CN110992688A - Intelligent traffic guidance system - Google Patents
Intelligent traffic guidance system Download PDFInfo
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- CN110992688A CN110992688A CN201911172558.7A CN201911172558A CN110992688A CN 110992688 A CN110992688 A CN 110992688A CN 201911172558 A CN201911172558 A CN 201911172558A CN 110992688 A CN110992688 A CN 110992688A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
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- G—PHYSICS
- 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
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
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Abstract
The application discloses traffic intelligence inducible system mainly includes: a plurality of inducement cards, each inducement card comprising: the system comprises a processing module, a meteorological monitoring module, a traffic flow monitoring module and a prompting module; the processing module is combined with weather information and vehicle information acquired by the weather monitoring module and the traffic flow monitoring module in real time, and based on a road condition prediction model determined by a machine learning algorithm, predicts the road condition of the road section where the guidance board is located, analyzes the prompt information including non-mandatory prompt information, variable speed limit information executed by mandatory vehicles and forbidden lane change information according to the road condition, and carries out traffic prompt on the vehicles passing through the road section through the prompt module. Therefore, the condition of the road is accurately predicted by a machine learning algorithm without artificially setting a speed limit value, the prompt information is accurately analyzed, the vehicle is effectively induced to run safely, and the safety of the road section is improved.
Description
Technical Field
The application relates to the field of intelligent traffic control, in particular to an intelligent traffic guidance system.
Background
With the accelerated development of modern society, road traffic networks such as expressway networks, national road network and the like are continuously extended and expanded, road passing conditions are increasingly improved, and vehicles can smoothly pass through the road networks. Due to different conditions, each passing road section in the road network needs to have proper speed limit to ensure the safety of vehicle passing. The speed limit guidance board becomes a basic traffic sign which must be arranged at the key position of the expressway and the national highway and is used for carrying out road condition prompt on speed limit values, road condition information and the like.
However, the existing speed limit guidance boards generally only prompt road conditions of passing vehicles on the road according to the speed limit value decided by the traffic command center, and although the weather conditions are also considered in the road condition prompt of some speed limit guidance boards, the current road condition analysis is not accurate, so that the road condition prompt has deviation.
Disclosure of Invention
The embodiment of the application provides an intelligent traffic guidance system to solve the problem that in the prior art, road condition prompts are inaccurate and have deviation.
In order to solve the technical problem, the embodiment of the application adopts the following technical scheme:
a traffic intelligence inducement system, comprising: the system comprises a plurality of guide cards, a plurality of display cards and a plurality of control modules, wherein the guide cards are respectively arranged on different road sections at preset road distances, and communication connection is established among the guide cards; wherein the inducement card further comprises: the system comprises a processing module, a meteorological monitoring module, a traffic flow monitoring module and a prompting module;
the weather monitoring module is used for monitoring and acquiring local weather information in real time and sending the local weather information to the processing module;
the traffic flow monitoring module is used for monitoring and acquiring vehicle information of each lane in a road section where the guidance board is located in real time and sending the vehicle information to the processing module;
the processing module is used for training based on a preset road condition model according to sample data screened from the historical data to obtain a road condition prediction model; inputting the received meteorological information and the received vehicle information into the road condition preset model, and determining the road condition corresponding to the road section where the guidance board is located; obtaining local prompt information based on the road condition analysis, and sending the local prompt information to the prompt module to prompt the traffic of the vehicles passing through the road section;
wherein the sample data comprises historical weather information and historical vehicle information; the local prompt message at least comprises: non-mandatory prompt information, mandatory variable speed limit information and forbidden lane change information executed by the vehicle.
Optionally, after determining the road condition of the road segment corresponding to the guidance board, the processing module is further configured to analyze other guidance boards of multiple rear road segments based on the road condition to obtain different remote prompting information, and send the different remote prompting information to the prompting module of the corresponding guidance board to perform different traffic prompts;
wherein, the remote prompt message at least comprises: non-mandatory prompt information, mandatory variable speed limit information and forbidden lane change information executed by the vehicle.
Optionally, the variable speed limit information in the different place prompt information is distributed progressively based on the distance between the other guidance cards belonging to the different place prompt information and the guidance cards.
Optionally, the processing module is further configured to identify an abnormally-driving vehicle on the road section where the guidance board is located based on the road condition, and send an early warning message to other guidance boards on the road sections where the front and rear vehicles are located, so as to perform early warning prompt on the front and rear vehicles.
Optionally, when the other guidance boards give an early warning prompt to the front and rear vehicles, a local prompting module is used for giving a text and/or light flashing prompt.
Optionally, the traffic intelligence guidance system further includes: a central control server;
the central control server is used for receiving the weather information and the vehicle information acquired by the guidance board in real time, inputting the weather information and the vehicle information into an accident preset model to obtain the current accident probability of the road section where the guidance board is located, and sending the accident probability to a prompt module of the guidance board for accident prompt;
the accident preset model is obtained by training the central control server based on historical meteorological information and historical vehicle information of a plurality of induction boards in the system.
Optionally, the traffic intelligence guidance system further includes: configuring a connected emergency lane occupation detection device for each guidance board;
the emergency lane occupation detection device is used for detecting the emergency lane in the road section where the induction board is located, and sending an alarm message to the induction board when the emergency lane is occupied, so that the occupied road vehicle is warned, and meanwhile, sending an early warning message to other induction boards on the road section where the rear vehicle is located, so that the rear vehicle is warned.
Optionally, the emergency lane occupancy detection device is a laser ranging sensor or an infrared vision sensor.
Optionally, the prompting module at least comprises a plurality of display screens, wherein each display screen is correspondingly arranged right above different lanes; and the display screen is used for displaying the variable speed limit value of the current lane in the road section where the guidance board is located and whether lane changing is forbidden.
Optionally, the non-mandatory prompt message includes: road condition information and road condition prompt information.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
according to the technical scheme, the processing module of the induction board is combined with the real-time acquired meteorological information and vehicle information, the road condition of the road section where the induction board is located is predicted based on a road condition prediction model determined by a machine learning algorithm, the prompting information including non-mandatory prompting information, variable speed limiting information executed by mandatory vehicles and forbidden lane changing information is analyzed according to the road condition, and the traffic prompting is carried out on the vehicles passing through the road section through the prompting module. Therefore, the condition of the road is accurately predicted by a machine learning algorithm without artificially setting a speed limit value, the prompt information is accurately analyzed, the vehicle is effectively induced to run safely, and the safety of the road section is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a schematic structural diagram of a traffic guidance system according to an embodiment of the present disclosure;
fig. 2 is a schematic view of a scene of a traffic guidance system provided in an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of an inducement board in a road scene according to an embodiment of the present disclosure;
fig. 4 is a second schematic structural diagram of a traffic intelligence guidance system provided in the embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following specific embodiments of the present application and the accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, 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 application.
The technical solutions provided by the embodiments of the present application are described in detail below with reference to the accompanying drawings.
Referring to fig. 1, a schematic structural diagram of a traffic intelligence guidance system provided in an embodiment of the present disclosure is shown, where the traffic intelligence guidance system mainly includes: a plurality of induction cards 1; wherein, each of the inducement cards 1 may further include: the system comprises support rods, a processing module 11, a meteorological monitoring module 12, a traffic flow monitoring module 13, a prompt module 14 and a power supply module. As further shown in fig. 2, the plurality of guidance boards 1 may be respectively arranged at different road sections at a preset distance, for example, one guidance board 1 is arranged at every 50 meters, the road condition in the road section 50 meters ahead of the position where the guidance board 1 is located may be analyzed at every guidance board 1, and communication connections are established between the plurality of guidance boards 1, so that the guidance boards 1 may interact and control each other. The preset road distance of 50 meters is only used as an example, and the road distance of adjacent guidance boards in this specification is not limited to 50 meters, and may be 1 kilometer, 500 meters, or other.
The weather monitoring module 12 is configured to monitor and collect local weather information in real time, and send the local weather information to the processing module 11; meanwhile, the traffic flow monitoring module 13 is configured to monitor and collect vehicle information in a road segment where the guidance board 1 is located in real time, and send the vehicle information to the processing module 11; it should be understood that the segment on which the inducement card 1 is located may be a segment within 50 meters ahead of the location on which the inducement card is located.
The processing module 11 is configured to obtain a road condition prediction model based on preset road condition model training according to sample data screened from the historical data; inputting the received meteorological information and the received vehicle information into the road condition preset model, and determining the road condition corresponding to the road section where the guidance board 1 is located; local prompt information is obtained based on the road condition analysis and is sent to the prompt module 14 to prompt the vehicles passing through the road section for traffic. The local prompt message at least comprises: non-mandatory prompt information, mandatory variable speed limit information and forbidden lane change information executed by the vehicle.
It should be understood that the historical data may be all data of the inducement board 1 before the current time, including historical weather information and historical vehicle information; wherein, the historical weather information can be obtained from the weather monitoring module 12 or the processing module 11, and the historical vehicle information can be obtained from the traffic flow monitoring module 13 or the processing module 11. The historical weather information and the historical vehicle information included in the sample data can be specifically weather features which are extracted from the historical weather information and represent the historical weather conditions, and vehicle features which are extracted from the historical vehicle information and represent the historical vehicle conditions. During specific training, the preset road condition module may be a model based on a clustering algorithm or other machine learning algorithms, which is not limited in this specification. The road condition prediction model may be trained in advance by the processing module 11 of the guidance board 1, and stored in the storage space of the processing module 11 for later use. Then, the road condition of the current road section can be predicted in real time based on the weather information and the vehicle information acquired in real time, and the local prompt information for prompting the backward vehicle is analyzed based on the prediction result.
In this embodiment, the processing module 11 may not only analyze non-forced prompt information such as a front road condition, but also analyze a variable speed limit value of each lane in a current road section and prompt information for forcing a vehicle to execute, such as whether to prohibit changing lanes, according to the road condition.
According to the technical scheme, the processing module of the induction board is combined with the real-time acquired meteorological information and vehicle information, the road condition of the road section where the induction board is located is predicted based on a road condition prediction model determined by a machine learning algorithm, the prompting information including non-mandatory prompting information, variable speed limiting information executed by mandatory vehicles and forbidden lane changing information is analyzed according to the road condition, and the traffic prompting is carried out on the vehicles passing through the road section through the prompting module. Therefore, the condition of the road is accurately predicted by a machine learning algorithm without artificially setting a speed limit value, the prompt information is accurately analyzed, the vehicle is effectively induced to run safely, and the safety of the road section is improved.
In the embodiment of the present specification, referring to fig. 3, a prompting module of an induction board at least includes a plurality of display screens, wherein each display screen is correspondingly disposed right above a different lane; and the display screen is used for displaying the variable speed limit value of the current lane in the road section where the guidance board is located and whether lane changing is forbidden. For example, the "speed limit 60, lane change" is indicated on the display screen corresponding to lane 1; the speed limit 90 and the lane change are indicated on a display screen corresponding to the 2 lanes; and a display screen corresponding to the 3 lanes is used for displaying the speed limit 120, and lane change is forbidden within 2 kilometers ahead.
In addition, non-mandatory prompt information including road condition information and road condition prompt information can be displayed on a display screen corresponding to each lane. For example, a message "enter iced road segment 1 km ahead, please crawl! Or, the '2 km ahead entering into a rainstorm area' is indicated on a display screen corresponding to the lane. And the road condition information and the road section in the prompt information can be obtained according to the predicted road condition analysis. It should be understood that the present specification is not limited to these optional prompt messages, and other messages that can be used as prompts or warnings can be displayed on the display screen directly above the corresponding lane as optional prompt messages.
Therefore, the rear driving vehicle can be prompted to pay attention to the front road condition conveniently, information for forcing the vehicle to execute is prompted, the driving vehicle is prevented from driving illegally, and the safety of the road section is improved.
Optionally, after determining the road condition corresponding to the road segment where the guidance board is located, the processing module 11 is further configured to analyze other guidance boards 1 of multiple rear road segments based on the road condition to obtain different place prompting information, and send the different place prompting information to the prompting module 14 of the corresponding guidance board 1 to perform different traffic prompting; wherein, the remote prompt message at least comprises: non-mandatory prompt information, mandatory variable speed limit information and forbidden lane change information executed by the vehicle.
It should be understood that the variable speed limit information in the different place prompt information is distributed progressively based on the distance between the other induction cards belonging to the different place prompt information and the induction card. Still referring to fig. 2, after the guidance board a determines the road condition of the local road section, the display screen of the guidance board a is displayed to indicate "speed limit 60 and lane change is prohibited for 1 km ahead", and the display screen of the guidance board b is displayed to indicate "speed limit 100 and lane change is prohibited for 2 km ahead", and the display screen of the guidance board b is displayed to indicate "speed limit 120 and lane change is prohibited for 3 km ahead". Wherein, the units behind the numbers in the three groups of the speed limit 60, the speed limit 100 and the speed limit 120 are kilometers per hour. Therefore, through the transmission of the progressive information, the road condition of the road section where the guiding board a is located is issued to the guiding board at the rear part, so that the rear guiding board can prompt the locally driven vehicles for different speed limits and lane change information, the remote prompt is flexibly and effectively realized, the safe driving of the vehicles is effectively induced, and the road section safety is further improved.
In consideration of the safety hazard caused by illegal driving of other vehicles, a driver mainly focuses on information such as the driving speed and the lane of the vehicle and ignores the information during driving of the vehicle. Therefore, the processing module of the guidance board in this specification is further configured to further identify an abnormally-driving vehicle in the road section where the guidance board is located based on the road condition predicted by the road condition prediction model, and when it is identified that an overspeed vehicle is located in the current road section, an early warning message may be sent to the guidance board in the road section where the front vehicle is located, and specifically, the early warning message may be displayed on a display screen of the guidance board in the road section where the front vehicle is located, for example, "an overspeed vehicle is located 500 meters behind, please pay attention to avoid |! "; when the current road section is identified to have the low-speed vehicle, the early warning message can be sent to the guidance board of the road section where the rear vehicle is located, and the early warning message can be specifically displayed in the display screen of the guidance board of the road section where the rear vehicle is located, for example, "there is a low-speed vehicle 200 meters ahead, please avoid rear-end collision! ". Wherein, the speed of the overspeed vehicle can be more than 120 km/h, and the speed of the low-speed vehicle can be less than 80 km/h. When the ultra-low vehicle speed is identified, the display screen and the warning lamp can be adopted to be matched with the flash mode for early warning. Therefore, the condition of the abnormal running vehicle is fed back to the front and rear vehicles through the induction boards, danger early warning is achieved, a vehicle driver can conveniently cope with the danger early warning in time, and the safety of a road section is improved.
Furthermore, the emergency processing capacity of the driver of the vehicle on the road condition is different, so that the probability of possible accidents on the road section can be accurately calculated in a machine learning mode, and the probability is prompted to the vehicle in an early warning mode. Specifically, still referring to fig. 1, the traffic intelligence guidance system further includes: a central control server 2; the central control server 2 is used for receiving the weather information and the vehicle information acquired by the guidance board 1 in real time, inputting the weather information and the vehicle information into an accident prediction model to obtain the current accident probability of the road section where the guidance board 1 is located, and sending the accident probability to the prompt module 14 of the guidance board 1 for accident prompt; the accident prediction model is obtained by training the central control server 2 based on historical meteorological information and historical vehicle information of a plurality of induction boards 1 in the system, and can be used for determining the accident occurrence probability of various road sections (turning road sections, bridge road sections, high-speed entrance/exit road sections, tunnel road sections and the like) in various climates (rain, snow, strong wind, fog and the like). Therefore, through early warning of the accident-prone road section, sufficient emergency processing time can be provided for a vehicle driver, so that the driving state of the vehicle can be adjusted in time, for example, the vehicle is decelerated and driven slowly, detour driving is achieved, the accident is reduced, and the safety of the road section is improved.
In the embodiment of the present specification, referring to fig. 4, the traffic intelligence guidance system further includes: an emergency lane occupation detection device 3 is configured and connected with each induction board 1; the emergency lane occupation detection device 3 is used for detecting the emergency lane in the road section where the induction board 1 is located, and when the emergency lane is detected to be occupied, sending an alarm message to the induction board 1 so as to give an alarm to the occupied vehicle, and simultaneously sending an early warning message to other induction boards 1 on the road section where the rear vehicle is located so as to give an early warning to the rear vehicle. For example, when the emergency lane in the road section where the guidance board is located is occupied by the vehicle a, the guidance board can give an alarm to the vehicle occupying the front lane in a voice broadcast manner, and the voice broadcast content may be "jing Axxx, please leave the emergency lane! The number of times of broadcasting can be repeated for a plurality of times to increase the warning effect. Meanwhile, the guidance board can also give an early warning prompt to the rear vehicle through the display screen, and the display content can be that the emergency lane 500 meters ahead is occupied, please notice! ". In fact, the guidance board can also send early warning information to the rear guidance board so that other guidance boards in the rear can give early warning prompts to the vehicle behind the guidance board through the display screen of the other guidance boards in the rear, and the display content can enable the emergency lane 1 kilometer in the front to be occupied and please notice! ".
Optionally, the emergency lane occupation detection device in this specification may be a laser ranging sensor, or an infrared vision sensor. The emergency lane detection device is preferably realized by an infrared vision sensor, and can detect whether vehicles exist in the emergency lane or whether the vehicles run in the emergency lane under the environment with low visibility, so that the emergency lane detection device is convenient to realize and has a good detection effect. Preferably, an LRB5000 remote laser rangefinder may be employed. The emergency lane occupation detection device can also adopt a radar device to enhance the detection under the condition of ultra-low visibility. It should be noted that the emergency lane occupation detection device is preferably only used for detecting whether the emergency lane is occupied within a certain distance ahead, and the license plate number does not need to be known. In a more preferable embodiment, the emergency lane occupation detection device can detect the speed of the vehicle in the emergency lane and give more accurate and comprehensive information to the traffic guidance system so as to improve the guidance efficiency.
It should be noted that the guidance board related in this description further includes other existing hardware devices or software modules, for example, a flashing warning light, a solar cell panel, and the like, which are not described herein again.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. An intelligent traffic guidance system, comprising: the system comprises a plurality of induction cards, a plurality of control boards and a plurality of display boards, wherein the induction cards are respectively arranged on different road sections at preset road distances, and direct or indirect communication connection is established among the induction cards; wherein the inducement card further comprises: the system comprises a processing module, a meteorological monitoring module, a traffic flow monitoring module and a prompting module;
the weather monitoring module is used for monitoring and acquiring local weather information in real time and sending the local weather information to the processing module;
the traffic flow monitoring module is used for monitoring and acquiring vehicle information of each lane in a road section where the guidance board is located in real time and sending the vehicle information to the processing module;
the processing module is used for training based on a preset road condition model according to sample data screened from the historical data to obtain a road condition prediction model; inputting the received meteorological information and the received vehicle information into the road condition preset model, and determining the road condition corresponding to the road section where the guidance board is located; obtaining local prompt information based on the road condition analysis, and sending the local prompt information to the prompt module to prompt the traffic of the vehicles passing through the road section;
wherein the sample data comprises historical weather information and historical vehicle information; the local prompt message at least comprises: non-mandatory prompt information, mandatory variable speed limit information and forbidden lane change information executed by the vehicle.
2. The system of claim 1, wherein the processing module, after determining the road condition corresponding to the road section where the guidance sign is located, is further configured to analyze other guidance signs of a plurality of road sections behind the forward direction based on the road condition to obtain different off-site guidance information, and send the information to the guidance sign-indicating module of the corresponding guidance sign to perform different traffic guidance;
wherein, the remote prompt message at least comprises: non-mandatory prompt information, mandatory variable speed limit information and forbidden lane change information executed by the vehicle.
3. The intelligent traffic guidance system according to claim 2, wherein the variable speed limit information in the different offsite guidance information is distributed progressively based on the distance between the other guidance cards belonging to the offsite guidance information and the guidance card.
4. The system of claim 2, wherein the processing module is further configured to identify an abnormally driving vehicle on the road section where the guidance board is located based on the road condition, and send an early warning message to other guidance boards on the road sections where the front and rear vehicles are located, so as to perform early warning prompt on the front and rear vehicles.
5. The intelligent traffic guidance system according to claim 4, wherein when the other guidance boards give warning to the front and rear vehicles, the other guidance boards give warning by flashing text and/or light through a local warning module.
6. The traffic intelligence inducement system of claim 1, further comprising: a central control server;
the central control server is used for receiving the weather information and the vehicle information acquired by the guidance board in real time, inputting the weather information and the vehicle information into an accident preset model to obtain the current accident probability of the road section where the guidance board is located, and sending the accident probability to a prompt module of the guidance board for accident prompt;
the accident preset model is obtained by training the central control server based on historical meteorological information and historical vehicle information of a plurality of induction boards in the system.
7. The traffic guidance system of any one of claims 1-6, further comprising: configuring a connected emergency lane occupation detection device for each guidance board;
the emergency lane occupation detection device is used for detecting the emergency lane in the road section where the induction board is located, and sending an alarm message to the induction board when the emergency lane is occupied, so that the occupied road vehicle is warned, and meanwhile, sending an early warning message to other induction boards on the road section where the rear vehicle is located, so that the rear vehicle is warned.
8. The intelligent traffic guidance system of claim 7, wherein the emergency lane occupancy detection device is a laser ranging sensor, or an infrared vision sensor.
9. The intelligent traffic guidance system according to any one of claims 1 to 6 and 8, wherein the prompting module at least comprises a plurality of display screens, wherein each display screen is correspondingly arranged right above a different lane; and the display screen is used for displaying the variable speed limit value of the current lane in the road section where the guidance board is located and whether lane changing is forbidden.
10. The traffic guidance system according to any one of claims 1 to 6 and 8, wherein the non-mandatory prompt information includes: road condition information and road condition prompt information.
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| CN201911172558.7A CN110992688A (en) | 2019-11-26 | 2019-11-26 | Intelligent traffic guidance system |
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| CN201911172558.7A CN110992688A (en) | 2019-11-26 | 2019-11-26 | Intelligent traffic guidance system |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991785A (en) * | 2021-05-17 | 2021-06-18 | 武汉中科通达高新技术股份有限公司 | Traffic guiding method and device |
| CN114285440A (en) * | 2021-12-27 | 2022-04-05 | 山东中网众信数字科技有限公司 | Application method of power line carrier communication in intelligent driving safety guidance system |
| CN114582126A (en) * | 2022-03-04 | 2022-06-03 | 深圳市综合交通与市政工程设计研究总院有限公司 | Intelligent management and control method and system suitable for ultra-long tunnel traffic and giving consideration to efficiency safety |
| CN114842656A (en) * | 2021-12-31 | 2022-08-02 | 视缘(上海)交通科技有限公司 | Restriction device with prompt system |
| CN115641736A (en) * | 2022-10-19 | 2023-01-24 | 中交一公局第七工程有限公司 | Intelligent induction system and method for special highway sections |
-
2019
- 2019-11-26 CN CN201911172558.7A patent/CN110992688A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112991785A (en) * | 2021-05-17 | 2021-06-18 | 武汉中科通达高新技术股份有限公司 | Traffic guiding method and device |
| CN114285440A (en) * | 2021-12-27 | 2022-04-05 | 山东中网众信数字科技有限公司 | Application method of power line carrier communication in intelligent driving safety guidance system |
| CN114285440B (en) * | 2021-12-27 | 2024-04-09 | 山东中网众信数字科技有限公司 | Application method of power carrier communication in intelligent driving safety induction system |
| CN114842656A (en) * | 2021-12-31 | 2022-08-02 | 视缘(上海)交通科技有限公司 | Restriction device with prompt system |
| CN114582126A (en) * | 2022-03-04 | 2022-06-03 | 深圳市综合交通与市政工程设计研究总院有限公司 | Intelligent management and control method and system suitable for ultra-long tunnel traffic and giving consideration to efficiency safety |
| CN115641736A (en) * | 2022-10-19 | 2023-01-24 | 中交一公局第七工程有限公司 | Intelligent induction system and method for special highway sections |
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