Vehicle control method, device, electronic device and vehicle

Abstract

The present disclosure provides a vehicle control method, device, electronic device, and vehicle, relating to the field of artificial intelligence, particularly autonomous driving and intelligent transportation, to at least address the technical issues of unstable deceleration and poor adaptability of autonomous vehicles in the prior art. A specific implementation scheme comprises: obtaining the current distance between the current vehicle and a target obstacle; in response to the current distance being less than a preset distance, obtaining the historical distance between the current vehicle and the target obstacle, wherein the historical distance represents the distance between the current vehicle and the target obstacle collected over a period of time before the current moment; determining a target acceleration for the current vehicle based on the current distance and the historical distance; and controlling the current vehicle based on the target acceleration.

Description

Technical Field

[0001] The present disclosure relates to the field of artificial intelligence technology, and in particular to the fields of autonomous driving and intelligent transportation. Background Art

[0002] With the rapid development of the smart car industry, more and more people are paying attention to self-driving vehicles to replace human driving in order to reduce travel pressure. The safety of the vehicle during driving is an important criterion for measuring the quality of an autonomous vehicle. However, most autonomous vehicles currently do not plan the trajectory scenarios for a period of time in the future when considering safety. This may cause the autonomous vehicle to collide with other obstacles that need to be avoided after changing the driving speed. Summary of the Invention

[0003] The present disclosure provides a method, an apparatus, an electronic device, and a vehicle for vehicle control.

[0004] According to one aspect of the present disclosure, a vehicle control method is provided, comprising: obtaining a current distance between a current vehicle and a target obstacle; in response to the current distance being less than a preset distance, obtaining a historical distance between the current vehicle and the target obstacle, wherein the historical distance is used to represent the distance between the current vehicle and the target obstacle collected within a period of time before the current moment; determining a target acceleration of the current vehicle based on the current distance and the historical distance; and controlling the current vehicle based on the target acceleration.

[0005] Optionally, determining the target acceleration of the current vehicle based on the current distance and the historical distance includes: determining the speed change of the current vehicle based on the current distance and the historical distance; determining the collision time between the current vehicle and the target obstacle; and determining the target acceleration based on the current acceleration, speed change and collision time of the current vehicle.

[0006] Optionally, based on the current distance and the historical distance, determining the speed change of the current vehicle includes: obtaining the difference between the distances collected at two adjacent moments in the current distance and the historical distance to obtain the distance difference; obtaining the difference between two adjacent moments to obtain the time difference; obtaining the ratio of the distance difference and the time difference to obtain the speed change.

[0007] Optionally, based on the current acceleration, speed change and collision time of the current vehicle, determining the target acceleration includes: obtaining a first weight corresponding to the historical time; obtaining the product of the first weight and the speed change to obtain a weighted change; obtaining the ratio of the weighted change and the collision time to obtain a compensated acceleration; obtaining the ratio of the current acceleration and the compensated acceleration to obtain the target acceleration.

[0008] Optionally, the method further includes: acquiring a first speed of the current vehicle and a second speed of the target obstacle; and determining a preset distance based on the second speed and the current distance.

[0009] Optionally, determining the preset distance based on the second speed and the current distance includes: processing the second speed and the current distance using a distance recognition model to obtain the preset distance.

[0010] Optionally, the method further includes: determining a predicted speed of the target obstacle within a preset time period based on the target acceleration, wherein the preset time period is used to represent a time period after the current time; and determining a target distance of the current vehicle within the preset time period based on the predicted speed.

[0011] According to another aspect of the present disclosure, a vehicle control device is also provided, including: a first distance acquisition module for acquiring a current distance between a current vehicle and a target obstacle; a second distance acquisition module for acquiring a historical distance between the current vehicle and the target obstacle in response to the current distance being less than a preset distance, wherein the historical distance is used to represent the distance between the current vehicle and the target obstacle collected within a period of time before the current moment; an acceleration determination module for determining a target acceleration of the current vehicle based on the current distance and the historical distance; and a vehicle control module for controlling the current vehicle based on the target acceleration.

[0012] Optionally, the acceleration determination module includes: a change determination unit, used to determine the speed change of the current vehicle based on the current distance and the historical distance; a time determination unit, used to determine the collision time between the current vehicle and the target obstacle; and an acceleration determination unit, used to determine the target acceleration based on the current acceleration, speed change and collision time of the current vehicle.

[0013] Optionally, the change determination unit is further used to obtain the difference between the distances collected at two adjacent moments in the current distance and the historical distance to obtain the distance difference; obtain the difference between two adjacent moments to obtain the time difference; obtain the ratio of the distance difference and the time difference to obtain the speed change.

[0014] Optionally, the acceleration determination unit is also used to obtain a first weight corresponding to the historical time; obtain the product of the first weight and the velocity change to obtain a weighted change; obtain the ratio of the weighted change and the collision time to obtain a compensated acceleration; obtain the ratio of the current acceleration and the compensated acceleration to obtain a target acceleration.

[0015] Optionally, the device further includes: a speed acquisition module for acquiring a first speed of the current vehicle and a second speed of the target obstacle; and a first distance determination module for determining a preset distance based on the second speed and the current distance.

[0016] Optionally, the distance determination module is further configured to process the second speed and the current distance using a distance recognition model to obtain a preset distance.

[0017] Optionally, the device also includes: a speed determination module, used to determine the predicted speed of the target obstacle within a preset time period based on the target acceleration, wherein the preset time period is used to represent a time period after the current time; and a second distance determination module, used to determine the target distance of the current vehicle within the preset time period based on the predicted speed.

[0018] According to another aspect of the present disclosure, an electronic device is also provided, comprising: 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 so that the at least one processor can execute any one of the above methods.

[0019] According to another aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is further provided, wherein the computer instructions are used to enable a computer to execute any one of the above methods.

[0020] According to another aspect of the present disclosure, a vehicle is provided, comprising any one of the above-mentioned devices.

[0021] 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

[0022] The accompanying drawings are provided to facilitate a better understanding of the present invention and do not constitute a limitation of the present disclosure.

[0023] Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a vehicle control method is shown;

[0024] Figure 2 is a flow chart of a vehicle control method provided according to an embodiment of the present disclosure;

[0025] Figure 3 is a schematic diagram of a historical data curve provided according to an embodiment of the present disclosure;

[0026] Figure 4 is a flow chart of a method for maintaining a safe distance according to an embodiment of the present invention;

[0027] Figure 5 This is a structural block diagram of a vehicle control device provided according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] 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.

[0029] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0030] To prevent excessive useless features with low relevance to the target model from affecting its accuracy, feature dimensionality reduction is often used to remove these useless features. The target model is then trained using the reduced useful feature areas, thereby improving its accuracy. However, the feature dimensionality reduction and target model training processes are typically separate. Often, during target model training, the target model is directly trained using the filtered, effective features, rather than being trained specifically based on feature label information as in supervised learning. This can lead to technical issues such as low recognition accuracy and efficiency for the trained target model.

[0031] According to an embodiment of the present disclosure, a vehicle control method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0032] The method embodiments provided in the embodiments of the present disclosure can be executed in a mobile terminal, a computer terminal or a similar electronic device. 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 processing, cellular phones, smart phones, wearable devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present disclosure described and / or required herein. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing a vehicle control method is shown.

[0033] like Figure 1 As shown, the computer terminal 100 includes a computing unit 101, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 102 or a computer program loaded from a storage unit 108 into a random access memory (RAM) 103. Various programs and data required for the operation of the computer terminal 100 can also be stored in the RAM 103. The computing unit 101, the ROM 102, and the RAM 103 are connected to each other via a bus 104. An input / output (I / O) interface 105 is also connected to the bus 104.

[0034] Multiple components in the computer terminal 100 are connected to the I / O interface 105, including an input unit 106, such as a keyboard, a mouse, etc.; an output unit 107, such as various types of displays, speakers, etc.; a storage unit 108, such as a magnetic disk, an optical disk, etc.; and a communication unit 109, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 109 allows the computer terminal 100 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0035] The computing unit 101 can be a variety of general-purpose and / or specialized processing components with processing and computing capabilities. Some examples of the computing unit 101 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 target model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 101 performs the vehicle control method described herein. For example, in some embodiments, the vehicle control method can be implemented as a computer software program that is tangibly contained in a machine-readable medium, such as a storage unit 108. In some embodiments, part or all of the computer program can be loaded and / or installed on the computer terminal 100 via the ROM 102 and / or the communication unit 109. When the computer program is loaded into the RAM 103 and executed by the computing unit 101, one or more steps of the vehicle control method described herein can be performed. Alternatively, in other embodiments, the computing unit 101 can be configured to perform the vehicle control method by any other appropriate means (e.g., by means of firmware).

[0036] 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 that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0037] It should be noted that, in some optional embodiments, the above Figure 1 The electronic device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of hardware elements and software elements. Figure 1 This is merely one example of a particular embodiment and is intended to illustrate the types of components that may be present in the electronic device described above.

[0038] Under the above operating environment, the present disclosure provides the following Figure 2 The vehicle control method shown can be Figure 1The computer terminal or similar electronic device shown is used for execution. Figure 2 This is a flow chart of a vehicle control method provided according to an embodiment of the present disclosure. Figure 2 As shown, the method may include the following steps:

[0039] Step S202: Obtain the current distance between the current vehicle and the target obstacle.

[0040] The above-mentioned current vehicle may refer to an autonomous driving vehicle, and the above-mentioned target obstacle may refer to a movable object near the autonomous driving vehicle, generally referring to other vehicles.

[0041] Optionally, the target obstacle may be in front of the autonomous driving vehicle or behind the autonomous driving vehicle.

[0042] During the driving process, high-precision maps can be used to collect the current distance between the autonomous driving vehicle and other vehicles in real time, and a historical distance curve can be generated based on the current distance collected over a period of time.

[0043] Optionally, in order to reduce the operating pressure of the autonomous driving vehicle's system, a smaller interval time may be set, and the autonomous driving vehicle collects the current distance once every interval time.

[0044] Optionally, the method of collecting the current distance may also include, but is not limited to: obtaining using a millimeter wave radar, obtaining using an infrared sensor, etc., without specific limitation.

[0045] Step S204 : In response to the current distance being less than the preset distance, obtaining a historical distance between the current vehicle and the target obstacle.

[0046] The historical distance is used to represent the distance between the current vehicle and the target obstacle collected within a period of time before the current moment.

[0047] The above-mentioned preset distance may refer to a safe distance between two vehicles, and the safe distance may generally be set to 10m.

[0048] Optionally, it is possible to determine in real time whether the current distance is less than the safety distance. If the current distance is less than the safety distance, it means that the autonomous driving vehicle is at risk of colliding with other vehicles. At this time, the historical distance can be obtained to assist the autonomous driving vehicle in changing its speed.

[0049] Optionally, the above-mentioned preset distance can be adaptively changed according to different road driving conditions and the driving speed of the autonomous driving vehicle. For example, when driving at high speed, the preset distance can be set to 80m; when driving at medium speed, the preset distance can be set to 50m; when driving at low speed, the preset distance can be set to 20m.

[0050] Optionally, if it is detected that there are other vehicles in front and behind the autonomous driving vehicle, and the distance between the two vehicles adjacent to the autonomous driving vehicle is less than a preset distance, the driving distance between the two vehicles can also be obtained, and the above preset distance can be set to half of the driving distance.

[0051] It should be noted that the above-mentioned method for setting the preset distance is only for exemplary reference and is not a specific limitation.

[0052] Step S206, determining the target acceleration of the current vehicle based on the current distance and the historical distance;

[0053] After obtaining the current distance and historical distance, the target acceleration used to change the speed of the autonomous driving vehicle can be dynamically calculated according to the current distance and the changing trend of the historical distance.

[0054] Step S208: Control the current vehicle based on the target acceleration.

[0055] After the target acceleration is calculated, the speed of the autonomous vehicle can be controlled to change according to the target acceleration to achieve dynamic adaptive changes in the speed of the autonomous vehicle and avoid collisions between the autonomous vehicle and other vehicles in front or behind.

[0056] According to the above steps S202 to S208 of the present disclosure, by dynamically changing the speed of the current vehicle according to the current distance and historical distance from the current vehicle to the target obstacle, the preset safe distance from the target obstacle is always maintained. This can ensure the safety of passengers while avoiding bumps caused by the autonomous driving vehicle moving or stopping suddenly, thereby solving the technical problems of unstable deceleration and poor adaptability of autonomous driving vehicles in the prior art.

[0057] In the above-mentioned embodiment of the present disclosure, determining the target acceleration of the current vehicle based on the current distance and the historical distance includes: determining the speed change of the current vehicle based on the current distance and the historical distance; determining the collision time between the current vehicle and the target obstacle; and determining the target acceleration based on the current acceleration, speed change and collision time of the current vehicle.

[0058] The above speed change is generally used to indicate the speed change trend of the autonomous driving vehicle in a period of time before the current moment, which can be the ratio of the change value of the current distance in the period to the value of the period.

[0059] In order to avoid the target acceleration suddenly disappearing when the autonomous vehicle changes its speed to a safe distance from other vehicles, causing discomfort to passengers on the autonomous vehicle, the current acceleration of the autonomous vehicle can be slowly reduced based on the above-mentioned speed change.

[0060] Specifically, when it is determined that the above-mentioned current distance is less than the safe distance, the above-mentioned saved historical distance curve can be first retrieved, and the speed change of the autonomous driving vehicle can be determined based on the historical distance curve. Then, the collision time between the autonomous driving vehicle and other vehicles while maintaining the current driving state is calculated, and the current acceleration of the autonomous driving vehicle is determined at the same time. Finally, based on the above-mentioned current acceleration, speed change, and collision event, the target acceleration required for the autonomous driving vehicle can be calculated, as shown below.

[0061] In the above embodiment of the present disclosure, determining the speed change of the current vehicle based on the current distance and the historical distance includes: obtaining the difference between the distances collected at two adjacent moments in the current distance and the historical distance to obtain the distance difference; obtaining the difference between two adjacent moments to obtain the time difference; obtaining the ratio of the distance difference and the time difference to obtain the speed change.

[0062] In order to improve the accuracy of the calculated target acceleration of the autonomous driving vehicle, the speed change can be calculated based on the above historical distance to assist in compensating the target acceleration. Figure 3 is a schematic diagram of a historical data curve provided according to an embodiment of the present disclosure, taking the target obstacle as another vehicle in front of the autonomous driving vehicle as an example. Figure 3 As shown in the figure, S represents the changing curve of the distance between the autonomous driving vehicle and other vehicles in a period of time before the current moment, T represents the time of the above period of time, and point A represents the distance between the two vehicles at 0.7s from the current moment.

[0063] When calculating the above speed change, the change at adjacent moments may be selected, or the change within a preset time may be selected. The specific setting may be based on actual needs and is not specifically limited.

[0064] Taking into account the complexity of calculation, the above speed change is generally the change within 1s before the current moment, that is, generally, the above time difference is 1s. At this time, the above distance difference can be obtained first according to the historical distance curve, such as Figure 3 As shown, the distance between the two vehicles is 3.58m at the current time, and the distance between the two vehicles at time -1s is 5.92m, so the distance difference at this time is 2.34m. Then, the above speed change is calculated according to the preset calculation formula.

[0065] Optionally, the preset calculation formula may be:

[0066]

[0067] Where s represents the distance between the two vehicles, t represents the time of the value acquisition, and i represents the number of acquisitions, for example Figure 3In the example, i=1 corresponds to time -1.1s, and i=3 corresponds to time -0.9s. According to the above formula, the speed change at this time is calculated to be 2.34m / s. It should be noted that the above preset calculation formula is only for illustrative purposes and is not limited to any specific calculation method as long as it can represent the speed change of the autonomous vehicle over a period of time.

[0068] In the above-mentioned embodiment of the present disclosure, determining the target acceleration based on the current acceleration, speed change and collision time of the current vehicle includes: obtaining a first weight corresponding to the historical time; obtaining the product of the first weight and the speed change to obtain a weighted change; obtaining the ratio of the weighted change and the collision time to obtain a compensated acceleration; obtaining the ratio of the current acceleration and the compensated acceleration to obtain the target acceleration.

[0069] The above-mentioned collision time may refer to the predicted time when the autonomous driving vehicle and other vehicles will collide in the future. The specific calculation method can be referred to in relevant literature and will not be repeated here.

[0070] In order to prevent the target acceleration from being unable to decrease slowly due to the speed change being too small, a first weight corresponding to the above period of time may be set to improve the quality of the above speed change.

[0071] Optionally, the first weight can be a fixed value set based on actual conditions, or a variable value based on the speed of the autonomous vehicle, such that the greater the current speed of the autonomous vehicle, the greater the first weight. The manner in which the first weight is set is not specifically limited.

[0072] After obtaining the speed change, the speed change can be weighted using a first weight, and then the compensation speed can be obtained using the weighted change. Finally, the target acceleration is obtained based on the current acceleration of the autonomous driving vehicle and the compensation acceleration, so as to slowly reduce the current acceleration and improve the passenger experience.

[0073] Specifically, the formula for calculating the weighted change is as follows:

[0074] bias=w t *Δv

[0075] Among them, bias represents the weighted change, w t Represents the first weight. The formula for calculating the compensation acceleration is as follows:

[0076]

[0077] Where v' represents the compensation acceleration and ttc represents the collision time. The formula for calculating the target acceleration is as follows:

[0078]

[0079] Where new_acc represents the target acceleration.

[0080] In the above embodiment of the present disclosure, the first speed of the current vehicle and the second speed of the target obstacle may also be acquired; and the preset distance may be determined based on the second speed and the current distance.

[0081] In addition to setting the safety distance based on the driving status of the autonomous vehicle as mentioned above, the safety distance can also be set dynamically based on the target obstacle, that is, the driving speed of the other vehicle, and the current distance between the two vehicles.

[0082] After the speed variation is acquired, determining the preset distance based on the second speed and the current distance includes: processing the second speed and the current distance using a distance recognition model to obtain the preset distance.

[0083] The aforementioned distance recognition model may refer to a model for calculating a safe distance, and any model can be used without specific limitation, as long as it can derive a new distance less than the input distance based on the input speed and input distance. As the current distance between the two vehicles increases and the second speed increases, the corresponding preset distance increases. The preset distance is then determined using the aforementioned distance recognition model, further ensuring the safety of the autonomous vehicle.

[0084] In the above embodiment of the present disclosure, the predicted speed of the target obstacle within a preset time period can also be determined based on the target acceleration, wherein the preset time period is used to represent the time period after the current time; and the target distance of the current vehicle within the preset time period is determined based on the predicted speed.

[0085] Optionally, after obtaining the target acceleration, the distance between the autonomous driving vehicle and other vehicles after the speed is changed according to the target acceleration can be further predicted. If the autonomous driving vehicle can accelerate or decelerate to a safe distance before the collision time, the autonomous driving vehicle can accelerate or decelerate according to the calculated target acceleration; if not, the target acceleration can be adaptively increased according to the above method to avoid a collision.

[0086] In order to clearly show the above embodiment, Figure 4 FIG. 1 is a flow chart of a method for maintaining a safe distance according to an embodiment of the present invention. Figure 4 As shown, the safe distance can be first calculated according to the actual situation, and then it is determined whether the current distance between the autonomous driving vehicle and the other vehicle is less than the safe distance. If it is less than, the distance-removing strategy shown in the aforementioned steps S202 to S208 is initiated to avoid a collision between the two vehicles; if it is not less than, the current driving state of the autonomous driving vehicle is maintained.

[0087] In the technical solutions disclosed herein, the collection, storage, use, processing, transmission, provision and disclosure of user personal information involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0088] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, or of course by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present disclosure.

[0089] The present disclosure also provides a vehicle control device for implementing the above-described embodiments and preferred embodiments. Details already described will not be repeated. As used below, the term "module" may refer to a combination of software and / or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementation using hardware, or a combination of software and hardware, is also possible and contemplated.

[0090] Figure 5 is a structural block diagram of a vehicle control device provided according to an embodiment of the present disclosure, such as Figure 5 As shown, a vehicle control device 500 includes: a first distance acquisition module 502 , a second distance acquisition module 504 , an acceleration determination module 506 , and a vehicle control module 508 .

[0091] Specifically, the first distance acquisition module 502 is used to obtain the current distance between the current vehicle and the target obstacle; the second distance acquisition module 504 is used to obtain the historical distance between the current vehicle and the target obstacle in response to the current distance being less than a preset distance, wherein the historical distance is used to represent the distance between the current vehicle and the target obstacle collected within a period of time before the current moment; the acceleration determination module 506 is used to determine the target acceleration of the current vehicle based on the current distance and the historical distance; and the vehicle control module 508 is used to control the current vehicle based on the target acceleration.

[0092] Optionally, the acceleration determination module 506 includes: a change determination unit, used to determine the speed change of the current vehicle based on the current distance and the historical distance; a time determination unit, used to determine the collision time between the current vehicle and the target obstacle; and an acceleration determination unit, used to determine the target acceleration based on the current acceleration, speed change and collision time of the current vehicle.

[0093] Optionally, the change determination unit is further used to obtain the difference between the distances collected at two adjacent moments in the current distance and the historical distance to obtain the distance difference; obtain the difference between two adjacent moments to obtain the time difference; obtain the ratio of the distance difference and the time difference to obtain the speed change.

[0094] Optionally, the acceleration determination unit is also used to obtain a first weight corresponding to the historical time; obtain the product of the first weight and the velocity change to obtain a weighted change; obtain the ratio of the weighted change and the collision time to obtain a compensated acceleration; obtain the ratio of the current acceleration and the compensated acceleration to obtain a target acceleration.

[0095] Optionally, the device further includes: a speed acquisition module for acquiring a first speed of the current vehicle and a second speed of the target obstacle; and a first distance determination module for determining a preset distance based on the second speed and the current distance.

[0096] Optionally, the distance determination module is further configured to process the second speed and the current distance using a distance recognition model to obtain a preset distance.

[0097] Optionally, the device also includes: a speed determination module, used to determine the predicted speed of the target obstacle within a preset time period based on the target acceleration, wherein the preset time period is used to represent a time period after the current time; and a second distance determination module, used to determine the target distance of the current vehicle within the preset time period based on the predicted speed.

[0098] It should be noted that the above modules can be implemented through software or hardware. For the latter, it can be implemented in the following ways, but not limited to: the above modules are all located in the same processor; or the above modules are located in different processors in any combination.

[0099] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, at least one processor; and 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 so that the at least one processor can perform the steps in any one of the above method embodiments.

[0100] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor, and the input / output device is connected to the processor.

[0101] Optionally, in the present disclosure, the processor may be configured to execute the following steps through a computer program:

[0102] S1, obtain the current distance between the current vehicle and the target obstacle;

[0103] S2, in response to the current distance being less than the preset distance, obtaining a historical distance between the current vehicle and the target obstacle;

[0104] S3, determining the target acceleration of the current vehicle based on the current distance and the historical distance;

[0105] S4, controlling the current vehicle based on the target acceleration.

[0106] Optionally, specific examples in this embodiment may refer to the examples described in the above embodiments and optional implementation modes, and this embodiment will not be described in detail here.

[0107] According to an embodiment of the present disclosure, the present disclosure also provides a non-transitory computer-readable storage medium storing computer instructions, wherein the non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions are configured to execute the steps of any of the above method embodiments during runtime.

[0108] Optionally, in this embodiment, the non-volatile storage medium may be configured to store a computer program for executing the following steps:

[0109] S1, obtain the current distance between the current vehicle and the target obstacle;

[0110] S2, in response to the current distance being less than the preset distance, obtaining a historical distance between the current vehicle and the target obstacle;

[0111] S3, determining the target acceleration of the current vehicle based on the current distance and the historical distance;

[0112] S4, controlling the current vehicle based on the target acceleration.

[0113] Alternatively, in this embodiment, the non-transitory computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any suitable combination of the above. More specific examples of readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the above.

[0114] According to an embodiment of the present disclosure, the present disclosure also provides a computer program product. The program code for implementing the audio processing 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 flowchart 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.

[0115] According to an embodiment of the present disclosure, the present disclosure further provides a vehicle, which is used to operate the above-mentioned vehicle control device, wherein the vehicle control method of the above-mentioned method embodiment is executed when the vehicle control device is running.

[0116] In the above embodiments of the present disclosure, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0117] In the several embodiments provided in the present disclosure, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0118] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected to achieve the purpose of the present embodiment according to actual needs.

[0119] In addition, the functional units in the various embodiments of the present disclosure may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0120] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present disclosure. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0121] The above is only a preferred embodiment of the present disclosure. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present disclosure. These improvements and modifications should also be regarded as within the scope of protection of the present disclosure.

Claims

1. A vehicle control method, comprising: Get the current distance between the current vehicle and the target obstacle; In response to the current distance being less than a preset distance, obtaining a historical distance between the current vehicle and the target obstacle, wherein the historical distance is used to represent the distance between the current vehicle and the target obstacle collected within a period of time before the current moment; determining a target acceleration of the current vehicle based on the current distance and the historical distance; controlling the current vehicle based on the target acceleration; Determining the target acceleration of the current vehicle based on the current distance and the historical distance includes: determining a speed change of the current vehicle based on the current distance and the historical distance; determining a collision time between the current vehicle and the target obstacle; obtaining a first weight corresponding to the historical time; obtaining a product of the first weight and the speed change to obtain a weighted change; obtaining a ratio of the weighted change to the collision time to obtain a compensated acceleration; and obtaining a ratio of the current acceleration to the compensated acceleration to obtain the target acceleration.

2. The method according to claim 1, wherein Determining the current vehicle speed change based on the current distance and the historical distance includes: Obtaining the difference between the current distance and the distances collected at two adjacent moments in the historical distance to obtain a distance difference; Obtaining the difference between the two adjacent moments to obtain a time difference; The ratio of the distance difference to the time difference is obtained to obtain the speed change.

3. The method according to claim 1, further comprising: Obtaining a first speed of the current vehicle and a second speed of the target obstacle; The preset distance is determined based on the second speed and the current distance.

4. The method according to claim 3, wherein: Determining the preset distance based on the second speed and the current distance includes: The second speed and the current distance are processed using a distance recognition model to obtain the preset distance.

5. The method according to claim 1, further comprising: determining a predicted speed of the target obstacle within a preset time period based on the target acceleration, wherein the preset time period is used to represent a time period after a current time; A target distance of the current vehicle within the preset time period is determined based on the predicted speed.

6. A vehicle control device comprising: A first distance acquisition module is used to obtain the current distance between the current vehicle and the target obstacle; a second distance acquisition module, configured to acquire, in response to the current distance being less than a preset distance, a historical distance between the current vehicle and the target obstacle, wherein the historical distance represents the distance between the current vehicle and the target obstacle collected within a period of time before the current moment; an acceleration determination module, configured to determine a target acceleration of the current vehicle based on the current distance and the historical distance; a vehicle control module, configured to control the current vehicle based on the target acceleration; The acceleration determination module includes: a change determination unit for determining a speed change of the current vehicle based on the current distance and the historical distance; a time determination unit for determining a collision time between the current vehicle and the target obstacle; and an acceleration determination unit for determining the target acceleration based on the current acceleration of the current vehicle, the speed change, and the collision time. The acceleration determination unit is also used to obtain a first weight corresponding to the historical time; obtain the product of the first weight and the velocity change to obtain a weighted change; obtain the ratio of the weighted change and the collision time to obtain a compensated acceleration; obtain the ratio of the current acceleration and the compensated acceleration to obtain the target acceleration.

7. The device according to claim 6, wherein The change determination unit is further used to obtain the difference between the current distance and the distance collected at two adjacent moments in the historical distance to obtain the distance difference; obtain the difference between the two adjacent moments to obtain the time difference; and obtain the ratio of the distance difference to the time difference to obtain the speed change.

8. The apparatus according to claim 6, further comprising: A speed acquisition module, configured to acquire a first speed of the current vehicle and a second speed of the target obstacle; The first distance determining module is configured to determine the preset distance based on the second speed and the current distance.

9. The device according to claim 8, wherein The distance determination module is further configured to process the second speed and the current distance using a distance recognition model to obtain the preset distance.

10. The apparatus according to claim 6, further comprising: a speed determination module, configured to determine a predicted speed of the target obstacle within a preset time period based on the target acceleration, wherein the preset time period is used to represent a time period after a current time; A second distance determination module is configured to determine a target distance of the current vehicle within the preset time period based on the predicted speed.

11. 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 5.

12. 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 5.

13. A computer program product comprising a computer program, which, when executed by a processor, implements the method according to any one of claims 1 to 5.

14. A vehicle comprising the device according to any one of claims 6 to 10.

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