CN112849144A - Vehicle control method, device and storage medium - Google Patents
Vehicle control method, device and storage medium Download PDFInfo
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- CN112849144A CN112849144A CN202110133550.0A CN202110133550A CN112849144A CN 112849144 A CN112849144 A CN 112849144A CN 202110133550 A CN202110133550 A CN 202110133550A CN 112849144 A CN112849144 A CN 112849144A
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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, or advanced driver assistance systems for ensuring comfort, stability and safety or drive control systems for propelling or retarding the vehicle
- B60W30/18—Propelling the vehicle
- B60W30/18009—Propelling the vehicle related to particular drive situations
- B60W30/18159—Traversing an intersection
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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
- B60W40/06—Road conditions
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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 control, and the method comprises the following steps: determining whether a lane line exists on a driving road surface of a current vehicle in a current statistical time period; if yes, obtaining the lane line type, and determining whether the lane line type jumps or not; if the jump occurs, acquiring a jump occurrence position and a jump occurrence type; acquiring the target quantity of other targets with the movement direction not parallel to the driving direction in the current statistical time period; acquiring a lane line distance between the current position and a visual end point of the lane line; acquiring the number of road edges of a road edge and the transverse distance between the road edge and a current vehicle; inputting the lane line type, the jumping occurrence position, the jumping occurrence type, the target number, the lane line distance, the road edge number and the transverse distance into a logistic regression classifier to obtain an intersection identification result; decelerating the driving when the driving to the intersection is identified; the intersection without the traffic lights can be identified, and the accuracy of intersection identification is improved.
Description
[ technical field ] A method for producing a semiconductor device
The application relates to a vehicle control method, a vehicle control device and a storage medium, and belongs to the technical field of automatic control.
[ background of the invention ]
Intersections are important components of the road network and are located where roads meet. The accurate intersection identification method has important significance for judging the current driving environment and optimizing the driving scheme by the vehicle.
In the prior art, identifying intersections in a driving area includes: acquiring the distance between a traffic light at an intersection and a current running vehicle; and judging whether the vehicle approaches the intersection or not according to the distance.
However, in real life, traffic lights are not arranged at a plurality of intersections, and at the moment, the intersections cannot be determined according to the distance between the traffic lights and the current running vehicles, so that the intersection identification accuracy is low.
[ summary of the invention ]
The application provides a vehicle control method, a vehicle control device and a storage medium, which can solve the problem that when an intersection is identified by a traffic light, the intersection without the traffic light cannot be identified. The application provides the following technical scheme:
in a first aspect, a vehicle control method is provided, the method comprising:
determining whether a lane line exists on a driving road surface of a current vehicle in a current statistical time period;
when the lane line exists on the driving road surface, acquiring the lane line type, and determining whether the lane line type jumps within the current statistical time period;
when the lane line type jumps, acquiring a jump occurrence position and a jump occurrence type;
acquiring the target quantity of other targets of which the movement direction is not parallel to the driving direction of the current vehicle in the current statistical time period;
acquiring a lane line distance between a current position and a visual end point of the lane line;
acquiring the number of road edges of the road edges on the running road surface and the transverse distance between the road edges and the current vehicle in the current statistical time period;
inputting the lane line type, the jump occurrence position, the jump occurrence type, the target number, the lane line distance, the road edge number and the transverse distance into a pre-trained logistic regression classifier to obtain an intersection recognition result;
and when the intersection identification result indicates that the vehicle is currently driven to the intersection, controlling the current vehicle to decelerate to drive so as to decelerate to pass through the intersection.
Optionally, when the intersection identification result indicates that the vehicle is currently driving to the intersection, after controlling the current vehicle to decelerate, the method further includes:
and outputting a prompt of leaving the intersection after the current vehicle speed reduction driving distance reaches a preset distance and a lane line is detected.
Optionally, after the distance traveled by the current vehicle at a decelerated speed reaches a preset distance and a lane line is detected, the method further includes:
and controlling the current vehicle to accelerate and continuing to perform intersection identification.
Optionally, the current statistical time period is a time length required when the current vehicle travels a preset distance; the starting time of the current statistical time period is the ending time of the last statistical time period, and the initial statistical time period is the time when the current vehicle starts the intersection detection function.
Optionally, the intersection identification result is used for indicating the probability of the existence of the intersection in the driving direction of the current vehicle.
Optionally, the lane line type includes: a dotted line type, a solid line type, a dotted line and solid line coexistence type.
Optionally, the running speed of the current vehicle after deceleration is lower than a preset speed.
In a second aspect, there is provided a vehicle control apparatus, the apparatus comprising:
the lane line detection module is used for determining whether a lane line exists on the driving road surface of the current vehicle in the current statistical time period;
the jump detection module is used for acquiring the lane line type when the lane line exists on the driving road surface and determining whether the lane line type jumps within the current statistical time period;
the hopping acquisition module is used for acquiring hopping generation positions and hopping generation types when the lane line types hop;
the target acquisition module is used for acquiring the target quantity of other targets of which the movement direction is not parallel to the running direction of the current vehicle in the current statistical time period;
the distance acquisition module is used for acquiring the distance between the current position and the visual end point of the lane line;
the road edge counting module is used for acquiring the number of road edges of the road edges on the driving road surface and the transverse distance between the road edges and the current vehicle in the current counting time period;
the intersection recognition module is used for inputting the lane line type, the jump occurrence position, the jump occurrence type, the target number, the lane line distance, the road edge number and the transverse distance into a pre-trained logistic regression classifier to obtain an intersection recognition result;
and the vehicle control module is used for controlling the current vehicle to run at a reduced speed to pass through the intersection when the intersection identification result indicates that the current vehicle runs to the intersection.
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: determining whether a lane line exists on a driving road surface of a current vehicle within a current statistical time period; when a lane line exists on a driving road surface, acquiring the type of the lane line, and determining whether the type of the lane line jumps within the current statistical time period; when the lane line type jumps, acquiring the jump occurrence position and the jump occurrence type; acquiring the target quantity of other targets with the movement direction not parallel to the driving direction of the current vehicle in the current statistical time period; acquiring a lane line distance between the current position and a visual end point of the lane line; acquiring the number of road edges on a driving road surface in the current statistical time period and the transverse distance between the road edges and the current vehicle; inputting the lane line type, the jumping occurrence position, the jumping occurrence type, the target number, the lane line distance, the road edge number and the transverse distance into a pre-trained logistic regression classifier to obtain an intersection recognition result; when the intersection identification result indicates that the vehicle is running to the intersection at present, controlling the vehicle to run at a reduced speed so as to pass through the intersection at a reduced speed; the problem that the intersection without the traffic light cannot be identified when the intersection is identified by the traffic light can be solved; the intersection is identified by combining the change of the lane line of the intersection, the running states of other targets and the change of the road edge, so that the accuracy of intersection identification can be improved.
Meanwhile, when the intersection is identified, the vehicle is controlled to decelerate, so that the safety of the vehicle passing through the intersection can be improved.
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.
[ description of the 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 ] embodiments
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.
Neural Network (Neural Network): the method is an algorithm model for simulating animal neural network behavior characteristics and performing distributed parallel information processing.
Logistic Regression Classifier (Logistic Regression Classifier): for estimating the likelihood of something, and also for classification. Regression refers to the estimation of unknown parameters of a known formula. Such as: given the formula y a x + b and the unknown parameters a and b, the values of a and b can be automatically estimated from many real (x, y) data (training samples).
Let the vector x' with p independent variables be (x)1，x2，…xp) The conditional probability P (Y ═ 1| x) ═ P is a probability of occurrence of an event according to the observed quantity. The logistic regression model can be expressed as:
P(Y＝1|x)＝π(x)＝1/(1+e-g(x))
the above function is called a logic function. Wherein g (x) is β0+β1x1+β2x2+…+βpxp。β0，β1，β2…βpI.e. the parameter to be estimated.
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 target vehicle in a communication manner, that is, the target vehicle is equipped with various sensors, such as: laser radar sensor, image sensor (or camera), etc. In practical implementation, the target vehicle may also be equipped with other types of sensors, and the present embodiment does not limit the types of sensors installed on the target vehicle. The electronic device may be an on-board computer on the target vehicle or a device independent from the target vehicle, and the embodiment does not limit the installation manner between the electronic device and the target 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, determining whether a lane line exists on a driving road surface of a current vehicle in a current statistical time period.
The starting time of the current statistical time period is the ending time of the last statistical time period, and the initial statistical time period is the time when the current vehicle starts the intersection detection function.
Optionally, the current timing of turning on the intersection detection function by the vehicle includes, but is not limited to, the following: starting when the current vehicle is started, or starting when the fact that a lane line exists on the running road surface of the current vehicle is determined; or, a control with an intersection detection function is arranged on the current vehicle, and the control is started based on the operation of the user on the control.
The electronic equipment determines whether a lane line exists on a driving road surface of a current vehicle, and comprises the following steps: acquiring a road surface image of a driving road surface, and carrying out target detection on the road surface image; and determining whether the lane line exists on the driving road surface according to the target detection result. Or comparing the road surface image with the template image, and determining that a lane line exists on the driving road surface when the road surface image has an image area matched with the template image; and when the image area matched with the template image does not exist in the road surface image, determining that no lane line exists on the driving road surface.
The road surface images include road surface images of the left and right sides of the current vehicle.
The way of detecting the target of the road image can be realized by a neural network model, such as: a YOLO algorithm, or a Single Shot multi box Detector (SSD) algorithm, and the like, and the type of the target detection algorithm is not limited in this embodiment. Accordingly, the target detection result is used to indicate whether a lane line exists on the traveling road surface.
The template image is an image of each lane line type, such as: the lane line types include: a dotted line type, a solid line type, a dotted line and solid line coexistence type; accordingly, the template image includes an image with a lane line of a dotted line type, an image with a lane line of a solid line type, and an image with a lane line of a dotted line and solid line coexistence type. Optionally, the template image corresponding to each type of lane line is at least one, and the at least one template image is used for reflecting different postures of the type of lane line, such as: different extension directions, different thicknesses, etc.
In this embodiment, the solid line type lane line may be an object that plays a role of interception, such as: the road edge, the road block, etc. or may also be a solid line drawn on the road surface, and the embodiment does not limit the implementation manner of the solid line type lane line.
Optionally, the duration of each statistical time period is fixed and is pre-stored in the electronic device; at this time, the duration of each statistical time period may be set by the user or stored in the electronic device by default. Or, the current statistical time period is a time period required when the current vehicle travels a preset distance. Wherein the preset distance may be set by a user or stored in the electronic device by default. The current statistical time period is a quotient of a preset distance divided by an average traveling speed, or a quotient of a preset distance divided by a historical maximum traveling speed of the current vehicle, and the determination manner of the current statistical time period is not limited in this embodiment.
Optionally, in the current statistical time period, if a lane line exists on the driving road surface of the current vehicle, executing step 102; if no lane line exists, the step is executed again.
And 102, when a lane line exists on a driving road surface, acquiring the type of the lane line, and determining whether the type of the lane line jumps within the current statistical time period.
The electronic device can obtain the type of the lane line by detecting the lane line.
Optionally, the scenarios in which the lane line type jumps within the current statistical time period include, but are not limited to, the following:
the first method comprises the following steps: the solid line type is jumped to a dotted line type or a dotted line and solid line coexistence type;
and the second method comprises the following steps: the dotted line type or the dotted line and solid line coexistence type jumps to the solid line type.
Generally speaking, the probability that the current vehicle approaches the intersection is higher in the second scenario.
Determining whether the lane line type jumps within the current statistical time period, including: determining whether the type of the lane line changes, if so, determining that jumping occurs; if not, no jump occurs.
And 103, acquiring a jump occurrence position and a jump occurrence type when the lane line type jumps.
The types of transition occurrences include, but are not limited to, the two scenarios listed in step 102, but may also include other scenarios, such as: jumping from the solid line type to the state that the lane line cannot be detected; or, the type of the coexistence of the dotted line and the solid line is changed into the type of the undetected lane line, and the types of the occurrence of the hopping are not listed in this embodiment.
Optionally, when the lane line type is not hopped, step 104 is executed after step 102.
And 104, acquiring the number of other targets of which the movement direction is not parallel to the driving direction of the current vehicle in the current statistical time period.
Since there may be a case where the moving direction of other vehicles or pedestrians is not parallel to the traveling manner of the current vehicle when the current vehicle is at the intersection, in this embodiment, it may be determined whether the current vehicle travels to the intersection by combining the target number of other targets.
The way for the electronic device to obtain the target number of other targets includes: acquiring n continuous driving images in the preset time length in front of the driving direction, and performing target identification on the driving images to obtain the number of designated targets in the n driving images and the movement direction of each designated target in the preset time length; and determining other targets with the movement directions not parallel to the driving direction in each designated target, and counting the number of the other targets.
Wherein the specified targets include, but are not limited to: vehicles (such as bicycles, electric vehicles, automobiles, etc.), pedestrians, etc. can move on the traveling road surface, and the present embodiment does not limit the type of the designated object.
Alternatively, when there is a lane line on the driving road, step 104 may be executed before steps 102 and 103, or may also be executed after steps 102 and 103, or may also be executed simultaneously with steps 102 and 103, and the execution timing of step 104 is not limited in this embodiment.
And 105, acquiring the distance between the current position and the visual end point of the lane line.
Optionally, an image sensor is mounted on the current vehicle, the image sensor is used for collecting a driving image in front of the driving direction, and the collection range of the image sensor comprises a front visual field range when the driver drives. The visual end point of the lane line is a critical point of the acquisition range of the lane line in the driving direction of the image sensor, or the end point of the lane line. In other words, the visual end point of the lane line is a point at which the lane line is not visible to human eyes from the viewpoint of the driver, or is an actual end point of the lane line.
Alternatively, when there is a lane line on the driving road, step 105 may be executed before steps 102 and 103, or may also be executed after steps 102 and 103, or may also be executed simultaneously with steps 102 and 103, and the execution timing of step 105 is not limited in this embodiment.
And 106, acquiring the number of the road edges of the road surface on the running road surface and the transverse distance between the road edges and the current vehicle in the current statistical time period.
The road edge is used as one of the lane lines, and when the electronic equipment detects the lane lines, whether the road edge exists on a driving road surface or not can be detected.
The road edge can be identified based on point cloud data collected by a laser radar installed on the current vehicle; or the road surface image is identified based on the road surface image collected by the image sensor installed on the current vehicle; or the road is identified based on the point cloud data and the road surface image, and the embodiment does not limit the way of identifying the road edge.
Here, the lateral distance is a distance in a direction parallel to the traveling road surface and perpendicular to the traveling direction.
Alternatively, when there is a lane line on the driving road, step 106 may be executed before steps 102 and 103, or may also be executed after steps 102 and 103, or may also be executed simultaneously with steps 102 and 103, and the execution timing of step 106 is not limited in this embodiment.
And step 107, inputting the lane line type, the jumping occurrence position, the jumping occurrence type, the target number, the lane line distance, the road edge number and the transverse distance into a pre-trained logistic regression classifier to obtain a crossing recognition result.
The intersection recognition result is used for indicating the probability of the intersection existing in the driving direction of the current vehicle.
The logistic regression classifier is obtained through training of multiple groups of training data, wherein each group of training data comprises a sample lane line type, a sample jump occurrence position, a sample jump occurrence type, a sample target number, a sample lane line distance, a sample road edge number, a sample transverse distance and a marking result of whether a certain vehicle is at an intersection or not in a certain driving time period.
And step 108, when the intersection identification result indicates that the vehicle is currently driven to the intersection, controlling the current vehicle to decelerate to drive so as to decelerate to pass through the intersection.
The running speed of the current vehicle after deceleration is lower than the preset speed. The preset speed may be a speed set by a user, or set in the electronic device by default, or determined based on the current vehicle speed, such as: the preset speed is obtained by subtracting a preset value from the current vehicle speed, and the setting mode of the preset speed is not limited in this embodiment.
Optionally, when the intersection identification result indicates that the vehicle is currently driving to the intersection, after controlling the current vehicle to decelerate, the method further includes: and outputting a prompt of leaving the intersection after the current distance of the vehicle in the deceleration running reaches a preset distance and the lane line is detected.
The output mode of the prompt for leaving the intersection includes but is not limited to: audio output, vibration output, and/or light prompt, etc., and the output mode is not limited in this embodiment.
Since the intersection usually does not have a lane line, when the lane line is detected, it is usually indicated that the intersection has been left, and at this time, in combination with the distance traveled by the current vehicle at a reduced speed, the accuracy of determining the intersection to be left can be improved.
The preset distance is the maximum distance required to be driven when each intersection in the current driving area passes through. Alternatively, the current driving area may be located by the locating component, and the current driving area may be divided by taking a street as a unit, or taking a village, a town, or a district as a unit, and the present embodiment does not limit the dividing manner of the current driving area.
Optionally, after the distance traveled by the current vehicle at the time of deceleration reaches a preset distance and a lane line is detected, the method further includes: and controlling the current vehicle to accelerate and continuing to perform intersection identification.
Illustratively, the speed of the current vehicle after acceleration is the speed before deceleration, or the speed redetermined according to the current running environment of the current vehicle, and the setting mode of the speed after acceleration is not limited in this embodiment.
In summary, the vehicle control method provided in this embodiment determines whether a lane line exists on the driving road of the current vehicle within the current statistical time period; when a lane line exists on a driving road surface, acquiring the type of the lane line, and determining whether the type of the lane line jumps within the current statistical time period; when the lane line type jumps, acquiring the jump occurrence position and the jump occurrence type; acquiring the target quantity of other targets with the movement direction not parallel to the driving direction of the current vehicle in the current statistical time period; acquiring a lane line distance between the current position and a visual end point of the lane line; acquiring the number of road edges on a driving road surface in the current statistical time period and the transverse distance between the road edges and the current vehicle; inputting the lane line type, the jumping occurrence position, the jumping occurrence type, the target number, the lane line distance, the road edge number and the transverse distance into a pre-trained logistic regression classifier to obtain an intersection recognition result; when the intersection identification result indicates that the vehicle is running to the intersection at present, controlling the vehicle to run at a reduced speed so as to pass through the intersection at a reduced speed; the problem that the intersection without the traffic light cannot be identified when the intersection is identified by the traffic light can be solved; the intersection is identified by combining the change of the lane line of the intersection, the running states of other targets and the change of the road edge, so that the accuracy of intersection identification can be improved.
Meanwhile, when the intersection is identified, the vehicle is controlled to decelerate, so that the safety of the vehicle passing through the intersection can be improved.
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 a lane line detection module 210, a jump detection module 220, a jump acquisition module 230, a target acquisition module 240, a distance acquisition module 250, a road edge statistics module 260, an intersection identification module 270, and a vehicle control module 280.
The lane line detection module 210 is configured to determine whether a lane line exists on a driving road of a current vehicle within a current statistical time period;
a jump detection module 220, configured to, when the lane line exists on the driving road surface, obtain the lane line type, and determine whether the lane line type jumps within the current statistical time period;
a jump obtaining module 230, configured to obtain a jump occurrence position and a jump occurrence type when the lane line type jumps;
a target obtaining module 240, configured to obtain the number of other targets whose motion directions are not parallel to the driving direction of the current vehicle within the current statistical time period;
a distance obtaining module 250, configured to obtain a lane line distance between the current position and a visual end point of the lane line;
the road edge counting module 260 is configured to obtain the number of road edges of the road edge on the driving road surface and the transverse distance between the road edge and the current vehicle in the current counting time period;
an intersection recognition module 270, configured to input the lane line type, the jump occurrence position, the jump occurrence type, the target number, the lane line distance, the road edge number, and the transverse distance into a pre-trained logistic regression classifier, so as to obtain an intersection recognition result;
and the vehicle control module 280 is used for controlling the current vehicle to run at a reduced speed to pass through the intersection when the intersection identification result indicates that the current vehicle runs to the intersection.
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), which is responsible for rendering and drawing the content required to be displayed on the display screen. 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 (10)
1. A vehicle control method, characterized by comprising:
determining whether a lane line exists on a driving road surface of a current vehicle in a current statistical time period;
when the lane line exists on the driving road surface, acquiring the lane line type, and determining whether the lane line type jumps within the current statistical time period;
when the lane line type jumps, acquiring a jump occurrence position and a jump occurrence type;
acquiring the target quantity of other targets of which the movement direction is not parallel to the driving direction of the current vehicle in the current statistical time period;
acquiring a lane line distance between a current position and a visual end point of the lane line;
acquiring the number of road edges of the road edges on the running road surface and the transverse distance between the road edges and the current vehicle in the current statistical time period;
inputting the lane line type, the jump occurrence position, the jump occurrence type, the target number, the lane line distance, the road edge number and the transverse distance into a pre-trained logistic regression classifier to obtain an intersection recognition result;
and when the intersection identification result indicates that the vehicle is currently driven to the intersection, controlling the current vehicle to decelerate to drive so as to decelerate to pass through the intersection.
2. The method according to claim 1, wherein after controlling the current vehicle to run at a reduced speed when the intersection identification result indicates that the current vehicle is currently running to the intersection, the method further comprises:
and outputting a prompt of leaving the intersection after the current vehicle speed reduction driving distance reaches a preset distance and a lane line is detected.
3. The method according to claim 2, further comprising, after the distance traveled by the current vehicle at a deceleration reaches a preset distance and a lane line is detected:
and controlling the current vehicle to accelerate and continuing to perform intersection identification.
4. The method of claim 1, wherein the current statistical time period is a time period required for the current vehicle to travel a preset distance; the starting time of the current statistical time period is the ending time of the last statistical time period, and the initial statistical time period is the time when the current vehicle starts the intersection detection function.
5. The method according to claim 1, wherein the intersection recognition result is used to indicate a probability that an intersection exists in the traveling direction of the current vehicle.
6. The method of claim 1, wherein the lane line type comprises: a dotted line type, a solid line type, a dotted line and solid line coexistence type.
7. The method according to any one of claims 1 to 6, wherein the current vehicle decelerated travel speed is lower than a preset speed.
8. A vehicle control apparatus, characterized in that the apparatus comprises:
the lane line detection module is used for determining whether a lane line exists on the driving road surface of the current vehicle in the current statistical time period;
the jump detection module is used for acquiring the lane line type when the lane line exists on the driving road surface and determining whether the lane line type jumps within the current statistical time period;
the hopping acquisition module is used for acquiring hopping generation positions and hopping generation types when the lane line types hop;
the target acquisition module is used for acquiring the target quantity of other targets of which the movement direction is not parallel to the running direction of the current vehicle in the current statistical time period;
the distance acquisition module is used for acquiring the distance between the current position and the visual end point of the lane line;
the road edge counting module is used for acquiring the number of road edges of the road edges on the driving road surface and the transverse distance between the road edges and the current vehicle in the current counting time period;
the intersection recognition module is used for inputting the lane line type, the jump occurrence position, the jump occurrence type, the target number, the lane line distance, the road edge number and the transverse distance into a pre-trained logistic regression classifier to obtain an intersection recognition result;
and the vehicle control module is used for controlling the current vehicle to run at a reduced speed to pass through the intersection when the intersection identification result indicates that the current vehicle runs to the intersection.
9. 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 7.
10. 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 7 when executed by a processor.
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