CN116202543A - Lane navigation method and system, computer equipment and storage medium - Google Patents
Lane navigation method and system, computer equipment and storage medium Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
- G01C21/3492—Special cost functions, i.e. other than distance or default speed limit of road segments employing speed data or traffic data, e.g. real-time or historical
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
- G01C21/3602—Input other than that of destination using image analysis, e.g. detection of road signs, lanes, buildings, real preceding vehicles using a camera
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/34—Route searching; Route guidance
- G01C21/36—Input/output arrangements for on-board computers
- G01C21/3626—Details of the output of route guidance instructions
- G01C21/3658—Lane guidance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
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Abstract
The invention relates to a lane navigation method and system, a computer device and a storage medium, comprising: receiving a front image in a current period acquired by a vehicle-mounted camera, inputting the front image into a pre-trained deep learning model for image recognition to obtain a Speed speed_A of a front vehicle A of a lane where a vehicle is located in the current period, a brake lamp lighting frequency count_A, a Speed speed_B of a front vehicle B of an adjacent lane and a brake lamp lighting frequency count_B; carrying out Bayesian statistics on the number of times of lighting the brake lamp count_A and the number of times of lighting the brake lamp count_B to obtain the braking deviation probability of the front vehicle A or the front vehicle B at the next moment; judging whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_A and the Speed speed_B and the braking deviation probability of the front vehicle A or the front vehicle B at the next moment; and outputting corresponding navigation indication information according to the judgment result, so that the accuracy of lane navigation can be improved.
Description
Technical Field
The invention relates to the technical field of vehicles, in particular to a lane navigation method and system, computer equipment and storage medium.
Background
The automobile navigation system has the functions of positioning, path searching and planning and the like, so that an automobile owner can know the exact position of the automobile owner at any time and any place when driving the automobile, and provides a driving route, the automobile owner can easily drive and travel efficiently, along with the continuous development of the automobile navigation system, the current part of the automobile navigation system can provide lane navigation according to the lane congestion condition, namely, the automobile owner is guided to avoid the congested lanes, but when the lane navigation of the current automobile navigation system judges the lane congestion condition, the congestion condition of each lane is generally pre-judged in advance based on the real-time position of the automobile and the map data of a third party, but the real-time data of the lanes collected by the map data of the third party possibly accords with the actual condition, so that the accuracy of the lane navigation of the method is still insufficient.
Disclosure of Invention
The invention aims to provide a lane navigation method and system, computer equipment and storage medium, which are used for carrying out lane navigation by combining real-time road conditions acquired and identified in the driving process of a vehicle, so as to improve the accuracy of the lane navigation.
To achieve the above object, an embodiment of the present invention provides a lane navigation method, including the steps of:
receiving a front image in a current period acquired by a vehicle-mounted camera, inputting the front image into a pre-trained deep learning model for image recognition to obtain a Speed speed_A of a front vehicle A of a lane where a vehicle is located in the current period, a brake lamp lighting frequency count_A, a Speed speed_B of a front vehicle B of an adjacent lane and a brake lamp lighting frequency count_B;
carrying out Bayesian statistics on the number of times of lighting the brake lamp count_A and the number of times of lighting the brake lamp count_B to obtain the braking deviation probability of the front vehicle A or the front vehicle B at the next moment;
judging whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_A and the Speed speed_B and the braking deviation probability of the front vehicle A or the front vehicle B at the next moment;
and outputting corresponding navigation indication information according to the judgment result.
Preferably, the determining whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_a and the Speed speed_b and the braking deviation probability of the preceding vehicle a or the preceding vehicle B at the next moment includes:
according to the brake deviation probability and the brake probability distribution type
Determining the magnitude relation between an a value and a b value in the brake probability distribution; wherein θ is the probability of the brake deviation of the front vehicle A at the next moment, and (1- θ) is the probability of the brake deviation of the front vehicle B;
judging whether the lane needs to be changed to the adjacent lane according to the size relation between the Speed speed_A and the Speed speed_B and the size relation between the value a and the value B.
Preferably, the determining whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_a and the Speed speed_b and the magnitude relation between the a value and the B value includes:
when the Speed speed_A is larger than the Speed speed_B, judging that lane changing to the adjacent lane is not needed;
when the Speed speed_A is equal to the Speed speed_B and the value a is smaller than or equal to the value B, judging that lane changing is not needed to be carried out on the adjacent lane, otherwise, lane changing is needed to be carried out on the adjacent lane;
and when the Speed speed_A is smaller than the Speed speed_B and the value a is larger than or equal to the value B, judging that the lane needs to be changed to the adjacent lane, and otherwise, not changing the lane to the adjacent lane.
Preferably, the outputting the corresponding navigation instruction information according to the result of the judgment includes:
when the judging result is negative, outputting navigation prompt information which continues to run along the lane where the vehicle is located or not prompting; and outputting navigation prompt information for changing lanes to the adjacent lanes when the judgment result is yes.
Another embodiment of the present invention proposes a lane navigation system including:
the image recognition unit is used for receiving a front image in a current period acquired by the vehicle-mounted camera, inputting the front image into a pre-trained deep learning model for image recognition to obtain the Speed speed_A, the brake lamp lighting frequency count_A, the Speed speed_B and the brake lamp lighting frequency count_B of the front vehicle B of the lane where the vehicle is located in the current period;
the counting unit is used for carrying out Bayesian statistics on the number of times of turning on the brake lamp count_A and the number of times of turning on the brake lamp count_B to obtain the braking deviation probability of the front vehicle A or the front vehicle B at the next moment;
the lane change decision unit is used for judging whether lane change to the adjacent lane is required according to the magnitude relation between the Speed speed_A and the Speed speed_B and the brake deviation probability of the front vehicle A or the front vehicle B at the next moment;
and the navigation indication unit is used for outputting corresponding navigation indication information according to the judgment result.
Preferably, the lane change decision unit includes:
weight calculation unit for calculating brake deviation probability and brake probability distribution type
Determining the magnitude relation between an a value and a b value in the brake probability distribution; wherein θ is the probability of the brake deviation of the front vehicle A at the next moment, and (1- θ) is the probability of the brake deviation of the front vehicle B;
and the judging unit is used for judging whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_A and the Speed speed_B and the magnitude relation between the value a and the value B.
Preferably, the judging unit is specifically configured to:
when the Speed speed_A is larger than the Speed speed_B, judging that lane changing to the adjacent lane is not needed;
when the Speed speed_A is equal to the Speed speed_B and the value a is smaller than or equal to the value B, judging that lane changing is not needed to be carried out on the adjacent lane, otherwise, lane changing is needed to be carried out on the adjacent lane;
and when the Speed speed_A is smaller than the Speed speed_B and the value a is larger than or equal to the value B, judging that the lane needs to be changed to the adjacent lane, and otherwise, not changing the lane to the adjacent lane.
Preferably, the navigation indication unit is specifically configured to:
when the judging result of the lane change decision unit is negative, outputting navigation prompt information which continues to run along the lane where the vehicle is located or not prompting; and outputting navigation prompt information for changing the lane to the adjacent lane when the judgment result of the lane changing decision unit is yes.
Another embodiment of the present invention also proposes a computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, said processor executing said program to implement the steps of the lane navigation method as described above.
Another embodiment of the present invention also proposes a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the steps of the lane navigation method described above.
The embodiment of the invention has at least the following beneficial effects:
the method comprises the steps of obtaining front vehicle brake lamp information and vehicle speed information of a lane where a vehicle is located, and brake lamp information and vehicle speed information of a front vehicle of an adjacent lane (a left lane or a right lane) through a deep learning model, obtaining front vehicle brake conditions (namely brake deviation probability) of the front vehicle of the lane where the vehicle is located and the front vehicle of the adjacent lane at the next moment through a Bayesian statistical model, judging congestion conditions of the lane where the vehicle is located and the adjacent lane by combining the vehicle speed information of the front vehicle of the lane where the vehicle is located and the front vehicle of the adjacent lane, determining whether to change the lane according to the congestion conditions, guiding drivers to avoid the congested lane, and selecting a smoother lane in advance.
Additional features and advantages of embodiments of the invention will be set forth in the description which follows.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a lane navigation method according to an embodiment of the invention.
FIG. 2 is an exemplary graph of a braking probability distribution curve according to an embodiment of the present invention.
Fig. 3 is a flow chart of a lane navigation system according to an embodiment of the invention.
Detailed Description
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In addition, numerous specific details are set forth in the following examples in order to provide a better illustration of the invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, well known means have not been described in detail in order to not obscure the present invention.
Referring to fig. 1, an embodiment of the present invention provides a lane navigation method, which includes the following steps:
step S100, receiving a front image in a current period acquired by a vehicle-mounted camera, and inputting the front image into a pre-trained deep learning model for image recognition to obtain a Speed speed_A of a front vehicle A of a lane where a vehicle is located in the current period, a brake light lighting frequency count_A, a Speed speed_B of a front vehicle B of an adjacent lane and a brake light lighting frequency count_B;
it should be noted that, the adjacent lane is a left adjacent lane or a right adjacent lane, and the time of one period is preferably but not limited to 1 minute; in this embodiment, the vehicle-mounted camera is a camera mounted in front of the vehicle, and in general, the current driving assistance system is configured with a front camera for acquiring a front image, and the front image of the vehicle is subjected to image recognition by using a deep learning model to obtain speed information of the front vehicle and the number of times of lighting a brake light is mature in the field of vehicle assistance driving, for example, the principle of the vehicle-mounted camera may be that the speed of the front vehicle is calculated by recognizing the front vehicle targets of a lane and an adjacent lane of the vehicle in the image, and determining the distance travelled by the front vehicle in time between the front vehicle and the front vehicle according to the distance between the front vehicle and the front vehicle in the front and rear frame images, which is merely an example of a simple principle, and of course, in order to improve the recognition accuracy, training and improvement are required continuously to be performed on model parameters; the identification of the number of the turn-on times of the brake lamp is easier to realize, whether the brake lamp is turned on or turned off can be determined by identifying the target of the brake lamp in the front image and judging the pixel change of the target of the brake lamp, and the number of the turn-on times of the brake lamp is further obtained, which is only an example introduction of a simple principle; it should be understood that there are many model structures of the deep learning model applied at present, and the present embodiment is not limited to a certain one;
step 200, performing bayesian statistics on the number of times of turning on the brake lamp count_a and the number of times of turning on the brake lamp count_b to obtain the braking deviation probability of the front vehicle a or the front vehicle B at the next moment;
specifically, the number of times of turning on the brake lamp count_a and the number of times of turning on the brake lamp count_b are input into a preset bayesian statistical model, probability statistics is carried out on the front vehicle braking conditions of the own vehicle lane and the adjacent lane in the current period, a braking deviation probability is output, the braking deviation probability can be set as the braking deviation probability of the front vehicle a or the front vehicle B at the next moment, which is obtained through the braking data prediction of the current period, and in the embodiment, lane changing decision can be carried out by using only one probability;
step S300, judging whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_A and the Speed speed_B and the braking deviation probability of the front vehicle A or the front vehicle B at the next moment;
specifically, as described above, in step S200, the probability of the brake deviation of the preceding vehicle a or the preceding vehicle B at the next time is obtained, and the sum of the probabilities of the brake deviations of the preceding vehicle a and the preceding vehicle B is 1, so that only one of the probabilities of the brake deviation of the preceding vehicle a or the probability of the brake deviation of the preceding vehicle B needs to be output;
further, the step S300 may include:
step S301, according to the brake deviation probability theta and the brake probability distribution type
Determining the magnitude relation between an a value and a b value in the brake probability distribution; wherein θ is the probability of the brake deviation of the front vehicle A at the next moment, and (1- θ) is the probability of the brake deviation of the front vehicle B;
specifically, brake probability distribution
As shown in FIG. 2, beta (2, 8) is a brake probability distribution curve with count_A being 2 and count_B being 8, and the corresponding brake deviation probability θ is 2/8; in FIG. 2, beta (5, 5) is a brake probability distribution curve with count_A being 5 and count_B being 5, and the corresponding brake deviation probability θ is 5/5; in FIG. 2, beta (8, 2) is a brake probability distribution curve with count_A being 8 and count_B being 2, and the corresponding brake deviation probability θ is 8/2; it should be appreciated that if the integral result of the brake probability distribution curve is the value of B (a, B), then the brake deviation probability θ is already statistically predicted by a bayesian statistical model, and therefore the brake probability distribution is
Only two parameters a and b, wherein a and b are values greater than or equal to 0, so that the magnitude relation between the value a and the value b can be determined;
step S302, judging whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_A and the Speed speed_B and the magnitude relation between the value a and the value B;
specifically, the speed of the front vehicle characterizes the smoothness of the front vehicle running in the road, reflects the traffic congestion condition to a certain extent, and meanwhile, the predicted braking probability of the front vehicle at the next moment also reflects the traffic congestion condition to a certain extent, so that the two aspects of information are required to be fused for judgment.
Further, the step S302 includes:
when the Speed speed_A is larger than the Speed speed_B, judging that lane changing to the adjacent lane is not needed;
when the Speed speed_A is equal to the Speed speed_B and the value a is smaller than or equal to the value B, judging that lane changing is not needed to be carried out on the adjacent lane, otherwise, lane changing is needed to be carried out on the adjacent lane;
when the Speed speed_A is smaller than the Speed speed_B and the value a is larger than or equal to the value B, judging that the lane needs to be changed to the adjacent lane, otherwise, not needing to be changed to the adjacent lane;
in particular, when said Speed speed_a is greater than said Speed speed_b, it is indicated that the traffic conditions of the lane in which the own vehicle is located are better than those of the adjacent lane when the preceding vehicle Speed condition is considered, in which case no lane change is considered, whether or not the value of a is greater than the value of B; when the Speed speed_a is equal to the Speed speed_b, the traffic conditions of the lane where the vehicle is located and the adjacent lanes are equal when the Speed condition of the front vehicle is considered, in this case, whether the value a is larger than the value B is considered, if the value a is larger than the value B, the probability that the front vehicle a possibly brakes at the next moment is larger than the probability that the front vehicle B possibly brakes is indicated, the lane needs to be changed, if the value a is smaller than or equal to the value B, the probability that the front vehicle a possibly brakes at the next moment is not larger than the probability that the front vehicle B possibly brakes is indicated, and therefore the lane does not need to be changed; when the Speed speed_a is smaller than the Speed speed_b, it is indicated that the traffic condition of the lane where the own vehicle is located is inferior to that of the adjacent lane when the Speed condition of the preceding vehicle is considered, in which case the lane change is performed if the value a is equal to or greater than the value B, i.e., as long as the probability that the preceding vehicle a may brake at the next moment is not smaller than the probability that the preceding vehicle B may brake.
Step 400, outputting corresponding navigation instruction information according to the judgment result.
Further, the step S400 includes:
when the judging result is negative, outputting navigation prompt information which continues to run along the lane where the vehicle is located or not prompting; and outputting navigation prompt information for changing lanes to the adjacent lanes when the judgment result is yes.
According to the method, the front vehicle brake lamp information and the vehicle speed information of the lane where the vehicle is located, the front vehicle brake lamp information and the vehicle speed information of the front vehicle of the adjacent lane (the left lane or the right lane) are acquired through a deep learning model, the front vehicle brake condition (namely the brake deviation probability) of the lane where the vehicle is located at the next moment and the front vehicle brake condition (namely the brake deviation probability) of the adjacent lane are acquired through a Bayesian statistical model, the congestion conditions of the lane where the vehicle is located and the adjacent lane are judged by combining the vehicle speed information of the front vehicle of the lane where the vehicle is located and the adjacent lane, whether a lane needs to be changed is determined according to the congestion conditions, so that a driver is guided to avoid the congested lane, and a smoother lane is selected in advance.
Referring to fig. 3, another embodiment of the present invention proposes a lane navigation system, which may be used to implement the method of the above embodiment, including:
the image recognition unit 1 is used for receiving a front image in a current period acquired by the vehicle-mounted camera, inputting the front image into a pre-trained deep learning model for image recognition to obtain the Speed speed_A, the brake light lighting frequency count_A, the Speed speed_B and the brake light lighting frequency count_B of the front vehicle B of a lane where the vehicle is located in the current period;
the counting unit 2 is used for carrying out Bayesian statistics on the number of times of lighting the brake lamp count_A and the number of times of lighting the brake lamp count_B to obtain the braking deviation probability of the front vehicle A or the front vehicle B at the next moment;
the lane change decision unit 3 is configured to determine whether a lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_a and the Speed speed_b and the braking deviation probability of the preceding vehicle a or the preceding vehicle B at the next moment;
and the navigation instruction unit 4 is used for outputting corresponding navigation instruction information according to the judgment result.
Further, the lane change decision unit 3 includes:
weight calculation unit for calculating brake deviation probability theta and brake probability distribution type
Determining the magnitude relation between an a value and a b value in the brake probability distribution; wherein, θ is the braking deviation probability of the front vehicle A at the next moment, and (1- θ) is the braking deviation probability of the front vehicle B;
and the judging unit is used for judging whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_A and the Speed speed_B and the magnitude relation between the value a and the value B.
Further, the judging unit is specifically configured to:
when the Speed speed_A is larger than the Speed speed_B, judging that lane changing to the adjacent lane is not needed;
when the Speed speed_A is equal to the Speed speed_B and the value a is smaller than or equal to the value B, judging that lane changing is not needed to be carried out on the adjacent lane, otherwise, lane changing is needed to be carried out on the adjacent lane;
and when the Speed speed_A is smaller than the Speed speed_B and the value a is larger than or equal to the value B, judging that the lane needs to be changed to the adjacent lane, and otherwise, not changing the lane to the adjacent lane.
Further, the navigation instruction unit 4 is specifically configured to:
when the judging result of the lane change decision unit is negative, outputting navigation prompt information which continues to run along the lane where the vehicle is located or not prompting; and outputting navigation prompt information for changing the lane to the adjacent lane when the judgment result of the lane changing decision unit is yes.
The system of the above-described embodiments is merely illustrative, in which the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the system solution of the embodiment.
It should be noted that, the system of the foregoing embodiment corresponds to the method of the foregoing embodiment, and therefore, a portion of the system of the foregoing embodiment that is not described in detail may be obtained by referring to the content of the method of the foregoing embodiment, that is, the specific step content described in the method of the foregoing embodiment may be understood as a function that can be implemented by the system of the foregoing embodiment, which is not described herein again.
Also, the lane guidance system of the above embodiment may be stored in a computer-readable storage medium if implemented in the form of a software functional unit and sold or used as a separate product.
Another embodiment of the present invention also proposes a computer device comprising: the lane guidance system according to the above embodiment; or, a memory and a processor, the memory having stored therein computer readable instructions which, when executed by the processor, cause the processor to perform the steps of the lane navigation method according to the above-described embodiments.
Of course, the computer device may also have a wired or wireless network interface, a keyboard, an input/output interface, and other components for implementing the functions of the device, which are not described herein.
The computer program may be divided into one or more units, which are stored in the memory and executed by the processor to accomplish the present invention, for example. The one or more elements may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments describe the execution of the computer program in the computer device.
The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is a control center of the computer device, connecting various interfaces and lines throughout the various portions of the computer device.
The memory may be used to store the computer program and/or elements, and the processor may implement various functions of the computer device by running or executing the computer program and/or elements stored in the memory, and invoking data stored in the memory. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Another embodiment of the present invention proposes a computer-readable storage medium on which a computer program is stored, which when executed by a processor implements the steps of the lane navigation method described in the above embodiments.
In particular, the computer-readable storage medium may include: any entity or device capable of carrying the computer program instructions, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier wave signal, a telecommunications signal, a software distribution medium, and so forth.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A lane navigation method, comprising the steps of:
receiving a front image in a current period acquired by a vehicle-mounted camera, inputting the front image into a pre-trained deep learning model for image recognition to obtain a Speed speed_A of a front vehicle A of a lane where a vehicle is located in the current period, a brake lamp lighting frequency count_A, a Speed speed_B of a front vehicle B of an adjacent lane and a brake lamp lighting frequency count_B;
carrying out Bayesian statistics on the number of times of lighting the brake lamp count_A and the number of times of lighting the brake lamp count_B to obtain the braking deviation probability of the front vehicle A or the front vehicle B at the next moment;
judging whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_A and the Speed speed_B and the braking deviation probability of the front vehicle A or the front vehicle B at the next moment;
and outputting corresponding navigation indication information according to the judgment result.
2. The method according to claim 1, wherein said determining whether a lane change to the adjacent lane is required based on the magnitude relation between the Speed speed_a and the Speed speed_b and the brake deviation probability of the preceding vehicle a or the preceding vehicle B at the next moment comprises:
according to the brake deviation probability and the brake probability distribution type
Determining the magnitude relation between an a value and a b value in the brake probability distribution; wherein θ is the probability of the brake deviation of the front vehicle A at the next moment, and (1- θ) is the probability of the brake deviation of the front vehicle B;
judging whether the lane needs to be changed to the adjacent lane according to the size relation between the Speed speed_A and the Speed speed_B and the size relation between the value a and the value B.
3. The method of claim 2, wherein said determining whether lane change to the adjacent lane is required based on the magnitude relationship of the Speed speed_a and the Speed speed_b and the magnitude relationship of the a value and the B value comprises:
when the Speed speed_A is larger than the Speed speed_B, judging that lane changing to the adjacent lane is not needed;
when the Speed speed_A is equal to the Speed speed_B and the value a is smaller than or equal to the value B, judging that lane changing is not needed to be carried out on the adjacent lane, otherwise, lane changing is needed to be carried out on the adjacent lane;
and when the Speed speed_A is smaller than the Speed speed_B and the value a is larger than or equal to the value B, judging that the lane needs to be changed to the adjacent lane, and otherwise, not changing the lane to the adjacent lane.
4. The method according to claim 1, wherein outputting the corresponding navigation instruction information according to the result of the determination comprises:
when the judging result is negative, outputting navigation prompt information which continues to run along the lane where the vehicle is located or not prompting; and outputting navigation prompt information for changing lanes to the adjacent lanes when the judgment result is yes.
5. A lane navigation system comprising the steps of:
the image recognition unit is used for receiving a front image in a current period acquired by the vehicle-mounted camera, inputting the front image into a pre-trained deep learning model for image recognition to obtain the Speed speed_A, the brake lamp lighting frequency count_A, the Speed speed_B and the brake lamp lighting frequency count_B of the front vehicle B of the lane where the vehicle is located in the current period;
the counting unit is used for carrying out Bayesian statistics on the number of times of turning on the brake lamp count_A and the number of times of turning on the brake lamp count_B to obtain the braking deviation probability of the front vehicle A or the front vehicle B at the next moment;
the lane change decision unit is used for judging whether lane change to the adjacent lane is required according to the magnitude relation between the Speed speed_A and the Speed speed_B and the brake deviation probability of the front vehicle A or the front vehicle B at the next moment;
and the navigation indication unit is used for outputting corresponding navigation indication information according to the judgment result.
6. The system of claim 5, wherein the lane change decision unit comprises:
weight calculation unit for calculating brake deviation probability and brake probability distribution type
Determining the magnitude relation between an a value and a b value in the brake probability distribution; wherein θ is the probability of the brake deviation of the front vehicle A at the next moment, and (1- θ) is the probability of the brake deviation of the front vehicle B;
and the judging unit is used for judging whether the lane needs to be changed to the adjacent lane according to the magnitude relation between the Speed speed_A and the Speed speed_B and the magnitude relation between the value a and the value B.
7. The system according to claim 6, wherein the judging unit is specifically configured to:
when the Speed speed_A is larger than the Speed speed_B, judging that lane changing to the adjacent lane is not needed;
when the Speed speed_A is equal to the Speed speed_B and the value a is smaller than or equal to the value B, judging that lane changing is not needed to be carried out on the adjacent lane, otherwise, lane changing is needed to be carried out on the adjacent lane;
and when the Speed speed_A is smaller than the Speed speed_B and the value a is larger than or equal to the value B, judging that the lane needs to be changed to the adjacent lane, and otherwise, not changing the lane to the adjacent lane.
8. The system according to claim 5, characterized in that said navigation instruction unit is specifically configured to:
when the judging result of the lane change decision unit is negative, outputting navigation prompt information which continues to run along the lane where the vehicle is located or not prompting; and outputting navigation prompt information for changing the lane to the adjacent lane when the judgment result of the lane changing decision unit is yes.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to perform the steps of the lane navigation method of any one of claims 1 to 4.
10. A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the lane navigation method of any of the preceding claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111431206.6A CN116202543A (en) | 2021-11-29 | 2021-11-29 | Lane navigation method and system, computer equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111431206.6A CN116202543A (en) | 2021-11-29 | 2021-11-29 | Lane navigation method and system, computer equipment and storage medium |
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| Publication Number | Publication Date |
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| CN116202543A true CN116202543A (en) | 2023-06-02 |
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| CN202111431206.6A Pending CN116202543A (en) | 2021-11-29 | 2021-11-29 | Lane navigation method and system, computer equipment and storage medium |
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| Country | Link |
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| CN (1) | CN116202543A (en) |
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