CN114550478A - ETC-based highway safety automatic driving recommendation method - Google Patents
ETC-based highway safety automatic driving recommendation method Download PDFInfo
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- CN114550478A CN114550478A CN202210183604.9A CN202210183604A CN114550478A CN 114550478 A CN114550478 A CN 114550478A CN 202210183604 A CN202210183604 A CN 202210183604A CN 114550478 A CN114550478 A CN 114550478A
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- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000005516 engineering process Methods 0.000 claims abstract description 17
- 230000002159 abnormal effect Effects 0.000 claims description 21
- 206010039203 Road traffic accident Diseases 0.000 claims description 4
- 238000004873 anchoring Methods 0.000 claims description 3
- 238000012423 maintenance Methods 0.000 claims description 3
- 230000004044 response Effects 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 abstract description 6
- 230000006399 behavior Effects 0.000 description 7
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
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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
- G08G1/0125—Traffic data processing
- G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
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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
-
- 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
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- Chemical & Material Sciences (AREA)
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Abstract
The invention discloses an ETC-based highway safety automatic driving recommendation method, which is characterized in that an accurate position of a vehicle is obtained in real time by utilizing an inertial navigation technology based on ETC data, a deep learning technology is adopted to predict the future vehicle traveling condition, then a database platform is constructed by adopting a block chain technology, the database is updated in real time, and finally whether the vehicle meets the automatic driving condition or not is judged by indexing the vehicle circumference range and combining the database comparison result.
Description
Technical Field
The invention relates to the technical field of road traffic safety, in particular to an ETC-based highway safety automatic driving recommendation method.
Background
With the rapid development of artificial intelligence technology in recent years, the realization of the intellectualization and automatic driving management and control of unmanned vehicles in the expressway range has become the key point of the development of the automobile industry. However, how to judge whether the vehicle enters the automatic driving condition safely or not in the field raises the problem to be solved, affects the passing efficiency of the unmanned vehicle to a certain extent, and is easy to cause traffic accidents.
Disclosure of Invention
The invention aims to provide an ETC-based highway safe automatic driving recommendation method.
The technical scheme adopted by the invention is as follows:
ETC-based highway safety automatic driving recommendation method comprises the following steps:
step 1, a platform is built, a cluster distributed architecture database is built, and road condition information of a road area is updated and stored in real time by using a block chain technology;
step 2, obtaining vehicle passing information by using the ETC portal;
step 3, the vehicle user sends a request for switching the automatic driving mode to the platform through the vehicle-mounted terminal;
step 4, the platform acquires the position of the vehicle, predicts the average vehicle speed and the driving behavior of the vehicle by combining the historical sequence information of the vehicle, and uploads the vehicle speed and the driving behavior to a database in real time;
specifically, based on existing data, we use the ETC portal as a node, and know the passing time and mileage of the vehicle between portals to calculate the average speed. And finally, predicting the average speed of the vehicle according to a deep learning GRU algorithm. The driving behavior refers to the historical observation of the driving condition of the vehicle on the highway, for example, a truck passes the highway 3 times a week, and the passing road sections are the same each time, so that the whole course of movement can be predicted in advance after the truck passes the highway.
Step 5, the platform searches whether abnormal information exists in the vehicle position range in real time based on a set range threshold; if so, notifying the user and switching to a manual driving mode; otherwise, allowing the switching to enter an automatic driving mode;
and 6, the current vehicle user acquires the platform feedback information and executes response operation.
Further, the vehicle passing information comprises vehicle type, entrance time, transaction time, license plate number and portal frame number;
further, the road condition information of the road area at least includes abnormal information affecting the driving of the vehicle.
The abnormal information comprises abnormal road conditions, abnormal maintenance stages, abnormal weather and dangerous vehicles.
Further, in step 4, angular rate and addition degree information of the vehicle are measured through a gyroscope and an accelerometer by using an inertial navigation technology, and position information of the vehicle is obtained through integral operation.
Further, the vehicle history sequence information in step 4 comprises the vehicle type and the speed.
Further, in the step 5, the value of the range threshold m is 0-60 km based on the current vehicle position range.
Further, the abnormal road condition area comprises traffic accidents, parking, anchoring and ground potholes of the front vehicles; the dangerous vehicles comprise two passengers and one passenger which is dangerous and overspeed.
The method adopts the technical scheme that the method is based on ETC data, utilizes an inertial navigation technology to obtain the accurate position of the vehicle in real time, adopts a deep learning technology to predict the trip condition of the vehicle in the future, then adopts a block chain technology to construct a database platform, updates the database in real time, and finally judges whether the vehicle meets the automatic driving condition by indexing the vehicle circumference range and combining the database comparison result.
Drawings
The invention is described in further detail below with reference to the accompanying drawings and the detailed description;
fig. 1 is a schematic flow chart of the safe and automatic highway driving recommendation method based on the ETC.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
As shown in fig. 1, the invention discloses an ETC-based highway safety automatic driving recommendation method, which comprises the following steps:
step 1, a platform is built to construct a cluster distributed architecture database, and the road condition information of a road area is updated and stored in real time by using a block chain technology;
step 2, obtaining vehicle passing information by using the ETC portal;
step 3, the vehicle user sends a request for switching the automatic driving mode to the platform through the vehicle-mounted terminal;
step 4, the platform acquires the position of the vehicle, predicts the average vehicle speed and the driving behavior of the vehicle by combining the historical sequence information of the vehicle, and uploads the vehicle speed and the driving behavior to a database in real time;
specifically, based on existing data, we calculate the average speed by using the ETC gate frames as nodes and knowing the passing time and mileage of the vehicle between the gate frames. And finally, predicting the average speed of the vehicle according to a deep learning GRU algorithm. The driving behavior refers to the historical observation of the driving condition of the vehicle on the highway, for example, a truck passes the highway 3 times a week, and the passing road sections are the same each time, so that the whole course of movement can be predicted in advance after the truck passes the highway. The platform predicts the average speed, driving behavior and other information of future vehicles by combining the historical sequence information of the vehicles through a deep learning technology.
Step 5, the platform searches whether abnormal information exists in the vehicle position range in real time based on a set range threshold; if so, notifying the user and switching to a manual driving mode; otherwise, allowing the switching to enter an automatic driving mode;
and 6, the current vehicle user acquires the platform feedback information and executes response operation.
Further, the vehicle passing information comprises vehicle type, entrance time, transaction time, license plate number and portal frame number;
further, the road condition information of the road area at least includes abnormal information affecting the driving of the vehicle.
The abnormal information comprises abnormal road conditions, abnormal maintenance stages, abnormal weather and dangerous vehicles.
Further, in step 4, angular rate and addition degree information of the vehicle are measured through a gyroscope and an accelerometer by using an inertial navigation technology, and position information of the vehicle is obtained through integral operation.
Further, the vehicle history sequence information in step 4 comprises the vehicle type and the speed.
Further, in the step 5, the value of the range threshold m is 0-60 km based on the current vehicle position range.
Further, the abnormal road condition area comprises traffic accidents, parking, anchoring and ground potholes of the front vehicles; the dangerous vehicles comprise two passengers and one passenger which is dangerous and overspeed.
The method adopts the technical scheme that the method is based on ETC data, utilizes an inertial navigation technology to obtain the accurate position of the vehicle in real time, adopts a deep learning technology to predict the trip condition of the vehicle in the future, then adopts a block chain technology to construct a database platform, updates the database in real time, and finally judges whether the vehicle meets the automatic driving condition by indexing the vehicle circumference range and combining the database comparison result.
It is to be understood that the embodiments described are only a few embodiments of the present application and not all embodiments. The embodiments and features of the embodiments in the present application may be combined with each other without conflict. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations. Thus, the detailed description of the embodiments of the present application is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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.
Claims (8)
1. ETC-based highway safety automatic driving recommendation method is characterized by comprising the following steps of: which comprises the following steps:
step 1, a platform is built, a cluster distributed architecture database is built, and road condition information of a road area is updated and stored in real time by using a block chain technology;
step 2, obtaining vehicle passing information by using the ETC portal;
step 3, the vehicle user sends a request for switching the automatic driving mode to the platform through the vehicle-mounted terminal;
step 4, the platform acquires the position of the vehicle, predicts the average vehicle speed and the driving behavior of the vehicle by combining the historical sequence information of the vehicle, and uploads the vehicle speed and the driving behavior to a database in real time;
step 5, the platform searches whether abnormal information exists in the vehicle position range in real time based on a set range threshold; if so, notifying the user and switching to a manual driving mode; otherwise, allowing the switching to enter an automatic driving mode;
and 6, the current vehicle user acquires the platform feedback information and executes response operation.
2. The ETC-based highway safe automatic driving recommendation method according to claim 1, characterized in that: the road condition information of the road area at least comprises abnormal information influencing the driving of the vehicle.
3. The ETC-based highway safe automatic driving recommendation method according to claim 2, characterized in that: the abnormal information comprises abnormal road conditions, abnormal maintenance stages, abnormal weather and dangerous vehicles.
4. The ETC-based highway safe automatic driving recommendation method according to claim 1, characterized in that: the vehicle passing information in the step 2 comprises vehicle types, entrance time, transaction time, license plate numbers and portal frame numbers.
5. The ETC-based highway safe automatic driving recommendation method according to claim 1, characterized in that: and 4, measuring the angular rate and the addition degree information of the vehicle through a gyroscope and an accelerometer by utilizing an inertial navigation technology, and obtaining the position information of the vehicle through integral operation.
6. The ETC-based highway safe automatic driving recommendation method according to claim 1, characterized by comprising the steps of: and 4, the vehicle historical sequence information comprises the vehicle type and the speed.
7. The ETC-based highway safe automatic driving recommendation method according to claim 1, characterized by comprising the steps of: and 5, taking the value of the range threshold m as 0-60 km based on the current vehicle position range.
8. The ETC-based highway safe automatic driving recommendation method according to claim 1, characterized in that: the abnormal road condition area comprises traffic accidents, parking, anchoring and ground potholes of the front vehicles; the dangerous vehicles comprise two passengers and one passenger which is dangerous and overspeed.
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Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008090348A (en) * | 2006-09-29 | 2008-04-17 | Nissan Diesel Motor Co Ltd | Operation controller for commercial vehicle, and operation control system using the same |
| JP2017157063A (en) * | 2016-03-03 | 2017-09-07 | トヨタ自動車株式会社 | Automatic driving vehicle |
| CN108090603A (en) * | 2016-11-22 | 2018-05-29 | 百度（美国）有限责任公司 | The method and system of vehicle group is managed for automatic driving vehicle |
| US20190041850A1 (en) * | 2017-08-04 | 2019-02-07 | Arnold Chase | Autonomous vehicle mode regulator |
| US20190204830A1 (en) * | 2016-10-12 | 2019-07-04 | Yazaki Corporation | Vehicle system |
| CN110070738A (en) * | 2019-05-27 | 2019-07-30 | 广州小鹏汽车科技有限公司 | Drive function recommended method, device and vehicle |
| CN110264720A (en) * | 2019-06-28 | 2019-09-20 | 腾讯科技（深圳）有限公司 | Driving mode reminding method, device, equipment and storage medium |
| JP2020152288A (en) * | 2019-03-22 | 2020-09-24 | トヨタ自動車株式会社 | Vehicular control apparatus |
| CN112434260A (en) * | 2020-10-21 | 2021-03-02 | 北京千方科技股份有限公司 | Road traffic state detection method and device, storage medium and terminal |
| US20210370983A1 (en) * | 2018-04-20 | 2021-12-02 | Sony Semiconductor Solutions Corporation | Information processing device, mobile device, information processing system, method, and program |
-
2022
- 2022-02-28 CN CN202210183604.9A patent/CN114550478B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008090348A (en) * | 2006-09-29 | 2008-04-17 | Nissan Diesel Motor Co Ltd | Operation controller for commercial vehicle, and operation control system using the same |
| JP2017157063A (en) * | 2016-03-03 | 2017-09-07 | トヨタ自動車株式会社 | Automatic driving vehicle |
| US20190204830A1 (en) * | 2016-10-12 | 2019-07-04 | Yazaki Corporation | Vehicle system |
| CN108090603A (en) * | 2016-11-22 | 2018-05-29 | 百度（美国）有限责任公司 | The method and system of vehicle group is managed for automatic driving vehicle |
| US20190041850A1 (en) * | 2017-08-04 | 2019-02-07 | Arnold Chase | Autonomous vehicle mode regulator |
| US20210370983A1 (en) * | 2018-04-20 | 2021-12-02 | Sony Semiconductor Solutions Corporation | Information processing device, mobile device, information processing system, method, and program |
| JP2020152288A (en) * | 2019-03-22 | 2020-09-24 | トヨタ自動車株式会社 | Vehicular control apparatus |
| CN110070738A (en) * | 2019-05-27 | 2019-07-30 | 广州小鹏汽车科技有限公司 | Drive function recommended method, device and vehicle |
| CN110264720A (en) * | 2019-06-28 | 2019-09-20 | 腾讯科技（深圳）有限公司 | Driving mode reminding method, device, equipment and storage medium |
| CN112434260A (en) * | 2020-10-21 | 2021-03-02 | 北京千方科技股份有限公司 | Road traffic state detection method and device, storage medium and terminal |
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