CN114822022A - Data processing method and device for cooperative vehicle and road sensing, vehicle and storage medium - Google Patents
Data processing method and device for cooperative vehicle and road sensing, vehicle and storage medium Download PDFInfo
- Publication number
- CN114822022A CN114822022A CN202210385501.0A CN202210385501A CN114822022A CN 114822022 A CN114822022 A CN 114822022A CN 202210385501 A CN202210385501 A CN 202210385501A CN 114822022 A CN114822022 A CN 114822022A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- data
- information
- source target
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- 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
- G08G1/0125—Traffic data processing
-
- 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
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
Abstract
The invention discloses a data processing method and device for one-vehicle-road cooperative sensing, a vehicle and a storage medium. Obtaining multisource target initial data of cooperative vehicle and road perception; determining data information of traffic participants irrelevant to the driving path of the vehicle according to the multi-source target initial data; filtering data information of irrelevant traffic participants in the multi-source target initial data to generate multi-source target associated data; and classifying the data information of each target object according to the data information of each target object, and using the classified data information of each target object as a multi-source target data set provided to a vehicle-end execution device of the vehicle. According to the technical scheme, the data volume provided to the vehicle-end execution equipment can be reduced, and the data processing pressure of the vehicle-end execution equipment is reduced.
Description
Technical Field
The invention relates to the technical field of vehicle networking, in particular to a data processing method and device for cooperative vehicle road sensing, a vehicle and a storage medium.
Background
The Vehicle-road cooperation V2X (Vehicle to X) technology is a general name of a wireless information exchange technology of "Vehicle to outside", and the V2X technology does not pass through a base station under a general condition, is not interfered by whether a network signal exists or not, and is directly used for information exchange between vehicles, vehicles and roadside equipment and the like, so that communication of near fields (within 1000 m) and short time delay (within 100 ms) can be realized, and an area without network coverage can also normally work, so that direct, rapid and reliable data exchange and transmission between vehicles and vehicles, and between vehicles and roadside equipment are realized.
However, as the amount of vehicles kept increases and the infrastructure of the vehicle network road side is continuously improved, the sources of network targets sensed by the vehicle end execution equipment are continuously increased, for example, real-time high-precision map information, traffic management information, full-time road side sensing information of a road side system and surrounding vehicles carrying the V2X technology, which makes a large amount of high-frequency and redundant V2X network information rush, and brings huge pressure and challenge to the processing amount of information by the vehicle end execution equipment.
Disclosure of Invention
The invention provides a data processing method and device based on cooperative vehicle-road sensing, a vehicle and a storage medium, which are used for reducing the data processing amount of vehicle-end execution equipment and relieving the processing pressure of the vehicle-end execution equipment.
According to an aspect of the present invention, a data processing method for cooperative vehicle and road sensing is provided, including:
acquiring multisource target initial data of cooperative vehicle and road perception; the multi-source target initial data comprises vehicle-end sensor sensing data acquired by a vehicle-end sensor based on a vehicle, networking terminal data and roadside sensing data acquired by vehicle-end networking terminal equipment, and satellite positioning information acquired based on a global satellite navigation system;
determining data information of traffic participants irrelevant to the driving path of the vehicle according to the multi-source target initial data;
filtering the data information of the irrelevant traffic participants in the multi-source target initial data to generate multi-source target relevant data; the multi-source target-related data includes data information of a target object associated with the vehicle travel path;
and classifying the data information of each target object according to the data information of each target object, and using the classified data information of each target object as a multi-source target data set provided to the vehicle-end execution equipment of the vehicle.
Optionally, obtaining multi-source target initial data of vehicle-road cooperative sensing includes:
the method comprises the steps that a vehicle-end sensor based on a vehicle acquires sensing data of the vehicle-end sensor, vehicle-end networking terminal equipment acquires road side sensing data and networking terminal sensing data of traffic participants, and a global satellite navigation system acquires satellite positioning information;
and fusing the sensing data of the internet terminal and the satellite positioning information, and taking the fused data as the initial data of the multi-source target.
Optionally, determining data information of a traffic participant unrelated to the driving path of the vehicle according to the multi-source target initial data includes:
determining the moving track and the driving path of each traffic participant, and the current moving track and the current driving path of the vehicle according to the multi-source target initial data;
and determining data information of the traffic participants irrelevant to the driving path of the vehicle according to the moving track and the driving path of each traffic participant and the current moving track and driving path of the vehicle.
Optionally, determining the movement track and the travel path of each traffic participant and the current movement track and the current travel path of the vehicle according to the multi-source target initial data includes:
positioning the vehicle and each traffic participant in the multi-source target initial data in a parabolic map of a map according to map information and positioning information in the multi-source target initial data;
determining lane information of the vehicles and the traffic participants in the parabolic map according to high-precision map information in the multi-source target initial data;
and determining the moving track and the driving path of each traffic participant in each lane and the current moving track and driving path of the vehicle according to the lane information, the navigation information of the vehicle and the navigation information of each traffic participant in the multi-source target initial data.
Optionally, determining data information of traffic participants irrelevant to the driving path of the vehicle according to the moving trajectory, the driving path and the displacement information of each traffic participant and the current moving trajectory, the driving path and the displacement information of the vehicle, including:
according to the driving path of each traffic participant and the current driving path of the vehicle, the traffic participants of which the driving paths are not overlapped with the driving paths of the vehicle are taken as first-class irrelevant traffic participants;
according to the movement tracks of other traffic participants except the first-class irrelevant traffic participant and the current movement track of the vehicle, taking the traffic participants of which the movement tracks are not overlapped with the current movement track of the vehicle as second-class irrelevant traffic participants;
according to the displacement information of other traffic participants except the first type of irrelevant traffic participant and the second type of irrelevant traffic participant and the displacement information of the vehicle, taking the traffic participant which does not collide with the vehicle as a third type of irrelevant traffic participant;
determining a set of data information associated with the traffic participant of the first type, data information associated with the traffic participant of the second type, and data information associated with the traffic participant of the third type in the multi-source target initial data as data information of traffic participants not associated with the travel path of the vehicle.
Optionally, classifying the data information of each target object according to the data information of each target object, including:
determining the motion direction of each target object relative to the vehicle according to the position information and the movement track of each target object in the multi-source target associated data and the position information and the movement track of the vehicle; the moving direction comprises at least one of the movement of the target object in the same direction as the vehicle, the movement of the target object in the opposite direction to the vehicle and the movement of the target object in the opposite direction to the vehicle;
determining the target objects with the same motion direction as the same type of target objects according to the motion direction of each target object;
and taking the data information of the multi-source target associated data and the target object of the same class as the data information of the same class.
Optionally, after classifying the data information of each target object according to the data information of each target object, the method further includes:
and tracking historical track and path prediction information of each target object based on the vehicle-end sensor sensing data, the networking terminal sensing data and the roadside sensing data.
Optionally, after classifying the data information of each target object according to the data information of each target object, the method further includes:
storing the multi-source target dataset in a local dynamic database.
Optionally, storing the classified data information of each target object in a local dynamic database, including:
sorting the multi-source target datasets according to target attributes of the multi-source target datasets;
sequentially storing the ordered multi-source target data sets into the local dynamic database; and each arrangement serial number corresponds to one multi-source target data set.
Optionally, the data processing method for cooperative vehicle and road sensing further includes:
and according to the arrangement sequence number of the multi-source target data sets, sequentially providing the multi-source target data sets for the vehicle-end execution equipment.
According to another aspect of the present invention, there is provided a data processing apparatus for cooperative vehicle infrastructure sensing, comprising:
the data acquisition module is used for acquiring multi-source target initial data of vehicle-road cooperative sensing; the multi-source target initial data comprises vehicle-end sensor sensing data acquired by a vehicle-end sensor based on a vehicle, roadside sensing data acquired by vehicle-end networking terminal equipment, networking terminal sensing data of traffic participants and satellite positioning information acquired by a global satellite navigation system;
the information determining module is used for determining data information of traffic participants irrelevant to the driving path of the vehicle according to the multi-source target initial data;
the data filtering module is used for filtering the data information of the irrelevant traffic participants in the multi-source target initial data to generate multi-source target associated data; the multi-source target-related data includes data information of a target object associated with the vehicle travel path;
and the data classification module is used for classifying the data information of each target object according to the data information of each target object, and using the classified data information of each target object as a multi-source target data set provided to vehicle-end execution equipment of the vehicle.
According to another aspect of the present invention, there is provided a vehicle including: the system comprises vehicle-end execution equipment, a vehicle-end sensor and a cooperative sensing processor;
the cooperative sensing processor is used for executing the data processing method for the vehicle-road cooperative sensing.
According to another aspect of the present invention, a computer-readable storage medium is provided, which stores computer instructions for causing a processor to implement the above-mentioned data processing method for vehicle-road cooperative sensing when executed.
According to the technical scheme, the multi-source target initial data are filtered, the data information of traffic participants irrelevant to the vehicle running path is removed, the multi-source target associated data are determined, compared with the multi-source target initial data, the data amount of the multi-source target associated data is less, meanwhile, the data information of each target object in the multi-source target associated data is classified and then is provided to the vehicle-end execution equipment, the data processing pressure of the vehicle-end execution equipment is reduced, meanwhile, the primary early warning reminding function can be achieved, the data processing speed and the reaction speed of the vehicle-end execution equipment can be improved, and therefore the vehicle can be controlled quickly and accurately. When the data processing method for cooperative vehicle road sensing is applied to the automatic driving vehicle, the safety and the stability of the automatic driving vehicle can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a data processing method for cooperative vehicle and road sensing according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a data processing method for cooperative vehicle and road sensing according to a second embodiment of the present invention;
FIG. 3 is a diagram of a transportation participant positioning location architecture suitable for use in accordance with a third embodiment of the present invention;
FIG. 4 is an illustration of another transportation participant positioning location architecture suitable for use in accordance with a third embodiment of the present invention;
fig. 5 is a schematic flow chart of a data processing method for cooperative vehicle and road sensing according to a third embodiment of the present invention;
FIG. 6 is a diagram of a kinematic orientation architecture to which a third embodiment of the present invention is applicable;
fig. 7 is a schematic flow chart of a data processing method for cooperative vehicle and road sensing according to a fourth embodiment of the present invention;
fig. 8 is a schematic structural diagram of a data processing apparatus for cooperative vehicle and road sensing according to a fifth embodiment of the present invention;
fig. 9 is a schematic structural diagram of a vehicle according to a sixth embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, 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 the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The embodiment of the invention provides a data processing method for cooperative vehicle-road sensing, which can be applied to automatic driving vehicles, and can reduce the data processing pressure of vehicle-end execution equipment and realize a primary early warning reminding function by processing multi-source target data of cooperative vehicle-road sensing and taking the processed multi-source target data as a multi-source target data set provided to the vehicle-end execution equipment; the data processing method for the cooperative vehicle and road perception can be executed by adopting the data processing device for the cooperative vehicle and road perception provided by the embodiment of the invention, the data processing device for the cooperative vehicle and road perception can be formed by hardware and/or software, and the data processing device for the cooperative vehicle and road perception can be integrated in a cooperative perception processor of a vehicle.
Example one
Fig. 1 is a schematic flow chart of a data processing method for vehicle-road cooperative sensing according to an embodiment of the present invention, as shown in fig. 1, the method includes:
and S110, obtaining multisource target initial data of cooperative vehicle and road perception.
The multi-source target initial data comprises vehicle-end sensor sensing data acquired by a vehicle-end sensor based on a vehicle, roadside sensing data acquired by vehicle-end networking terminal equipment, networking terminal sensing data of traffic participants and satellite positioning information acquired by a global satellite navigation system.
The vehicle-end sensor of the vehicle comprises but is not limited to a vehicle-mounted camera, a laser radar, a millimeter wave radar and the like, and the vehicle-end sensing data comprises but is not limited to the position, the speed, the time, the navigation information and the like of the vehicle; traffic participants include, but are not limited to, pedestrians, other vehicles, and the like; the vehicle-end internet connection terminal equipment acquires internet connection terminal perception data of the traffic participants, wherein the internet connection terminal perception data is data sent by other vehicles carrying a vehicle-road cooperative perception technology; the roadside sensing data acquired by the vehicle end networking terminal equipment are data and the like sent by the vehicle networking roadside infrastructure; data sent by the vehicle networking road side infrastructure is acquired by road side sensors in the vehicle networking road side infrastructure, the road side sensors can specifically comprise road side laser radars, cameras, millimeter wave radars and the like, and the data sent by the vehicle networking road side infrastructure comprises but is not limited to the position, speed, time, navigation information and the like of each traffic participant; the data sent by other vehicles carrying the vehicle-road cooperative sensing technology includes but is not limited to vehicle-end sensing data of a vehicle-end sensor of the vehicle, data sent by other vehicles carrying the vehicle-road cooperative sensing technology, data sent by infrastructure on the side of the internet of vehicles, and the like; the method comprises the steps that a local positioning and time service module carries out positioning through a global satellite navigation satellite system based on satellite positioning information acquired by the global satellite navigation system, and the current three-dimensional coordinate information, speed information and time information of a vehicle are determined by utilizing the pseudo range, ephemeris, satellite transmitting time and the like of a satellite.
Optionally, after the vehicle-end sensor based on the vehicle acquires the vehicle-end sensor sensing data, the vehicle-end internet terminal device acquires the roadside sensing data, the internet terminal sensing data of the traffic participants, and the satellite positioning information acquired based on the global satellite navigation system, the vehicle-end sensor sensing data, the internet terminal sensing data, the roadside sensing data, and the satellite positioning information are fused, and the fused data is used as the multi-source target initial data.
Illustratively, part of repeated data is arranged among vehicle-end sensor sensing data based on a vehicle, traffic participants acquired by vehicle-end networking terminal equipment, sensing data of the traffic participants and roadside sensing data; the repeated data is data representing the same event, the data representing the same event is unified, and the process of fusing the vehicle-end sensing data, the road-side sensing data and the satellite positioning information is the process of removing redundant data. After redundant data in a standard data set formed by vehicle-end sensing data, roadside sensing and satellite positioning information are removed, multi-source target initial data can be obtained, and compared with the standard data set, the data volume of the multi-source target initial data is relatively reduced.
And S120, determining data information of traffic participants irrelevant to the driving path of the vehicle according to the initial data of the multi-source target.
Specifically, the multi-source target initial data includes data such as navigation information, speed, position, and time information of the vehicle, and the multi-source target initial data further includes data such as navigation information, speed, position, and time information of traffic participants, so that an object unlikely to collide with the vehicle among the traffic participants can be known as a traffic participant unrelated to the traveling path of the vehicle, and data associated with the unrelated traffic participant in the multi-source target initial data can be determined as data information of the traffic participant unrelated to the traveling path of the vehicle.
S130, filtering data information of irrelevant traffic participants in the multi-source target initial data to generate multi-source target relevant data.
Wherein the multi-source object-related data includes data information of a target object associated with a vehicle travel path.
Specifically, traffic participants irrelevant to the traveling path of the vehicle do not collide with the vehicle, so that the primary early warning prompt can be realized by only providing data information relevant to traffic participants possibly colliding with the vehicle to the vehicle-end execution device without providing data information relevant to irrelevant traffic participants to the vehicle-end execution device, the traffic participants possibly colliding with the vehicle are used as target objects relevant to the traveling path of the vehicle, and the data information relevant to the target objects is multi-source target relevant data. And (3) data information of irrelevant traffic participants in the multi-source target initial data is filtered, namely the data information of the traffic participants irrelevant to the driving path of the vehicle is removed from the multi-source target initial data, so that the relevant data of the multi-source target can be determined.
And S140, classifying the data information of each target object according to the data information of each target object, and using the classified data information of each target object as a multi-source target data set provided to vehicle-end execution equipment of the vehicle.
Specifically, attributes such as positions, speeds, displacement directions and the like of different target objects have differences, the target objects can be classified based on the attributes such as the positions, the speeds, the displacement directions and the like of the different target objects, the target objects with the same attribute are classified into one class, and data information associated with the same class of target objects in the multi-source target associated data is used as the same class of data information. The classified data information of the target objects can further reflect the collision probability between each target object and the vehicle, and the classified data information of the target objects is used as a multi-source target data set provided to the vehicle-end executing equipment of the vehicle, so that the vehicle-end executing equipment can perform early warning reminding based on the classified data information of the target objects and execute corresponding control functions.
According to the embodiment of the invention, the multi-source target data cooperatively sensed by the vehicle road are fused, the redundant repeated data are removed to be used as the multi-source target initial data, the multi-source target initial data are filtered, the data information of traffic participants irrelevant to the vehicle driving path is removed, and the multi-source target associated data are determined. When the data processing method for cooperative vehicle road sensing is applied to the automatic driving vehicle, the safety and the stability of the automatic driving vehicle can be improved.
Example two
The embodiment provides a specific method for determining data information of a traffic participant irrelevant to a driving path of a vehicle on the basis of the above embodiment, and the specific method specifically comprises the following steps: determining the moving track and the driving path of each traffic participant of the multi-source target initial data and the current moving track and driving path of the vehicle according to the multi-source target initial data; and determining data information of the traffic participants which is not related to the running path of the vehicle according to the moving track and the running path of each traffic participant and the current moving track and the current running path of the vehicle. Fig. 2 is a schematic flow chart of a data processing method for vehicle-road cooperative sensing provided in the second embodiment of the present invention, and as shown in fig. 2, the method includes:
s210, obtaining multisource target initial data of vehicle-road cooperative perception.
The multi-source target initial data comprises vehicle-end sensor sensing data acquired by a vehicle-end sensor based on a vehicle, roadside sensing data acquired by vehicle-end networking terminal equipment, networking terminal sensing data of traffic participants and satellite positioning information acquired by a global satellite navigation system.
And S220, determining the moving track and the driving path of each traffic participant, and the current moving track and the current driving path of the vehicle according to the multi-source target initial data.
The multi-source target initial data further comprises the position, the speed, the time, the navigation information and the like of each traffic participant, and accordingly the moving track and the driving path of each traffic participant, and the current moving track and the driving path of the vehicle can be determined.
Optionally, according to map information and positioning information in the multi-source target initial data, positioning the vehicle and each traffic participant in the multi-source target initial data in a parabolic map of the map; determining lane information of vehicles and all traffic participants in the parabolic map according to high-precision map information in the multi-source target initial data; and determining the moving track and the driving path of each traffic participant in each lane and the current moving track and driving path of the vehicle according to the lane information, the navigation information of the vehicle in the multi-source target initial data and the navigation information of each traffic participant.
Specifically, as shown in fig. 3, according to the multi-source target initial data, the vehicles and the traffic participants in the multi-source target initial data are positioned in a parabolic map of the map (as shown in fig. 3 (a)); then, the vehicle and each traffic participant are placed on a lane line (as shown in fig. 3(b)) by fusing with a high-precision map and a global satellite navigation system; further, the navigation information of the vehicle is fused to obtain the movement track and the traveling path of each traffic participant, and the current movement track and the traveling path of the vehicle (see fig. 3 (c)).
And S230, determining data information of the traffic participants irrelevant to the running path of the vehicle according to the moving track and the running path of each traffic participant and the current moving track and running path of the vehicle.
The current position, the driving lane, the speed, the expected driving lane, and the like of the traffic participant can be determined according to the moving track and the driving path of each traffic participant, and the current position, the driving lane, the speed, the expected driving lane, and the like of the vehicle can be determined according to the current moving track and the driving path of the vehicle, so that the traffic participant which cannot be assembled with the vehicle can be determined as an irrelevant traffic participant, and the data information of the traffic participant irrelevant to the driving path of the vehicle can be further determined.
Optionally, according to the driving path of each traffic participant and the current driving path of the vehicle, the traffic participants of which the driving paths are not overlapped with the driving paths of the vehicle are used as first-class irrelevant traffic participants; according to the movement tracks of other traffic participants except the first-class irrelevant traffic participant and the current movement track of the vehicle, taking the traffic participants of which the movement tracks are not overlapped with the current movement track of the vehicle as second-class irrelevant traffic participants; according to the displacement information of other traffic participants except the first-class irrelevant traffic participant and the second-class irrelevant traffic participant and the displacement information of the vehicle, taking the traffic participant which does not collide with the vehicle as a third-class irrelevant traffic participant; and determining a set of data information associated with the traffic participants irrelevant to the first type, data information associated with the traffic participants irrelevant to the second type and data information associated with the traffic participants irrelevant to the third type in the multi-source target initial data as data information of the traffic participants irrelevant to the driving path of the vehicle.
Specifically, as shown in fig. 4, traffic participants whose travel paths are unrelated to the travel path of the vehicle and do not overlap each other in the multi-source target initial data are taken as first-type unrelated traffic participants, and data information of the first-type unrelated traffic participants is data information to be filtered (as shown in fig. 4 (a)); determining the movement tracks of the vehicle and the traffic participants according to the navigation route information in the multi-source target initial data so as to identify the traffic participants which are not overlapped with the movement tracks of the vehicle as second-class irrelevant traffic participants, wherein the data information of the second-class irrelevant traffic participants is also data information needing to be filtered (as shown in fig. 4 (b)); finally, the predicted time of collision between the vehicle and the transportation participants is calculated according to the data information of the transportation participants screened in the first two steps, so as to further identify the high-risk targets, namely the transportation participants who are likely to collide with the vehicle, and the transportation participants who are not likely to collide with the vehicle as the third type of irrelevant transportation participants, wherein the data information of the first type of irrelevant transportation participants is also the data information which needs to be filtered (as shown in fig. 4 (c)).
S240, filtering data information of irrelevant traffic participants in the multi-source target initial data to generate multi-source target relevant data.
Wherein the multi-source object-related data includes data information of a target object associated with a vehicle travel path.
And S250, classifying the data information of each target object according to the data information of each target object, and taking the classified data information of each target object as a multi-source target data set provided to the vehicle-end execution equipment of the vehicle.
According to the embodiment of the invention, the movement track and the driving path of each traffic participant, the current movement track and the driving path of the vehicle are determined according to the multi-source target initial data, and the data information of the traffic participants irrelevant to the driving path of the vehicle is determined according to the movement track and the driving path of each traffic participant, the current movement track and the driving path of the vehicle, so that the data information of the traffic participants irrelevant to the driving path of the vehicle in the multi-source target initial data can be accurately removed, and therefore, on the premise of realizing early warning reminding, the data quantity provided to the vehicle-end execution equipment is reduced, the data processing speed and the reaction speed of the vehicle-end execution equipment can be further improved, and the vehicle can be further controlled quickly and accurately. When the data processing method for cooperative vehicle road sensing is applied to the automatic driving vehicle, the safety and the stability of the automatic driving vehicle can be improved.
Optionally, after classifying the data information of each target object according to the data information of each target object, the method further includes: and tracking historical track and path prediction information of each target object based on vehicle end sensor sensing data, networking terminal sensing data and roadside sensing data. Therefore, the road side sensing data acquired by the traffic participants can utilize and manage the historical track and the path prediction information of the vehicle, and can be accurately classified on special road conditions such as a curved road, a sloping road and the like.
EXAMPLE III
The present embodiment provides a specific method how to classify data information of a target object based on the above embodiments, and specifically includes: determining the motion direction of each target object relative to the vehicle according to the position information and the moving track of each target object in the multi-source target associated data and the position information and the moving track of the vehicle; determining target objects with the same motion direction as the same type of target objects according to the motion direction of each target object; and taking data information of the multi-source target associated data and the same type of target object as the same type of data information ". Fig. 5 is a schematic flow chart of a data processing method for vehicle-road cooperative sensing provided in the third embodiment of the present invention, and as shown in fig. 5, the method includes:
s310, obtaining multisource target initial data of vehicle-road cooperative perception.
And S320, determining data information of traffic participants irrelevant to the driving path of the vehicle according to the multi-source target initial data.
S330, filtering data information of irrelevant traffic participants in the multi-source target initial data to generate multi-source target relevant data.
S340, determining the motion direction of each target object relative to the vehicle according to the position information and the moving track of each target object in the multi-source target related data and the position information and the moving track of the vehicle.
As shown in fig. 6, the moving direction includes at least one of the movement of the target object in the same direction as the vehicle, the movement of the target object in the opposite direction to the vehicle, and the movement of the target object in the direction opposite to the vehicle.
And S350, determining the target objects with the same motion direction as the same type of target objects according to the motion direction of each target object.
Specifically, a target object which is on the same lane as the vehicle and has the same moving direction is taken as a target object which moves in the same direction as the vehicle, and the target objects are classified into the same type of target object (as shown in fig. 6 (a)); taking a target object which is on the same lane as the vehicle and has the opposite moving direction as a target object which moves in the opposite direction of the vehicle, and classifying the target objects into the same type of target object (as shown in fig. 6 (b)); a target object which is not in the same lane with the vehicle but the driving direction of which intersects with the driving direction of the vehicle is taken as a target object moving in the direction intersecting with the vehicle, and the target objects of the type are classified as the same type of target object (as shown in fig. 6 (c)). When the moving directions of the target objects are classified, the target objects can be further classified by using data such as lane width, lane orientation, lane line, lane curvature and the like in the high-precision map information, so that the target objects can be more accurately classified.
And S360, taking the data information of the multi-source target associated data and the same type of target object as the same type of data information.
Specifically, data information corresponding to each target object moving in the same direction as the vehicle is classified into the same type of data information; classifying data information corresponding to each target object moving in the reverse direction of the vehicle into the same type of data information; classifying data information corresponding to each target object which does the intersection movement with the vehicle into the same type of data information; in this way, classification of data information of each target object can be realized.
And S370, taking the data information of each classified target object as a multi-source target data set provided to the vehicle-end execution equipment of the vehicle.
According to the embodiment of the invention, the moving direction of each target object relative to the vehicle is determined according to the position information and the moving track of each target object in the multi-source target associated data, the target objects with the same moving direction are determined as the same type of target objects according to the moving direction of each target object, and the data information of the multi-source target associated data and the data information of the same type of target objects are used as the same type of data information, so that the target objects can be classified quickly and accurately, and the front-end processing speed and accuracy of the multi-source target data can be improved.
Example four
On the basis of the above embodiments, the present embodiment further provides a method for storing data information of a classification target object. Fig. 7 is a schematic flowchart of a data processing method for vehicle-road cooperative sensing according to a fourth embodiment of the present invention, and as shown in fig. 7, the method includes:
and S410, obtaining multi-source target initial data of cooperative vehicle and road perception.
And S420, determining data information of traffic participants irrelevant to the driving path of the vehicle according to the multi-source target initial data.
And S430, filtering data information of irrelevant traffic participants in the multi-source target initial data to generate multi-source target relevant data.
And S440, classifying the data information of each target object according to the data information of each target object, and using the classified data information of each target object as a multi-source target data set provided to the vehicle-end execution equipment of the vehicle.
And S450, storing the multi-source target data set in a local dynamic database.
The multi-source target data set is stored in the local dynamic database, so that the multi-source target data set can be called in time when the vehicle-end execution equipment needs; when the vehicle-end execution equipment is not needed, the data processing pressure of the vehicle-end execution equipment can be further reduced, and the power consumption consumed by data processing of the vehicle-end execution equipment is reduced.
Optionally, the data information of each classified target object may be stored in the following manner: sorting the multi-source target data sets according to the target attributes of the multi-source target data sets; and sequentially storing the sequenced multi-source target data sets into a local dynamic database.
And each arrangement serial number corresponds to one multi-source target data set. Target attributes of the multi-source target data set include, but are not limited to, attributes of data source, type, time, and the like, and the priority of the multi-source target data can be determined by sorting the multi-source target data set. In an exemplary embodiment, the multi-source target dataset is classified according to a temporal attribute of the multi-source target dataset.
Optionally, after the multi-source target data sets are sorted, the multi-source target data sets can be provided to the vehicle-end execution equipment according to the arrangement sequence numbers of the multi-source target data sets, so that the real-time performance, the stability and the accuracy of data provided to the vehicle-end execution equipment can be improved, and the operation safety and the stability of the vehicle-end execution equipment can be improved.
EXAMPLE five
Based on the same inventive concept as the embodiment, the embodiment of the invention further provides a data processing device for cooperative vehicle and road sensing, which can be applied to automatic driving vehicles, and can reduce the data processing pressure of vehicle-end execution equipment and realize a primary early warning reminding function by processing the multi-source target data cooperatively sensed by the vehicle and taking the processed multi-source target data as a multi-source target data set provided to the vehicle-end execution equipment; the data processing device for vehicle-road cooperative sensing can be used for executing the data processing method for vehicle-road cooperative sensing provided by the embodiment of the invention. Therefore, the data processing device for vehicle-road cooperative sensing has the technical characteristics of the data processing method for vehicle-road cooperative sensing in the above embodiment, and can achieve the beneficial effects of the data processing method for vehicle-road cooperative sensing in the above embodiment, and reference may be made to the description of the data processing method for vehicle-road cooperative sensing in the above embodiment for the same points.
Fig. 8 is a schematic structural diagram of a data processing apparatus for vehicle-road cooperative sensing according to a fifth embodiment of the present invention, as shown in fig. 8, the apparatus includes, but is not limited to, a data acquisition module 810, an information determination module 820, a data filtering module 830, and a data classification module 840.
The data acquisition module 810 is used for acquiring multi-source target initial data of vehicle-road cooperative sensing; the multi-source target initial data comprises vehicle-end sensor sensing data acquired by a vehicle-end sensor based on a vehicle, roadside sensing data acquired by vehicle-end networking terminal equipment, networking terminal sensing data of traffic participants and satellite positioning information acquired by a global satellite navigation system;
the information determination module 820 is used for determining data information of traffic participants irrelevant to the driving path of the vehicle according to the multi-source target initial data;
the data filtering module 830 is configured to filter data information of the unrelated traffic participants in the multi-source target initial data, and generate multi-source target related data; the multi-source target-related data includes data information of a target object associated with a vehicle travel path;
the data classification module 840 is configured to classify the data information of each target object according to the data information of each target object, and use the classified data information of each target object as a multi-source target data set provided to a vehicle-end execution device of the vehicle.
According to the embodiment of the invention, the initial data of the multi-source target is filtered, the data information of the traffic participants irrelevant to the vehicle running path is removed, and the associated data of the multi-source target is determined, compared with the initial data of the multi-source target, the data amount of the associated data of the multi-source target is less, and meanwhile, the data information of each target object in the associated data of the multi-source target is classified and then provided to the vehicle-end execution equipment, so that the data processing pressure of the vehicle-end execution equipment is reduced, and meanwhile, the primary early warning reminding function can be realized, so that the data processing speed and the reaction speed of the vehicle-end execution equipment can be improved, and the vehicle can be controlled quickly and accurately. When the data processing device for the cooperative vehicle road sensing is applied to the automatic driving vehicle, the safety and the stability of the automatic driving vehicle can be improved.
EXAMPLE six
Based on the same inventive concept as the above embodiment, an embodiment of the present invention further provides a vehicle, fig. 9 is a schematic structural diagram of a vehicle according to a sixth embodiment of the present invention, and as shown in fig. 9, the vehicle 900 at least includes a vehicle-end execution device 910, a vehicle-end sensor 920, and a cooperative sensing processor 930; the cooperative sensing processor 930 is configured to execute a data processing method for vehicle-road cooperative sensing according to any embodiment of the present invention.
It should be noted that fig. 9 is a block diagram of an exemplary vehicle 900 suitable for implementing the embodiment of the present invention, and is not intended to limit the function and the scope of the embodiment of the present invention.
A connection bus represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, Industry Standard Architecture (ISA) bus, Micro Channel Architecture (MCA) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and Peripheral Component Interconnect (PCI) bus.
A variety of computer system readable media may also be provided in the vehicle 900. Such media may be any available media that is accessible by a vehicle-end execution device and includes both volatile and nonvolatile media, removable and non-removable media.
The memory may include computer system readable media in the form of volatile memory, such as random access memory and/or cache memory. The vehicle 900 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, the storage system may be used to read from and write to non-removable, nonvolatile magnetic media, may provide a disk drive to read from or write to a removable, nonvolatile magnetic disk (e.g., a "floppy disk"), and may provide a removable, nonvolatile optical disk
An optical disk drive for reading from and writing to a disk (e.g., a CD-ROM, DVD-ROM, or other optical media). In these cases, each drive may be connected to the connection bus by one or more data media interfaces. The system memory may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention. 6]
A program/utility having a set (at least one) of program modules may be stored, for example, in system memory, such program modules including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination may comprise an implementation of a network environment. The program modules generally perform the functions and/or methodologies of the described embodiments of the invention.
The cooperative sensing processor executes various functional applications and data processing by running the program stored in the system memory, for example, implementing the data processing method for vehicle-road cooperative sensing provided by the embodiment of the present invention.
EXAMPLE seven
On the basis of the foregoing embodiments, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the data processing method for vehicle-road cooperative sensing provided in any embodiment of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (13)
1. A data processing method for cooperative vehicle and road perception is characterized by comprising the following steps:
acquiring multisource target initial data of cooperative vehicle and road perception; the multi-source target initial data comprises vehicle-end sensor sensing data acquired by a vehicle-end sensor based on a vehicle, roadside sensing data acquired by vehicle-end networking terminal equipment, networking terminal sensing data of a traffic participant and satellite positioning information acquired by a global satellite navigation system;
determining data information of traffic participants irrelevant to the driving path of the vehicle according to the multi-source target initial data;
filtering the data information of the irrelevant traffic participants in the multi-source target initial data to generate multi-source target relevant data; the multi-source target-related data includes data information of a target object associated with the vehicle travel path;
and classifying the data information of each target object according to the data information of each target object, and using the classified data information of each target object as a multi-source target data set provided to the vehicle-end execution equipment of the vehicle.
2. The data processing method for cooperative vehicle and road perception according to claim 1, wherein the obtaining of the initial data of the multi-source target of cooperative vehicle and road perception includes:
the method comprises the steps that a vehicle-end sensor based on a vehicle acquires sensing data of the vehicle-end sensor, vehicle-end networking terminal equipment acquires road side sensing data and networking terminal sensing data of traffic participants, and a global satellite navigation system acquires satellite positioning information;
and fusing the vehicle-end sensor sensing data with the networking terminal sensing data, the roadside sensing data and the satellite positioning information, and taking the fused data as the multi-source target initial data.
3. The data processing method for cooperative vehicle and road perception according to claim 1, wherein determining data information of traffic participants unrelated to the driving path of the vehicle according to the multi-source target initial data comprises:
determining the moving track and the driving path of each traffic participant, and the current moving track and the current driving path of the vehicle according to the multi-source target initial data;
and determining data information of the traffic participants irrelevant to the driving path of the vehicle according to the moving track and the driving path of each traffic participant and the current moving track and driving path of the vehicle.
4. The data processing method for cooperative vehicle and road perception according to claim 3, wherein determining the movement track and the driving path of each traffic participant and the current movement track and the driving path of the vehicle according to the multi-source target initial data comprises:
positioning the vehicle and each traffic participant in the multi-source target initial data in a parabolic map of a map according to map information and positioning information in the multi-source target initial data;
determining lane information of the vehicles and the traffic participants in the parabolic map according to high-precision map information in the multi-source target initial data;
and determining the moving track and the driving path of each traffic participant in each lane and the current moving track and driving path of the vehicle according to the lane information, the navigation information of the vehicle and the navigation information of each traffic participant in the multi-source target initial data.
5. The data processing method for vehicle-road cooperative sensing according to claim 4, wherein determining data information of traffic participants irrelevant to the driving route of the vehicle according to the moving track, the driving route and the displacement information of each traffic participant and the current moving track, the driving route and the displacement information of the vehicle comprises:
according to the driving path of each traffic participant and the current driving path of the vehicle, the traffic participants of which the driving paths are not overlapped with the driving paths of the vehicle are taken as first-class irrelevant traffic participants;
according to the movement tracks of other traffic participants except the first-class irrelevant traffic participant and the current movement track of the vehicle, taking the traffic participants of which the movement tracks are not overlapped with the current movement track of the vehicle as second-class irrelevant traffic participants;
according to the displacement information of other traffic participants except the first type of irrelevant traffic participant and the second type of irrelevant traffic participant and the displacement information of the vehicle, taking the traffic participant which does not collide with the vehicle as a third type of irrelevant traffic participant;
determining a set of data information associated with the traffic participant unrelated to the first class, data information associated with the traffic participant unrelated to the second class, and data information associated with the traffic participant unrelated to the third class in the multi-source target initial data as data information of traffic participants unrelated to the traveling path of the vehicle.
6. The data processing method for cooperative vehicle and road perception according to claim 1, wherein classifying the data information of each target object according to the data information of each target object includes:
determining the motion direction of each target object relative to the vehicle according to the position information and the movement track of each target object in the multi-source target associated data and the position information and the movement track of the vehicle; the moving direction comprises at least one of the movement of the target object in the same direction as the vehicle, the movement of the target object in the opposite direction to the vehicle and the movement of the target object in the opposite direction to the vehicle;
determining the target objects with the same motion direction as the same type of target objects according to the motion direction of each target object;
and taking the data information of the multi-source target associated data and the target object of the same class as the data information of the same class.
7. The method for processing the data of the cooperative vehicle and road perception according to claim 1, further comprising, after classifying the data information of each of the target objects according to the data information of each of the target objects:
and tracking historical track and path prediction information of each target object based on the vehicle-end sensor sensing data, the networking terminal sensing data and the roadside sensing data.
8. The method for processing the data of the cooperative vehicle and road perception according to claim 1, further comprising, after classifying the data information of each of the target objects according to the data information of each of the target objects:
storing the multi-source target dataset in a local dynamic database.
9. The method for processing the data through cooperative vehicle and road perception according to claim 8, wherein the step of storing the classified data information of each target object in a local dynamic database includes:
sorting the multi-source target data sets according to target attributes of the multi-source target data sets;
sequentially storing the ordered multi-source target data sets into the local dynamic database; and each arrangement serial number corresponds to one multi-source target data set.
10. The data processing method for cooperative vehicle and road perception according to claim 9, further comprising:
and according to the arrangement sequence number of the multi-source target data sets, sequentially providing the multi-source target data sets for the vehicle-end execution equipment.
11. A data processing device for cooperative vehicle and road perception is characterized by comprising:
the data acquisition module is used for acquiring multi-source target initial data of vehicle-road cooperative sensing; the multi-source target initial data comprises vehicle-end sensor sensing data acquired by a vehicle-end sensor based on a vehicle, roadside sensing data acquired by vehicle-end networking terminal equipment, networking terminal data of traffic participants and satellite positioning information acquired by a global satellite navigation system;
the information determining module is used for determining data information of traffic participants irrelevant to the driving path of the vehicle according to the multi-source target initial data;
the data filtering module is used for filtering the data information of the irrelevant traffic participants in the multi-source target initial data to generate multi-source target associated data; the multi-source target-related data includes data information of a target object associated with the vehicle travel path;
and the data classification module is used for classifying the data information of each target object according to the data information of each target object, and using the classified data information of each target object as a multi-source target data set provided to vehicle-end execution equipment of the vehicle.
12. A vehicle, characterized by comprising: the system comprises vehicle-end execution equipment, a vehicle-end sensor and a cooperative sensing processor;
the cooperative sensing processor is used for executing the data processing method of vehicle-road cooperative sensing of any one of claims 1 to 10.
13. A computer-readable storage medium storing computer instructions for causing a processor to implement the method for processing data for vehicle-road cooperative sensing according to any one of claims 1 to 10 when executed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210385501.0A CN114822022A (en) | 2022-04-13 | 2022-04-13 | Data processing method and device for cooperative vehicle and road sensing, vehicle and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210385501.0A CN114822022A (en) | 2022-04-13 | 2022-04-13 | Data processing method and device for cooperative vehicle and road sensing, vehicle and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114822022A true CN114822022A (en) | 2022-07-29 |
Family
ID=82536658
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210385501.0A Pending CN114822022A (en) | 2022-04-13 | 2022-04-13 | Data processing method and device for cooperative vehicle and road sensing, vehicle and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114822022A (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190219668A1 (en) * | 2018-01-12 | 2019-07-18 | Tiejun Shan | Method of using a Direct Sequence Spread Spectrum in Vehicle Location Approximation when using Orthogonal Frequency-Division Multiplexing |
| CN110276972A (en) * | 2019-07-16 | 2019-09-24 | 启迪云控(北京)科技有限公司 | A kind of object cognitive method and system based on car networking |
| CN111354182A (en) * | 2018-12-20 | 2020-06-30 | 阿里巴巴集团控股有限公司 | Driving assisting method and system |
| CN112712719A (en) * | 2020-12-25 | 2021-04-27 | 北京百度网讯科技有限公司 | Vehicle control method, vehicle-road coordination system, road side equipment and automatic driving vehicle |
| CN114301792A (en) * | 2021-12-29 | 2022-04-08 | 北京经纬恒润科技股份有限公司 | Traffic flow simulation method and traffic flow sensor |
-
2022
- 2022-04-13 CN CN202210385501.0A patent/CN114822022A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190219668A1 (en) * | 2018-01-12 | 2019-07-18 | Tiejun Shan | Method of using a Direct Sequence Spread Spectrum in Vehicle Location Approximation when using Orthogonal Frequency-Division Multiplexing |
| CN111354182A (en) * | 2018-12-20 | 2020-06-30 | 阿里巴巴集团控股有限公司 | Driving assisting method and system |
| CN110276972A (en) * | 2019-07-16 | 2019-09-24 | 启迪云控(北京)科技有限公司 | A kind of object cognitive method and system based on car networking |
| CN112712719A (en) * | 2020-12-25 | 2021-04-27 | 北京百度网讯科技有限公司 | Vehicle control method, vehicle-road coordination system, road side equipment and automatic driving vehicle |
| CN114301792A (en) * | 2021-12-29 | 2022-04-08 | 北京经纬恒润科技股份有限公司 | Traffic flow simulation method and traffic flow sensor |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Muhammad et al. | Deep learning for safe autonomous driving: Current challenges and future directions | |
| Darms et al. | Obstacle detection and tracking for the urban challenge | |
| CN109188438B (en) | Yaw angle determination method, device, equipment and medium | |
| Tang et al. | On-road object detection and tracking based on radar and vision fusion: A review | |
| Bai et al. | Infrastructure-based object detection and tracking for cooperative driving automation: A survey | |
| US20150166071A1 (en) | Method and system for information use | |
| CN113165670A (en) | Intelligent driving method, device, storage medium and computer program | |
| WO2018154367A1 (en) | System and method for target track management of an autonomous vehicle | |
| EP3660807A1 (en) | Server device and vehicle | |
| CN114212110A (en) | Obstacle trajectory prediction method, obstacle trajectory prediction device, electronic device, and storage medium | |
| Janeera et al. | Internet of things and artificial intelligence-enabled secure autonomous vehicles for smart cities | |
| CN114475656A (en) | Travel track prediction method, travel track prediction device, electronic device, and storage medium | |
| JP2014002477A (en) | Vehicle group management apparatus | |
| CN113454555A (en) | Trajectory prediction for driving strategies | |
| CN111785000B (en) | Vehicle state data uploading method and device, electronic equipment and storage medium | |
| US11433892B2 (en) | Assertive vehicle detection model generation | |
| Sohail et al. | Radar sensor based machine learning approach for precise vehicle position estimation | |
| JP2021140367A (en) | Communication device, vehicle, program, and communication method | |
| CN114822022A (en) | Data processing method and device for cooperative vehicle and road sensing, vehicle and storage medium | |
| EP4160269A1 (en) | Systems and methods for onboard analysis of sensor data for sensor fusion | |
| Kheder et al. | Iot-based vision techniques in autonomous driving: A review | |
| Kumar et al. | A novel algorithm to track closely spaced road vehicles using a low density flash lidar | |
| CN114895274A (en) | Guardrail identification method | |
| EP4239616A1 (en) | Object tracking device and object tracking method | |
| CN114154510A (en) | Control method and device for automatic driving vehicle, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |