CN108180915A - Vehicle location sorting technique, device, vehicle and storage medium - Google Patents
Vehicle location sorting technique, device, vehicle and storage medium Download PDFInfo
- Publication number
- CN108180915A CN108180915A CN201711342140.7A CN201711342140A CN108180915A CN 108180915 A CN108180915 A CN 108180915A CN 201711342140 A CN201711342140 A CN 201711342140A CN 108180915 A CN108180915 A CN 108180915A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- parameter
- target
- target vehicle
- network model
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Radar, Positioning & Navigation (AREA)
- Theoretical Computer Science (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Traffic Control Systems (AREA)
Abstract
This disclosure relates to a kind of vehicle location sorting technique, device, vehicle and storage medium, the method includes：Obtain the vehicle parameter for the target vehicle being located at around this vehicle；According to the vehicle parameter of the target vehicle, the vehicle parameter of described vehicle and default neural network model, determine that the target vehicle is classified relative to the position of described vehicle.Through the above scheme, vehicle location classification is carried out using neural network model, accelerates the arithmetic speed of vehicle location classification, improve the reliability of vehicle location classification.
Description
Technical field
This disclosure relates to automobile technical field, and in particular, to a kind of vehicle location sorting technique, device, vehicle and deposit
Storage media.
Background technology
V2X (vehicle to everything, vehicle and external information switching technology), is visited using wireless communication, sensing
The technologies such as survey collect the information such as vehicle, road, environment, by vehicle and vehicle, Che Yu roads information exchange and share, set vehicle and basis
It is intelligent coordinated with coordinating between applying, so as to fulfill intelligent traffic administration system control, Vehicular intelligent control and Intelligent Dynamic information clothes
The integrated network of business.
In the relevant technologies, in the vehicle for being equipped with V2X, pass through TC (target classification, target identification)
Module screens the vehicle location in the range of 360 degree, but the algorithm logic of TC modules is more inflexible and formula is complicated so that
When classifying to vehicle location, occupancy resource is excessive, the response time is long.
Invention content
To overcome the problems in correlation technique, the purpose of the disclosure is to provide a kind of vehicle location sorting technique, dress
It puts, vehicle and storage medium.
According to the embodiment of the present disclosure in a first aspect, provide a kind of vehicle location sorting technique, the method includes：
Obtain the vehicle parameter for the target vehicle being located at around this vehicle；
According to the vehicle parameter of the target vehicle, the vehicle parameter of described vehicle and default neural network model, really
The fixed target vehicle is classified relative to the position of described vehicle.
Optionally, the vehicle parameter according to the target vehicle, the vehicle parameter of described vehicle and default nerve
Network model determines that the target vehicle is classified relative to the position of described vehicle, including：
Using the vehicle parameter of the vehicle parameter of the target vehicle and described vehicle as the default neural network mould
The input data of type inputs the input layer of the default neural network model；
The input data is handled by two hidden layers of the default neural network model, by the position
Classification is exported by the output layer of the default neural network model.
Optionally, the vehicle parameter of the target vehicle includes：The latitude of the longitude of the target vehicle, the target vehicle
Degree, the speed of the target vehicle, the course angle of the target vehicle；The vehicle parameter of described vehicle includes：Described vehicle
Longitude, the latitude of described vehicle, the speed of described vehicle, the course angle of described vehicle.
Optionally, in the vehicle parameter according to the target vehicle, the vehicle parameter of described vehicle and default god
Through network model, after determining position classification of the target vehicle relative to described vehicle, the method further includes：
Classified according to the position, determine early warning scene corresponding with position classification；
According to early warning scene and the vehicle parameter of the target vehicle, it is determined whether generation vehicle early warning information, it is described
Warning information is used to prompt the driver of described vehicle to perform operation corresponding with the warning information.
According to the second aspect of the embodiment of the present disclosure, a kind of vehicle location sorter is provided, described device includes：
Acquisition module, for obtaining the vehicle parameter for the target vehicle being located at around this vehicle；
Processing module, for vehicle parameter, the vehicle parameter of described vehicle and the default god according to the target vehicle
Through network model, determine that the target vehicle is classified relative to the position of described vehicle.
Optionally, the processing module includes：
Input submodule, for using the vehicle parameter of the vehicle parameter of the target vehicle and described vehicle as described in
The input data of default neural network model inputs the input layer of the default neural network model；
Submodule is handled, for being carried out by two hidden layers of the default neural network model to the input data
Processing is exported position classification by the output layer of the default neural network model.
Optionally, the vehicle parameter of the target vehicle includes：The latitude of the longitude of the target vehicle, the target vehicle
Degree, the speed of the target vehicle, the course angle of the target vehicle；The vehicle parameter of described vehicle includes：Described vehicle
Longitude, the latitude of described vehicle, the speed of described vehicle, the course angle of described vehicle.
Optionally, described device further includes：
First determining module for classifying according to the position, determines early warning scene corresponding with position classification；
Second determining module, for the vehicle parameter according to early warning scene and the target vehicle, it is determined whether generation
Vehicle early warning information, the warning information are used to prompt the driver of described vehicle to perform behaviour corresponding with the warning information
Make.
According to the third aspect of the embodiment of the present disclosure, a kind of vehicle is provided, the vehicle includes：
For the memory of storage control executable instruction；
Controller, for performing the vehicle location sorting technique of disclosure first aspect offer.
According to the fourth aspect of the embodiment of the present disclosure, a kind of computer readable storage medium is provided, is stored thereon with calculating
Machine program instruction realizes the vehicle location sorting technique that disclosure first aspect provides when the program instruction is executed by processor
Step.
In the disclosure, the vehicle parameter of the target vehicle around this vehicle is located at by acquisition, according to the target vehicle
Vehicle parameter, the vehicle parameter of described vehicle and default neural network model, determine the target vehicle relative to described
The position classification of vehicle, i.e., carry out vehicle location classification using neural network model, accelerate the arithmetic speed of vehicle location classification,
Improve the reliability of vehicle location classification.
Other feature and advantage of the disclosure will be described in detail in subsequent specific embodiment part.
Description of the drawings
Attached drawing is for providing further understanding of the disclosure, and a part for constitution instruction, with following tool
Body embodiment is used to explain the disclosure, but do not form the limitation to the disclosure together.In the accompanying drawings：
Fig. 1 is a kind of flow chart of vehicle location sorting technique shown in one exemplary embodiment of the disclosure.
Fig. 2 is the schematic diagram of the default neural network model shown in one exemplary embodiment of the disclosure.
Fig. 3 is the schematic diagram of the vehicle location classification shown in one exemplary embodiment of the disclosure.
Fig. 4 is the position classification and the correspondence schematic diagram of early warning scene shown in one exemplary embodiment of the disclosure.
Fig. 5 is a kind of schematic diagram of vehicle location sorter shown in one exemplary embodiment of the disclosure.
Specific embodiment
The specific embodiment of the disclosure is described in detail below in conjunction with attached drawing.It should be understood that this place is retouched
The specific embodiment stated is only used for describing and explaining the disclosure, is not limited to the disclosure.In the absence of conflict, originally
The feature in embodiment and embodiment in invention can be combined with each other.
As shown in Figure 1, the flow chart for a kind of vehicle location sorting technique shown in one exemplary embodiment of the disclosure, it should
Method includes the following steps.
In step s 11, the vehicle parameter for the target vehicle being located at around this vehicle is obtained；
In step s 12, according to the vehicle parameter of the target vehicle, the vehicle parameter of described vehicle and default nerve
Network model determines that the target vehicle is classified relative to the position of described vehicle.
In the disclosure, target vehicle can be the vehicle that the distance between this vehicle meets a pre-determined distance, in a reality
It applies in example, target vehicle is the vehicle for being less than 500m with this vehicle distance.Alternatively, target vehicle can be led to this vehicle
The vehicle of letter.Target vehicle can also be the vehicle for meeting other conditions, and the disclosure does not limit.
The vehicle parameter of the vehicle parameter of the target vehicle and described vehicle can pass through target vehicle and this vehicle
The sensor of upper setting obtains.In one embodiment, by the speed of velocity sensor collection vehicle, pass through angle sensor
The azimuth of device collection vehicle acquires the distance between target vehicle and this vehicle by radar, passes through GPS (Global
Positioning System, global positioning system) obtain location information of vehicle etc..Vehicle parameter can be according to actual needs
It is set, in one embodiment, vehicle parameter can include the lateral shift of vehicle, vertical misalignment, speed, azimuth
Etc. information.In another embodiment, vehicle parameter includes the information such as the longitude of vehicle, latitude.
Sending module and receiving module can be both provided on the target vehicle and described vehicle, is adopted in target vehicle
After the vehicle parameter for collecting the target vehicle, by the sending module on target vehicle by the vehicle parameter of the target vehicle
Other vehicles are sent to, described vehicle receives the target vehicle parameter by receiving module.
The vehicle parameter of described vehicle and the vehicle parameter of the target vehicle can be identical parameters, or different
Parameter.In one embodiment, longitude, latitude, speed of the vehicle parameter of described vehicle for this vehicle, the vehicle of the target vehicle
The longitude, latitude, speed of parameter for target vehicle.In another embodiment, the vehicle parameter of described vehicle is this vehicle
Longitude, latitude, speed, the vehicle parameter of the target vehicle are the lateral shift of target vehicle, vertical misalignment, speed.
It, can be according to reality equipped with default neural network model, default neural network model on this vehicle in the disclosure
It is set, in one embodiment, neural network model includes input layer, hidden layer, output layer, and input layer is hidden
The number of neuronal structure that layer and output layer are included can be set according to actual needs.For example, input layer includes 10
A neuron, hidden layer include 8 neurons, and output layer includes 10 neurons.Default neural network model can also include
Multiple hidden layers, the disclosure are not specifically limited.The disclosure is by default neural network model to target vehicle relative to this vehicle
Position classify, therefore, can be using the vehicle parameter of the vehicle parameter of the target vehicle and described vehicle as pre-
If the input data of the input layer of neural network model, the target vehicle is relative to the position classification of described vehicle as output
The output data of layer.
Optionally, the vehicle parameter according to the target vehicle, the vehicle parameter of described vehicle and default nerve
Network model determines that the target vehicle is classified relative to the position of described vehicle, including：The vehicle of the target vehicle is joined
Input data of the vehicle parameter of several and described vehicles as the default neural network model inputs the default nerve net
The input layer of network model；The input data is handled by two hidden layers of the default neural network model, it will
The position classification is exported by the output layer of the default neural network model.
As shown in Fig. 2, the schematic diagram for the default neural network model shown in one exemplary embodiment of the disclosure, this is default
Neural network model includes an input layer (input), two hidden layers (hidden1, hidden2) and an output layer
(output).Input layer includes 8 neurons, and each hidden layer includes 4 neurons, and output layer includes 17 neurons.
The default neural network model can be established by way of machine learning.In one embodiment, pass through
Pilot steering pattern obtains the training data needed for machine learning, and training data is pre-processed to meet machine learning
Form, every group of training data can include：Eight input datas are as input array [X], and an output data is as a result
Sample [Y].By ready training data by group input neural network, neural network is trained, in a certain amount of training
Afterwards, neural network can generate the decision logic of oneself.The algorithmic formula of specific neural network is as follows：
When establishing neural network model, at random to the weight assignment of each neuron, h is calculated using above-mentioned formulaΘ(x(i)) and the error amount of J (Θ) and each node of use backward pass-algorithm calculating, gradient is used to decline and checks algorithm inspection
The correctness that gradient declines calculates the minimum value of J (Θ) using gradient decline or other algorithms, so as to obtain neural network mould
The optimal algorithm of type.
Determine that target vehicle is classified relative to the position of this vehicle using default neural network model in the disclosure, therefore,
Each neuron of input layer can correspond to a vehicle parameter, and each neuron of output layer corresponds to a position classification knot
Fruit.
In one embodiment, the vehicle parameter of the target vehicle includes：The longitude of the target vehicle, the target
The latitude of vehicle, the speed of the target vehicle, the course angle of the target vehicle；The vehicle parameter of described vehicle includes：Institute
State the course angle of the longitude of this vehicle, the latitude of described vehicle, the speed of described vehicle, described vehicle.Above-mentioned eight vehicle parameters
Eight neurons of input layer can be corresponded to, as one group of default neural network model of input data input.
The output layer of default neural network is to classify relative to 17 positions of this vehicle.It please refers to Fig.3, shows for the disclosure one
The schematic diagram of vehicle location classification that example property implementation exemplifies.In this embodiment, 17 position classification are respectively the friendship of this vehicle
Fork left side intersects right side, front, left forward side, right forward side, a left front distant place, a right preceding distant place, rear, left rear side, rear
Right side, a left back distant place, it is right after a distant place, opposite front, to the left, to the right, to a distant place to the left, to remote to the right
Side.Wherein, opposite front, to the right, to a distant place to the left, to a distant place to the right, intersect left side and be not shown.
In the disclosure, the vehicle parameter of the vehicle parameter of the target vehicle and described vehicle is inputted into the default god
After network model, by the processing of two hidden layers, the one kind that can be mapped in above-mentioned 17 kinds of vehicle locations classification.
Optionally, in the vehicle parameter according to the target vehicle, the vehicle parameter of described vehicle and default god
Through network model, after determining position classification of the target vehicle relative to described vehicle, the method further includes：According to institute
Rheme puts the vehicle parameter of classification and the target vehicle, it is determined whether generation vehicle early warning information, the warning information are used
Operation corresponding with the warning information is performed in the driver of described vehicle of prompting.
It,, can also be according to institute's rheme after the position classification that vehicle is determined in order to ensure traffic safety in the disclosure
The vehicle parameter of classification and target vehicle is put, to determine whether travel shape in shop safety between target vehicle and this vehicle
State.In one embodiment, the vehicle parameter of target vehicle can be speed, acceleration, target vehicle and the sheet of target vehicle
The distance of vehicle, according to the position classify determine target vehicle relative to this vehicle azimuth information, according to the speed of target vehicle,
The distance of acceleration and target vehicle and this vehicle determines whether target vehicle and this vehicle have the possibility of collision.In target vehicle
When having the possibility of collision with this vehicle, vehicle early warning information is generated, and the prior-warning device for passing through this vehicle carries out early warning, to prompt this vehicle
Driver taken appropriate measures according to actual conditions.
Optionally, in the vehicle parameter according to the target vehicle, the vehicle parameter of described vehicle and default god
Through network model, after determining position classification of the target vehicle relative to described vehicle, the method further includes：According to institute
Rheme puts classification, determines early warning scene corresponding with position classification；According to early warning scene and the vehicle of the target vehicle
Parameter, it is determined whether generation vehicle early warning information, the warning information are used to prompting the driver of described vehicle to perform and institute
State the corresponding operation of warning information.
In the disclosure, the classification of different positions may be corresponding with different early warning scenes, for example, when the position is classified as
When intersecting left side, corresponding early warning scene can include crossing anti-collision warning and early warning to lose control of one's vehicle.As shown in figure 4, for this
Position classification shown in one exemplary embodiment and the correspondence schematic diagram of early warning scene are disclosed.In this embodiment, intersect
The corresponding early warning scene in left side and intersection right side includes crossing anti-collision warning, to lose control of one's vehicle early warning；The corresponding early warning field in front
Scape includes urgent anti-collision warning, to lose control of one's vehicle early warning, emergency brake early warning, early warning of overtaking other vehicles；Left forward side and right forward side correspond to
Early warning scene include：Early warning, emergency brake early warning to lose control of one's vehicle；The corresponding early warning scene in rear includes early warning to lose control of one's vehicle；
The corresponding early warning scene in left rear side and right rear side includes：Early warning, lane change early warning to lose control of one's vehicle；Opposite front is corresponding pre-
Alert scene includes early warning to lose control of one's vehicle；Early warning scene corresponding to the left is included：Early warning, early warning of overtaking other vehicles to lose control of one's vehicle；It is opposite
The corresponding early warning scene in right side includes early warning to lose control of one's vehicle.
Under different early warning scenes, according to the vehicle parameter of the vehicle parameter of target vehicle and/or this vehicle come further
Determine whether to generate vehicle early warning information.For example, early warning scene be to the left when, according to the speed of target vehicle and this
The speed of vehicle, to determine whether to generate warning information of overtaking other vehicles.Certainly, position classification can basis with the correspondence of early warning scene
Actual needs is adjusted, and the disclosure is not specifically limited.
As shown in figure 5, the schematic diagram for a kind of vehicle location sorter shown in one exemplary embodiment of the disclosure, institute
Device is stated to include：
Acquisition module 51, for obtaining the vehicle parameter for the target vehicle being located at around this vehicle；
Processing module 52, for vehicle parameter, the vehicle parameter of described vehicle and default according to the target vehicle
Neural network model determines that the target vehicle is classified relative to the position of described vehicle.
Optionally, processing module 52 includes：
Input submodule, for using the vehicle parameter of the vehicle parameter of the target vehicle and described vehicle as described in
The input data of default neural network model inputs the input layer of the default neural network model；
Submodule is handled, for being carried out by two hidden layers of the default neural network model to the input data
Processing is exported position classification by the output layer of the default neural network model.
Optionally, the vehicle parameter of the target vehicle includes：The latitude of the longitude of the target vehicle, the target vehicle
Degree, the speed of the target vehicle, the course angle of the target vehicle；The vehicle parameter of described vehicle includes：Described vehicle
Longitude, the latitude of described vehicle, the speed of described vehicle, the course angle of described vehicle.
Optionally, described device further includes：
Determining module, according to position classification and the vehicle parameter of the target vehicle, it is determined whether generation vehicle
Warning information, the warning information are used to prompt the driver of described vehicle to perform operation corresponding with the warning information.
Based on same design, the disclosure also provides a kind of vehicle, and the vehicle includes：Finger is can perform for storage control
The memory of order；Controller, for performing the position sorting technique of the vehicle of disclosure offer.
Based on same design, the disclosure also provides a kind of computer readable storage medium, is stored thereon with computer program
The step of instruction, the vehicle location sorting technique that the realization disclosure provides when which is executed by processor.
The preferred embodiment of the disclosure is described in detail above in association with attached drawing, still, the disclosure is not limited to above-mentioned reality
The detail in mode is applied, in the range of the technology design of the disclosure, a variety of letters can be carried out to the technical solution of the disclosure
Monotropic type, these simple variants belong to the protection domain of the disclosure.
It is further to note that specific technical features described in the above specific embodiments, in not lance
In the case of shield, can be combined by any suitable means, in order to avoid unnecessary repetition, the disclosure to it is various can
The combination of energy no longer separately illustrates.
In addition, arbitrary combination can also be carried out between a variety of different embodiments of the disclosure, as long as it is without prejudice to originally
Disclosed thought should equally be considered as disclosure disclosure of that.
Claims (10)
1. a kind of vehicle location sorting technique, which is characterized in that the method includes：
Obtain the vehicle parameter for the target vehicle being located at around this vehicle；
According to the vehicle parameter of the target vehicle, the vehicle parameter of described vehicle and default neural network model, institute is determined
Target vehicle is stated relative to the position of described vehicle to classify.
2. vehicle location sorting technique according to claim 1, which is characterized in that the vehicle according to the target vehicle
Parameter, the vehicle parameter of described vehicle and default neural network model, determine the target vehicle relative to described vehicle
Position classification, including：
Using the vehicle parameter of the vehicle parameter of the target vehicle and described vehicle as the default neural network model
Input data inputs the input layer of the default neural network model；
The input data is handled by two hidden layers of the default neural network model, the position is classified
It is exported by the output layer of the default neural network model.
3. vehicle location sorting technique according to claim 1 or 2, which is characterized in that the vehicle ginseng of the target vehicle
Number includes：The longitude of the target vehicle, the latitude of the target vehicle, the speed of the target vehicle, the target vehicle
Course angle；The vehicle parameter of described vehicle includes：The longitude of described vehicle, the latitude of described vehicle, described vehicle speed,
The course angle of described vehicle.
4. vehicle location sorting technique according to claim 1, which is characterized in that described according to the target vehicle
Vehicle parameter, the vehicle parameter of described vehicle and default neural network model, determine the target vehicle relative to described
After the position classification of vehicle, the method further includes：
Classified according to the position, determine early warning scene corresponding with position classification；
According to early warning scene and the vehicle parameter of the target vehicle, it is determined whether generation vehicle early warning information, the early warning
Information is used to prompt the driver of described vehicle to perform operation corresponding with the warning information.
5. a kind of vehicle location sorter, which is characterized in that described device includes：
Acquisition module, for obtaining the vehicle parameter for the target vehicle being located at around this vehicle；
Processing module, for vehicle parameter, the vehicle parameter of described vehicle and the default nerve net according to the target vehicle
Network model determines that the target vehicle is classified relative to the position of described vehicle.
6. vehicle location sorter according to claim 5, which is characterized in that the processing module includes：
Input submodule, for using the vehicle parameter of the vehicle parameter of the target vehicle and described vehicle as described default
The input data of neural network model inputs the input layer of the default neural network model；
Submodule is handled, at by two hidden layers of the default neural network model to the input data
Reason is exported position classification by the output layer of the default neural network model.
7. vehicle location sorter according to claim 5 or 6, which is characterized in that the vehicle ginseng of the target vehicle
Number includes：The longitude of the target vehicle, the latitude of the target vehicle, the speed of the target vehicle, the target vehicle
Course angle；The vehicle parameter of described vehicle includes：The longitude of described vehicle, the latitude of described vehicle, described vehicle speed,
The course angle of described vehicle.
8. vehicle location sorter according to claim 5, which is characterized in that described device further includes：
First determining module for classifying according to the position, determines early warning scene corresponding with position classification；
Second determining module, for the vehicle parameter according to early warning scene and the target vehicle, it is determined whether generation vehicle
Warning information, the warning information are used to prompt the driver of described vehicle to perform operation corresponding with the warning information.
9. a kind of vehicle, which is characterized in that the vehicle includes：
For the memory of storage control executable instruction；
Controller requires 1~4 any one of them method for perform claim.
10. a kind of computer readable storage medium, is stored thereon with computer program instructions, which is characterized in that the program instruction
The step of method according to any one of claims 1 to 4 is realized when being executed by processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711342140.7A CN108180915A (en) | 2017-12-14 | 2017-12-14 | Vehicle location sorting technique, device, vehicle and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711342140.7A CN108180915A (en) | 2017-12-14 | 2017-12-14 | Vehicle location sorting technique, device, vehicle and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108180915A true CN108180915A (en) | 2018-06-19 |
Family
ID=62546047
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711342140.7A Pending CN108180915A (en) | 2017-12-14 | 2017-12-14 | Vehicle location sorting technique, device, vehicle and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108180915A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111210661A (en) * | 2020-01-16 | 2020-05-29 | 北京汽车集团有限公司 | Intersection vehicle anti-collision method and device |
| CN112583872A (en) * | 2019-09-29 | 2021-03-30 | 华为技术有限公司 | Communication method and device |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09245284A (en) * | 1996-03-13 | 1997-09-19 | Toshiba Corp | Traffic situation monitoring device |
| CN1656524A (en) * | 2002-03-22 | 2005-08-17 | 易卜拉欣·纳拉 | Vehicle navigation, collision avoidance and control system |
| CN103903479A (en) * | 2014-04-23 | 2014-07-02 | 奇瑞汽车股份有限公司 | Vehicle safety driving pre-warning method and system and vehicle terminal device |
| CN107346612A (en) * | 2016-05-06 | 2017-11-14 | 中国科学院微电子研究所 | Vehicle anti-collision method and system based on Internet of Vehicles |
-
2017
- 2017-12-14 CN CN201711342140.7A patent/CN108180915A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH09245284A (en) * | 1996-03-13 | 1997-09-19 | Toshiba Corp | Traffic situation monitoring device |
| CN1656524A (en) * | 2002-03-22 | 2005-08-17 | 易卜拉欣·纳拉 | Vehicle navigation, collision avoidance and control system |
| CN103903479A (en) * | 2014-04-23 | 2014-07-02 | 奇瑞汽车股份有限公司 | Vehicle safety driving pre-warning method and system and vehicle terminal device |
| CN107346612A (en) * | 2016-05-06 | 2017-11-14 | 中国科学院微电子研究所 | Vehicle anti-collision method and system based on Internet of Vehicles |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112583872A (en) * | 2019-09-29 | 2021-03-30 | 华为技术有限公司 | Communication method and device |
| WO2021057972A1 (en) * | 2019-09-29 | 2021-04-01 | 华为技术有限公司 | Communication method and apparatus |
| CN112583872B (en) * | 2019-09-29 | 2022-05-13 | 华为云计算技术有限公司 | Communication method and device |
| CN111210661A (en) * | 2020-01-16 | 2020-05-29 | 北京汽车集团有限公司 | Intersection vehicle anti-collision method and device |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Bachute et al. | Autonomous driving architectures: insights of machine learning and deep learning algorithms | |
| US11673550B2 (en) | Agent prioritization for autonomous vehicles | |
| CN109808700A (en) | System and method for mapping road interfering object in autonomous vehicle | |
| CN110531753A (en) | Control system, control method and the controller of autonomous vehicle | |
| CN1915725B (en) | Driver assistance system | |
| CN109781129A (en) | A kind of road surface safety detection system and method based on inter-vehicular communication | |
| CN109522784A (en) | For distinguishing the device and method that can be crossed object and object can not be crossed | |
| CN107577231A (en) | Formulating method, device and the automatic driving vehicle of the control decision of vehicle | |
| CN109472986A (en) | For determining the existing system and method for traffic control personnel and traffic control signals object | |
| CN109284764B (en) | System and method for object classification in autonomous vehicles | |
| CN107316500A (en) | Threat computational methods, target selecting method and application in automatic Pilot | |
| CN110182217A (en) | A kind of traveling task complexity quantitative estimation method towards complicated scene of overtaking other vehicles | |
| CN105719509A (en) | Automobile collision early warning radar information system | |
| US20220111859A1 (en) | Adaptive perception by vehicle sensors | |
| US11887323B2 (en) | Self-supervised estimation of observed vehicle pose | |
| CN112896185A (en) | Intelligent driving behavior decision planning method and system for vehicle-road cooperation | |
| CN105151044A (en) | Method and device for aided driving of vehicle | |
| US11164457B2 (en) | Vehicle control system | |
| CN108180915A (en) | Vehicle location sorting technique, device, vehicle and storage medium | |
| CN112406905A (en) | Data processing method and device based on vehicle, computer and storage medium | |
| CN112784867A (en) | Training deep neural networks using synthetic images | |
| CN114119625A (en) | Segmentation and classification of point cloud data | |
| CN114283593A (en) | Vehicle as a sensing platform for phase timing effectiveness of traffic signal lights | |
| US20210264284A1 (en) | Dynamically routed patch discriminator | |
| CN108973995A (en) | Environment sensing data processing method, device and vehicle for assisting driving |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20180619 |