CN110716558A - Automatic driving system for non-public road based on digital twin technology - Google Patents
Automatic driving system for non-public road based on digital twin technology Download PDFInfo
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- CN110716558A CN110716558A CN201911145647.2A CN201911145647A CN110716558A CN 110716558 A CN110716558 A CN 110716558A CN 201911145647 A CN201911145647 A CN 201911145647A CN 110716558 A CN110716558 A CN 110716558A
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- 238000013507 mapping Methods 0.000 claims description 10
- 230000005540 biological transmission Effects 0.000 claims description 4
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0221—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving a learning process
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0276—Control of position or course in two dimensions specially adapted to land vehicles using signals provided by a source external to the vehicle
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Abstract
The invention discloses an automatic driving system for a non-public road based on a digital twinning technology, which comprises an automatic driving cloud server and an automatic driving vehicle, wherein the automatic driving cloud server and the automatic driving vehicle perform data interaction through a high-speed wireless communication link and transmit vehicle position/posture information and vehicle control information, the automatic driving cloud server is internally provided with a digital twinning vehicle, a map module and a vehicle automatic driving algorithm, and the automatic driving vehicle comprises a vehicle positioning unit, a vehicle motion controller and an obstacle detection module. Meanwhile, a simple and flexible vehicle management and control and remote takeover means is provided.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to an automatic driving system for a non-public road based on a digital twinning technology.
Background
Low-speed vehicles (less than 30 km/h) running on non-public roads are one of the main scenes for landing of current automatic driving technology, and include, but are not limited to, a transfer car, a logistics car used in a park or a transportation hub, an inspection vehicle used in a substation or a power plant, an AGV used in an intelligent plant, and the like.
The automatic driving systems currently used by non-public road automatic driving vehicles can be classified into one of the following two technologies:
1) tracking type
The marks for the automatic driving vehicle to recognize, such as magnetic nails, magnetic stripes, route marks and the like, are arranged on the running road in advance. Autonomous vehicles recognize these signs through sensors mounted on the vehicle (e.g., magnetic induction sensors, vision sensors) and control the vehicle to move along the course designated by the signs. Many AGVs used in a factory are of this type.
The tracking type automatic driving vehicle has the advantages of simple and reliable algorithm of automatic driving and low cost. The disadvantages are also evident:
the mobile terminal can only move along a fixed path, and when the path is planned again, the path mark needs to be rearranged;
needs to arrange the route mark in advance and has limited application occasions
2) Autonomous navigation type
The method is an automatic driving algorithm used by the current mainstream non-public road low-speed vehicles, and mainly adopts the automatic driving technology currently applied to passenger vehicles. The system comprises a whole set of sensors for positioning and sensing, such as an integrated inertial navigation system (IMU + GNSS), a vision sensor and a laser radar, and also comprises an expensive high-performance automatic driving computing platform for environment sensing, vehicle positioning, path planning and vehicle control.
The autonomous navigation type vehicle has the advantages that the vehicle moves completely autonomously, and can move autonomously only by designating the target position, autonomously plan a path and avoid obstacles in real time.
The disadvantages of the autonomous navigation vehicle are mainly the following two aspects:
the cost is high, and in order to achieve high reliability, an expensive laser radar, a combined inertial navigation system and a high-performance computing platform must be used;
the perception sensor is very easy to be affected by weather factors and can not work all the day.
Disclosure of Invention
The invention aims to provide an automatic driving system for a non-public road based on a digital twin technology, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
the automatic driving system comprises an automatic driving cloud server and an automatic driving vehicle, wherein the automatic driving cloud server and the automatic driving vehicle perform data interaction through a high-speed wireless communication link and transmit vehicle position/posture information and vehicle control information, a digital twin vehicle, a map module and a vehicle automatic driving algorithm are arranged in the automatic driving cloud server, and the automatic driving vehicle comprises a vehicle positioning unit, a vehicle motion controller and an obstacle detection module.
As a further scheme of the invention: the map module is internally stored with a three-dimensional high-precision map and semantic data of an area to be operated by the automatic driving vehicle, and the three-dimensional high-precision map and the semantic data are acquired in advance by using surveying and mapping means, field surveying and mapping and the like and are accurate to centimeter level.
As a further scheme of the invention: the surveying and mapping means is the SLAM (simultaneous localization and mapping) technique or aerial three-dimensional oblique photography.
As a further scheme of the invention: the wireless communication link comprises a 5G communication system and a high-speed data transmission radio station.
As a further scheme of the invention: the digital twin vehicle is a three-dimensional geometric data and high-precision vehicle dynamics mathematical model of an automatic driving vehicle, and simultaneously comprises an obstacle detection virtual sensor and a map virtual sensor, wherein the virtual sensor is used for acquiring static space information and moving target information in a map module.
As a further scheme of the invention: the vehicle positioning unit acquires motion information of the autonomous vehicle and uses the information to control the motion of the digital twin vehicle in the map module through a high-speed wireless communication network.
As a further scheme of the invention: and the automatic driving cloud server generates vehicle motion control data according to the real-time position of the digital twin vehicle and the information of the digital twin vehicle virtual sensor.
As a further scheme of the invention: the vehicle motion controller acquires vehicle motion control data from the automatic driving cloud server through a high-speed wireless communication network, and integrates information from the obstacle detection module to control the automatic driving vehicle to move.
As a further scheme of the invention: the obstacle detection module acquires obstacle information in front of the operation of the automatic driving vehicle and sends the obstacle information to the vehicle motion controller, and the vehicle motion controller fuses the obstacle information to determine whether the vehicle should be stopped or stopped to avoid obstacles or continuously control the vehicle to move according to the instruction of the automatic driving cloud server.
Compared with the prior art, the invention has the beneficial effects that: 1. the automatic driving control is carried out on the vehicle by using the automatic driving algorithm placed at the cloud end, so that the cost of the automatic driving system of the single vehicle is greatly reduced;
2. the physical autonomous vehicle does not need to be equipped with a complex sensor, but only needs a high-precision positioning unit and a simple obstacle detection sensor. For a non-public road low-speed automatic driving vehicle in the field, all-weather work can be realized, and the used sensor is basically not influenced by the weather environment;
3. semantic data of a high-precision map which is mapped in advance is stored in the cloud, and the semantic data of the surrounding environment for vehicle track planning can be directly obtained by the automatic driving algorithm of the cloud without a complex perception algorithm and a sensor fusion algorithm, so that the performance requirement on a cloud server is lowered, and a large number of automatic driving vehicles can be controlled by the automatic driving algorithm of the cloud in real time;
4. the global path planning and the real-time path planning of all automatic driving vehicles are realized at the cloud. Therefore, the cooperative operation of a plurality of automatic driving vehicles can be coordinated by virtualizing various transportation facilities in the cloud. For example, when the running tracks of a plurality of automatic driving vehicles are crossed, virtual traffic lights can be used at the cloud end to coordinate the running of the automatic driving vehicles;
5. the real-time track tracking and management and control of the automatic driving vehicle can conveniently carry out remote take-over of the automatic driving vehicle.
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Fig. 1 is a schematic diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, embodiment 1: in the embodiment of the invention, the automatic driving system for the non-public road based on the digital twin technology comprises an automatic driving cloud server and an automatic driving vehicle, wherein the automatic driving cloud server and the automatic driving vehicle perform data interaction through a high-speed wireless communication link and transmit vehicle position/posture information and vehicle control information, the automatic driving cloud server is internally provided with the digital twin vehicle, a map module and a vehicle automatic driving algorithm, and the automatic driving vehicle comprises a vehicle positioning unit, a vehicle motion controller and an obstacle detection module.
The map module stores three-dimensional high-precision maps and semantic data of the areas where the automatic driving vehicles need to operate, and the three-dimensional high-precision maps and the semantic data are obtained in advance by using surveying and mapping means, field surveying and mapping and the like and are accurate to centimeter level.
The surveying and mapping means are aerial three-dimensional oblique photography and SLAM (simultaneous localization and mapping) technology.
The wireless communication link comprises a 5G communication system and a high-speed data transmission station.
The digital twin vehicle is a three-dimensional geometric data and high-precision vehicle dynamics mathematical model of an automatic driving vehicle, and simultaneously comprises an obstacle detection virtual sensor and a map virtual sensor, wherein the virtual sensor is used for acquiring static space information and moving target information in a map module.
The vehicle positioning unit acquires the motion information of the automatic driving vehicle and controls the motion of the digital twin vehicle in the map module through a high-speed wireless communication network.
And the automatic driving cloud server generates vehicle motion control data according to the real-time position of the digital twin vehicle and the information of the digital twin vehicle virtual sensor.
The vehicle motion controller acquires vehicle motion control data from the automatic driving cloud server through a high-speed wireless communication network, and integrates information from the obstacle detection module to control the automatic driving vehicle to move.
The obstacle detection module acquires obstacle information in front of the operation of the automatic driving vehicle and sends the obstacle information to the vehicle motion controller, and the vehicle motion controller fuses the obstacle information to determine whether the vehicle should be stopped or stopped to avoid obstacles or continue to control the vehicle to move according to the instruction of the automatic driving cloud server.
The working principle of the system is divided into an automatic driving cloud server and an automatic driving vehicle, which are respectively described as follows:
the automatic driving cloud server side: the digital twin vehicle obtains the position and posture data of the automatic driving vehicle through the high-speed wireless communication network, and moves the digital twin vehicle to the corresponding position in the high-precision map module according to the position and posture data.
The vehicle automatic driving algorithm acquires virtual sensor information of the digital twin vehicle module in real time, wherein the sensor information comprises obstacle information, travelable path information and the like around the position of the digital twin vehicle in the high-precision map module. The vehicle automatic driving algorithm plans the running track of the vehicle according to the information and the global path planning information which is formulated in advance, calculates the motion control data (an accelerator, a brake, a direction instruction and the like) of the vehicle, and then sends the data to the automatic driving vehicle through a high-speed wireless communication link.
Automatic driving vehicle end: the vehicle motion controller receives vehicle motion control data (accelerator, brake, direction instructions and the like) from the automatic driving cloud server and directly controls physical actuating mechanisms (an accelerator actuator, a brake actuator, a steering actuator and the like) of the automatic driving vehicle. In the process, the vehicle motion controller inquires information from the obstacle detection module in real time, if an obstacle exists in front of the vehicle motion controller, a braking instruction is executed, the vehicle is stopped until the obstacle disappears, and the vehicle continues to operate according to the instruction from the automatic driving cloud server.
The vehicle-mounted high-precision positioning unit acquires the position and posture information of the automatic driving vehicle in motion in real time, and then sends the information to the automatic driving cloud server through the high-speed wireless communication link for updating the corresponding position and posture of the digital twin vehicle in the high-precision map in real time.
The whole process described in the two sections forms a closed loop and operates in real time at a rate not lower than 100Hz to ensure that the autonomous vehicle is controllable.
Example 2: on the basis of the embodiment 1, the wireless communication link includes a 5G communication system and a high-speed data transmission station, and a common communication mode in the current market can be selected, so that the application range is increased.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
Claims (8)
1. The automatic driving system for the non-public road based on the digital twin technology comprises an automatic driving cloud server and an automatic driving vehicle and is characterized in that the automatic driving cloud server and the automatic driving vehicle perform data interaction through a high-speed wireless communication link and transmit vehicle position/posture information and vehicle control information, the automatic driving cloud server is internally provided with a digital twin vehicle, a map module and a vehicle automatic driving algorithm, and the automatic driving vehicle comprises a vehicle positioning unit, a vehicle motion controller and an obstacle detection module.
2. The automatic driving system for the non-public road based on the digital twin technology as claimed in claim 1, wherein a three-dimensional high-precision map and semantic data of an area where the automatic driving vehicle is to run are stored in the map module, and are obtained in advance by means of surveying and mapping, on-site surveying and the like, and are accurate to centimeter level.
3. The automatic driving system for non-public roads based on digital twin technology as claimed in claim 2, wherein the mapping means is SLAM technology or aerial three-dimensional oblique photography.
4. The automatic driving system for the non-public road based on the digital twin technology as claimed in claim 1, wherein the wireless communication link comprises a 5G communication system and a high speed data transmission station.
5. The automatic driving system for the non-public road based on the digital twin technology as claimed in claim 1, wherein the digital twin vehicle is a mathematical model of three-dimensional geometric data and high precision vehicle dynamics of the automatic driving vehicle, and comprises an obstacle detection virtual sensor and a map virtual sensor, and the virtual sensor is used for acquiring static spatial information and moving target information in a map module.
6. The automatic driving system for the non-public road based on the digital twin technology as claimed in claim 1, wherein the vehicle locating unit obtains movement information of the automatic driving vehicle and uses the information to control the movement of the digital twin vehicle in the map module through a high speed wireless communication network.
7. The automatic driving system for the non-public road based on the digital twin technology as claimed in claim 1, wherein the automatic driving cloud server generates vehicle motion control data according to the real-time position of the digital twin vehicle and information of the digital twin vehicle virtual sensor.
8. The automatic driving system for the non-public road based on the digital twin technology as claimed in claim 1, wherein the obstacle detection module obtains information of an obstacle in front of the automatic driving vehicle and sends the information to the vehicle motion controller, and the vehicle motion controller integrates the information of the obstacle to determine whether the vehicle should be stopped and the obstacle should be avoided or continues to control the vehicle to move according to the instruction of the automatic driving cloud server.
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Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111930026A (en) * | 2020-08-20 | 2020-11-13 | 北京经纬恒润科技有限公司 | Test method and device |
| CN112037543A (en) * | 2020-09-14 | 2020-12-04 | 中德（珠海）人工智能研究院有限公司 | Urban traffic light control method, device, equipment and medium based on three-dimensional modeling |
| CN112925291A (en) * | 2021-01-22 | 2021-06-08 | 大连理工大学 | Digital twin automatic driving test method based on camera dark box |
| CN113132474A (en) * | 2021-04-14 | 2021-07-16 | 东软睿驰汽车技术（沈阳）有限公司 | Control method, device and equipment for automatically driving vehicle |
| CN113242320A (en) * | 2021-07-08 | 2021-08-10 | 国汽智控(北京)科技有限公司 | Intelligent driving system, method, device and storage medium |
| CN113359709A (en) * | 2021-05-19 | 2021-09-07 | 中山大学 | Unmanned motion planning method based on digital twins |
| CN113436463A (en) * | 2021-06-28 | 2021-09-24 | 江苏智库智能科技有限公司 | 5G-based four-way shuttle vehicle multi-vehicle scheduling method |
| CN113626155A (en) * | 2021-10-11 | 2021-11-09 | 国汽智控(北京)科技有限公司 | Control method, equipment and storage medium for computing resources in edge cloud server |
| CN113645201A (en) * | 2021-07-27 | 2021-11-12 | 西安电子科技大学 | Application agent system and method based on digital Internet of vehicles |
| CN113687659A (en) * | 2021-10-26 | 2021-11-23 | 武汉鼎元同立科技有限公司 | Optimal trajectory generation method and system based on digital twinning |
| CN113741442A (en) * | 2021-08-25 | 2021-12-03 | 中国矿业大学 | Monorail crane automatic driving system and method based on digital twin driving |
| CN113867354A (en) * | 2021-10-11 | 2021-12-31 | 电子科技大学 | Regional traffic flow guiding method for intelligent cooperation of automatic driving of multiple vehicles |
| CN114518743A (en) * | 2022-02-21 | 2022-05-20 | 合肥工业大学 | Intelligent networking automobile positioning disturbance early warning method based on multi-dimensional space-time twin control model |
| CN114715197A (en) * | 2022-06-10 | 2022-07-08 | 深圳市爱云信息科技有限公司 | Automatic driving safety method and system based on digital twin DaaS platform |
| CN115840404A (en) * | 2022-12-21 | 2023-03-24 | 浙江大学 | Cloud control automatic driving system based on automatic driving special road network and digital twin map |
| WO2023051289A1 (en) * | 2021-09-30 | 2023-04-06 | 达闼机器人股份有限公司 | Navigation method and apparatus for unmanned device, medium, and unmanned device |
| WO2023151548A1 (en) * | 2022-02-08 | 2023-08-17 | 达闼机器人股份有限公司 | Navigation method and apparatus, and program and computer-readable storage medium |
| RU2805887C1 (en) * | 2021-08-25 | 2023-10-24 | Китайский Университет Горного Дела И Технологии | Automatic control system for monorail lift based on application of virtual layout method |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180329433A1 (en) * | 2017-05-10 | 2018-11-15 | General Electric Company | Self-localized mobile sensor network for autonomous robotic inspection |
| US20190266295A1 (en) * | 2018-02-28 | 2019-08-29 | Toyota Jidosha Kabushiki Kaisha | Proactive vehicle maintenance scheduling based on digital twin simulations |
| US20190287079A1 (en) * | 2018-03-19 | 2019-09-19 | Toyota Jidosha Kabushiki Kaisha | Sensor-based digital twin system for vehicular analysis |
-
2019
- 2019-11-21 CN CN201911145647.2A patent/CN110716558A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20180329433A1 (en) * | 2017-05-10 | 2018-11-15 | General Electric Company | Self-localized mobile sensor network for autonomous robotic inspection |
| US20190266295A1 (en) * | 2018-02-28 | 2019-08-29 | Toyota Jidosha Kabushiki Kaisha | Proactive vehicle maintenance scheduling based on digital twin simulations |
| US20190287079A1 (en) * | 2018-03-19 | 2019-09-19 | Toyota Jidosha Kabushiki Kaisha | Sensor-based digital twin system for vehicular analysis |
Non-Patent Citations (2)
| Title |
|---|
| 刘琪 等: "基于5G的车联网体系架构及其应用研究", 《移动通信》 * |
| 林述涛: "面向多源数据融合的交通基础设施", 《公路交通科技》 * |
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| RU2805887C1 (en) * | 2021-08-25 | 2023-10-24 | Китайский Университет Горного Дела И Технологии | Automatic control system for monorail lift based on application of virtual layout method |
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| WO2023024476A1 (en) * | 2021-08-25 | 2023-03-02 | 中国矿业大学 | Digital twin drive-based autonomous driving system and method for monorail crane |
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| CN113867354A (en) * | 2021-10-11 | 2021-12-31 | 电子科技大学 | Regional traffic flow guiding method for intelligent cooperation of automatic driving of multiple vehicles |
| CN113867354B (en) * | 2021-10-11 | 2023-05-02 | 电子科技大学 | Regional traffic flow guiding method for intelligent cooperation of automatic driving multiple vehicles |
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| CN113687659B (en) * | 2021-10-26 | 2022-01-25 | 武汉鼎元同立科技有限公司 | Optimal trajectory generation method and system based on digital twinning |
| CN113687659A (en) * | 2021-10-26 | 2021-11-23 | 武汉鼎元同立科技有限公司 | Optimal trajectory generation method and system based on digital twinning |
| WO2023151548A1 (en) * | 2022-02-08 | 2023-08-17 | 达闼机器人股份有限公司 | Navigation method and apparatus, and program and computer-readable storage medium |
| CN114518743A (en) * | 2022-02-21 | 2022-05-20 | 合肥工业大学 | Intelligent networking automobile positioning disturbance early warning method based on multi-dimensional space-time twin control model |
| CN114518743B (en) * | 2022-02-21 | 2024-04-09 | 合肥工业大学 | Intelligent networking automobile positioning disturbance early warning method based on multidimensional space-time twin control model |
| CN114715197A (en) * | 2022-06-10 | 2022-07-08 | 深圳市爱云信息科技有限公司 | Automatic driving safety method and system based on digital twin DaaS platform |
| WO2023236562A1 (en) * | 2022-06-10 | 2023-12-14 | 深圳市爱云信息科技有限公司 | Digital twin daas platform-based automatic driving safety method and system |
| CN115840404A (en) * | 2022-12-21 | 2023-03-24 | 浙江大学 | Cloud control automatic driving system based on automatic driving special road network and digital twin map |
| CN115840404B (en) * | 2022-12-21 | 2023-11-03 | 浙江大学 | Cloud control automatic driving system based on automatic driving special road network and digital twin map |
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