CN110689748A - Unmanned route adjusting method, device and system and storage medium - Google Patents
Unmanned route adjusting method, device and system and storage medium Download PDFInfo
- Publication number
- CN110689748A CN110689748A CN201810737472.3A CN201810737472A CN110689748A CN 110689748 A CN110689748 A CN 110689748A CN 201810737472 A CN201810737472 A CN 201810737472A CN 110689748 A CN110689748 A CN 110689748A
- Authority
- CN
- China
- Prior art keywords
- route
- street lamp
- vehicle
- unmanned
- state
- 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
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
Abstract
The present disclosure relates to a method, an apparatus, a system, and a storage medium for unmanned route adjustment, the method including: acquiring a first route currently driven by the unmanned vehicle; determining N street lamp states of N street lamps positioned in front of the vehicle on the first street lamp, wherein N is a positive integer greater than 1; when the N street lamp states meet a preset condition, planning a second route for the vehicle to run; and adjusting the running route of the vehicle from the first route to the second route. The scheme provided by the disclosure greatly reduces the data processing amount of the unmanned system, simultaneously reduces the complexity of data processing, and improves the path adjustment efficiency of the unmanned vehicle.
Description
Technical Field
The present disclosure relates to the field of unmanned driving, and in particular, to an unmanned driving route adjusting method, an apparatus, and a storage medium.
Background
The unmanned vehicle is an intelligent vehicle which realizes unmanned driving through a computer system, and has wide prospect and high practical value in the fields of urban traffic, public safety and the like. In the related art, when planning a driving route of an unmanned vehicle, it is necessary to comprehensively analyze road data and environmental data around the road, such as radar data, image data, GPS data, and the like, and since these data are often various and large in number, the workload of unmanned route planning is large and the computational complexity is high.
Disclosure of Invention
To overcome the problems in the related art, the present disclosure provides an unmanned route adjustment method, apparatus, system, and storage medium.
According to a first aspect of embodiments of the present disclosure, there is provided an unmanned route adjustment method, the method including:
acquiring a first route currently driven by the unmanned vehicle;
determining N street lamp states of N street lamps positioned in front of the vehicle on the first street lamp, wherein N is a positive integer greater than 1;
when the N street lamp states meet a preset condition, planning a second route for the vehicle to run;
and adjusting the running route of the vehicle from the first route to the second route.
Optionally, the street lamp state is one of a traffic unblocked state, a traffic jam state and a traffic restricted state.
Optionally, when the N street lamp states satisfy a preset condition, planning a second route on which the vehicle travels, including:
and when the traffic restriction state exists in the N street lamp states, planning a second route for the vehicle to run.
Optionally, when the traffic restriction state does not exist in the N street lamp states, and when the N street lamp states satisfy a preset condition, planning a second route on which the vehicle travels, includes:
determining a number of the traffic congestion states among the N street light states;
and when the ratio of the number of the traffic jam states to N meets a preset range, planning a second route of the vehicle.
Optionally, the planning a second route traveled by the vehicle includes:
determining M routes from the current position to a destination of the vehicle, wherein M is a positive integer larger than 1;
and determining the second route in the M routes according to the street lamp state on each route in the M routes.
Optionally, the determining the second route in the M routes according to the street lamp status on each route in the M routes includes:
determining T routes with the street lamp state being the traffic restriction state in the M routes, wherein T is a positive integer greater than 1;
determining a ratio of a first target street lamp state quantity to a second target street lamp state quantity of each of the T routes, wherein the first target street lamp state is the traffic jam state, and the second target street lamp state is the smooth traffic state;
and determining the route with the minimum ratio as the second route.
According to a second aspect of the embodiments of the present disclosure, there is provided an unmanned route adjustment device, the device including:
the acquisition module is used for acquiring a first route currently driven by the unmanned vehicle;
the processing module is used for determining N street lamp states of N street lamps positioned in front of the vehicle on the first street lamp, wherein N is a positive integer greater than 1;
the planning module is used for planning a second route for the vehicle to run when the states of the N street lamps meet a preset condition;
and the adjusting module is used for adjusting the running route of the vehicle from the first route to the second route.
Optionally, the street lamp state is one of a traffic unblocked state, a traffic jam state and a traffic restricted state.
Optionally, the processing module includes:
and the first processing submodule is used for planning a second route for the vehicle to run when the traffic restriction state exists in the N street lamp states.
Optionally, the processing module includes:
a first determining submodule for determining the number of the traffic congestion states among the N street lamp states;
and the second processing submodule is used for planning a second route of the vehicle when the ratio of the number of the traffic jam states to the N meets a preset range.
Optionally, the processing module includes:
the second determining submodule is used for determining M routes from the current position to the destination of the vehicle, and M is a positive integer larger than 1;
and the third processing submodule is used for determining the second route in the M routes according to the street lamp state on each route in the M routes.
Optionally, the processing module includes:
a third determining submodule, configured to determine, among the M routes, T routes where the street lamp state is the traffic restriction state does not exist, where T is a positive integer greater than 1;
a fourth determining submodule, configured to determine a ratio of a first target street lamp state quantity to a second target street lamp state quantity of each of the T routes, where the first target street lamp state is the traffic jam state, and the second target street lamp state is the clear traffic state;
and the fifth determining submodule is used for determining the route with the minimum ratio as the second route.
According to a third aspect of embodiments of the present disclosure, there is provided an unmanned route adjustment system, the system comprising:
the street lamp comprises a street lamp network, a memory and a processor, wherein the street lamp network, the memory and the processor are in communication connection;
the network of street lamps comprises a plurality of street lamps, each street lamp in the network of street lamps comprising: the street lamp comprises a light source, a heat dissipation assembly and a street lamp processing module, wherein the heat dissipation assembly comprises heat-conducting silicone grease, the street lamp processing module and the light source are arranged above the heat dissipation assembly, and the street lamp processing module is used for determining the street lamp state of each street lamp;
the memory is for storing computer program instructions which, when executed by the processor, implement the method of the first aspect of the disclosure.
Optionally, the heat-conducting silicone grease is prepared from a specific composition, the specific composition comprises 10-60 parts by weight of silicone oil, 50-150 parts by weight of a first filler, 50-150 parts by weight of a second filler and optionally an auxiliary agent, based on 100 parts by weight of the silicone oil; the first filler comprises a metal heat conductor and a phase-change material, and the weight ratio of the metal heat conductor to the phase-change material is 1: (0.2 to 2.5); the second filler comprises carbon nanotubes and graphene, and the weight ratio of the carbon nanotubes to the graphene is 1: (1-20).
According to a fourth aspect of embodiments of the present disclosure, there is provided a computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method according to the first aspect of the present disclosure.
According to the scheme, the current first route of the unmanned vehicle and N street lamp states of N street lamps positioned in front of the vehicle in the first route are obtained, road condition information of the first route is determined, when the N street lamp states meet a preset condition, a second route is planned for the vehicle again, and the driving route of the vehicle is adjusted to the second route from the first route. Therefore, the method in the embodiment of the disclosure can adjust the route of the unmanned vehicle only according to the street lamp state, thereby greatly reducing data processing, reducing the complexity of data processing, and improving the efficiency of adjusting the route of the unmanned vehicle.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a flowchart illustrating an unmanned route adjustment method according to an exemplary embodiment of the present disclosure.
Fig. 2 is a flowchart illustrating a method of determining a second route according to an exemplary embodiment of the present disclosure.
Fig. 3 is a flowchart illustrating an implementation manner of step S22 according to an exemplary embodiment of the present disclosure.
Fig. 4 is a schematic view illustrating an unmanned route adjustment apparatus according to an exemplary embodiment of the present disclosure.
Fig. 5 is a schematic view of a street lamp according to an exemplary embodiment of the present disclosure.
Detailed Description
The following detailed description of specific embodiments of the present disclosure is provided in connection with the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the present disclosure, are given by way of illustration and explanation only, not limitation.
The embodiment of the disclosure provides an unmanned route adjusting method, which is applied to an unmanned system, wherein the unmanned system comprises a road lamp network, a processor and a memory. The street lamp network may include all street lamps arranged in a preset area.
As shown in fig. 1, a flowchart of an unmanned route adjustment method according to an exemplary embodiment of the present disclosure is shown, and the method includes the following steps.
In step S11, a first route on which the unmanned vehicle is currently traveling is acquired;
in step S12, N street lamp states of N street lamps located ahead of the vehicle on the first street lamp are determined, N being a positive integer greater than 1;
in step S13, when the N street lamp states satisfy a preset condition, planning a second route for the vehicle to travel;
in step S14, the travel route of the vehicle is adjusted from the first route to the second route.
In the present disclosure, the initial route, i.e., the first route, may be determined based on the starting position, the destination of the unmanned vehicle, and an electronic map stored in the unmanned system. The first route may be a route with the shortest distance from the starting position to the destination, may also be a route with the optimal traffic condition from the starting position to the destination, and may also be another route, which is not limited in this disclosure. After the first route is determined, the first route may be saved to a memory of the unmanned system. Since the road condition is changed at any time, as the vehicle travels on the first route, the road condition of the first route may also be changed, for example, a traffic accident occurs on a road in front of the vehicle, or traffic jam occurs, and if the route is not adjusted, the traffic jam may be more serious.
In the present disclosure, the street lamps in the unmanned system can obtain respective street lamp states, and in one embodiment, the street lamps can determine the street lamp states according to data collected by sensors arranged on the street lamps, for example, the street lamp states are determined according to traffic flow information collected by a traffic flow sensor.
The street lamp state can be used for representing the road condition of the road section where the street lamp is located, and can be set according to actual needs. For example, the street lamp state may be a traffic unblocked state, a traffic jam state, a traffic accident state, a road construction state, and the like. To detect the road condition of the first route, N street lamp states of N street lamps located in front of the vehicle on the first route may be determined. The N street lamps may be all street lamps in front of the vehicle, may also be street lamps in front of the vehicle on the right in the driving direction, and may also be street lamps located within a preset distance in front of the vehicle in the driving direction, and the like.
It should be understood that the preset conditions in the present disclosure can be set according to actual needs. In one embodiment, the preset condition is that all of the N street lamp states are traffic jam states, that is, when all of the front sides of the vehicles are jammed, the driving route is re-planned. In another embodiment, the preset condition is that the street lamp status of the street lamp closest to the vehicle is a traffic jam status.
And when the N street lamp states meet the preset conditions, the fact that the road condition in front of the vehicle is not ideal is shown, and the current driving route needs to be adjusted. In one embodiment, the road conditions of all directions at the next intersection of the vehicle can be determined according to the street lamp state, and the vehicle can travel in the direction which has better road conditions and can reach the destination. In another embodiment, all the routes where the current position of the vehicle can reach the destination are determined, the road condition of each route is determined according to the street lamp state on each route, and the route with the best road condition is used as the second route.
For convenience of explanation of the present disclosure, the street lamp state is one of a traffic smooth state, a traffic jam state, and a traffic restriction state.
In one embodiment, a speed sensor is provided on the street light for detecting the speed of a vehicle passing under the street light. When the speed of the vehicle is greater than or equal to a first threshold value (such as 30km/h), determining that the street lamp state is a smooth traffic state; when the vehicle speed is less than a first threshold value and more than or equal to a second threshold value (for example, less than 30km/h and more than or equal to 5km/h), determining that the street lamp state is a traffic jam state; and when the vehicle speed is less than a second threshold value (such as 5km/h) or the vehicle speed is the reverse vehicle speed, determining that the street lamp state is a traffic restriction state. Of course, other sensors, such as an image acquisition device, may be further disposed on the street lamp, and the state of the street lamp is determined according to the image information acquired by the image acquisition device.
Optionally, when the N street lamp states satisfy a preset condition, planning a second route on which the vehicle travels, including: and when the traffic restriction state exists in the N street lamp states, planning a second route for the vehicle to run.
In the disclosure, if the traffic restriction state exists in the N street lamp states, it indicates that at least one road section in front of the unmanned vehicle is a restriction, where the restriction may be a no-pass due to road collapse, facility damage, or the like, or a temporary restriction caused by traffic control or traffic accident, but in any case, the vehicle is caused to wait, increasing the driving time. In this case, the present disclosure needs to plan a driving route for the vehicle again in order to make the vehicle arrive at the destination as soon as possible.
Optionally, when the traffic restriction state does not exist in the N street lamp states, and when the N street lamp states satisfy a preset condition, planning a second route on which the vehicle travels, includes: determining a number of the traffic congestion states among the N street light states; and when the ratio of the number of the traffic jam states to N meets a preset range, planning a second route of the vehicle.
It should be understood that, when there is no traffic restriction state in the N street lamp states, it indicates that the road condition of each road segment on the first route is either congested or smooth. In this disclosure, the congestion condition of the first route may be determined by a ratio of a congested road segment to the entire road segment, that is, a ratio of the number of the traffic congestion states to N is calculated, and when the ratio satisfies a preset range, for example, the ratio is greater than 60%, it is indicated that the first route is heavily congested, at this time, a driving route is planned for the vehicle again, and of course, the preset range may be set according to actual needs, which is not limited in this disclosure.
As shown in fig. 2, a flowchart of a method for determining a second route according to an exemplary embodiment of the present disclosure is shown, and the method includes the following steps.
In step S21, M routes from the current position to the destination of the vehicle are determined, M being a positive integer greater than 1;
in step S22, the second route is determined among the M routes according to the street lamp status on each of the M routes.
When the vehicle travels on the first route, if the road ahead is congested, the current location of the vehicle is obtained, and the location may be obtained by a Global Positioning System (GPS) provided in the vehicle. And determining M routes on the electronic map according to the current position and the destination of the vehicle. In one embodiment, the M routes are all routes from the current location to the destination. In another embodiment, the M routes are routes having a distance from the current location to the destination that is less than a predetermined distance. In another embodiment, the M routes are routes that have a budgeted time to travel from the current location to the destination that is less than a preset time.
After the M routes are determined, the street lamp state on each route in the M routes is obtained, and an optimal route is determined as the second route according to the street lamp state on each route. For example, the route with the shortest distance is selected as the second route, or the route with the best road condition is selected as the second route, or the route with the shortest time to reach the destination is selected as the second route.
As shown in fig. 3, a flowchart of one implementation of step S22 shown for an exemplary embodiment of the present disclosure includes the following steps.
In step S221, it is determined that there are no T routes in which the street lamp status is the traffic restriction status among the M routes, where T is a positive integer greater than 1;
in step S222, determining a ratio of a first target street lamp state quantity to a second target street lamp state quantity of each of the T routes, where the first target street lamp state is the traffic jam state, and the second target street lamp state is the clear traffic state;
in step S223, the route with the smallest ratio is determined as the second route.
In this embodiment, the street lamps whose street lamp states are traffic restricted are determined first, and then the routes in which these street lamps are located are excluded, so that only the traffic jam state and the traffic free state are included in the street lamp states of the remaining routes. And respectively counting the number of the street lamps in the traffic jam state and the number of the street lamps in the traffic unblocked state in each route, calculating the ratio of the two, finally comparing the ratio of each route, and taking the route with the minimum ratio as the second route.
According to the method, the route of the unmanned vehicle can be adjusted according to the street lamp state, data processing is greatly reduced, the complexity of data processing is reduced, and the route adjustment efficiency of the unmanned vehicle is improved.
Based on the same inventive concept, the embodiment of the disclosure also provides an unmanned route adjusting device. Fig. 4 is a schematic diagram illustrating an unmanned route adjustment device according to an exemplary embodiment of the present disclosure. As shown in fig. 4, an unmanned route adjustment device provided in an embodiment of the present disclosure includes:
the acquiring module 41 is used for acquiring a first route currently driven by the unmanned vehicle;
a processing module 42, configured to determine N street lamp states of N street lamps located in front of the vehicle on the first street lamp, where N is a positive integer greater than 1;
the planning module 43 is configured to plan a second route where the vehicle runs when the N street lamp states meet a preset condition;
and an adjusting module 44, configured to adjust the driving route of the vehicle from the first route to the second route.
Optionally, the street lamp state is one of a traffic unblocked state, a traffic jam state and a traffic restricted state.
Optionally, the processing module 42 comprises:
and the first processing submodule is used for planning a second route for the vehicle to run when the traffic restriction state exists in the N street lamp states.
Optionally, the processing module 42 comprises:
a first determining submodule for determining the number of the traffic congestion states among the N street lamp states;
and the second processing submodule is used for planning a second route of the vehicle when the ratio of the number of the traffic jam states to the N meets a preset range.
Optionally, the processing module 42 comprises:
the second determining submodule is used for determining M routes from the current position to the destination of the vehicle, and M is a positive integer larger than 1;
and the third processing submodule is used for determining the second route in the M routes according to the street lamp state on each route in the M routes.
Optionally, the processing module 42 comprises:
a third determining submodule, configured to determine, among the M routes, T routes where the street lamp state is the traffic restriction state does not exist, where T is a positive integer greater than 1;
a fourth determining submodule, configured to determine a ratio of a first target street lamp state quantity to a second target street lamp state quantity of each of the T routes, where the first target street lamp state is the traffic jam state, and the second target street lamp state is the clear traffic state;
and the fifth determining submodule is used for determining the route with the minimum ratio as the second route.
With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.
Based on the same inventive concept, the embodiment of the disclosure also provides an unmanned route adjusting system. Fig. 5 is a schematic view of a street lamp according to an exemplary embodiment of the present disclosure. As shown in fig. 5, an unmanned route adjustment system provided by an embodiment of the present disclosure includes: the street lamp network 51, the memory 52 and the processor 53 are connected in a two-to-two communication manner, and the street lamp network 51, the memory 52 and the processor 53 are connected in a two-to-two communication manner.
The network of street lamps 51 comprises a plurality of street lamps, each street lamp of the network of street lamps comprising: the street lamp comprises a light source, a heat dissipation assembly and a street lamp processing module, wherein the heat dissipation assembly comprises heat-conducting silicone grease, the street lamp processing module and the light source are arranged above the heat dissipation assembly, and the street lamp processing module is used for determining the street lamp state of each street lamp;
the memory 52 is used to store computer program instructions that, when executed by the processor 53, implement the unmanned route adjustment method provided by the present disclosure.
Because the processing module and the light source are arranged above the heat dissipation assembly, the heat dissipation assembly can better dissipate heat of the assemblies, the condition that the street lamp cannot determine the state of the street lamp due to the fact that the temperature of the assemblies is too high due to poor heat dissipation is guaranteed as far as possible, and the normal work of the street lamp is further guaranteed.
Optionally, the heat-conducting silicone grease is prepared from a specific composition, the specific composition comprises 10-60 parts by weight of silicone oil, 50-150 parts by weight of a first filler, 50-150 parts by weight of a second filler and optionally an auxiliary agent, based on 100 parts by weight of the silicone oil; the first filler comprises a metal heat conductor and a phase-change material, and the weight ratio of the metal heat conductor to the phase-change material is 1: (0.2 to 2.5); the second filler comprises carbon nanotubes and graphene, and the weight ratio of the carbon nanotubes to the graphene is 1: (1-20).
Preferably, the content of the first filler is 20 to 40 parts by weight, the content of the second filler is 80 to 120 parts by weight, and the content of the auxiliary agent is 0 to 10 parts by weight, based on 100 parts by weight of the silicone oil;
further preferably, R is 6.5 to 35.5 as calculated by the following formula:
r ═ 0.656w (second filler) -1.581w (first filler) +0.11w (adjuvant),
wherein w (first filler) represents parts by weight of the first filler relative to 100 parts by weight of the silicone oil,
w (second filler) represents parts by weight of the second filler relative to 100 parts by weight of the silicone oil,
w (adjuvant) represents the parts by weight of adjuvant with respect to 100 parts by weight of silicone oil.
The heat-conducting silicone grease composition adopts the metal heat conductor and the phase-change material as the first filler, and compared with the traditional heat-conducting silicone grease which only adopts the metal heat conductor as the filler, the heat-conducting silicone grease composition can effectively improve the absorption rate of heat of a heat source and has the effects of quickly absorbing heat and transferring heat; meanwhile, the carbon nano tube and the graphene are used as second fillers, so that the heat conductivity coefficient is greatly improved, the compatibility with silicone oil is facilitated, and the quality and the performance of the specific composition are further improved.
The heat conduction silicone grease prepared from the composition can effectively improve the heat conduction and heat dissipation efficiency of the heat dissipation assembly. Because the radiating efficiency is improved, a good radiating effect can be realized by adopting a radiating assembly with smaller volume, so that more space can be saved, a light source, a processing module and other assemblies can be conveniently placed, and the whole volume of the street lamp is reduced. Especially when carrying out intelligent transformation to current street lamp, the street lamp of less volume can be installed in current old street lamp body, and need not to change whole street lamp holders, and the transformation cost is lower, efficiency is higher.
Based on the same inventive concept, the disclosed embodiments also provide a computer-readable storage medium, on which computer program instructions are stored, which when executed by a processor implement the steps of the unmanned route adjustment method provided by the disclosed embodiments. For example, the computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
The preferred embodiments of the present disclosure are described in detail with reference to the accompanying drawings, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all belong to the protection scope of the present disclosure.
It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.
In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.
Claims (10)
1. An unmanned route adjustment method, characterized in that the method comprises:
acquiring a first route currently driven by the unmanned vehicle;
determining N street lamp states of N street lamps positioned in front of the vehicle on the first street lamp, wherein N is a positive integer greater than 1;
when the N street lamp states meet a preset condition, planning a second route for the vehicle to run;
and adjusting the running route of the vehicle from the first route to the second route.
2. The unmanned route adjustment method of claim 1, wherein the street lamp status is one of a clear traffic status, a congested traffic status, and a restricted traffic status.
3. The unmanned route adjustment method of claim 2, wherein when the N street lamp states satisfy a preset condition, planning a second route for the vehicle to travel comprises:
and when the traffic restriction state exists in the N street lamp states, planning a second route for the vehicle to run.
4. The unmanned route adjustment method of claim 3, wherein when the traffic restriction state does not exist in the N street lamp states, and when the N street lamp states satisfy a preset condition, planning a second route on which the vehicle travels comprises:
determining a number of the traffic congestion states among the N street light states;
and when the ratio of the number of the traffic jam states to N meets a preset range, planning a second route of the vehicle.
5. The unmanned route adjustment method of claim 2, wherein the planning the second route traveled by the vehicle comprises:
determining M routes from the current position to a destination of the vehicle, wherein M is a positive integer larger than 1;
and determining the second route in the M routes according to the street lamp state on each route in the M routes.
6. The unmanned route adjustment method according to claim 5, wherein the determining the second route among the M routes according to the street lamp state on each of the M routes includes:
determining T routes with the street lamp state being the traffic restriction state in the M routes, wherein T is a positive integer greater than 1;
determining a ratio of a first target street lamp state quantity to a second target street lamp state quantity of each of the T routes, wherein the first target street lamp state is the traffic jam state, and the second target street lamp state is the smooth traffic state;
and determining the route with the minimum ratio as the second route.
7. An unmanned route adjustment device, characterized in that the device comprises:
the acquisition module is used for acquiring a first route currently driven by the unmanned vehicle;
the processing module is used for determining N street lamp states of N street lamps positioned in front of the vehicle on the first street lamp, wherein N is a positive integer greater than 1;
the planning module is used for planning a second route for the vehicle to run when the states of the N street lamps meet a preset condition;
and the adjusting module is used for adjusting the running route of the vehicle from the first route to the second route.
8. An unmanned route adjustment system, the system comprising:
the street lamp comprises a street lamp network, a memory and a processor, wherein the street lamp network, the memory and the processor are in communication connection;
the network of street lamps comprises a plurality of street lamps, each street lamp in the network of street lamps comprising: the street lamp comprises a light source, a heat dissipation assembly and a street lamp processing module, wherein the heat dissipation assembly comprises heat-conducting silicone grease, the street lamp processing module and the light source are arranged above the heat dissipation assembly, and the street lamp processing module is used for determining the street lamp state of each street lamp;
the memory for storing computer program instructions which, when executed by the processor, implement the method of any one of claims 1-6.
9. The unmanned route adjustment system according to claim 8, wherein the heat conductive silicone grease is prepared from a specific composition comprising silicone oil, a first filler, a second filler and optionally an auxiliary agent, wherein based on 100 parts by weight of the silicone oil, the first filler is contained in an amount of 10 to 60 parts by weight, the second filler is contained in an amount of 50 to 150 parts by weight, and the auxiliary agent is contained in an amount of 0 to 20 parts by weight; the first filler comprises a metal heat conductor and a phase-change material, and the weight ratio of the metal heat conductor to the phase-change material is 1: (0.2 to 2.5); the second filler comprises carbon nanotubes and graphene, and the weight ratio of the carbon nanotubes to the graphene is 1: (1-20).
10. A computer-readable storage medium, on which computer program instructions are stored, which program instructions, when executed by a processor, carry out the steps of the method according to any one of claims 1 to 6.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810737472.3A CN110689748A (en) | 2018-07-06 | 2018-07-06 | Unmanned route adjusting method, device and system and storage medium |
| PCT/CN2018/121820 WO2020006991A1 (en) | 2018-07-06 | 2018-12-18 | Road lamp, driverless route adjustment method, apparatus, and system, and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810737472.3A CN110689748A (en) | 2018-07-06 | 2018-07-06 | Unmanned route adjusting method, device and system and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110689748A true CN110689748A (en) | 2020-01-14 |
Family
ID=69107391
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810737472.3A Pending CN110689748A (en) | 2018-07-06 | 2018-07-06 | Unmanned route adjusting method, device and system and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110689748A (en) |
Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102634212A (en) * | 2012-04-23 | 2012-08-15 | 钱新明 | Heat conductive silicone grease composition |
| CN103854471A (en) * | 2012-11-30 | 2014-06-11 | 北京掌城科技有限公司 | Traffic information issuing method |
| CN204014185U (en) * | 2014-06-25 | 2014-12-10 | 万源市海铝科技有限公司 | A kind of Internet of Things intelligent road lamp system |
| CN104282167A (en) * | 2013-07-12 | 2015-01-14 | 观致汽车有限公司 | Vehicle traffic state display and warning system and multi-media control device based on GPS |
| CN105258707A (en) * | 2015-11-24 | 2016-01-20 | 上海斐讯数据通信技术有限公司 | Navigation optimization method and system, and electronic equipment |
| CN105674223A (en) * | 2016-01-25 | 2016-06-15 | 郑州天阳新能源科技有限公司 | LED street lamp system having environment and road condition monitoring functions |
| CN105788330A (en) * | 2016-04-29 | 2016-07-20 | 百度在线网络技术（北京）有限公司 | Real-time road-condition early warning method of unmanned vehicle and apparatus thereof |
| CN105825684A (en) * | 2016-05-24 | 2016-08-03 | 佛山市中德光电特种照明有限公司 | Smart city cloud street lamp system |
| CN106767871A (en) * | 2016-12-27 | 2017-05-31 | 上海斐讯数据通信技术有限公司 | A kind of navigation system and its application method of the preset mode based on high in the clouds |
| CN106781490A (en) * | 2016-12-29 | 2017-05-31 | 广州交通信息化建设投资营运有限公司 | Urban highway traffic analysis ＆ appraisement on operation system |
| CN106935053A (en) * | 2015-12-29 | 2017-07-07 | 天津华方科技有限公司 | Cloud computing platform optimizes the Internet of Things self-driving automotive fittings of traffic route |
| CN106939988A (en) * | 2017-05-10 | 2017-07-11 | 湖州明朔光电科技有限公司 | Road lighting light source module, light fixture and illuminator lamp |
| CN107045790A (en) * | 2016-12-30 | 2017-08-15 | 浙江海洋大学 | A kind of vehicle flowrate intelligent monitoring management method |
| CN107099146A (en) * | 2017-06-05 | 2017-08-29 | 合肥展游软件开发有限公司 | A kind of CPU high-efficiency heat conduction materials and preparation method thereof |
| CN206522699U (en) * | 2017-01-06 | 2017-09-26 | 成都聚立汇信科技有限公司 | Fault detect and wireless transmission circuit structure for street lamp |
| CN107202586A (en) * | 2016-03-16 | 2017-09-26 | 高德信息技术有限公司 | A kind of air navigation aid and device |
| CN107396518A (en) * | 2017-08-16 | 2017-11-24 | 芜湖晶鑫光电照明有限公司 | A kind of road lamp wireless intelligent monitor system |
| CN107967812A (en) * | 2016-10-19 | 2018-04-27 | 高德信息技术有限公司 | A kind of definite method and device of navigation way traffic behavior |
| CN207279475U (en) * | 2017-08-01 | 2018-04-27 | 雷宗平 | A kind of wisdom street lamp |
| CN207334567U (en) * | 2017-08-14 | 2018-05-08 | 官久见 | A kind of multi-functional municipal road lamp lighting device |
| CN108109415A (en) * | 2017-12-20 | 2018-06-01 | 东风汽车集团有限公司 | A kind of net connection automatic driving control system and method with anti-congestion |
| CN108182817A (en) * | 2018-01-11 | 2018-06-19 | 北京图森未来科技有限公司 | Automatic Pilot auxiliary system, trackside end auxiliary system and vehicle-mounted end auxiliary system |
-
2018
- 2018-07-06 CN CN201810737472.3A patent/CN110689748A/en active Pending
Patent Citations (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102634212A (en) * | 2012-04-23 | 2012-08-15 | 钱新明 | Heat conductive silicone grease composition |
| CN103854471A (en) * | 2012-11-30 | 2014-06-11 | 北京掌城科技有限公司 | Traffic information issuing method |
| CN104282167A (en) * | 2013-07-12 | 2015-01-14 | 观致汽车有限公司 | Vehicle traffic state display and warning system and multi-media control device based on GPS |
| CN204014185U (en) * | 2014-06-25 | 2014-12-10 | 万源市海铝科技有限公司 | A kind of Internet of Things intelligent road lamp system |
| CN105258707A (en) * | 2015-11-24 | 2016-01-20 | 上海斐讯数据通信技术有限公司 | Navigation optimization method and system, and electronic equipment |
| CN106935053A (en) * | 2015-12-29 | 2017-07-07 | 天津华方科技有限公司 | Cloud computing platform optimizes the Internet of Things self-driving automotive fittings of traffic route |
| CN105674223A (en) * | 2016-01-25 | 2016-06-15 | 郑州天阳新能源科技有限公司 | LED street lamp system having environment and road condition monitoring functions |
| CN107202586A (en) * | 2016-03-16 | 2017-09-26 | 高德信息技术有限公司 | A kind of air navigation aid and device |
| CN105788330A (en) * | 2016-04-29 | 2016-07-20 | 百度在线网络技术（北京）有限公司 | Real-time road-condition early warning method of unmanned vehicle and apparatus thereof |
| CN105825684A (en) * | 2016-05-24 | 2016-08-03 | 佛山市中德光电特种照明有限公司 | Smart city cloud street lamp system |
| CN107967812A (en) * | 2016-10-19 | 2018-04-27 | 高德信息技术有限公司 | A kind of definite method and device of navigation way traffic behavior |
| CN106767871A (en) * | 2016-12-27 | 2017-05-31 | 上海斐讯数据通信技术有限公司 | A kind of navigation system and its application method of the preset mode based on high in the clouds |
| CN106781490A (en) * | 2016-12-29 | 2017-05-31 | 广州交通信息化建设投资营运有限公司 | Urban highway traffic analysis ＆ appraisement on operation system |
| CN107045790A (en) * | 2016-12-30 | 2017-08-15 | 浙江海洋大学 | A kind of vehicle flowrate intelligent monitoring management method |
| CN206522699U (en) * | 2017-01-06 | 2017-09-26 | 成都聚立汇信科技有限公司 | Fault detect and wireless transmission circuit structure for street lamp |
| CN106939988A (en) * | 2017-05-10 | 2017-07-11 | 湖州明朔光电科技有限公司 | Road lighting light source module, light fixture and illuminator lamp |
| CN107099146A (en) * | 2017-06-05 | 2017-08-29 | 合肥展游软件开发有限公司 | A kind of CPU high-efficiency heat conduction materials and preparation method thereof |
| CN207279475U (en) * | 2017-08-01 | 2018-04-27 | 雷宗平 | A kind of wisdom street lamp |
| CN207334567U (en) * | 2017-08-14 | 2018-05-08 | 官久见 | A kind of multi-functional municipal road lamp lighting device |
| CN107396518A (en) * | 2017-08-16 | 2017-11-24 | 芜湖晶鑫光电照明有限公司 | A kind of road lamp wireless intelligent monitor system |
| CN108109415A (en) * | 2017-12-20 | 2018-06-01 | 东风汽车集团有限公司 | A kind of net connection automatic driving control system and method with anti-congestion |
| CN108182817A (en) * | 2018-01-11 | 2018-06-19 | 北京图森未来科技有限公司 | Automatic Pilot auxiliary system, trackside end auxiliary system and vehicle-mounted end auxiliary system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11796332B2 (en) | Generation of optimal trajectories for navigation of vehicles | |
| US20200042019A1 (en) | Management of multiple autonomous vehicles | |
| US10229600B2 (en) | Navigation system with traffic flow mechanism and method of operation thereof | |
| US20190250639A1 (en) | Vehicle platoon system control for intersections | |
| US20230113298A1 (en) | Autonomous vehicle fleet management for reduced traffic congestion | |
| US11940234B2 (en) | Sensor housing | |
| US9146121B2 (en) | Navigation system with obstacle accommodating emergency route planning mechanism and method of operation thereof | |
| JP5796740B2 (en) | Traffic information notification system, traffic information notification program, and traffic information notification method | |
| US11449705B2 (en) | Field theory based perception for autonomous vehicles | |
| CN102243811B (en) | Vehicular navigation system and recommendation paths search method | |
| US11885893B2 (en) | Localization based on predefined features of the environment | |
| US20120209517A1 (en) | Navigation system with accessory control mechanism and method of operation thereof | |
| US11803184B2 (en) | Methods for generating maps using hyper-graph data structures | |
| BR102013008342A2 (en) | method of performing a road network search and system for estimating a vehicle's cruising speed range | |
| CN110750103B (en) | Unmanned aerial vehicle navigation method and device, street lamp and readable storage medium | |
| US11898859B2 (en) | System and method for updating map data | |
| CN102252687A (en) | Method and device for acquiring fastest running path of vehicle | |
| US9726509B1 (en) | Profile aware navigation | |
| EP4002322A1 (en) | System and method for determining dynamic road capacity data for traffic condition | |
| CN110689748A (en) | Unmanned route adjusting method, device and system and storage medium | |
| US20220340139A1 (en) | Vehicle Route Modification to Improve Vehicle Location Information | |
| CN108615385B (en) | Road condition assessment method and device, vehicle and cloud server | |
| US20220185272A1 (en) | Lane connectors as a constraint | |
| CN115752499A (en) | Path planning method and device, terminal equipment and vehicle | |
| US20220207996A1 (en) | Method, apparatus, and system for real-time traffic based location referencing with offsets for road incident reporting |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20200714 Address after: 050035 No. 9, the Yellow River Avenue, hi tech Zone, Hebei, Shijiazhuang Applicant after: DONGXU OPTOELECTRONIC TECHNOLOGY Co.,Ltd. Address before: The 100070 Beijing Seahawks Fengtai District Science City Road No. 9 Building No. 2 room 266 (Park) Applicant before: TUNGHSU TECHNOLOGY GROUP Co.,Ltd. Applicant before: DONGXU OPTOELECTRONIC TECHNOLOGY Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200114 |
|
| RJ01 | Rejection of invention patent application after publication |