Car following control method, electronic equipment and program product
By obtaining and analyzing the current driving scenario information of the target vehicle, determining the collection of follow-up scenarios, and performing comprehensive follow-up control, the problem that the follow-up control method in the existing technology cannot adapt to multiple different scenarios, and achieving higher scene adaptability and safety.
Patent Information
- Application Number
- CN202510161344.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-02-13
- Publication Date
- 2025-05-06
AI Technical Summary
The existing car follow-up control method cannot adapt to many different scenarios, resulting in the inability to effectively adjust the car follow-up distance in low-speed congestion or inclement weather, which increases safety risks.
By obtaining the current driving scenario information of the target vehicle, determine the collection of follow-up scenarios, including congestion, bad weather, large vehicles and downhill scenarios, and conduct comprehensive follow-up control based on these scenarios to dynamically adjust the follow-up distance.
It improves the scenario adaptability of car follow-up control, can adjust car follow-up distance more safely and effectively in different scenarios, reducing safety risks.
Smart Images
Figure CN119928852A_ABST
Abstract
Description
Technical Field
[0001] The present disclosure relates to the field of intelligent driving technology, and in particular to a vehicle following control method, electronic equipment and program product. Background Art
[0002] In the assisted driving scenario, when there is a vehicle target in front of the vehicle, the vehicle needs to follow the vehicle in front, and the distance to the vehicle needs to be controlled during the following process. In the prior art, the following distance is mainly determined in real time based on the vehicle speed and the fixed time interval (unit s) of the vehicle (vehicle speed × time interval). The time interval usually needs to be set by the driver and is divided into fixed gears. Once the driver sets the time interval, the following distance during the assisted driving process will remain the same at the same speed. It is impossible to make adaptive adjustments according to different scenarios. For example, in a low-speed congestion scenario, adopting a fixed time interval may easily lead to being squeezed by the vehicle next to you. In severe weather conditions, adopting the same time interval may increase safety risks. Summary of the invention
[0003] In view of this, the embodiments of the present disclosure provide a vehicle following control method, an electronic device, and a program product to solve the problem that the existing vehicle following control method has poor adaptability to a variety of different scenarios.
[0004] In a first aspect, the present disclosure provides a vehicle following control method, comprising:
[0005] Obtain the current driving scene information of the target vehicle;
[0006] Determining a vehicle following scene set of the target vehicle according to the current driving scene information, wherein the vehicle following scene set includes at least one vehicle following scene;
[0007] The target vehicle is controlled to follow the vehicle according to the following scenarios included in the following scenario set.
[0008] In a second aspect, the present disclosure provides an electronic device, including:
[0009] at least one processor; and
[0010] a memory communicatively connected to the at least one processor; wherein,
[0011] The memory stores at least one computer program that can be executed by the at least one processor, and the at least one computer program is executed by the at least one processor so that the at least one processor can execute the vehicle following control method as described in the first aspect.
[0012] In a third aspect, the present disclosure provides a computer program product, which includes a computer program. When the computer program is executed in a processor, the vehicle following control method described in the first aspect is implemented.
[0013] Optionally, the computer program may be stored in a readable storage medium of a computer device or in the cloud; and the processor of the computer device reads the computer program from the readable storage medium or in the cloud.
[0014] The embodiments provided by the present disclosure obtain the current driving scene information of the target vehicle, determine each current following scene of the target vehicle according to the current driving scene information, and form a following scene set, so that various following scenes in which the target vehicle is currently located can be obtained at the same time, and then the target vehicle can be controlled to follow the target vehicle by integrating the various following scenes in which the target vehicle is currently located, so as to adapt to the various following scenes in which the target vehicle is currently located, thereby improving the scene adaptability of the following control. BRIEF DESCRIPTION OF THE DRAWINGS
[0015] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.
[0016] Figure 1 The figure is a flow chart of the vehicle following control method in the embodiment of the present disclosure;
[0017] Figure 2 The figure is a schematic diagram of the architecture of a vehicle following control system in an embodiment of the present disclosure;
[0018] Figure 3 Shown is a block diagram of a vehicle following control device in an embodiment of the present disclosure;
[0019] Figure 4 FIG. 1 is a schematic diagram of the structure of an electronic device in an embodiment of the present disclosure. DETAILED DESCRIPTION
[0020] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.
[0021] In the absence of conflict, the various embodiments of the present disclosure and the various features therein may be combined with each other.
[0022] As used herein, the term "and / or" includes any and all combinations of one or more of the associated listed items.
[0023] The terms used herein are only used to describe specific embodiments and are not intended to limit the present disclosure. As used herein, the singular forms "a" and "the" are also intended to include plural forms, unless the context clearly indicates otherwise. It will also be understood that when the terms "including" and / or "made of" are used in this specification, the presence of the features, wholes, steps, operations, elements and / or components is specified, but the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof is not excluded. "Connected" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.
[0024] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted as having an idealized or overly formal meaning unless explicitly defined as such herein.
[0025] Exemplary Methods
[0026] The method provided by the embodiment of the present disclosure may be applied to a main controller of a vehicle, or may be applied to other terminals capable of communicating with the vehicle and / or sensors installed on the vehicle.
[0027] The method provided by the embodiment of the present disclosure is as follows: Figure 1 As shown, mainly including 101-103:
[0028] 101, obtaining current driving scene information of the target vehicle.
[0029] Exemplarily, obtaining the current driving scene information of the target vehicle includes: obtaining the current driving scene information through a variety of sensors installed on the target vehicle, the various sensors including cameras, millimeter-wave radars, inertial (IMU) sensors, positioning devices, etc.
[0030] 102. Determine a vehicle following scene set of the target vehicle according to the current driving scene information, where the vehicle following scene set includes at least one vehicle following scene.
[0031] For example, see Figure 2The following control system architecture diagram shown in the figure includes the following devices: camera, millimeter wave radar sensor, positioning device, IMU sensor, scene recognition and matching device, scene coupling judgment device and time-distance controller. The camera and millimeter wave radar mainly input the recognized surrounding environment information and target information to the scene recognition and matching device. The positioning device and IMU sensor input the geographical location and posture information of the vehicle to the scene recognition and matching device.
[0032] The scene recognition and matching device determines the time-distance scene in which the vehicle is currently located by acquiring the current driving scene information, which includes surrounding environment information, driving data of the vehicle, etc., wherein the surrounding environment information includes the current road speed limit, the length of the following vehicle target, the current road slope, the current weather conditions, etc.; the driving data of the vehicle includes posture information, actual driving speed, pre-configured vehicle cruising speed, etc. In addition, the driving scene information may also include information such as the geographical location of the vehicle.
[0033] In some embodiments, the following scene set includes at least one of the following following scenes: a congested following scene; a large vehicle following scene; a downhill following scene; and a bad weather following scene. In addition, following scenes can also be defined according to road grades, such as a highway following scene, a national highway following scene, and a city main road following scene.
[0034] In some embodiments, the current driving scene information includes the actual driving speed of the target vehicle, the pre-configured vehicle cruising speed and the current road speed limit; determining the following scene set of the target vehicle based on the current driving scene information includes: when the current driving scene information satisfies a first condition, determining that the following scene set of the target vehicle includes a congested following scene; the first condition includes: the product of the vehicle cruising speed and the congestion coefficient is greater than the actual driving speed, and the actual driving speed is less than the current road speed limit.
[0035] The first condition is expressed as: <v set *v_CongestionPercent and v <v limit
[0036] Where v is the actual speed of the vehicle, v set is the cruising speed set by the driver, v_CongestionPercent is the congestion coefficient, which is a constant. For example, the congestion coefficient ranges from 30% to 50%. limit The current road speed limit is obtained through the positioning device combined with map information.
[0037] In addition, other methods can also be used to identify congested car-following scenarios, for example, identifying congested car-following scenarios based on the congestion conditions of a navigation map, etc., which are not listed here one by one.
[0038] In some embodiments, the current driving scene information includes the length of the following target; determining the following scene set of the target vehicle based on the current driving scene information includes: when the current driving scene information satisfies a second condition, determining that the following scene set of the target vehicle includes a large vehicle following scene; the second condition includes: the length of the following target is greater than a length threshold, and the vehicle type of the following target is a specified type.
[0039] The second condition is expressed as: D length >d_trucklength and the target type is truck or bus type, where D length is the length of the following target located in front of the vehicle determined by the sensor, and d_trucklength is the length threshold, which is a pre-configured constant. For example, the setting range of the length threshold is 5 to 7 meters.
[0040] In some embodiments, the current driving scene information includes the current road slope of the target vehicle; determining the following scene set of the target vehicle based on the current driving scene information includes: when the current driving scene information satisfies a third condition, determining that the following scene set of the target vehicle includes a downhill following scene; the third condition includes that the current road slope is greater than a slope threshold.
[0041] The third condition is expressed as: slope >σ_road
[0042] where σ slope is the road slope (%) of the target vehicle, σ_road is the slope threshold, which is a pre-configured constant, for example, the slope threshold is 10%.
[0043] The current road slope of the vehicle can be obtained in a variety of ways, such as through a road slope sensor carried by the vehicle itself or through the current road slope marked on the navigation map. Of course, other ways can also be used to obtain the current road slope, such as combining image sensors, laser radars, etc. to detect the road slope.
[0044] In some embodiments, the current driving scene information includes current weather conditions; determining the following scene set for the target vehicle based on the current driving scene information includes: when the current weather conditions indicate that the target vehicle is currently in bad weather, determining that the following scene set for the target vehicle includes a bad weather following scene.
[0045] The judgment of bad weather is mainly based on the environmental information perceived by the sensor, and the weather environment is judged through the existing perception model, mainly including rainy and snowy weather, sandstorm weather, etc. Bad weather can be divided into three following scenarios: weak bad weather, moderate bad weather, and strong bad weather.
[0046] In addition, other methods can be used to identify bad weather following scenarios, for example, identifying bad weather following scenarios through the vehicle wiper status, and identifying the severity of the weather through the gear position of the vehicle wiper. The higher the gear position, the worse the weather.
[0047] 103 . Perform vehicle following control on the target vehicle according to the vehicle following scenario included in the vehicle following scenario set.
[0048] In some embodiments, the following control of the target vehicle according to the following scene included in the following scene set includes:
[0049] When the number of the following vehicle scenes is one, obtaining a following vehicle control strategy corresponding to the following vehicle scene, determining a following vehicle distance value according to the following vehicle control strategy corresponding to the following vehicle scene, and performing following vehicle control on the target vehicle according to the following vehicle distance value;
[0050] When the number of the following scenarios is at least two, obtain a following control strategy corresponding to each of the following scenarios, determine a fused following control strategy based on the following control strategy corresponding to each of the following scenarios, determine a following distance value based on the fused following control strategy, and perform following control on the target vehicle based on the following distance value.
[0051] For example, when a single car following scene is included in the car following scene set, the single car following scene is directly input into Figure 2 The time distance controller in the following scene determines the following control strategy corresponding to the single following scene through the time distance controller.
[0052] For example, when the vehicle following scene set includes at least two vehicle following scenes, all vehicle following scenes in the vehicle following scene set are input into Figure 2 The scene coupling judgment device in the embodiment determines a fusion following control strategy based on the acquired multiple following scenes, and then performs following control according to the fusion following control strategy through the time headway controller.
[0053] In some embodiments, determining the fused following control strategy according to the following control strategy corresponding to each of the following scenarios includes: obtaining the adjustment direction of the following distance in the following control strategy corresponding to each of the following scenarios, and determining the weight value corresponding to each of the following scenarios; the adjustment direction includes increasing the following distance and decreasing the following distance; determining the fused following control strategy according to the weight value corresponding to each of the following scenarios and the following control strategy corresponding to each of the following scenarios.
[0054] In the case where multiple following scenarios exist simultaneously, the weight value corresponding to each following scenario is determined according to the adjustment direction of the following distance in the following control strategy corresponding to the multiple following scenarios existing simultaneously, so that the determined fusion following control strategy can comprehensively consider the multiple following scenarios existing simultaneously, improve the adaptability to the multiple following scenarios existing simultaneously, and improve the scenario adaptability of the following control. In addition, the method of integrating multiple following control strategies in the disclosed embodiment is not limited to the number of following scenarios.
[0055] In some embodiments, obtaining the adjustment direction of the following distance in the following control strategy corresponding to each of the following scenarios and determining the weight value corresponding to each of the following scenarios include: determining a first subset and a second subset according to the adjustment direction of the following distance in the following control strategy corresponding to each of the following scenarios, wherein each of the following scenarios in the first subset corresponds to increasing the following distance, and each of the following scenarios in the second subset corresponds to reducing the following distance; determining the weight value corresponding to each of the following scenarios in the first subset according to the number of following scenarios included in the first subset, and determining the weight value corresponding to each of the following scenarios in the second subset according to the number of following scenarios included in the second subset.
[0056] Exemplarily, all the following scenarios that reduce the following distance in the following scenario set correspond to the first weight value λ minus , all the following scenarios that increase the following distance in the following scenario set correspond to the second weight value λ plus , the first weight value and the second weight value are expressed as follows:
[0057]
[0058] N plus represents the number of all following scenarios that increase the following distance in the following scenario set, that is, the number of following scenarios included in the first subset, N minus It represents the number of all following scenarios that reduce the following distance in the following scenario set, that is, the number of following scenarios included in the second subset. △λ represents the weight gain coefficient of a single following scenario, which is a preset constant. For example, the weight gain coefficient is 0.1. λ0 represents the basic weight value, which is a preset constant. For example, the basic weight value is 1.0.
[0059] In some embodiments, the fused following control strategy is determined according to the weight value corresponding to each of the following scenarios and the following control strategy corresponding to each of the following scenarios, including: determining the fused time distance offset according to the time distance offset under each of the following scenarios and the weight value corresponding to each of the following scenarios; determining the fused following distance offset according to the following distance offset under each of the following scenarios and the weight value corresponding to each of the following scenarios; determining the fused following control strategy according to the fused time distance offset and the fused following distance offset; the fused following control strategy includes the fused time distance offset and the fused following distance offset.
[0060] For example, when the following scene set includes the congestion following scene, the downhill following scene, the large vehicle following scene, and the bad weather following scene, it is assumed that the following control strategy for the large vehicle following scene does not include the following time offset, and it is assumed that the following control strategy for the bad weather following scene does not include the following distance offset. Based on this assumption, the fused following time offset and the fused following distance offset are respectively expressed as:
[0061] △t fusion =△t congestion *λ minus +△t slope *λ plus +△t weather *λ plus
[0062] △s fusion =△s congestion *λ minus +△s truck *λ plus +△s slope *λ plus
[0063] △t fusion Indicates the fusion following vehicle time offset, △t congestion Indicates the time offset of following vehicles in congested following scenarios, △t slope Indicates the time distance offset of following a vehicle in a downhill following scenario, △t weather Indicates the time offset of following a vehicle in a bad weather following scenario; minus represents the first weight value, λ plus represents the second weight value, wherein the first weight value is the weight value of the congested following scenario where the following distance offset is a negative value (i.e., the following distance is reduced), and the second weight value is the weight value of the downhill following scenario and the bad weather following scenario where the following distance offset is a positive value (i.e., the following distance is increased);
[0064] △s fusionIndicates the offset of the fused following distance, △s congestion Indicates the following distance offset in the congested following scenario, △s truck Indicates the following distance offset in the large vehicle following scenario, △s slope Indicates the following distance offset in downhill following scenarios.
[0065] In some embodiments, determining the following distance value according to the fused following control strategy includes: adding the basic time distance value and the fused time distance offset to obtain a fused following time distance value; multiplying the current driving speed of the target vehicle by the fused following time distance value to obtain a fused following distance adjustment value; and adding the conventional following distance, the fused following distance adjustment value and the fused following distance offset to obtain a following distance value.
[0066] Exemplarily, the fusion following control strategy is expressed as:
[0067] S=v*(t set +△t fusion )+s0+△s fusion
[0068] Among them, S represents the following distance value, t set is the basic time interval value, is the time interval value corresponding to the time interval gear set by the user, △t fusion is the fusion following distance value, △s fusion To integrate the following distance offset, v represents the current speed of the target vehicle, and s0 represents the normal following distance of the target vehicle.
[0069] t set Can be set to be associated with the driving speed, for example, setting t set The mapping relationship between the target vehicle and the driving speed is obtained from the mapping relationship according to the current driving speed of the target vehicle. set .
[0070] In some embodiments, when the number of following scenarios is one and it is a congested following scenario, determining the following distance value according to the following control strategy corresponding to the following scenario includes: adding the basic time distance value and the congested following time distance offset to obtain the congested following time distance value, multiplying the current driving speed of the target vehicle by the congested following time distance value to obtain the congested following distance adjustment value; adding the normal following distance, the congested following distance adjustment value and the congested following distance offset to obtain the following distance value; wherein the congested following time distance offset and the congested following distance offset are both negative values.
[0071] The following control strategy in the congested following scenario is as follows:
[0072] S=v*(t set+△t congestion )+s0+△s congestion
[0073] Among them, S is the following distance, v is the current speed of the vehicle, and t set is the basic time interval value, is the time interval value corresponding to the time interval gear set by the user, △t congestion is the time distance offset of following a vehicle in a congested scene, is a preset constant (for example, the time distance offset is 0.1s to 0.2s), s0 is the normal following distance, △s congestion is the following distance offset for following and stopping in congested scenes, which is a preset constant (for example, the following distance offset is 0.5m to 1m). In congested scenes, in order to prevent jamming and improve traffic efficiency, the actual following distance should be reduced, so △t congestion and △s congestion Is a negative value.
[0074] t set Can be set to be associated with the driving speed, for example, setting t set The mapping relationship between the target vehicle and the driving speed is obtained from the mapping relationship according to the current driving speed of the target vehicle. set Similarly, we can also set △t congestion The mapping relationship between the target vehicle and the driving speed is used to obtain the corresponding △t from the mapping relationship according to the current driving speed of the target vehicle. congestion .
[0075] In some embodiments, when the number of following scenarios is one and it is a large vehicle following scenario, the following distance value is determined according to the following control strategy corresponding to the following scenario, including: determining the product of the basic time distance value and the current driving speed of the target vehicle to obtain a large vehicle following distance adjustment value; adding the conventional following distance, the large vehicle following distance adjustment value and the large vehicle following distance offset to obtain the following distance value; wherein, the following distance offset in the large vehicle following scenario is a positive value.
[0076] The following control strategy for the large vehicle following scenario is as follows:
[0077] S=v*t set +s0+△s truck
[0078] Among them, S is the following distance, v is the current speed of the vehicle, and t set Indicates the time interval value corresponding to the time interval gear set by the user, △s truck is the following distance offset in the large vehicle following scenario, which is a preset constant (for example, the following distance offset is 0.5m to 1m). When following a large vehicle, in order to improve the following safety and driver safety experience, the following distance should be increased, △s truck is the value.
[0079] t set Can be set to be associated with the driving speed, for example, setting t set The mapping relationship between the target vehicle and the driving speed is obtained from the mapping relationship according to the current driving speed of the target vehicle. set .
[0080] In some embodiments, when the number of following scenarios is one and it is a downhill following scenario, the following distance value is determined according to the following control strategy corresponding to the following scenario, including: adding the basic time distance value and the downhill following time distance offset to obtain the downhill following time distance value, multiplying the current driving speed of the target vehicle by the downhill following time distance value to obtain the downhill following distance adjustment value; adding the conventional following distance, the downhill following distance adjustment value and the downhill following distance offset to obtain the following distance value; wherein, the downhill following time distance offset and the downhill following distance offset are both positive values.
[0081] The following control strategy for the downhill following scenario is as follows:
[0082] S=v*(t set +△t slope )+s0+△s slope
[0083] Among them, S is the following distance, v is the current speed of the vehicle, and t set is the basic time interval value, is the time interval value corresponding to the time interval gear set by the user, △t slope is the following distance offset in the downhill following scenario, and is a preset constant, △s slope is the following distance offset in downhill scenarios, which is a preset constant (e.g. 0.5m to 1m). In downhill scenarios, considering vehicle control safety, the following distance should be appropriately increased, △t slope and △s slope Is a positive value.
[0084] t set Can be set to be associated with the driving speed, for example, setting t set The mapping relationship between the target vehicle and the driving speed is obtained from the mapping relationship according to the current driving speed of the target vehicle. set Similarly, we can also set △t slope The mapping relationship between the target vehicle and the driving speed is used to obtain the corresponding △t from the mapping relationship according to the current driving speed of the target vehicle. slope .
[0085] In some embodiments, when the number of following scenarios is one and it is a severe weather following scenario, the following distance value is determined according to the following control strategy corresponding to the following scenario, including: adding the basic time distance value and the severe weather following time distance offset to obtain the severe weather following time distance value, multiplying the current driving speed of the target vehicle by the severe weather following time distance value to obtain the severe weather following distance adjustment value; adding the conventional following distance and the severe weather following distance adjustment value to obtain the following distance value; wherein the severe weather following time distance offset is a positive value.
[0086] In some embodiments, the severe weather following vehicle time offset is positively correlated with the severity of the weather in the severe weather following vehicle scenario.
[0087] The following control strategy for the following scenario in bad weather is as follows:
[0088] S=v*(t set +△t weather )+s0
[0089] Where S represents the following distance value, t set is the basic time distance value, is the time distance value corresponding to the time distance gear set by the user, s0 represents the normal following distance of the target vehicle, △t weather is the follow-up stop distance offset in bad weather, is a preset constant, △t weather Choose different values (0.1s to 0.5s) according to the severity of the bad weather, the stronger the weather, the shorter the △t weather In bad weather conditions, to ensure driving safety, the following distance should be increased, △t weather Is a positive value.
[0090] t set Can be set to be associated with the driving speed, for example, setting t set The mapping relationship between the target vehicle and the driving speed is obtained from the mapping relationship according to the current driving speed of the target vehicle. set Similarly, we can also set △t weather The mapping relationship between the target vehicle and the driving speed is used to obtain the corresponding △t from the mapping relationship according to the current driving speed of the target vehicle. weather .
[0091] The embodiments provided by the present disclosure obtain the current driving scene information of the target vehicle, determine each current following scene of the target vehicle according to the current driving scene information, and form a following scene set, so that various following scenes in which the target vehicle is currently located can be obtained at the same time, and then the target vehicle can be controlled to follow the target vehicle by integrating the various following scenes in which the target vehicle is currently located, so as to adapt to the various following scenes in which the target vehicle is currently located, thereby improving the scene adaptability of the following control.
[0092] The target vehicle may encounter different scenarios during driving. The disclosed embodiment analyzes the various scenarios that may be encountered, identifies typical scenarios that affect the following distance, including congested following scenarios, large vehicle following scenarios, downhill following scenarios, and bad weather following scenarios, and analyzes these scenarios to formulate effective following control strategies. When multiple following scenarios appear at the same time, the following control strategies for different scenarios determine the weight of each scenario, so as to merge the following control strategies for multiple different scenarios and obtain a merged following control strategy that can adapt to these multiple scenarios.
[0093] When the target vehicle is driving, it can judge the current set of following scenarios of its own vehicle based on its own vehicle's perception system and scene judgment strategy, and thus match the following control strategy in a targeted manner.
[0094] The disclosed embodiment introduces more following scenarios and following control strategies, making following control more flexible and more adaptable to actual needs. Moreover, by integrating multiple scenarios, the following distance can be adaptively adjusted when multiple following scenarios occur at the same time, further improving the following adaptability.
[0095] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not repeat them. It can be understood by those skilled in the art that in the above-mentioned method of the specific implementation, the specific execution order of each step should be determined by its function and possible internal logic, and the execution order between the steps is not limited to being implemented according to the step number.
[0096] In addition, the present disclosure also provides a vehicle following control device, an electronic device, and a computer program product, all of which can be used to implement any vehicle following control method provided by the present disclosure. The corresponding technical solutions and descriptions are referred to the corresponding records in the method part and will not be repeated here.
[0097] Exemplary Devices
[0098] Figure 3 A block diagram of a vehicle following control device provided in an embodiment of the present disclosure, the vehicle following control device mainly includes:
[0099] An acquisition module 301 is used to acquire current driving scene information of a target vehicle;
[0100] A determination module 302 is used to determine a vehicle following scene set of the target vehicle according to the current driving scene information, wherein the vehicle following scene set includes at least one vehicle following scene;
[0101] The control module 303 is used to perform vehicle following control on the target vehicle according to the vehicle following scenarios included in the vehicle following scenario set.
[0102] Exemplary Electronic Devices
[0103] Figure 4 A block diagram of an electronic device provided in an embodiment of the present disclosure.
[0104] Reference Figure 4 An embodiment of the present disclosure provides an electronic device, which includes: at least one processor 401; at least one memory 402, and one or more I / O interfaces 403, connected between the processor 401 and the memory 402; wherein the memory 402 stores one or more computer programs that can be executed by the at least one processor 401, and the one or more computer programs are executed by the at least one processor 401 so that the at least one processor 401 can execute the above-mentioned vehicle following control method.
[0105] Each module in the above electronic device can be implemented in whole or in part by software, hardware, or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to each module.
[0106] Exemplary computer program products and storage media
[0107] The embodiment of the present disclosure also provides a computer program product, including a computer program, which implements the above-mentioned vehicle following control method when executed in a processor.
[0108] The computer program may be stored in a readable storage medium of a computer device or in the cloud; the processor of the computer device reads the computer program from the readable storage medium or in the cloud.
[0109] The computer program product may be implemented in hardware, software or a combination thereof. In one optional embodiment, the computer program product is implemented as a computer storage medium. In another optional embodiment, the computer program product is implemented as a software product, such as a software development kit (SDK).
[0110] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In a hardware implementation, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable storage medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium).
[0111] As is known to those of ordinary skill in the art, the term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable program instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technology, portable compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. In addition, it is known to those of ordinary skill in the art that communication media typically contain computer-readable program instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transmission mechanism, and may include any information delivery medium.
[0112] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.
[0113] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.
[0114] The computer program product described herein may be implemented in hardware, software, or a combination thereof. In one optional embodiment, the computer program product is embodied as a computer storage medium, and in another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), etc.
[0115] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.
[0116] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, thereby producing a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device that implements the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions cause the computer, programmable data processing device, and / or other equipment to work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.
[0117] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.
[0118] The flow chart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to multiple embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a part of a module, program segment or instruction, and a part of the module, program segment or instruction includes one or more executable instructions for realizing the specified logical function. In some alternative implementations, the function marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two continuous square boxes can actually be executed substantially in parallel, and they can sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or action, or can be implemented with a combination of special hardware and computer instructions.
[0119] The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure. Any modifications, equivalent substitutions, etc. made within the spirit and principles of the present disclosure should be included in the protection scope of the present disclosure.
Claims
1. A vehicle following control method, characterized in that: include: Obtain the current driving scene information of the target vehicle; Determining a vehicle following scene set of the target vehicle according to the current driving scene information, wherein the vehicle following scene set includes at least one vehicle following scene; The target vehicle is controlled to follow the vehicle according to the following scenarios included in the following scenario set.
2. The method according to claim 1, characterized in that The following control of the target vehicle according to the following scene included in the following scene set includes: When the number of the following vehicle scenes is one, obtaining a following vehicle control strategy corresponding to the following vehicle scene, determining a following vehicle distance value according to the following vehicle control strategy corresponding to the following vehicle scene, and performing following vehicle control on the target vehicle according to the following vehicle distance value; When the number of the following scenarios is at least two, obtain a following control strategy corresponding to each of the following scenarios, determine a fused following control strategy based on the following control strategy corresponding to each of the following scenarios, determine a following distance value based on the fused following control strategy, and perform following control on the target vehicle based on the following distance value.
3. The method according to claim 2, characterized in that The determining of the fused following control strategy according to the following control strategy corresponding to each following scenario includes: Obtaining an adjustment direction of the following distance in the following control strategy corresponding to each of the following scenarios, and determining a weight value corresponding to each of the following scenarios; the adjustment direction includes increasing the following distance and decreasing the following distance; A fused following control strategy is determined according to a weight value corresponding to each of the following scenarios and a following control strategy corresponding to each of the following scenarios.
4. The method according to claim 3, characterized in that The obtaining the adjustment direction of the following distance in the following control strategy corresponding to each following scenario and determining the weight value corresponding to each following scenario includes: Determine a first subset and a second subset according to the adjustment direction of the following distance in the following control strategy corresponding to each of the following scenarios, wherein each of the following scenarios in the first subset corresponds to increasing the following distance, and each of the following scenarios in the second subset corresponds to reducing the following distance; According to the number of vehicle following scenes included in the first subset, the weight value corresponding to each vehicle following scene in the first subset is determined, and according to the number of vehicle following scenes included in the second subset, the weight value corresponding to each vehicle following scene in the second subset is determined.
5. The method according to claim 3, characterized in that: The determining of the fused following control strategy according to the weight value corresponding to each following scenario and the following control strategy corresponding to each following scenario includes: Determining a fused time-distance offset according to the time-distance offset in each of the following vehicle scenarios and a weight value corresponding to each of the following vehicle scenarios; Determining a fused following distance offset according to the following distance offset in each of the following scenarios and a weight value corresponding to each of the following scenarios; A fusion following vehicle control strategy is determined according to the fusion time distance offset and the fusion following vehicle distance offset; the fusion following vehicle control strategy includes the fusion time distance offset and the fusion following vehicle distance offset.
6. The method according to claim 3, characterized in that The determining the following distance value according to the fused following control strategy includes: The basic time distance value and the fused time distance offset are accumulated to obtain a fused following vehicle time distance value; Multiplying the current driving speed of the target vehicle by the fused following vehicle time distance value to obtain a fused following vehicle distance adjustment value; The conventional following distance, the fused following distance adjustment value and the fused following distance offset are accumulated to obtain a following distance value.
7. The method according to claim 2, characterized in that When the number of the following scenes is one and it is a congested following scene, determining the following distance value according to the following control strategy corresponding to the following scene includes: The basic time distance value and the congested following time distance offset are accumulated to obtain a congested following time distance value, and the current driving speed of the target vehicle is multiplied by the congested following time distance value to obtain a congested following distance adjustment value; The normal following distance, the congested following distance adjustment value and the congested following distance offset are accumulated to obtain a following distance value; wherein the congested following distance time offset and the congested following distance offset are both negative values.
8. The method according to claim 2, characterized in that: When the number of the following vehicle scenes is one and it is a large vehicle following scene, determining the following vehicle distance value according to the following vehicle control strategy corresponding to the following vehicle scene includes: Determine the product of the basic time headway value and the current driving speed of the target vehicle to obtain a vehicle following distance adjustment value; The conventional following distance, the large vehicle following distance adjustment value and the large vehicle following distance offset are accumulated to obtain the following distance value; wherein the following distance offset in the large vehicle following scenario is a positive value.
9. The method according to claim 2, characterized in that: When the number of the following scenes is one and it is a downhill following scene, determining the following distance value according to the following control strategy corresponding to the following scene includes: The basic time distance value and the downhill following time distance offset are accumulated to obtain the downhill following time distance value, and the current driving speed of the target vehicle is multiplied by the downhill following time distance value to obtain the downhill following distance adjustment value; The conventional following distance, the downhill following distance adjustment value and the downhill following distance offset are accumulated to obtain a following distance value; wherein the downhill following distance offset and the downhill following distance offset are both positive values.
10. The method according to claim 2, characterized in that When the number of the following scenes is one and it is a following scene in bad weather, determining the following distance value according to the following control strategy corresponding to the following scene includes: The basic time distance value and the bad weather following time distance offset are accumulated to obtain the bad weather following time distance value, and the current driving speed of the target vehicle is multiplied by the bad weather following time distance value to obtain the bad weather following distance adjustment value; The conventional following distance and the inclement weather following distance adjustment value are added to obtain a following distance value; wherein the inclement weather following distance offset is a positive value.
11. The method according to claim 10, characterized in that The bad weather following vehicle time distance offset is positively correlated with the severity of the weather in the bad weather following vehicle scenario.
12. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores at least one computer program executable by the at least one processor, and the at least one computer program is executed by the at least one processor so that the at least one processor can execute the vehicle following control method as described in any one of claims 1-11.
13. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed in a processor, the vehicle following control method according to any one of claims 1 to 11 is implemented.