CN111409455A - Vehicle speed control method and device, electronic device and storage medium - Google Patents
Vehicle speed control method and device, electronic device and storage medium Download PDFInfo
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- 238000007499 fusion processing Methods 0.000 claims abstract description 10
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- 238000004590 computer program Methods 0.000 claims description 4
- 230000004927 fusion Effects 0.000 claims description 4
- 238000012821 model calculation Methods 0.000 claims description 4
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60K—ARRANGEMENT OR MOUNTING OF PROPULSION UNITS OR OF TRANSMISSIONS IN VEHICLES; ARRANGEMENT OR MOUNTING OF PLURAL DIVERSE PRIME-MOVERS IN VEHICLES; AUXILIARY DRIVES FOR VEHICLES; INSTRUMENTATION OR DASHBOARDS FOR VEHICLES; ARRANGEMENTS IN CONNECTION WITH COOLING, AIR INTAKE, GAS EXHAUST OR FUEL SUPPLY OF PROPULSION UNITS IN VEHICLES
- B60K31/00—Vehicle fittings, acting on a single sub-unit only, for automatically controlling vehicle speed, i.e. preventing speed from exceeding an arbitrarily established velocity or maintaining speed at a particular velocity, as selected by the vehicle operator
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/18—Propelling the vehicle
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/04—Traffic conditions
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W60/00—Drive control systems specially adapted for autonomous road vehicles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
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Abstract
The application provides a speed control method and device of a vehicle, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring running state information, surrounding target information and external sending information of a vehicle; carrying out fusion processing on the driving state information, the surrounding target information and the outside sending information to obtain a model input parameter; inputting the model input parameters into a vehicle speed control model, and calculating and outputting speed control state parameters of the vehicle; and adjusting the speed of the vehicle in real time according to the speed control state parameter. The method is used for automatically controlling the vehicle speed and improving the safety of automatic driving.
Description
Technical Field
The present disclosure relates to the field of intelligent driving, and in particular, to a method and an apparatus for controlling a speed of a vehicle, an electronic device, and a storage medium.
Background
The automatic driving system can realize automatic driving of the vehicle according to the set vehicle speed and the set environmental condition, completes coordination and coordination of actions of an accelerator, a brake, a clutch and a gear shift, and is a high-automation system simulating the intelligence and the behaviors of a human driver. The automatic driving system of the automobile not only relates to simple control of the speed of the automobile, but also has the characteristics of nonlinearity, time variation and time lag, and has more influence factors, so that the automatic driving is still not popularized, and the main factor is low safety.
Disclosure of Invention
The embodiment of the application provides a speed control method of a vehicle, which is used for automatically controlling the speed of the vehicle and improving the safety of automatic driving.
The present application provides a speed control method of a vehicle, the method including:
acquiring running state information, surrounding target information and external sending information of a vehicle;
carrying out fusion processing on the driving state information, the surrounding target information and the outside sending information to obtain a model input parameter;
inputting the model input parameters into a vehicle speed control model, and calculating and outputting speed control state parameters of the vehicle;
and adjusting the speed of the vehicle in real time according to the speed control state parameter.
In one embodiment, the acquiring the driving state information of the vehicle includes:
and acquiring speed information, direction information and power information of the vehicle.
In one embodiment, the surrounding target information includes first target information and second target information; acquiring the surrounding target information of the vehicle, including:
acquiring a target image through an image sensor, and detecting first target information according to the target image;
and detecting surrounding targets through a radar sensor to obtain second target information of the surrounding targets.
In one embodiment, obtaining the outside transmission information of the vehicle includes:
acquiring traffic information and weather information of an area where the vehicle is located from a cloud;
and acquiring all vehicle position information in a preset range around the vehicle in the local area network.
In an embodiment, the model input parameters comprise a first partial parameter and a second partial parameter; the fusion processing of the driving state information of the vehicle, the surrounding target information and the outside transmission information to obtain a model input parameter includes:
vectorizing the running state information to obtain the first part of parameters;
carrying out redundancy removal processing on the surrounding target information, and rejecting the same data;
comparing the surrounding target information with the outside transmitted information, and reserving information with higher confidence level in the same information to obtain surrounding state information;
and vectorizing the surrounding state information to obtain the second part of parameters.
In an embodiment, the comparing the surrounding target information with the external sending information, and retaining information with a higher confidence level in the same information to obtain surrounding state information includes:
calculating a sensor confidence and a network confidence of the vehicle;
if the sensor confidence is greater than the network confidence, the external sending information in the same information is abandoned, otherwise, the surrounding target information in the same information is abandoned, and the surrounding state information is obtained.
In one embodiment, the calculating the sensor confidence and the network confidence of the vehicle includes:
determining the confidence coefficient of the sensor according to the real-time weather condition and the integrity and definition of the image acquired by the image sensor;
and determining the network confidence according to the real-time network delay condition.
In another aspect, the present application also provides a speed control apparatus of a vehicle, the apparatus including:
the information acquisition module is used for acquiring the running state information, the surrounding target information and the external sending information of the vehicle;
the information fusion module is used for carrying out fusion processing on the running state information, the surrounding target information and the outside sending information of the vehicle to obtain a model input parameter;
the model calculation module is used for inputting the model input parameters into a vehicle speed control model, and calculating and outputting speed control state parameters of the vehicle;
and the speed adjusting module is used for adjusting the speed of the vehicle in real time according to the speed control state parameter.
Further, the present application also provides an electronic device, including:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to execute a speed control method of a vehicle provided herein.
Further, the present application also provides a computer-readable storage medium storing a computer program executable by a processor to perform the speed control method of the vehicle provided by the present application.
According to the technical scheme provided by the embodiment of the application, the driving state information, the surrounding target information and the outside sending information of the vehicle are acquired and fused, the speed control state parameters are input into the vehicle speed control model, the speed control state parameters are output, and the speed of the vehicle is adjusted in real time based on the parameters.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required to be used in the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic application scenario diagram of a speed control method for a vehicle according to an embodiment of the present application;
FIG. 2 is a schematic flow chart illustrating a method for controlling a speed of a vehicle according to an embodiment of the present disclosure;
FIG. 3 is a detailed flowchart of step S220 in the corresponding embodiment of FIG. 2;
fig. 4 is a block diagram of a speed control apparatus of a vehicle according to another embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
Like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures. Meanwhile, in the description of the present application, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Fig. 1 is a schematic view of an application scenario of a speed control method of a vehicle according to an embodiment of the present application. As shown in fig. 1, the application scenario includes a vehicle 110. The vehicle 110 may adopt the speed control method of the vehicle provided in the embodiment to automatically control the speed of the vehicle, thereby improving the safety of automatic driving.
The vehicle 110 may include an onboard sensing system 111 and an onboard controller 112. The onboard controller 112 is connected to the onboard sensing system 111. The vehicle-mounted sensing system 111 may include a state sensor, an image sensor, and a radar sensor, and the vehicle-mounted sensing system 111 sends the acquired information to the vehicle-mounted controller 112, so that the vehicle-mounted controller 112 may control the speed of the vehicle 110 by using the vehicle speed control method provided in the embodiment.
In an embodiment, the application scenario may further include the cloud 120. The cloud 120 may be connected to the vehicle-mounted controller 112 through a network, and send information to the vehicle-mounted controller 112, so that the vehicle-mounted controller 112 may control the speed of the vehicle 110 according to the information sent by the cloud 120 and the information obtained by the vehicle-mounted sensing system 111.
The application also provides an electronic device. The electronic device may be the onboard controller 112 shown in fig. 1. As shown in fig. 1, the onboard controller 112 may include a processor 1121 and a memory 1122 for storing instructions executable by the processor 1121; wherein the processor 1121 is configured to execute a speed control method of a vehicle provided herein.
The Memory 1122 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as a Static Random Access Memory (SRAM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), an Erasable Programmable Read-Only Memory (EPROM), a Programmable Read-Only Memory (PROM), a Read-Only Memory (ROM), a magnetic Memory, a flash Memory, a magnetic disk, or an optical disk.
The present application also provides a computer-readable storage medium storing a computer program executable by the processor 1121 to perform the speed control method of the vehicle provided herein.
Fig. 2 is a schematic flow chart of a speed control method of a vehicle according to an embodiment of the present application. As shown in fig. 2, the method comprises the following steps S210-S240.
Step S210: the driving state information of the vehicle, the surrounding target information, and the outside transmission information are acquired.
The driving state information may be information describing a driving state of the vehicle, such as a speed, an acceleration, a direction, and the like of the vehicle. The surrounding target information may be all target information within a preset range around the vehicle, where the target may be a vehicle, a pedestrian, a road block, a traffic light, a traffic sign, etc., and the target information may be position information, displacement information, traffic indication information, etc. of the target. The external transmission information may be external transmission information received by the vehicle through a network, and the information may provide auxiliary information related to the vehicle traveling, such as surrounding vehicle information, traffic information, and the like, within the entire vehicle network. The acquisition of the driving state information, the surrounding target information, and the external transmission information may be performed synchronously or step by step.
Step S220: and carrying out fusion processing on the driving state information, the surrounding target information and the outside sending information to obtain a model input parameter.
The driving state information, the surrounding target information, and the outside transmission information may be obtained through different ways. Among these pieces of information, there may be duplicated information, information that needs to be spliced, or information that needs to be discarded, and for example, there may be duplication or conflict between the surrounding target information and the positions of surrounding vehicles in the outside transmission information. In this step, the manner of performing fusion processing on all the acquired information includes: redundant data information is removed, or more accurate information is obtained through splicing and fusing some information, and unreliable information is abandoned. And the information obtained after the fusion processing is used as a model input parameter.
Step S230: and inputting the model input parameters into a vehicle speed control model, and calculating and outputting speed control state parameters of the vehicle.
The vehicle speed control model may be a neural network-based model trained based on artificial driving data, and the model may calculate and output speed control state parameters based on input parameters. The speed control state parameter can be a vehicle running signal for adjusting the accelerator opening degree and the brake oil pressure parameter of the vehicle, and performing emergency braking, brake releasing and the like.
Step S240: and adjusting the speed of the vehicle in real time according to the speed control state parameter.
After the vehicle speed control model outputs the speed control state parameters, a controller carried on the vehicle can adjust the accelerator opening, the brake oil pressure and the brake state of the vehicle according to the speed control state parameters, so that the control on the vehicle speed is realized.
According to the technical scheme provided by the embodiment of the application, the driving state information, the surrounding target information and the outside sending information of the vehicle are acquired and fused, the speed control state parameters are input into the vehicle speed control model, the speed control state parameters are output, and the speed of the vehicle is adjusted in real time based on the parameters.
In one embodiment, acquiring the driving state information of the vehicle may include: speed information, direction information, and power information of the vehicle are acquired. The speed information may include speed, acceleration, and continuous acceleration and deceleration time of the vehicle; the directional information may include a tire rotation angle of the vehicle; the power information may include a percentage of throttle opening and a percentage of brake oil pressure of the vehicle. This information can be acquired by means of on-board state sensors.
In an embodiment, the surrounding target information may include first target information and second target information. The first target information is target information in a preset range around the vehicle, which is acquired by an image sensor; the second target information is target information in a preset range around the vehicle, which is acquired by the radar sensor. For the purpose of distinction, the first object information and the second object information are referred to. The two parts of information can complement each other, and the more accurate and more complete detection of the surrounding targets is realized.
Acquiring the surrounding target information of the vehicle may include: acquiring a target image through an image sensor, and detecting first target information according to the target image; and detecting surrounding targets through a radar sensor to obtain second target information of the surrounding targets.
The image sensors can be arranged at a plurality of positions of the vehicle, target images in all directions in a preset range around the vehicle can be collected through rotation, and the target images can be images of the vehicle, pedestrians, roadblocks, traffic indicator lights, traffic signs and the like. The image sensor may capture the target image at a preset frequency, for example, 8 frames per second. Detecting a target image acquired by an image sensor, and acquiring position information of surrounding vehicles, pedestrians and roadblocks; according to the continuous multiple target images, displacement information and speed information of surrounding vehicles and pedestrians can be obtained; according to the images of the traffic indicator lights and the traffic signs, the image information and the character information can be identified, and the color of the traffic lights, the steering signs, the countdown seconds, the traffic indication information and the like can be extracted.
The radar sensor collects information about the second target by sending a radar signal to the surroundings, and if the signal encounters a target, it may reflect back, and the radar sensor may determine the position of the target by the time from the transmission of the signal to the reception of the echo. The vehicle may be equipped with a plurality of radar sensors that respectively transmit radar signals in a plurality of directions. Thus, the radar sensor can acquire position information of an object within a preset range around the vehicle and determine speed information of the object through continuous detection.
In one embodiment, obtaining the outside transmission information of the vehicle includes: acquiring traffic information and weather information of an area where the vehicle is located from a cloud; and acquiring all vehicle position information in a preset range around the vehicle in the local area network.
The vehicle is connected with the cloud end through the network, the cloud end can acquire the position of the vehicle in real time, and traffic information and weather information of the area where the vehicle is located are sent to the vehicle. The traffic information can be traffic light information, traffic instructions, roadblock information and the like; the weather information may be visibility information of an area where the vehicle is located, severe weather prompt information, and the like. Meanwhile, the vehicle can share the position information with other connected vehicles in the local area network, so that the position information of all vehicles in a preset range around the vehicle can be obtained.
In an embodiment, the model input parameters may comprise a first partial parameter and a second partial parameter. As shown in fig. 3, step S220 in fig. 2 may include the following steps S221-S224.
Step S221: and vectorizing the running state information to obtain the first part of parameters.
And converting the running state information into an input vector which can be input into a vehicle speed control model, wherein the input vector is a first part parameter.
Step S222: and performing redundancy removal processing on the surrounding target information, and eliminating the same data.
When acquiring the surrounding target information, it may be acquired by both the image sensor and the radar sensor, which results in a possibility of overlapping portions of the information acquired by both types of sensors. In this step, the same data is culled.
Step S223: and comparing the surrounding target information with the outside transmitted information, and keeping information with higher confidence level in the same information to obtain surrounding state information.
The surrounding target information may include position information, traffic information, and the like of vehicles within a preset surrounding range, and such information may be included in the external transmission information. And comparing the surrounding target information with the outside transmitted information, and identifying the information indicating the same target. For the same information, the information with lower confidence coefficient in the two is abandoned, and the information with higher confidence coefficient is reserved. The confidence coefficient is a parameter describing whether certain information is accurate and reliable. For the purpose of discrimination, the information retained after this step is completed is referred to as surrounding state information.
In an embodiment, step S223 may include: calculating a sensor confidence and a network confidence of the vehicle; if the sensor confidence is greater than the network confidence, the external sending information in the same information is abandoned, otherwise, the surrounding target information in the same information is abandoned, and the surrounding state information is obtained.
Wherein, the sensor confidence and the network confidence can be determined by the image quality and the network quality acquired by the sensor respectively. Since the surrounding target information is acquired by the vehicle-mounted sensor and the outside transmitted information is acquired from the outside through the network, the image quality and the network quality acquired by the vehicle sensor directly determine the confidence of the surrounding target information and the outside transmitted information of the vehicle. In one embodiment, the sensor confidence and the network confidence of the vehicle are calculated, and the sensor confidence is determined according to the real-time weather condition, the integrity and the definition of the image acquired by the image sensor; and determining the network confidence according to the real-time network delay condition. Since weather conditions may have an effect on the quality of the image captured by the sensor, the weather conditions may be used as one factor in considering the confidence of the sensor, and the completeness and clarity of the image captured by the sensor may be used as another factor. The network confidence may be determined by the network delay, the greater the network delay, the lower the network confidence. And after the confidence coefficient of the sensor is compared with the confidence coefficient of the network, the information with lower confidence coefficient is abandoned, and the surrounding state information can be obtained.
For example, the vehicle is about to travel to the intersection, the peripheral target information acquired by the sensor prompts that the green light countdown of the intersection is 1 second, and the external sending information acquired by the network prompts that the green light countdown of the intersection is 7 seconds. At the moment, the weather condition is poor, the image acquired by the sensor is unclear, the confidence coefficient of the sensor is 0.3 after normalization, the network delay is low at the moment, the confidence coefficient of the network is 0.95 after normalization, the information of 1 second when the green light of the intersection counts down is discarded after comparison, and the information of 7 seconds when the green light of the intersection counts down is retained.
By comparing the sensor confidence coefficient with the network confidence coefficient, information with higher confidence coefficient in the same information of the surrounding state information and the external sending information is reserved, the reliability of the information can be ensured to a greater extent, and the safety of automatic driving is improved.
Step S224: and vectorizing the surrounding state information to obtain the second part of parameters.
In the synchronization step S221, the surrounding state information is converted into an input vector as a second partial parameter of the model input in this step.
The first part of parameters and the second part of parameters can be spliced to obtain model input parameters, and then the model input parameters are input into the vehicle speed control model to obtain speed control state parameters, so that the speed of the vehicle can be controlled according to the speed control state parameters, and automatic driving is realized.
The following are embodiments of the apparatus of the present application that may be used to implement embodiments of the method for controlling the speed of a vehicle of the present application as described above. For details which are not disclosed in the embodiments of the apparatus of the present application, please refer to the embodiments of the speed control method of the vehicle of the present application.
Fig. 4 is a block diagram of a vehicle speed control device according to an embodiment of the present application. As shown in fig. 4, the apparatus includes: an information acquisition module 410, an information fusion module 420, a model calculation module 430, and a speed adjustment module 440.
An information obtaining module 410, configured to obtain driving state information of a vehicle, surrounding target information, and external sending information;
an information fusion module 420, configured to perform fusion processing on the driving state information of the vehicle, the surrounding target information, and the external sending information to obtain a model input parameter;
a model calculation module 430 for inputting the model input parameters into a vehicle speed control model, calculating and outputting speed control state parameters of the vehicle;
and a speed adjusting module 440, configured to adjust the speed of the vehicle in real time according to the speed control state parameter.
The implementation processes of the functions and actions of the modules in the device are specifically described in the implementation processes of the corresponding steps in the speed control method of the vehicle, and are not described again here.
In the embodiments provided in the present application, the disclosed apparatus and method can be implemented in other ways. The apparatus embodiments described above are merely illustrative, and for example, the flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Claims (7)
1. A speed control method of a vehicle, characterized by comprising:
acquiring running state information, surrounding target information and external sending information of a vehicle;
carrying out fusion processing on the driving state information, the surrounding target information and the outside sending information to obtain a model input parameter;
inputting the model input parameters into a vehicle speed control model, and calculating and outputting speed control state parameters of the vehicle;
and adjusting the speed of the vehicle in real time according to the speed control state parameter.
2. The speed control method of a vehicle according to claim 1, wherein the acquiring the running state information of the vehicle includes:
and acquiring speed information, direction information and power information of the vehicle.
3. The speed control method of the vehicle according to claim 1, characterized in that the surrounding target information includes first target information and second target information; acquiring the surrounding target information of the vehicle, including:
acquiring a target image through an image sensor, and detecting first target information according to the target image;
and detecting surrounding targets through a radar sensor to obtain second target information of the surrounding targets.
4. The speed control method of a vehicle according to claim 1, wherein acquiring the outside world transmission information of a vehicle includes:
acquiring traffic information and weather information of an area where the vehicle is located from a cloud;
and acquiring all vehicle position information in a preset range around the vehicle in the local area network.
5. A speed control apparatus of a vehicle, characterized by comprising:
the information acquisition module is used for acquiring the running state information, the surrounding target information and the external sending information of the vehicle;
the information fusion module is used for carrying out fusion processing on the running state information, the surrounding target information and the outside sending information of the vehicle to obtain a model input parameter;
the model calculation module is used for inputting the model input parameters into a vehicle speed control model, and calculating and outputting speed control state parameters of the vehicle;
and the speed adjusting module is used for adjusting the speed of the vehicle in real time according to the speed control state parameter.
6. An electronic device, characterized in that the electronic device comprises:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to perform the speed control method of the vehicle of any one of claims 1-4.
7. A computer-readable storage medium, characterized in that the storage medium stores a computer program executable by a processor to perform the method of controlling the speed of a vehicle according to any one of claims 1 to 4.
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| CN202010226578.4A CN111409455A (en) | 2020-03-26 | 2020-03-26 | Vehicle speed control method and device, electronic device and storage medium |
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| CN202010226578.4A CN111409455A (en) | 2020-03-26 | 2020-03-26 | Vehicle speed control method and device, electronic device and storage medium |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112406873A (en) * | 2020-11-19 | 2021-02-26 | 东风汽车有限公司 | Longitudinal control model parameter confirmation method, vehicle control method, storage medium, and electronic device |
| CN113433548A (en) * | 2021-06-24 | 2021-09-24 | 中国第一汽车股份有限公司 | Data monitoring method, device, equipment and storage medium |
| CN114023081A (en) * | 2021-11-02 | 2022-02-08 | 北京世纪好未来教育科技有限公司 | Vehicle speed measuring method, device and system and storage medium |
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