at time t.

[0050] In the present disclosure, t may be the time period of vehicle startup, generally 0-8s, the stage of converting electrical energy into kinetic energy.

[0051] In the disclosed embodiment, i(t) and e(t) are related to motor characteristic parameters, internal and external friction torques, damping torque, moment of inertia, and angular velocity ω(t) at time t. Furthermore, since the motor drives the vehicle, the motor's angular velocity ω(t) is related to the autonomous vehicle's speed v(t).

[0052] Furthermore, in the embodiment of the present disclosure, the relationship between the angular velocity and the vehicle speed may be:

[0053] ω=2π / T=V / R

[0054] Where ω represents the angular velocity, T represents the cycle time, V represents the vehicle speed, and R represents the travel arc.

[0055] In the disclosed embodiments, the motor transient current and voltage are related to motor characteristic parameters, internal and external friction torque, damping torque, moment of inertia, and angular velocity. Therefore, the motor transient current and voltage can be expressed using angular velocity. Furthermore, based on the relationship between angular velocity and vehicle speed and acceleration, the motor transient current and voltage can be expressed using vehicle speed and acceleration.

[0056] In step S320 , a first conversion relationship between the vehicle speed and the motor transient current and a second conversion relationship between the vehicle speed and the motor transient voltage are obtained.

[0057] In the present disclosure, the vehicle speed may be represented by angular velocity. Furthermore, based on the relationship between the angular velocity and the motor transient current, a first conversion relationship between the vehicle speed and the motor transient current is determined. Based on the relationship between the angular velocity and the motor transient voltage, a second conversion relationship between the vehicle speed and the motor transient current is determined.

[0058] In step S330 , the energy function is converted based on the first conversion relationship and the second conversion relationship to determine an energy consumption cost function based on the vehicle driving speed.

[0059] In the disclosed embodiment, a motor transient current expression representing the motor transient current based on the vehicle speed and the vehicle acceleration can be determined based on a first conversion relationship between the motor transient current and the vehicle speed and a relationship between the vehicle speed and the vehicle acceleration. A voltage expression representing the motor transient voltage based on the vehicle speed can be determined based on a second conversion relationship between the vehicle speed and the motor transient voltage.

[0060] Furthermore, the product of the motor transient current expression and the motor transient voltage expression is simplified to obtain an energy consumption cost function including a first undetermined coefficient.

[0061] For example, the energy consumption cost function including the first undetermined coefficient can be expressed by the vehicle acceleration and the vehicle speed, as follows:

[0062]

[0063] Among them, a 2 (t) represents the vehicle acceleration, and w1, w2, w3, w4, w5, and w6 represent the first unknown coefficients.

[0064] Since the initial speed of the vehicle is very close to the final speed of the vehicle, the present disclosure simplifies the formula of the energy consumption cost function of the first undetermined coefficient to obtain the final formula of the energy consumption cost function including the first undetermined coefficient, as follows:

[0065]

[0066] Therefore, the expression of motor energy consumption can be converted into an expression based on vehicle speed and vehicle acceleration.

[0067] The following embodiments of the present disclosure will illustrate how to determine the first undetermined coefficient.

[0068] In the disclosed embodiment, as can be seen from the above embodiment, i(t) and e(t) are related to motor characteristic parameters. i(t) and e(t) can be expressed as a linear combination of v(t) and a(t). The linear combination of the motor transient current and the motor transient voltage is as follows:

[0069] i(t)=c1+c2v(t)+c3a(t)

[0070] e(t)=c4+c5v(t)+c6a(t)

[0071] Among them, c1, c2, c3, c4, c5, and c6 are the second unknown coefficients.

[0072] In the embodiment of the present disclosure, the product of i(t) and e(t) represents the energy consumed by the calculation. It also represents energy consumption. When the linear combination expression is the same as the result of the energy consumption cost function, the vehicle acceleration and vehicle speed for driving the vehicle are obtained. The relationship between the first undetermined coefficient and the second undetermined coefficient can be determined.

[0073] The relationship between the first undetermined coefficient and the second undetermined coefficient is as follows:

[0074] w1=c3c6

[0075] w2=c2c5

[0076] w3=c1c5+c2c4

[0077] w4=c1c4

[0078] In the disclosed embodiment, the second undetermined coefficients can be calibrated by collecting motor current and motor voltage values ​​for multiple groups of vehicles at different vehicle speeds and accelerations. The motor current and motor voltage values ​​for multiple groups of vehicles at different vehicle speeds and accelerations can be collected using sensors. Based on the collected vehicle accelerations and speeds, a linear combination expression for the motor transient current and motor transient voltage is calculated, thereby determining the values ​​of all the second undetermined coefficients.

[0079] Furthermore, according to the value of the second undetermined coefficient and the relationship between the first undetermined coefficient and the second undetermined coefficient, the first undetermined coefficient is determined to obtain the energy consumption cost function. That is,

[0080]

[0081] At this time, w1, w2, w3, and w4 are known values.

[0082] The following embodiments of the present disclosure will illustrate how to determine the target planning cost function of an autonomous driving vehicle.

[0083] Figure 4 A flow chart of a method for determining a target planning cost function provided by an embodiment of the present disclosure is shown. Figure 4 As shown in , the method may include:

[0084] In step S410 , a speed cost function and an energy consumption cost function are obtained.

[0085] In the present disclosure, the speed constraint cost function is determined by the vehicle's speed, and the speed cost function can be determined by acquisition. The energy consumption cost function is predetermined by the vehicle's speed.

[0086] It should be noted that, for the convenience of description, the present disclosure refers to the function used to calculate the cost related to speed, speed constraint (eg, maximum speed), and obstacles as a speed cost function.

[0087] In step S420 , the sum of the energy consumption cost function, the speed cost function, and the target planning cost function of the previous driving path is determined as the target planning cost function.

[0088] In the embodiment of the present disclosure, the target planning cost function can be a recursive function, which can determine the minimum value of the sum of the energy consumption cost function, the speed cost function and the target planning cost function of the previous driving path as the target planning cost function of the current driving path.

[0089] For example, the current target planning cost function is represented by F, and when the speed of any path is k, the cost function related to speed, speed constraint (e.g., maximum speed), and obstacle (i.e., speed cost function) is represented by C, and the energy consumption cost function is represented by E. Then, the target planning cost function is as follows:

[0090] F(v (k) ,i)=min 0≤j≤M (F(v j ,i-1)+C(v (j) ,v (k) ,v m (i),obs)+E(vj,v(k),vm(i)))

[0091] Where C represents the cost related to speed, speed constraints (e.g., maximum speed), and obstacles when the autonomous vehicle is traveling on the i-th segment of the path at speed k; F on the right side of the equation represents the total cost of the previous segment of any path at speed j; F on the left side of the equation represents the total cost of any path at speed k; and v represents the vehicle's speed.

[0092] The present disclosure can calculate the vehicle driving speed at which the function is the minimum value through a dynamic programming method, thereby determining the speed planning of the autonomous driving vehicle on each driving path and realizing the control of the energy consumption of the autonomous driving system.

[0093] The following embodiments of the present disclosure will illustrate a method for determining the vehicle speed corresponding to the minimum value of the target planning cost function.

[0094] Figure 5 FIG. 1 shows a flow chart of a method for determining a vehicle speed provided by an embodiment of the present disclosure, such as Figure 5 As shown in , the method may include:

[0095] In step S510, the current driving path of the autonomous driving vehicle is obtained, and the speed constraint corresponding to the driving path is determined.

[0096] In step S520, based on the speed constraint, the vehicle speed corresponding to the minimum value of the target planning cost function is calculated.

[0097] In the disclosed embodiment, speed constraints may be pre-set for the path of the autonomous vehicle, and a correspondence between each path and the set speed constraints may be obtained. Based on the correspondence between the paths and the set speed constraints, the speed constraint corresponding to the current path may be obtained.

[0098] Furthermore, the vehicle speed corresponding to the minimum value of the target planning cost function is calculated according to the determined speed constraint condition. The vehicle speed corresponding to the minimum value of the target planning cost function is used as the speed of the autonomous driving vehicle traveling the current driving path.

[0099] The following embodiments of the present disclosure will illustrate setting speed constraints for the path traveled by an autonomous vehicle.

[0100] In the disclosed embodiment, corresponding speed constraints may be set for each driving path segment based on the road speed limit parameters, autonomous driving vehicle configuration parameters, and vehicle kinematic parameters of each driving path segment.

[0101] For example, the speed constraint condition may include at least one of the following constraints:

[0102] v(0)=v0

[0103] v(t f )=v f

[0104] v0≤v m

[0105] v f ≤v m

[0106] Among them, v0 represents the initial speed of the vehicle, v f Indicates the vehicle's terminal speed, v m Indicates the maximum speed of the vehicle.

[0107] In an embodiment of the present disclosure, before obtaining the current driving path of the autonomous driving vehicle, the planned path of the determined autonomous driving vehicle may be divided to obtain at least one driving path.

[0108] For example, Figure 6 FIG. 1 shows a schematic diagram of the relationship between a segmented planned path and a vehicle's driving speed provided by an embodiment of the present disclosure. Figure 6 As shown in , the planned path L is divided into N parts, so that for any segment (for example, segment i), a speed constraint can be added and the maximum speed v of the vehicle can be determined. m (i).

[0109] Maximum vehicle speed v m(i) It can be determined based on the road speed limit parameters of the driving path, the configuration parameters of the autonomous driving vehicle, and the vehicle kinematic parameters.

[0110] The following embodiments of the present disclosure will illustrate controlling an autonomous driving vehicle based on the vehicle's driving speed.

[0111] Figure 7 A flow chart of a method for controlling an autonomous driving vehicle provided by an embodiment of the present disclosure is shown. Figure 7 As shown in , the method may include:

[0112] In step S710, the vehicle speed of each driving path is obtained to obtain the speed trajectory of the planned path.

[0113] In step S720 , the energy consumption of the autonomous driving vehicle is controlled based on the speed trajectory.

[0114] In the embodiment of the present disclosure, the vehicle driving speed of each driving path determined in the above embodiment can be obtained, so as to obtain the speed curve of the autonomous driving vehicle driving the planned path and determine the speed trajectory of the planned path.

[0115] In the present disclosure, weight coefficients can also be set for the multiple sub-functions included in the target planning cost function. Based on the obtained speed curve, combined with the weight coefficients of the energy consumption cost function and the speed constraint cost function, the autonomous vehicle is dynamically controlled to control the energy consumption of the autonomous vehicle.

[0116] Among them, the weight coefficients of the energy consumption cost function and the speed constraint cost function can be dynamically determined according to the needs of body perception optimization, safety optimization, and energy consumption optimization.

[0117] The present invention introduces energy consumption cost in the speed planning of the planning and control process of the autonomous driving vehicle to obtain a speed trajectory that meets both safety and physical perception indicators while also meeting low energy requirements, thereby achieving the effect of effectively saving energy.

[0118] Based on Figure 2 The same principle as shown in the method, Figure 8 A schematic diagram of the structure of an automatic driving vehicle control device provided by an embodiment of the present disclosure is shown in FIG. Figure 8 As shown, the autonomous driving vehicle control device 800 may include:

[0119] The determination module 801 is used to determine the energy consumption cost function of the autonomous driving vehicle and determine the target planning cost function based on the energy consumption cost function; the calculation module 802 is used to calculate the vehicle driving speed corresponding to the minimum value of the target planning cost function; the control module 803 is used to control the autonomous driving vehicle based on the vehicle driving speed.

[0120] In an embodiment of the present disclosure, the determination module 801 is used to obtain an energy function for calculating the energy consumption of the motor of the autonomous driving vehicle, where the energy function is the product of the transient current of the motor of the autonomous driving vehicle and the transient voltage of the motor of the autonomous driving vehicle; obtain a first conversion relationship between the vehicle driving speed and the transient current of the motor and a second conversion relationship between the vehicle driving speed and the transient voltage of the motor; based on the first conversion relationship and the second conversion relationship, convert the energy function to determine an energy consumption cost function based on the vehicle driving speed.

[0121] In the embodiment of the present disclosure, the determination module 801 is used to obtain the current driving path of the autonomous driving vehicle and determine the target planning cost function of the current driving path; based on the driving path, determine the vehicle driving speed corresponding to the minimum value of the target planning cost function; obtain the speed cost function, and determine the sum of the energy consumption cost function, the speed cost function, and the target planning cost function of the previous driving path as the target planning cost function of the current driving path.

[0122] In the embodiment of the present disclosure, the calculation module 802 is used to obtain the current driving path of the autonomous driving vehicle and determine the speed constraint corresponding to the driving path; based on the speed constraint, calculate the vehicle driving speed corresponding to the minimum value of the target planning cost function.

[0123] In the embodiment of the present disclosure, the determination module 801 is further used to set corresponding speed constraint conditions for each section of the driving path based on the road speed limit parameters, autonomous driving vehicle configuration parameters and vehicle kinematic parameters of each section of the driving path.

[0124] In the embodiment of the present disclosure, the determination module 801 is further used to determine the planned path of the autonomous driving vehicle and divide the planned path to obtain at least one driving path.

[0125] In the embodiment of the present disclosure, the control module 803 is used to obtain the vehicle driving speed of each driving path to obtain the speed trajectory of the planned path; and based on the speed trajectory, control the energy consumption of the autonomous driving vehicle.

[0126] In the technical solutions disclosed herein, the acquisition, storage, and application of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0127] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0128] Figure 9A schematic block diagram of an example electronic device 900 that can be used to implement embodiments of the present disclosure 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 assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0129] like Figure 9 As shown, the device 900 includes a computing unit 901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 902 or a computer program loaded from a storage unit 908 into a random access memory (RAM) 903. Various programs and data required for the operation of the device 900 can also be stored in the RAM 203. The computing unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input / output (I / O) interface 1205 is also connected to the bus 904.

[0130] Various components in the device 900 are connected to the I / O interface 905, including an input unit 906, such as a keyboard, a mouse, etc.; an output unit 907, such as various types of displays, speakers, etc.; a storage unit 908, such as a magnetic disk, an optical disk, etc.; and a communication unit 909, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 909 allows the device 900 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0131] The computing unit 901 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 901 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 901 performs the various methods and processes described above, such as the autonomous vehicle control method. For example, in some embodiments, the autonomous vehicle control method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program can be loaded and / or installed on the device 900 via the ROM 902 and / or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the computing unit 901 can be configured to perform the autonomous vehicle control method by any other appropriate means (e.g., by means of firmware).

[0132] Various embodiments of the systems and techniques described 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), system-on-chip systems (SOCs), 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 comprising 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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 through computer programs running on the respective computers and having a client-server relationship with each other. The server may be a cloud server, a server in a distributed system, or a server integrated with a blockchain.

[0138] 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 limited herein.

[0139] 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 method for controlling an autonomous vehicle, characterized in that: The method comprises: Determining an energy consumption cost function of the autonomous driving vehicle, and determining a target planning cost function based on the energy consumption cost function; Calculating the vehicle speed corresponding to the minimum value of the target planning cost function; controlling the autonomous vehicle based on the vehicle's speed; The step of determining the energy consumption cost function of the autonomous driving vehicle includes: Obtaining an energy function for calculating energy consumption of a motor of the autonomous driving vehicle, where the energy function is the product of a transient current of the motor of the autonomous driving vehicle and a transient voltage of the motor of the autonomous driving vehicle; Acquire a first conversion relationship between the vehicle speed and the transient current of the motor and a second conversion relationship between the vehicle speed and the transient voltage of the motor; Based on the first conversion relationship and the second conversion relationship, the energy function is converted to determine an energy consumption cost function based on the vehicle driving speed.

2. The method according to claim 1, characterized in that The determining of the target planning cost function based on the energy consumption cost function includes: Obtaining a speed cost function and the energy consumption cost function; The sum of the energy consumption cost function, the speed cost function, and the target planning cost function of the previous driving path is determined as the target planning cost function of the current driving path.

3. The method according to claim 1 or 2, characterized in that The determining, based on the driving path, a vehicle driving speed corresponding to a minimum value of the target planning cost function includes: Obtaining a current driving path of the autonomous vehicle and determining a speed constraint corresponding to the driving path; Based on the speed constraint, the vehicle speed corresponding to the minimum value of the target planning cost function is calculated.

4. The method according to claim 3, characterized in that The speed constraint condition is determined as follows: Based on the road speed limit parameters, autonomous driving vehicle configuration parameters and vehicle kinematic parameters of each section of the driving path, corresponding speed constraint conditions are set for each section of the driving path.

5. The method according to claim 3, characterized in that Before obtaining the current driving path of the autonomous driving vehicle, the method further includes: Determine a planned path for the autonomous driving vehicle and divide the planned path into at least one driving path.

6. The method according to claim 1 or 2, characterized in that The controlling the autonomous driving vehicle based on the vehicle speed includes: Obtain the vehicle speed for each section of the route and obtain the speed trajectory of the planned route; Based on the speed trajectory, energy consumption of the autonomous driving vehicle is controlled.

7. An automatic driving vehicle control device, characterized in that: The device comprises: a determination module configured to obtain an energy function for calculating energy consumption of a motor of the autonomous driving vehicle, the energy function being the product of a transient current of the motor of the autonomous driving vehicle and a transient voltage of the motor of the autonomous driving vehicle; obtain a first conversion relationship between a vehicle speed and the transient current of the motor and a second conversion relationship between the vehicle speed and the transient voltage of the motor; convert the energy function based on the first conversion relationship and the second conversion relationship, determine an energy consumption cost function based on the vehicle speed, and determine a target planning cost function based on the energy consumption cost function; A calculation module, configured to calculate a vehicle speed corresponding to a minimum value of the target planning cost function; A control module is used to control the autonomous driving vehicle based on the vehicle's driving speed.

8. The device according to claim 7, characterized in that The determining module is configured to: Obtain the current driving path of the autonomous vehicle and determine the target planning cost function of the current driving path; Based on the driving path, determining the vehicle driving speed corresponding to the minimum value of the target planning cost function; Obtain the speed cost function and compare the energy consumption cost function with the speed cost function and the previous driving The sum of the target planning cost functions of the paths is determined as the target planning cost function of the current driving path.

9. The device according to claim 7 or 8, characterized in that The computing module is configured to: Obtaining a current driving path of the autonomous vehicle and determining a speed constraint corresponding to the driving path; Based on the speed constraint, the vehicle speed corresponding to the minimum value of the target planning cost function is calculated.

10. The device according to claim 9, characterized in that The determining module is further configured to: Based on the road speed limit parameters, autonomous driving vehicle configuration parameters and vehicle kinematic parameters of each section of the driving path, corresponding speed constraint conditions are set for each section of the driving path.

11. The device according to claim 9, characterized in that The determining module is further configured to: Determine a planned path for the autonomous driving vehicle and divide the planned path into at least one driving path.

12. The device according to claim 7 or 8, characterized in that The control module is used to: Obtain the vehicle speed for each section of the route and obtain the speed trajectory of the planned route; Based on the speed trajectory, energy consumption of the autonomous driving vehicle is controlled.

13. An electronic device comprising: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium storing computer instructions, wherein: The computer instructions are used to cause the computer to execute the method according to any one of claims 1 to 6.

15. A computer product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 6.

16. An autonomous driving product, comprising an autonomous driving system, wherein the autonomous driving system implements the method according to any one of claims 1 to 6 when executed by a processor.

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