CN112918488B - Vehicle control method, device and storage medium - Google Patents
Vehicle control method, device and storage medium Download PDFInfo
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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
- B60W60/001—Planning or execution of driving tasks
- B60W60/0015—Planning or execution of driving tasks specially adapted for safety
- B60W60/0016—Planning or execution of driving tasks specially adapted for safety of the vehicle or its occupants
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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
- B60W30/14—Adaptive cruise control
- B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding 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
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/10—Longitudinal speed
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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
- B60W2554/00—Input parameters relating to objects
- B60W2554/80—Spatial relation or speed relative to objects
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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
- B60W2554/00—Input parameters relating to objects
- B60W2554/80—Spatial relation or speed relative to objects
- B60W2554/805—Azimuth angle
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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
- B60W2754/00—Output or target parameters relating to objects
- B60W2754/10—Spatial relation or speed relative to objects
- B60W2754/30—Longitudinal distance
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Abstract
The application relates to a vehicle control method, a vehicle control device and a storage medium, which belong to the technical field of automatic driving, and the method comprises the following steps: acquiring vehicle information acquired by a sensor on a current vehicle; calculating the average speed of the current vehicle within a preset time; counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within a preset time period according to the vehicle information; inputting the average speed, the number of vehicles and the density of the vehicles into a pre-trained decision tree to obtain traffic flow state classification; determining a following time distance between the current vehicle and the front vehicle based on the traffic flow state classification; maintaining the distance between the vehicles ahead according to the following distance; the problem that the following distance of the vehicle is fixed and the vehicle is not flexible when the following distance is fixed can be solved; the following vehicle distance can be adaptively adjusted according to the traffic flow state, so that the flexibility of vehicle control can be improved.
Description
Technical Field
The application relates to a vehicle control method, a vehicle control device and a storage medium, and belongs to the technical field of automatic driving.
Background
An autonomous vehicle is an intelligent vehicle that is unmanned via a computer system. An autonomous vehicle generally has an Adaptive Cruise Control (ACC) function. The vehicle having the ACC function may be kept at a preset distance from the preceding vehicle to avoid a collision.
In the existing ACC control mode, the following distance of a vehicle is usually fixed, and the control mode is not flexible enough.
Disclosure of Invention
The application provides a vehicle control method, a vehicle control device and a storage medium, and can solve the problem that a vehicle is fixed in following distance and not flexible enough when the vehicle is fixed in following distance. The application provides the following technical scheme:
in a first aspect, a vehicle control method is provided, the method comprising:
acquiring vehicle information acquired by a sensor on a current vehicle, wherein the vehicle information comprises the position, the speed and the orientation angle of other vehicles relative to the current vehicle;
calculating the average speed of the current vehicle within a preset time length;
counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within the preset time period according to the vehicle information;
inputting the average vehicle speed, the number of vehicles and the vehicle density into a pre-trained decision tree to obtain traffic flow state classification;
determining a following time distance between the current vehicle and a preceding vehicle based on the traffic flow state classification;
and keeping the distance between the front vehicles according to the following distance.
Optionally, the traffic state classification includes a congestion state and a non-congestion state, and the determining a following distance between the current vehicle and a vehicle ahead based on the traffic state classification includes:
when the traffic flow state is classified as the congestion state, determining the following time interval as a first following time interval;
when the traffic flow state is classified as the non-congestion state, determining the following time interval as a second following time interval;
wherein, the first car following time interval is less than the second car following time interval.
Optionally, the decision tree includes a plurality of network branches, the network branches include a first branch and a second branch, the first branch corresponds to a congested state, and the second branch corresponds to a non-congested state, the method further includes:
determining the data volume processed by the network branch corresponding to the current group of input data in the decision tree;
and calculating the ratio of the data volume to the total historical data volume to obtain the probability corresponding to the traffic state classification.
Optionally, after the calculating a ratio of the data amount to a total amount of historical data and obtaining a probability corresponding to the traffic state classification, the method further includes:
determining whether to determine the following time distance based on the traffic flow state classification according to the probability;
when the following time interval is determined to be determined based on the traffic state classification, triggering and executing the step of determining the following time interval between the current vehicle and the front vehicle based on the traffic state classification;
when it is determined that the following time interval is not determined based on the traffic flow state classification, counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within the preset time length according to the vehicle information again; and inputting the average vehicle speed, the number of the vehicles and the vehicle density into a pre-trained decision tree to obtain the classification of the traffic flow state.
Optionally, the determining whether to determine the following headway based on the traffic flow state classification according to the probability includes:
when the probability is less than or equal to a probability threshold, determining not to determine the following headway based on the traffic flow state classification;
and when the probability is larger than a probability threshold value, determining that the following time interval is determined based on the traffic flow state classification.
Optionally, the determining of the vehicle density of the other vehicles within the second range based on the current vehicle includes:
determining vehicle densities of the other vehicles within a second range of a first distance in a longitudinal direction from the current vehicle and a second distance in a lateral direction from the current vehicle.
Optionally, the method further comprises:
determining whether to start a following distance adjusting function;
when the following distance adjusting function is determined to be started, the step of keeping the distance between the front vehicles according to the following distance is triggered and executed;
and when the following distance adjusting function is determined not to be started, keeping the distance between the front vehicles according to the default following distance.
In a second aspect, there is provided a vehicle control apparatus, the apparatus comprising:
the information acquisition module is used for acquiring vehicle information acquired by a sensor on a current vehicle, wherein the vehicle information comprises the position, the speed and the orientation angle of other vehicles relative to the current vehicle;
the vehicle speed calculation module is used for calculating the average vehicle speed of the current vehicle within a preset time length;
the data statistics module is used for counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within the preset time length according to the vehicle information;
the state classification module is used for inputting the average vehicle speed, the number of the vehicles and the vehicle density into a decision tree trained in advance to obtain traffic flow state classification;
the time distance adjusting module is used for determining the vehicle following time distance between the current vehicle and the front vehicle based on the traffic flow state classification;
and the vehicle control module is used for keeping the distance between the front vehicles according to the following vehicle distance.
In a third aspect, a vehicle control apparatus is provided, the apparatus comprising a processor and a memory; the memory stores therein a program that is loaded and executed by the processor to implement the vehicle control method provided by the first aspect.
In a fourth aspect, a computer-readable storage medium is provided, in which a program is stored, the program being executed by a processor for implementing the vehicle control method provided in the first aspect.
The beneficial effect of this application lies in: acquiring vehicle information acquired by a sensor on a current vehicle; calculating the average speed of the current vehicle within a preset time; counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within a preset time period according to the vehicle information; inputting the average speed, the number of vehicles and the density of the vehicles into a pre-trained decision tree to obtain traffic flow state classification; determining a following time distance between the current vehicle and the front vehicle based on the traffic flow state classification; maintaining the distance between the vehicles ahead according to the following distance; the problem that the following distance of the vehicle is fixed and the vehicle is not flexible when the following distance is fixed can be solved; the following vehicle distance can be adaptively adjusted according to the traffic flow state, so that the flexibility of vehicle control can be improved.
In addition, whether the following time interval is adjusted or not is determined according to the probability corresponding to the traffic state classification, so that the following time interval can be adjusted under the condition that the confidence coefficient of the traffic state classification is high, and the driving safety of the vehicle is guaranteed.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical solutions of the present application more clear and clear, and to implement the technical solutions according to the content of the description, the following detailed description is made with reference to the preferred embodiments of the present application and the accompanying drawings.
Drawings
FIG. 1 is a flow chart of a vehicle control method provided by one embodiment of the present application;
FIG. 2 is a block diagram of a vehicle control apparatus provided in an embodiment of the present application;
fig. 3 is a block diagram of a vehicle control device according to an embodiment of the present application.
Detailed Description
The following detailed description of embodiments of the present application will be described in conjunction with the accompanying drawings and examples. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.
First, several terms referred to in the present application will be described.
Automatic driving (Self-driving): the intelligent automobile is an intelligent automobile which can realize automatic driving through a computer system.
Adaptive Cruise Control (ACC): is an intelligent automatic control system. During the running process of the vehicle, a vehicle distance sensor (such as a radar sensor) arranged at the front part of the vehicle continuously scans the road in front of the vehicle, and meanwhile, a wheel speed sensor collects a vehicle speed signal. When the distance between the vehicle and the front vehicle is too small, the ACC control unit can appropriately brake the wheels and reduce the output power of the engine through the coordination action of the anti-lock braking system and the engine control system, so that the vehicle and the front vehicle can always keep a safe distance. When the distance between the vehicle and the front vehicle is increased to a safe distance, the ACC control unit controls the vehicle to run according to the set vehicle speed.
Following the vehicle distance: for each vehicle, the vehicle distance between the vehicle and the preceding vehicle in the longitudinal direction is divided by the vehicle speed of the vehicle.
Decision Tree (Decision Tree): in machine learning, a decision tree is a predictive model that represents a mapping between object attributes and object values. A decision tree is a tree-like structure in which each internal node represents a test on an attribute, each branch represents a test output, and each leaf node represents a category.
Optionally, the execution subject of each embodiment is taken as an example of an electronic device with computing capability, the electronic device may be a terminal or a server, the terminal may be a vehicle-mounted computer, a mobile phone, a computer, a notebook computer, a tablet computer, and the like, and the type of the terminal and the type of the electronic device are not limited in this embodiment.
In this embodiment, the electronic device is connected to a sensor on the current vehicle in a communication manner, such as: and the system is respectively in communication connection with a laser radar sensor, an image sensor, a millimeter wave radar sensor and the like so as to acquire the position, the speed and the orientation angle of other vehicles around the current vehicle for detection. In practical implementations, the current vehicle may also be equipped with other types of sensors, such as: the speed sensor is used for acquiring the speed of the current vehicle, and the present embodiment does not limit the type of the sensor installed on the current vehicle. The electronic device may be an on-board computer on the current vehicle or a device independent from the current vehicle, and the embodiment does not limit the installation manner between the electronic device and the current vehicle.
Fig. 1 is a flowchart of a vehicle control method according to an embodiment of the present application. The method at least comprises the following steps:
step 101, vehicle information collected by a sensor on a current vehicle is obtained, and the vehicle information comprises the position, the speed and the orientation angle of other vehicles relative to the current vehicle.
In other embodiments, the vehicle information may further include information such as a change rate of the orientation angle, and the content of the vehicle information is not limited in this embodiment.
And 102, calculating the average speed of the current vehicle in a preset time period.
The preset duration may be set by a user; or default setting in the electronic device, the present embodiment does not limit the setting mode and value of the preset duration.
And 103, counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within a preset time period according to the vehicle information.
Optionally, the first range and the second range are the same or different; the first range may include the second range when the first range and the second range are different.
In one example, the second range is a range that is a first distance from the current vehicle in the longitudinal direction and a second distance from the current vehicle in the lateral direction. Accordingly, the vehicle density of other vehicles in the second range determined based on the current vehicle includes: vehicle densities of other vehicles within a second range of a first distance from the current vehicle in the longitudinal direction and a second distance from the current vehicle in the lateral direction are determined.
The first distance and the second distance may be set by a user, or may also be default values in the electronic device, the first distance may be 200 meters, the second distance may be 7 meters, of course, the first distance and the second distance may also be other numerical values, and the setting mode and the value of the first distance and the second distance are not limited in this embodiment.
And 104, inputting the average speed, the number of vehicles and the density of the vehicles into a pre-trained decision tree to obtain the traffic flow state classification.
The decision tree is obtained by using a plurality of groups of training data to train in advance, each group of training data comprises sample data and a label, and the sample data comprises sample average speed, sample vehicle number and sample vehicle density; the label is used for indicating the real traffic state classification corresponding to the sample data. The decision tree is built based on supervised learning.
Optionally, the decision tree includes a plurality of network branches, and the network branches include a first branch and a second branch, the first branch corresponding to a congested state, and the second branch corresponding to a non-congested state. The number of the first branches is at least one, and the number of the second branches is at least one. At this time, the decision tree may also output a probability corresponding to the traffic flow state classification.
The process of calculating the probability corresponding to the traffic flow state classification through the decision tree comprises the following steps: determining the data volume processed by the network branch corresponding to the current group of input data in the decision tree; and calculating the ratio of the data volume to the total historical data volume to obtain the probability corresponding to the traffic flow state classification.
At this time, after calculating the ratio of the data volume to the total historical data volume and obtaining the probability corresponding to the traffic state classification, the method further includes: determining whether to determine the following vehicle distance based on the traffic flow state classification according to the probability; when determining that the following vehicle distance is determined based on the traffic flow state classification, triggering to execute the step 105; when it is determined that the following vehicle distance is not determined based on the traffic state classification, step 103 is executed again.
Wherein, confirm whether to confirm to follow the vehicle distance based on traffic flow state classification according to the probability, include: when the probability is smaller than or equal to the probability threshold value, determining that the following vehicle distance is not determined based on the traffic flow state classification; and when the probability is larger than the probability threshold value, determining the following vehicle distance based on the traffic flow state classification.
Because the probability corresponding to the traffic state classification is in positive correlation with the confidence, in the embodiment, whether the following time interval is adjusted or not is determined according to the probability corresponding to the traffic state classification, so that the following time interval can be adjusted only under the condition that the confidence of the traffic state classification is high, and the driving safety of the vehicle is ensured.
And 105, determining the following time distance between the current vehicle and the front vehicle based on the traffic flow state classification.
The following time interval and the congestion degree indicated by the traffic flow state classification are in a negative correlation relationship. In one example, the traffic status classification includes a congested state and a non-congested state, and determining a following headway between a current vehicle and a preceding vehicle based on the traffic status classification includes: when the traffic flow state is classified into a congestion state, determining that the following vehicle distance is a first following time distance; when the traffic flow state is classified into a non-congestion state, determining that the following vehicle distance is a second following vehicle time distance; wherein, the first car following distance is less than the second car following time distance.
And 106, keeping the distance between the front vehicles according to the following vehicle distance.
Optionally, before step 106, the electronic device may further determine whether to turn on a following distance adjustment function; when the following distance adjusting function is determined to be started, a step of keeping the distance between the front vehicles according to the following distance is triggered and executed; when determining not to start the following vehicle distance adjustment function, keeping the distance between the vehicles ahead according to the default following vehicle distance.
In summary, in the vehicle control method provided by the embodiment, the vehicle information acquired by the sensor on the current vehicle is acquired; calculating the average speed of the current vehicle within a preset time; counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within a preset time period according to the vehicle information; inputting the average vehicle speed, the number of vehicles and the vehicle density into a pre-trained decision tree to obtain traffic flow state classification; determining a following time distance between the current vehicle and the front vehicle based on the traffic flow state classification; maintaining the distance between the vehicles ahead according to the following distance; the problem that the following distance of the vehicle is fixed and the vehicle is not flexible when the following distance is fixed can be solved; the following vehicle distance can be adaptively adjusted according to the traffic flow state, so that the flexibility of vehicle control can be improved.
In addition, whether the following time interval is adjusted or not is determined according to the probability corresponding to the traffic state classification, so that the following time interval can be adjusted under the condition that the confidence coefficient of the traffic state classification is high, and the driving safety of the vehicle is guaranteed.
Fig. 2 is a block diagram of a vehicle control device according to an embodiment of the present application. The device at least comprises the following modules: the system comprises an information acquisition module 210, a vehicle speed calculation module 220, a data statistics module 230, a state classification module 240, a time distance adjustment module 250 and a vehicle control module 260.
The information acquisition module 210 is configured to acquire vehicle information acquired by a sensor on a current vehicle, where the vehicle information includes a position, a speed, and an orientation angle of another vehicle relative to the current vehicle;
the vehicle speed calculating module 220 is used for calculating the average vehicle speed of the current vehicle within a preset time length;
the data statistics module 230 is configured to count, within the preset time period, the number of vehicles of other vehicles within a first range determined based on the current vehicle and the vehicle density of other vehicles within a second range determined based on the current vehicle according to the vehicle information;
the state classification module 240 is configured to input the average vehicle speed, the number of vehicles, and the vehicle density into a pre-trained decision tree to obtain a traffic flow state classification;
a time interval adjusting module 250, configured to determine a following time interval between the current vehicle and a preceding vehicle based on the traffic flow state classification;
a vehicle control module 260 for maintaining a distance between the vehicles ahead according to the following vehicle distance.
For relevant details reference is made to the above-described method embodiments.
It should be noted that: in the vehicle control device provided in the above embodiment, when performing vehicle control, only the division of the above functional modules is taken as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of the vehicle control device is divided into different functional modules to complete all or part of the above described functions. In addition, the vehicle control device and the vehicle control method provided by the above embodiment belong to the same concept, and the specific implementation process is described in the method embodiment, which is not described herein again.
Fig. 3 is a block diagram of a vehicle control device according to an embodiment of the present application. The apparatus comprises at least a processor 301 and a memory 302.
Processor 301 may include one or more processing cores, such as: 4 core processors, 8 core processors, etc. The processor 301 may be implemented in at least one hardware form of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 301 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also called a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 301 may be integrated with a GPU (Graphics Processing Unit) that is responsible for rendering and drawing content that the display screen needs to display. In some embodiments, the processor 301 may further include an AI (Artificial Intelligence) processor for processing computing operations related to machine learning.
Memory 302 may include one or more computer-readable storage media, which may be non-transitory. Memory 302 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 302 is used to store at least one instruction for execution by processor 301 to implement a vehicle control method provided by method embodiments herein.
In some embodiments, the vehicle control device may further include: a peripheral interface and at least one peripheral. The processor 301, memory 302 and peripheral interface may be connected by bus or signal lines. Each peripheral may be connected to the peripheral interface via a bus, signal line, or circuit board. Illustratively, peripheral devices include, but are not limited to: radio frequency circuit, touch display screen, audio circuit, power supply, etc.
Of course, the vehicle control device may include fewer or more components, and the embodiment is not limited thereto.
Optionally, the present application also provides a computer-readable storage medium, in which a program is stored, the program being loaded and executed by a processor to implement the vehicle control method of the above-described method embodiment.
Optionally, the present application also provides a computer product including a computer-readable storage medium, in which a program is stored, the program being loaded and executed by a processor to implement the vehicle control method of the above-mentioned method embodiment.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
The above is only one specific embodiment of the present application, and any other modifications based on the concept of the present application are considered as the protection scope of the present application.
Claims (9)
1. A vehicle control method, characterized by comprising:
acquiring vehicle information acquired by a sensor on a current vehicle, wherein the vehicle information comprises the position, the speed and the orientation angle of other vehicles relative to the current vehicle;
calculating the average speed of the current vehicle within a preset time length;
counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within the preset time period according to the vehicle information;
inputting the average vehicle speed, the number of vehicles and the vehicle density into a pre-trained decision tree to obtain a traffic flow state classification, wherein the decision tree comprises a plurality of network branches, each network branch comprises a first branch and a second branch, the first branch corresponds to a congestion state, and the second branch corresponds to a non-congestion state, and the method comprises the following steps: determining the data volume processed by the network branch corresponding to the current group of input data in the decision tree; calculating the ratio of the data volume to the total historical data volume to obtain the probability corresponding to the traffic state classification;
determining a following time distance between the current vehicle and a preceding vehicle based on the traffic flow state classification;
and keeping the distance between the front vehicles according to the following distance.
2. The method of claim 1, wherein the traffic status classification includes a congested status and an uncongested status, and wherein determining a following headway between the current vehicle and a preceding vehicle based on the traffic status classification comprises:
when the traffic flow state is classified as the congestion state, determining the following time interval as a first following time interval;
when the traffic flow state is classified as the non-congestion state, determining the following time interval as a second following time interval;
wherein, the first car following time interval is less than the second car following time interval.
3. The method according to claim 1, wherein after calculating the ratio of the data amount to the total amount of the historical data and obtaining the probability corresponding to the traffic state classification, the method further comprises:
determining whether to determine the following time distance based on the traffic flow state classification according to the probability;
when the following time interval is determined to be determined based on the traffic state classification, triggering and executing the step of determining the following time interval between the current vehicle and the front vehicle based on the traffic state classification;
when it is determined that the following time interval is not determined based on the traffic flow state classification, counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within the preset time length according to the vehicle information again; and inputting the average vehicle speed, the number of the vehicles and the vehicle density into a pre-trained decision tree to obtain the classification of the traffic flow state.
4. The method of claim 3, wherein the determining whether to determine the following headway based on the traffic state classification according to the probability comprises:
determining not to determine the following headway based on the traffic flow state classification when the probability is less than or equal to a probability threshold;
and when the probability is larger than a probability threshold value, determining that the following time interval is determined based on the traffic flow state classification.
5. The method of claim 1, wherein the determining the vehicle density of other vehicles within the second range based on the current vehicle comprises:
determining vehicle densities of the other vehicles within a second range of a first distance in a longitudinal direction from the current vehicle and a second distance in a lateral direction from the current vehicle.
6. The method of any of claims 1 to 5, further comprising:
determining whether to start a following distance adjusting function;
when the following distance adjusting function is determined to be started, the step of keeping the distance between the front vehicles according to the following distance is triggered and executed;
and when the following distance adjusting function is determined not to be started, keeping the distance between the front vehicles according to the default following distance.
7. A vehicle control apparatus, characterized by comprising:
the information acquisition module is used for acquiring vehicle information acquired by a sensor on a current vehicle, wherein the vehicle information comprises the position, the speed and the orientation angle of other vehicles relative to the current vehicle;
the vehicle speed calculation module is used for calculating the average vehicle speed of the current vehicle within a preset time length;
the data statistics module is used for counting the number of other vehicles in a first range determined based on the current vehicle and the vehicle density of other vehicles in a second range determined based on the current vehicle within the preset time length according to the vehicle information;
a state classification module, configured to input the average vehicle speed, the number of vehicles, and the vehicle density into a pre-trained decision tree to obtain a traffic flow state classification, where the decision tree includes a plurality of network branches, the network branches include a first branch and a second branch, the first branch corresponds to a congested state, and the second branch corresponds to a non-congested state, and the state classification module includes: determining the data volume processed by the network branch corresponding to the current group of input data in the decision tree; calculating the ratio of the data volume to the total historical data volume to obtain the probability corresponding to the traffic state classification;
the time distance adjusting module is used for determining the vehicle following time distance between the current vehicle and the front vehicle based on the traffic flow state classification;
and the vehicle control module is used for keeping the distance between the front vehicles according to the following distance.
8. A vehicle control apparatus, characterized in that the apparatus comprises a processor and a memory; the memory stores therein a program that is loaded and executed by the processor to implement the vehicle control method according to any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that the storage medium has stored therein a program for implementing the vehicle control method according to any one of claims 1 to 6 when executed by a processor.
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