CN115331448B - Intelligent navigation method and device based on reverse vehicle - Google Patents
Intelligent navigation method and device based on reverse vehicle Download PDFInfo
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- CN115331448B CN115331448B CN202211264490.7A CN202211264490A CN115331448B CN 115331448 B CN115331448 B CN 115331448B CN 202211264490 A CN202211264490 A CN 202211264490A CN 115331448 B CN115331448 B CN 115331448B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
- G08G1/096805—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route
Abstract
The invention provides an intelligent navigation method and device based on a reverse vehicle, wherein the method comprises the following steps: the method comprises the steps of acquiring predicted running paths of a first vehicle and a second vehicle, splitting the predicted running paths into a plurality of road sections, and judging whether the predicted running road sections need to be bypassed or not by using the first vehicle and the second vehicle which run in opposite directions. The invention has the beneficial effects that: the method and the device realize that whether congestion or traffic accidents happen in the front or not according to the reverse vehicle, can acquire real-time information and provide better driving experience for vehicle owners.
Description
Technical Field
The invention relates to the field of intelligent driving, in particular to an intelligent navigation method and device based on a reverse vehicle.
Background
At present, road condition information acquired by a vehicle during driving mainly comes from a driving system, and the driving system mainly obtains traffic information uploaded by a vehicle owner in front, so that congestion occurs in the current side or a traffic accident occurs, the vehicle receives the information with a certain delay, and the traffic jam further occurs, and bad driving experience is brought to the vehicle owner.
Disclosure of Invention
The invention mainly aims to provide an intelligent navigation method and device based on a reverse vehicle, and aims to solve the problem that when congestion occurs in the front or a car accident occurs, the vehicle receiving information has certain delay, so that the vehicle cannot be avoided in advance.
The invention provides an intelligent navigation method based on a reverse vehicle, which comprises the following steps:
acquiring a first predicted travelling path of a first vehicle and a second predicted travelling path of a second vehicle;
splitting the first predicted travel path into a plurality of first road segments and splitting the second predicted travel path into a plurality of second road segments;
the first vehicle generates a first passing path fingerprint based on a first path segment passing in real time, and the second vehicle generates a second passing path fingerprint based on a second path segment passing in real time; the first road section which is passed by the first vehicle in real time is the first road section which is passed by the first vehicle recently, and the second road section which is passed by the second vehicle in real time is the second road section which is passed by the second vehicle recently;
obtaining a target first road segment of the predicted driving of the first vehicle based on the first predicted driving path and generating a first target path fingerprint, and obtaining a target second road segment of the predicted driving of the second vehicle based on the second predicted driving path and generating a second target path fingerprint;
the first vehicle broadcasting based on the first target path fingerprint and the first pass path fingerprint, the second vehicle broadcasting based on the second target path fingerprint and the second pass path fingerprint;
when the first target path fingerprint matches the second pass path fingerprint and/or the second target path fingerprint matches the first pass path fingerprint, the first vehicle receiving link information for a second link transmitted by the second vehicle passing in real time and/or the second vehicle receiving link information for a first link transmitted by a first vehicle passing in real time; the road section information is acquired by the first vehicle and the second vehicle in real time;
the first vehicle and/or the second vehicle determining whether a predicted travel link needs to be bypassed based on the received link information;
if so, the first vehicle and/or the second vehicle sends a detour instruction to a navigation system and receives a new navigation path sent by the navigation system to continue driving.
Further, the first vehicle and the second vehicle are provided with cameras on vehicle bodies running close to each other, and before the step of receiving the section information of the second section of the real-time passage transmitted by the second vehicle and/or the section information of the first section of the real-time passage transmitted by the first vehicle when the first target path fingerprint is matched with the second passage path fingerprint and/or the second target path fingerprint is matched with the first passage path fingerprint, the method further includes:
the first vehicle and the second vehicle shoot a plurality of running pictures of opposite lanes through cameras respectively arranged on a vehicle body;
the first vehicle or the second vehicle calculates the visual scores of the shot running pictures; wherein the visual score is calculated by the formula
，
，
The pixel values of the pixel points with coordinates i, j,
the pixel value of the o-th adjacent pixel point of the pixel point with the coordinates of i and j is represented, p is the number of the adjacent pixel points,
indicating a brightness variation value, m and n indicating the width and height of the running picture, respectively,
representing a visual score;
judging whether the number of the running photos with the visual scores reaching the preset value is larger than the preset number or not;
if the number of the driving pictures is larger than the preset number, inputting the driving pictures into a preset traffic smoothness model to obtain corresponding road section information; the traffic smoothness model is formed by training according to various driving pictures and corresponding smoothness labels.
Further, after the step of the first vehicle broadcasting based on the first target route fingerprint and the first through-route fingerprint, and the step of the second vehicle broadcasting based on the second target route fingerprint and the second through-route fingerprint, the method further includes:
judging whether a third vehicle meets the condition that a third passing path fingerprint of the third vehicle is matched with a first target path fingerprint of the first vehicle;
if so, calculating the visual score of the driving picture shot by the third vehicle;
judging whether the visual score of the running picture shot by the third vehicle is larger than the visual score of the running picture shot by the second vehicle;
and if so, selecting the road section information of the third vehicle and sending the road section information to the first vehicle.
Further, the step of the first vehicle receiving the section information of the second section of the real-time passage transmitted by the second vehicle and/or the second vehicle receiving the section information of the first section of the real-time passage transmitted by the first vehicle when the first target path fingerprint matches with the second passage path fingerprint and/or the second target path fingerprint matches with the first passage path fingerprint includes:
when the first target path fingerprint matches the second pass path fingerprint and/or the second target path fingerprint matches the first pass path fingerprint;
the first vehicle receives second identity information of the second vehicle transmitted by the second vehicle and/or the second vehicle receives first identity information of the first vehicle transmitted by the first vehicle;
the first vehicle packages and uploads the second identity information and the road section information of the first road section passing through in real time to a navigation system, and/or the second vehicle packages and uploads the first identity information and the road section information of the second road section passing through in real time to the navigation system;
and sending the road section information of the second road section which is transmitted by the second vehicle and passes through in real time to the first vehicle through the navigation system, and/or sending the road section information of the first road section which is transmitted by the first vehicle and passes through in real time to the second vehicle through the navigation system.
Further, the step of inputting the driving picture into a preset traffic smoothness model to obtain corresponding road section information includes:
recognizing the driving photograph through the accident recognition model, and judging whether an accident event occurs therein;
if so, marking the place where the traffic accident event occurs, and combining the smoothness identified by the traffic smoothness model to form the road section information.
The invention also provides an intelligent navigation device based on a reverse vehicle, which comprises:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a first predicted running path of a first vehicle and a second predicted running path of a second vehicle;
the splitting module is used for splitting the first predicted driving path into a plurality of first road sections and splitting the second predicted driving path into a plurality of second road sections;
the first generation module is used for generating a first passing path fingerprint based on a first path which is passed by the first vehicle in real time, and generating a second passing path fingerprint based on a second path which is passed by the second vehicle in real time; the first road section which passes through in real time is a first road section which is passed through by a first vehicle recently, and the second road section which passes through in real time is a second road section which is passed through by a second vehicle recently;
the second generation module is used for obtaining a target first road section of the predicted running of the first vehicle based on the first predicted running path, generating a first target path fingerprint, obtaining a target second road section of the predicted running of the second vehicle based on the second predicted running path, and generating a second target path fingerprint;
a broadcast module for the first vehicle to broadcast based on the first target path fingerprint and the first pass path fingerprint, the second vehicle to broadcast based on the second target path fingerprint and the second pass path fingerprint;
a receiving module, configured to receive, by the first vehicle, link information of a second link transmitted by the second vehicle in real time and/or receive, by the second vehicle, link information of a first link transmitted by the first vehicle in real time when the first target path fingerprint matches the second passing path fingerprint and/or the second target path fingerprint matches the first passing path fingerprint; the road section information is acquired by the first vehicle and the second vehicle in real time;
a judging module, which is used for the first vehicle and/or the second vehicle to judge whether the road section which is predicted to be driven needs to be bypassed or not based on the received road section information;
and the detouring module is used for sending a detouring instruction to the navigation system and receiving a new navigation path sent by the navigation system to continue driving if the first vehicle and/or the second vehicle are/is in the same state.
Further, the intelligent navigation device based on reverse vehicle still includes:
the device comprises a setting module, a driving module and a driving module, wherein the setting module is used for shooting a plurality of driving pictures of opposite lanes by the first vehicle and the second vehicle through cameras respectively arranged on a vehicle body;
the calculating module is used for calculating the visual scores of the taken running pictures of the first vehicle or the second vehicle; wherein the visual score is calculated by the formula
，
，
The pixel values of the pixel points with coordinates i, j,
the pixel value of the o-th adjacent pixel point of the pixel point with the coordinates of i and j is represented, p is the number of the adjacent pixel points,
indicating a brightness variation value, m and n indicating the width and height of the running picture, respectively,
representing a visual score;
the score judging module is used for judging whether the number of the driving photos with the visual scores reaching the preset value is larger than the preset number;
the input module is used for inputting the driving pictures into a preset traffic smoothness model to obtain corresponding road section information if the number of the driving pictures is larger than the preset number; the traffic smoothness model is trained according to various driving pictures and corresponding smoothness labels.
Further, the intelligent navigation device based on reverse vehicle still includes:
the matching module is used for judging whether a third vehicle meets the condition that a third passing path fingerprint of the third vehicle is matched with a first target path fingerprint of the first vehicle;
the score calculating module is used for calculating the visual score of the driving picture shot by the third vehicle if the score is positive;
the score judging module is used for judging whether the visual score of the running picture shot by the third vehicle is larger than the visual score of the running picture shot by the second vehicle;
and the information sending module is used for selecting the road section information of the third vehicle and sending the road section information to the first vehicle if the road section information is the road section information of the third vehicle.
Further, the receiving module includes:
a matching sub-module for matching the first target path fingerprint with the second pass path fingerprint and/or the second target path fingerprint with the first pass path fingerprint;
the receiving submodule is used for receiving second identity information of the second vehicle transmitted by the second vehicle and/or receiving first identity information of the first vehicle transmitted by the first vehicle by the second vehicle;
the uploading sub-module is used for the first vehicle to package and upload the second identity information and the road section information of the first road section passing through in real time to a navigation system, and/or the second vehicle to package and upload the first identity information and the road section information of the second road section passing through in real time to the navigation system;
and the sending submodule is used for sending the road section information of the second road section which is transmitted by the second vehicle and passes through in real time to the first vehicle through the navigation system, and/or sending the road section information of the first road section which is transmitted by the first vehicle and passes through in real time to the second vehicle through the navigation system.
Further, the input module includes:
an identification sub-module for identifying the driving photograph through the car accident identification model and judging whether a car accident event occurs therein;
and the marking sub-module is used for marking based on the place where the traffic accident happens if the traffic accident happens and forming the road section information by combining the smoothness identified by the traffic smoothness model.
The invention has the beneficial effects that: by acquiring the predicted driving paths of the first vehicle and the second vehicle, splitting the predicted driving paths into a plurality of road sections and judging whether the predicted driving road sections need to be bypassed by using the first vehicle and the second vehicle which drive in opposite directions, whether congestion or traffic accidents happen in the front or not is determined according to the reverse vehicle, real-time information can be acquired, and better driving experience is provided for a vehicle owner.
Drawings
FIG. 1 is a flow chart illustrating a method for intelligent reverse vehicle based navigation according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a structure of an intelligent navigation device based on a reverse vehicle according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that all directional indicators (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative position relationship between the components, the motion situation, etc. in a specific posture (as shown in the drawings), and if the specific posture is changed, the directional indicator is changed accordingly, and the connection may be a direct connection or an indirect connection.
The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and B, may mean: a exists alone, A and B exist simultaneously, and B exists alone.
In addition, descriptions such as "first", "second", etc. in the present invention are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In addition, technical solutions between various embodiments may be combined with each other, but must be realized by a person skilled in the art, and when the technical solutions are contradictory or cannot be realized, such a combination should not be considered to exist, and is not within the protection scope of the present invention.
Referring to fig. 1, the present invention provides an intelligent navigation method based on a reverse vehicle, including:
s1: acquiring a first predicted travelling path of a first vehicle and a second predicted travelling path of a second vehicle;
s2: splitting the first predicted travel path into a plurality of first road segments and splitting the second predicted travel path into a plurality of second road segments;
s3: the second vehicle generates a second pass through first path fingerprint based on the first path segment passing through in real time; the first road section which is passed by the first vehicle in real time is the first road section which is passed by the first vehicle recently, and the second road section which is passed by the second vehicle in real time is the second road section which is passed by the second vehicle recently;
s4: obtaining a target first road segment of the predicted driving of the first vehicle based on the first predicted driving path and generating a first target path fingerprint, and obtaining a target second road segment of the predicted driving of the second vehicle based on the second predicted driving path and generating a second target path fingerprint;
s5: the first vehicle broadcasting based on the first target path fingerprint and the first pass path fingerprint, the second vehicle broadcasting based on the second target path fingerprint and the second pass path fingerprint;
s6: when the first target path fingerprint matches the second pass path fingerprint and/or the second target path fingerprint matches the first pass path fingerprint, the first vehicle receiving link information for a second link transmitted by the second vehicle passing in real time and/or the second vehicle receiving link information for a first link transmitted by a first vehicle passing in real time; the road section information is acquired by the first vehicle and the second vehicle in real time;
s7: the first vehicle and/or the second vehicle determining whether a predicted travel link needs to be bypassed based on the received link information;
s8: if yes, the first vehicle and/or the second vehicle sends a detour instruction to a navigation system and receives a new navigation path sent by the navigation system to continue driving.
As described in step S1 above, the first predicted travel path of the first vehicle and the second predicted travel path of the second vehicle are acquired. The first vehicle and the second vehicle are vehicles running on a highway, and the first predicted running path and the second predicted running path of the first vehicle and the second vehicle are paths intelligently planned by the navigation system according to the input starting point and the input end point, so that the first vehicle and the second vehicle can directly obtain the predicted running path.
As described in step S2, the first predicted travel path is split into a plurality of first road segments, and the second predicted travel path is split into a plurality of second road segments, where the splitting manner is not limited, and it should be noted that there may be some intersections between the split road segments, and the split road segments are not too long, otherwise, when the first vehicle and the second vehicle are subsequently matched, there may be some difficulties, certainly, too short, which easily causes the first vehicle to be matched with a plurality of vehicles, and because the real-time performance of the first vehicle and the second vehicle does not change the data particularly much, the distance of the road segments may be extended appropriately under the condition that the vehicle matching is ensured.
As described in step S3, the first vehicle generates a first passing path fingerprint based on a first path segment that passes through in real time, and the second vehicle generates a second passing path fingerprint based on a second path segment that passes through in real time; the first road section passed by the first vehicle in real time is the first road section passed by the first vehicle recently, and the second road section passed by the second vehicle in real time is the second road section passed by the second vehicle recently. The first passing path fingerprint and the second passing path fingerprint are generated according to a starting point and an end point of a road section which is passed by a vehicle in real time, and when the first passing path fingerprint and the second passing path fingerprint are matched, only whether the head and the tail of the first passing path fingerprint of the first vehicle are just opposite to the tail of the second target path fingerprint needs to be judged, wherein the road section which is passed by the vehicle just runs through is the first road section which is just passed by the vehicle, the vehicle is supposed to run through three road sections including ABC, when the vehicle passes through the road section A, the vehicle runs through the road section B and reaches the road section C immediately, the passing path fingerprint of the vehicle is generated according to the starting point and the end point of the road section A, and the starting point and the end point of the road section A are determined according to the running direction of the vehicle.
As described in the above step S4, the target first link for the predicted travel of the first vehicle is obtained based on the first predicted travel path, and the first target path fingerprint is generated, and the target second link for the predicted travel of the second vehicle is obtained based on the second predicted travel path, and the second target path fingerprint is generated, similarly, assuming that the vehicle travels with three links ABC, and when the vehicle passes through the link a, travels on the link B, and arrives immediately at the link C, the target path fingerprint of the vehicle is generated with the start point and the end point of the link C, and the determination of the start point and the end point of the link C is determined according to the travel direction of the vehicle. When the first vehicle and the second vehicle are matched, whether the head and the tail of the first passing path fingerprint of the first vehicle and the head and the tail of the second target path fingerprint are opposite or not is judged.
As described in the above step S5, the first vehicle broadcasts based on the first target route fingerprint and the first through-route fingerprint, and the second vehicle broadcasts based on the second target route fingerprint and the second through-route fingerprint, where the broadcasting may be performed by the first vehicle communicating with the wireless communication device of the second vehicle, for example: such as WiFi, zigbee, bluetooth, Z-Wave, etc. The base station may be arranged in the middle of the road section to transmit information, and the information is forwarded through the intelligent gateway.
As described in the above step S6, when the first target route fingerprint matches the second passing route fingerprint and/or the second target route fingerprint matches the first passing route fingerprint, the first vehicle receives the link information of the second link transmitted by the second vehicle and/or the second vehicle receives the link information of the first link transmitted by the first vehicle; the road section information is acquired by the first vehicle and the second vehicle in real time, and matching only needs to judge whether the head and the tail of the first passing path fingerprint of the first vehicle and the head and the tail of the second target path fingerprint are just opposite or whether the head and the tail of the first passing path fingerprint of the second vehicle and the head and the tail of the first target path fingerprint are just opposite. After matching, data connection between the first vehicle and the second vehicle can be established, or connection between the vehicle and the intelligent gateway can be established, so that corresponding road section information can be conveniently acquired.
As described in the foregoing steps S7 to S8, the first vehicle and/or the second vehicle determines whether to bypass the predicted driving road segment based on the received road segment information, and when the predicted driving road segment needs to be bypassed, the first vehicle and/or the second vehicle sends a bypass instruction to the navigation system, and receives a new navigation path sent by the navigation system to continue driving, where the navigation system may mark that the road segment does not pass through, so the navigation system may set a new navigation path based on the instruction and send the new navigation path to the vehicle, and of course, after determining the new navigation path, the new navigation path may also be sent to the client to determine the distance and path of the bypass, so as to facilitate the selection of the user, thereby determining whether a front accident or a car accident has occurred according to a reverse vehicle, and acquiring real-time congestion information, and providing better driving experience for the car owner.
In one embodiment, the first vehicle and the second vehicle are provided with cameras on bodies traveling close to each other, and before step S6, when the first target route fingerprint matches the second passing route fingerprint and/or the second target route fingerprint matches the first passing route fingerprint, the first vehicle receives link information of a second link passed through in real time transmitted by the second vehicle and/or the second vehicle receives link information of a first link passed through in real time transmitted by the first vehicle, the method further includes:
s501: the first vehicle and the second vehicle shoot a plurality of running pictures of opposite lanes through cameras respectively arranged on a vehicle body;
s502: the first vehicle or the second vehicle calculates the visual scores of the shot running pictures; wherein the visual score is calculated by the formula
，
，
The pixel values of the pixel points with coordinates i, j,
the pixel value of the o-th adjacent pixel point of the pixel point with the coordinates of i and j is represented, p is the number of the adjacent pixel points,
indicating a brightness variation value, m and n indicating the width and height of the driving picture, respectively,
representing a visual score;
s503: judging whether the number of the running photos with the visual scores reaching the preset value is larger than the preset number or not;
s504: if the number of the driving pictures is larger than the preset number, inputting the driving pictures into a preset traffic smoothness model to obtain corresponding road section information; the traffic smoothness model is trained according to various driving pictures and corresponding smoothness labels.
As described in step S501, the first vehicle and the second vehicle are provided with cameras on the bodies that run close to each other, the road generally runs right in china, the camera is provided on the left side of the vehicle, and the first vehicle and the second vehicle can take pictures on the lanes running in the opposite direction, and the pictures can be taken at intervals, for example, once every 5 seconds, so that multiple running pictures can be obtained for the same road segment, and in some embodiments, the pictures can also be obtained from the pictures taken by the drive recorder.
As described in step S502, the first vehicle or the second vehicle calculates the visual scores of the respective driving pictures, and since the vehicle is not necessarily driving near the innermost lane while driving, the driving pictures may not have data of the road, and therefore the formula is adopted as
，
When the field of view shot by the vehicle is small, it is generally considered that the vehicle is blocked, and the blocked object may be another vehicle or a roadside blocking object, but after being blocked, because the field of view is small, the variation of the pixel point is relatively small, and therefore, the adjacent vision difference, that is, the brightness variation value is adopted
The pixel points in the application are not the pixel points in the traditional sense, and can be the average values of a plurality of pixel points, so that the error caused by the fact that the difference between the pixel values of the pixel points and the pixel points around the pixel points is not large due to local reasons is avoided.
As described in the above steps S503 to S504, it is determined whether the number of the driving photographs with the visual scores reaching the preset value is larger than a preset number; if the number of the driving pictures is larger than the preset number, inputting the driving pictures into a preset traffic smoothness model to obtain corresponding road section information; the traffic smoothness model is trained according to various driving pictures and corresponding smoothness labels. It should be noted that the preset value is a specific value set by a researcher according to an actual situation, when the value is greater than the specific value, the driving photo can be considered to have an analysis value, if the value is not greater than the specific value, the driving photo is considered to have no analysis value, and of course, some shielding situations are inevitable in the driving process, so that it is only necessary to ensure that at least a preset number of driving photos are greater than the preset value, and then the driving photos are input into a preset traffic smoothness model to obtain corresponding road section information; the traffic smoothness model is formed by training according to various driving pictures and corresponding smoothness labels, and is a neural network model.
In one embodiment, after the step S5 of the first vehicle broadcasting based on the first target route fingerprint and the first through-route fingerprint, and the second vehicle broadcasting based on the second target route fingerprint and the second through-route fingerprint, the method further comprises:
s601: judging whether a third vehicle meets the condition that a third passing path fingerprint of the third vehicle is matched with the first target path fingerprint of the first vehicle;
s602: if so, calculating the visual score of the driving picture shot by the third vehicle;
s603: judging whether the visual score of the running picture shot by the third vehicle is larger than the visual score of the running picture shot by the second vehicle;
s604: and if so, selecting the road section information of the third vehicle and sending the road section information to the first vehicle.
As described in the foregoing steps S601-S604, in an actual situation, there is not necessarily only one second vehicle, and there may be a third vehicle, when the field of view of the third vehicle is better, that is, the angle shot by the camera of the third vehicle is better, or the third vehicle is a large vehicle, for example, a truck, the information shot by the third vehicle has more analysis value, and the judgment method is to calculate a visual score, and when the visual score shot by the third vehicle is greater than the visual score of the driving picture shot by the second vehicle, the road information of the third vehicle may be selected and sent to the first vehicle, it should be noted that the third vehicle and the second vehicle are vehicles driving in the same direction, and each vehicle may also broadcast the shot visual score in a broadcast manner, thereby facilitating the screening of the first vehicle.
In one embodiment, the step S6 of the first vehicle receiving the section information of the second section transmitted by the second vehicle in real time and/or the second vehicle receiving the section information of the first section transmitted by the first vehicle in real time when the first target path fingerprint matches the second passing path fingerprint and/or the second target path fingerprint matches the first passing path fingerprint includes:
s611: when the first target path fingerprint matches the second pass path fingerprint and/or the second target path fingerprint matches the first pass path fingerprint;
s612: the first vehicle receives second identity information of the second vehicle transmitted by the second vehicle and/or the second vehicle receives first identity information of the first vehicle transmitted by the first vehicle;
s613: the first vehicle packages and uploads the second identity information and the road section information of the first road section passing through in real time to a navigation system, and/or the second vehicle packages and uploads the first identity information and the road section information of the second road section passing through in real time to the navigation system;
s614: and the navigation system sends the road section information of the second road section which is transmitted by the second vehicle and passes through in real time to the first vehicle, and/or the navigation system sends the road section information of the first road section which is transmitted by the first vehicle and passes through in real time to the second vehicle.
As described in the above steps S611-S614, the link information is implemented by the navigation system, when the first target path fingerprint matches the second pass path fingerprint and/or the second target path fingerprint matches the first pass path fingerprint; the first vehicle receives second identity information of a second vehicle transmitted by the second vehicle and/or the second vehicle receives first identity information of the first vehicle transmitted by the first vehicle, wherein the first identity information is unique identification information of the first vehicle, and similarly, the second identity information is unique identification information of the second vehicle, which may be an account number of the first vehicle on a navigation system, the first vehicle packages the second identity information and road section information of a first road section passing through in real time and uploads the second identity information and road section information of a second road section passing through in real time to the navigation system, the navigation system transmits the road section information of the second road section passing through in real time transmitted by the second vehicle to the first vehicle, and/or the navigation system transmits the road section information of the first road section passing through in real time transmitted by the first vehicle to the second vehicle.
In an embodiment, the step S504 of inputting the driving picture into a preset traffic smoothness model to obtain corresponding road segment information includes:
s5041: recognizing the driving picture through the traffic accident recognition model, and judging whether a traffic accident event occurs;
s5042: if so, marking the place where the traffic accident event occurs, and combining the smoothness identified by the traffic smoothness model to form the road section information.
As described in the above steps S5041 to S5042, the judgment and prevention of the car accident event are achieved by recognizing the driving photos through the car accident recognition model trained by a large number of driving photos and corresponding tags, and after the car accident event occurs, the driving photos can be marked for prevention.
Referring to fig. 2, the present invention also provides an intelligent navigation device based on a reverse vehicle, comprising:
an obtaining module 10 for obtaining a first predicted travel path of a first vehicle and a second predicted travel path of a second vehicle;
a splitting module 20, configured to split the first predicted travel path into a plurality of first road segments and split the second predicted travel path into a plurality of second road segments;
a first generating module 30, configured to generate a first passing path fingerprint based on a first path segment that the first vehicle passes through in real time, and generate a second passing path fingerprint based on a second path segment that the second vehicle passes through in real time; the first road section which passes through in real time is a first road section which is passed through by a first vehicle recently, and the second road section which passes through in real time is a second road section which is passed through by a second vehicle recently;
a second generating module 40, configured to obtain a target first road segment of the predicted travel of the first vehicle based on the first predicted travel path and generate a first target path fingerprint, and obtain a target second road segment of the predicted travel of the second vehicle based on the second predicted travel path and generate a second target path fingerprint;
a broadcast module 50 for the first vehicle to broadcast based on the first target path fingerprint and the first pass path fingerprint, and the second vehicle to broadcast based on the second target path fingerprint and the second pass path fingerprint;
a receiving module 60, configured to receive, by the first vehicle, the link information of the second link transmitted by the second vehicle in real time and/or receive, by the second vehicle, the link information of the first link transmitted by the first vehicle in real time when the first target path fingerprint matches the second passing path fingerprint and/or the second target path fingerprint matches the first passing path fingerprint; the road section information is acquired by the first vehicle and the second vehicle in real time;
a determining module 70, configured to determine whether a road segment predicted to be traveled needs to be bypassed by the first vehicle and/or the second vehicle based on the received road segment information;
and a detour module 80, configured to, if yes, send a detour instruction to the navigation system by the first vehicle and/or the second vehicle, and receive a new navigation path sent by the navigation system to continue driving.
In one embodiment, the intelligent navigation device based on a reverse vehicle further comprises:
the device comprises a setting module, a driving module and a driving module, wherein the setting module is used for shooting a plurality of driving pictures of opposite lanes by the first vehicle and the second vehicle through cameras respectively arranged on a vehicle body;
the calculating module is used for calculating the visual scores of the taken running pictures of the first vehicle or the second vehicle; wherein the visual score is calculated by the formula
，
，
The pixel values of the pixel points with coordinates i, j,
the pixel value of the o-th adjacent pixel point of the pixel point with the coordinates of i and j is represented, p is the number of the adjacent pixel points,
indicating a brightness variation value, m and n indicating the width and height of the driving picture, respectively,
representing a visual score;
the score judging module is used for judging whether the number of the driving photos with the visual scores reaching the preset value is larger than the preset number;
the input module is used for inputting the driving pictures into a preset traffic smoothness model to obtain corresponding road section information if the number of the driving pictures is larger than the preset number; the traffic smoothness model is trained according to various driving pictures and corresponding smoothness labels.
In one embodiment, the intelligent navigation device based on reverse vehicle further comprises:
the matching module is used for judging whether a third vehicle meets the condition that a third passing path fingerprint of the third vehicle is matched with a first target path fingerprint of the first vehicle;
the score calculating module is used for calculating the visual score of the driving picture shot by the third vehicle if the score is positive;
the score judging module is used for judging whether the visual score of the running picture shot by the third vehicle is larger than the visual score of the running picture shot by the second vehicle;
and the information sending module is used for selecting the road section information of the third vehicle and sending the road section information to the first vehicle if the road section information is positive.
In one embodiment, the receiving module includes:
a matching sub-module for matching the first target path fingerprint with the second pass path fingerprint and/or the second target path fingerprint with the first pass path fingerprint;
the receiving submodule is used for receiving second identity information of the second vehicle transmitted by the second vehicle and/or receiving first identity information of the first vehicle transmitted by the first vehicle by the second vehicle;
the first vehicle is used for acquiring second identity information of a second road section passing through in real time, and uploading the second identity information and road section information of the first road section passing through in real time to a navigation system;
and the sending submodule is used for sending the road section information of the second road section which is transmitted by the second vehicle and passes through in real time to the first vehicle through the navigation system, and/or sending the road section information of the first road section which is transmitted by the first vehicle and passes through in real time to the second vehicle through the navigation system.
In one embodiment, the input module includes:
an identification sub-module for identifying the driving photograph through the car accident identification model and judging whether a car accident event occurs therein;
and the marking sub-module is used for marking the place where the car accident happens and combining the smoothness identified by the traffic smoothness model to form the road section information if the traffic accident happens.
The invention has the beneficial effects that: by acquiring the predicted driving paths of the first vehicle and the second vehicle, splitting the predicted driving paths into a plurality of road sections and judging whether the predicted driving road sections need to be bypassed by using the first vehicle and the second vehicle which drive in opposite directions, whether congestion or traffic accidents happen in the front or not is determined according to the reverse vehicle, real-time information can be acquired, and better driving experience is provided for a vehicle owner.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of another identical element in a process, apparatus, article, or method comprising the element.
The embodiment of the application can acquire and process related data based on an artificial intelligence technology. Among them, artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.
The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (10)
1. An intelligent navigation method based on a reverse vehicle is characterized by comprising the following steps:
acquiring a first predicted travelling path of a first vehicle and a second predicted travelling path of a second vehicle;
splitting the first predicted travel path into a plurality of first road segments and splitting the second predicted travel path into a plurality of second road segments;
the first vehicle generates a first passing path fingerprint based on a first path segment passing in real time, and the second vehicle generates a second passing path fingerprint based on a second path segment passing in real time; the first road section which passes through in real time is a first road section which is passed through by a first vehicle recently, and the second road section which passes through in real time is a second road section which is passed through by a second vehicle recently;
obtaining a target first road segment of the first vehicle predicted to travel based on the first predicted travel path and generating a first target path fingerprint, and obtaining a target second road segment of the second vehicle predicted to travel based on the second predicted travel path and generating a second target path fingerprint;
the first vehicle broadcasting based on the first target path fingerprint and the first pass path fingerprint, the second vehicle broadcasting based on the second target path fingerprint and the second pass path fingerprint;
when the first target route fingerprint matches the second pass-through route fingerprint and/or the second target route fingerprint matches the first pass-through route fingerprint, the first vehicle receiving link information for a second link of the real-time pass-through transmitted by the second vehicle and/or the second vehicle receiving link information for a first link of the real-time pass-through transmitted by the first vehicle; the road section information is acquired by the first vehicle and the second vehicle in real time;
the first vehicle and/or the second vehicle determining whether a predicted travel link needs to be bypassed based on the received link information;
if so, the first vehicle and/or the second vehicle sends a detour instruction to a navigation system and receives a new navigation path sent by the navigation system to continue driving.
2. The intelligent reverse vehicle-based navigation method according to claim 1, wherein the first vehicle and the second vehicle are provided with cameras on bodies traveling close to each other, and before the step of the first vehicle receiving the link information of the second link transmitted by the second vehicle and/or the link information of the first link transmitted by the first vehicle when the first target route fingerprint matches the second pass route fingerprint and/or the second target route fingerprint matches the first pass route fingerprint, the method further comprises:
the first vehicle and the second vehicle shoot a plurality of driving pictures of opposite lanes through cameras respectively arranged on a vehicle body;
the first vehicle or the second vehicle calculates the visual scores of the shot running pictures; wherein the visual score is calculated by the formula
，
，
The pixel values of the pixel points with coordinates i, j,
the pixel value of the o-th adjacent pixel point of the pixel point with the coordinates of i and j is represented, p is the number of the adjacent pixel points,
indicating a brightness variation value, m and n indicating the width and height of the driving picture, respectively,
representing a visual score;
judging whether the number of the running photos with the visual scores reaching the preset value is larger than the preset number or not;
if the number of the driving pictures is larger than the preset number, inputting the driving pictures into a preset traffic smoothness model to obtain corresponding road section information; the traffic smoothness model is trained according to various driving pictures and corresponding smoothness labels.
3. The reverse vehicle based intelligent navigation method of claim 2, wherein the step of the first vehicle broadcasting based on the first target route fingerprint and the first through-route fingerprint and the second vehicle broadcasting based on the second target route fingerprint and the second through-route fingerprint is followed by further comprising:
judging whether a third vehicle meets the condition that a third passing path fingerprint of the third vehicle is matched with the first target path fingerprint of the first vehicle;
if so, calculating the visual score of the driving picture shot by the third vehicle;
judging whether the visual score of the running picture shot by the third vehicle is larger than the visual score of the running picture shot by the second vehicle;
and if so, selecting the road section information of the third vehicle and sending the road section information to the first vehicle.
4. The reverse vehicle-based smart navigation method according to claim 1, wherein the step of the first vehicle receiving the section information of the second section through which the second vehicle transmits in real time and/or the step of the second vehicle receiving the section information of the first section through which the first vehicle transmits in real time when the first target path fingerprint matches the second pass path fingerprint and/or the second target path fingerprint matches the first pass path fingerprint comprises:
when the first target path fingerprint matches the second pass path fingerprint and/or the second target path fingerprint matches the first pass path fingerprint;
the first vehicle receives second identity information of the second vehicle transmitted by the second vehicle and/or the second vehicle receives first identity information of the first vehicle transmitted by the first vehicle;
the first vehicle packages and uploads the second identity information and the road section information of the first road section passing through in real time to a navigation system, and/or the second vehicle packages and uploads the first identity information and the road section information of the second road section passing through in real time to the navigation system;
and sending the road section information of the second road section which is transmitted by the second vehicle and passes through in real time to the first vehicle through the navigation system, and/or sending the road section information of the first road section which is transmitted by the first vehicle and passes through in real time to the second vehicle through the navigation system.
5. The intelligent navigation method based on the reverse vehicle as claimed in claim 2, wherein the step of inputting the driving picture into a preset traffic smoothness model to obtain corresponding road section information comprises:
recognizing the driving picture through the traffic accident recognition model, and judging whether a traffic accident event occurs;
if yes, marking is carried out on the basis of the place where the car accident happens, and the road section information is formed by combining the smoothness degree identified by the traffic smoothness degree model.
6. An intelligent navigation device based on a reverse vehicle, comprising:
the system comprises an acquisition module, a control module and a control module, wherein the acquisition module is used for acquiring a first predicted running path of a first vehicle and a second predicted running path of a second vehicle;
a splitting module for splitting the first predicted travel path into a plurality of first road segments and splitting the second predicted travel path into a plurality of second road segments;
the first generation module is used for generating a first passing path fingerprint based on a first path which is passed by the first vehicle in real time, and generating a second passing path fingerprint based on a second path which is passed by the second vehicle in real time; the first road section which passes through in real time is a first road section which is passed through by a first vehicle recently, and the second road section which passes through in real time is a second road section which is passed through by a second vehicle recently;
a second generation module, configured to obtain a target first road segment of the first vehicle predicted to travel based on the first predicted travel path, generate a first target path fingerprint, obtain a target second road segment of the second vehicle predicted to travel based on the second predicted travel path, and generate a second target path fingerprint;
a broadcast module to broadcast by the first vehicle based on the first target route fingerprint and the first pass route fingerprint, and to broadcast by the second vehicle based on the second target route fingerprint and the second pass route fingerprint;
a receiving module, configured to receive, by the first vehicle, link information of a second link transmitted by the second vehicle in real time and/or receive, by the second vehicle, link information of a first link transmitted by the first vehicle in real time when the first target path fingerprint matches the second passing path fingerprint and/or the second target path fingerprint matches the first passing path fingerprint; the road section information is acquired by the first vehicle and the second vehicle in real time;
a judging module, which is used for the first vehicle and/or the second vehicle to judge whether the road section which is predicted to be driven needs to be bypassed or not based on the received road section information;
and the detouring module is used for sending a detouring instruction to the navigation system and receiving a new navigation path sent by the navigation system to continue driving if the first vehicle and/or the second vehicle are/is in the same state.
7. The reverse vehicle-based smart navigation device as recited in claim 6, further comprising:
the device comprises a setting module, a driving module and a driving module, wherein the setting module is used for shooting a plurality of driving pictures of opposite lanes by the first vehicle and the second vehicle through cameras respectively arranged on a vehicle body;
the calculating module is used for calculating the visual scores of the shot running pictures of the first vehicle or the second vehicle; wherein the visual score is calculated by the formula
，
，
The pixel values of the pixel points with coordinates i, j,
the pixel value of the o-th adjacent pixel point of the pixel point with the coordinates of i and j is represented, p is the number of the adjacent pixel points,
indicating a brightness variation value, m and n indicating the width and height of the running picture, respectively,
representing a visual score;
the score judging module is used for judging whether the number of the driving photos with the visual scores reaching the preset value is larger than the preset number;
the input module is used for inputting the driving pictures into a preset traffic smoothness model to obtain corresponding road section information if the number of the driving pictures is larger than the preset number; the traffic smoothness model is formed by training according to various driving pictures and corresponding smoothness labels.
8. The reverse vehicle-based smart navigation device according to claim 7, further comprising:
the matching module is used for judging whether a third vehicle meets the condition that a third passing path fingerprint of the third vehicle is matched with a first target path fingerprint of the first vehicle;
the score calculating module is used for calculating the visual score of the driving picture shot by the third vehicle if the score is positive;
the score judging module is used for judging whether the visual score of the running picture shot by the third vehicle is larger than the visual score of the running picture shot by the second vehicle;
and the information sending module is used for selecting the road section information of the third vehicle and sending the road section information to the first vehicle if the road section information is positive.
9. The intelligent reverse vehicle-based navigation device of claim 6, wherein the receiving module comprises:
a matching sub-module for matching the first target path fingerprint with the second pass path fingerprint and/or the second target path fingerprint with the first pass path fingerprint;
the receiving submodule is used for the first vehicle to receive second identity information of the second vehicle transmitted by the second vehicle and/or receive first identity information of the first vehicle transmitted by the first vehicle by the second vehicle;
the uploading sub-module is used for the first vehicle to package and upload the second identity information and the road section information of the first road section passing through in real time to a navigation system, and/or the second vehicle to package and upload the first identity information and the road section information of the second road section passing through in real time to the navigation system;
and the sending submodule is used for sending the road section information of the second road section which is transmitted by the second vehicle and passes through in real time to the first vehicle through the navigation system, and/or sending the road section information of the first road section which is transmitted by the first vehicle and passes through in real time to the second vehicle through the navigation system.
10. The intelligent reverse vehicle-based navigation device of claim 7, wherein the input module comprises:
an identification sub-module for identifying the driving photograph through the car accident identification model and judging whether a car accident event occurs therein;
and the marking sub-module is used for marking the place where the car accident happens and combining the smoothness identified by the traffic smoothness model to form the road section information if the traffic accident happens.
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| US7994902B2 (en) * | 2009-02-25 | 2011-08-09 | Southwest Research Institute | Cooperative sensor-sharing vehicle traffic safety system |
| KR20150070801A (en) * | 2013-12-17 | 2015-06-25 | 현대자동차주식회사 | Method for transmitting traffic information using vehicle to vehicle communications |
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