CN110967008A - Automatic driving full scene positioning method for commercial vehicle - Google Patents
Automatic driving full scene positioning method for commercial vehicle Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
- G01C21/28—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network with correlation of data from several navigational instruments
- G01C21/30—Map- or contour-matching
- G01C21/32—Structuring or formatting of map data
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/03—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers
- G01S19/10—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing dedicated supplementary positioning signals
- G01S19/12—Cooperating elements; Interaction or communication between different cooperating elements or between cooperating elements and receivers providing dedicated supplementary positioning signals wherein the cooperating elements are telecommunication base stations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/40—Correcting position, velocity or attitude
- G01S19/41—Differential correction, e.g. DGPS [differential GPS]
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/48—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system
- G01S19/49—Determining position by combining or switching between position solutions derived from the satellite radio beacon positioning system and position solutions derived from a further system whereby the further system is an inertial position system, e.g. loosely-coupled
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Abstract
The invention discloses an automatic driving full scene positioning method for a commercial vehicle, and belongs to the technical field of unmanned driving of commercial vehicles. The method comprises the following specific steps: detecting a scene in front of the vehicle through a camera, judging whether the scene in front of the vehicle is a tunnel scene or a gallery bridge scene or not, sending a judgment result to an automatic driving control system, and if so, positioning by adopting a high-precision map; if not, simultaneously positioning by adopting GPS positioning and inertial navigation positioning. According to the invention, the tunnel and corridor bridge scenes are detected in advance through the binocular camera, the command is sent to the vehicle control system in real time, the high-precision map laser point cloud positioning mode is switched, and the full-scene high-precision positioning of the commercial vehicle is ensured.
Description
Technical Field
The invention particularly relates to an automatic driving full scene positioning method for a commercial vehicle, and belongs to the technical field of unmanned driving of commercial vehicles.
Background
The automatic driving process is the same as the driving process of a human driver, and the automatic driving also needs 5 steps of sensing, high-precision positioning, predicting, planning and controlling. Human perception is achieved through eyes and ears, and automatic driving is achieved through sensors such as laser radars, millimeter-wave radars and cameras. Then, high-precision positioning is carried out, and the person can judge the position and the direction of the person by comparing the environment information heard by the person with the memorized information. And finally, the human driver operates the automobile to drive to the destination after thinking and judging. The automatic driving makes a lane and path plan through artificial intelligence algorithm decision, gives instructions to controllers for braking, steering, accelerating and the like, and controls the vehicle to drive to a destination. Therefore, the importance of positioning in the development and implementation process of unmanned driving is known, and once the positioning is inaccurate or invalid, the unmanned vehicle loses direction and cannot plan a path, so that the driving safety of automatic driving is seriously influenced.
Disclosure of Invention
Therefore, the invention provides a method for positioning the automatic driving full scene of the commercial vehicle, aiming at the defects in the prior art, the camera is used for detecting the position of the vehicle in the tunnel and gallery bridge scene, the high-precision map positioning mode is seamlessly switched, the problem of positioning drift of the automatic driving commercial vehicle in the tunnel and gallery bridge is solved, and the automatic full scene positioning of the commercial vehicle is enhanced.
The specific technical scheme is as follows:
a full scene positioning method for automatic driving of a commercial vehicle comprises the following steps:
detecting a scene in front of the vehicle through a camera, judging whether the scene in front of the vehicle is a tunnel scene or a gallery bridge scene or not, sending a judgment result to an automatic driving control system, and if so, positioning by adopting a high-precision map; if not, simultaneously positioning by adopting GPS positioning and inertial navigation positioning.
Further, the high-precision map positioning specifically comprises:
the laser radar generates a high-precision map through an SLAM (instant positioning and map construction) technology, and the laser radar compares, matches and preprocesses the point cloud data with landmarks on the high-precision map pre-stored in a system to acquire the global position and the driving direction of the vehicle in the high-precision map.
Further, in the method, the camera is a binocular camera.
Furthermore, the method corrects the motion error of inertial navigation positioning through GPS positioning and overcomes the defect of low GPS positioning updating frequency through inertial navigation positioning.
Furthermore, in the method, the GPS positioning is realized by an automatic driving GPS receiver, a cloud system, a park satellite reference station and a GPS satellite.
Further, the cloud system receives an error value sent by the campus satellite reference station and sends the error value to the autopilot GPS receiver.
Furthermore, the automatic driving GPS receiver receives GPS position information from a GPS satellite, and the GPS position information is adjusted according to the error value to obtain an accurate actual position.
Further, the campus satellite reference station receives a GPS position from a GPS satellite, and calculates an error value according to an actual position of the reference station.
The invention has the beneficial effects that: compared with the prior art, the automatic driving full scene positioning method of the commercial vehicle has the following advantages:
the tunnel and corridor bridge scenes are detected in advance through the binocular camera, an instruction is sent to the whole vehicle control system in real time, a high-precision map laser point cloud positioning mode is switched, and the full-scene high-precision positioning of the commercial vehicle is guaranteed.
Drawings
FIG. 1 is a schematic diagram of a full-scene positioning method for automatic driving of a commercial vehicle according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided with reference to the accompanying drawings:
as shown in fig. 1, the positioning method of the present invention works according to the following principle:
the localization method includes global localization and local localization based on features
1) Global positioning-RTK differential positioning
Differential positioning is also called differential GPS technology, and a GPS receiver is arranged on a reference station for observation. The distance corrections from the reference station to the satellite are calculated based on the known precise coordinates of the reference station and transmitted by the reference station in real time, as shown in fig. 1. The GPS antenna on the automatic driving vehicle receives the satellite data and the correction number sent by the reference station at the same time, and corrects the positioning result, thereby improving the positioning precision:
ground satellite reinforced base station: arranging a fixed GPS receiver and a communication auxiliary system;
satellite: can receive GPS and big dipper satellite signal;
unmanned commodity circulation car end: a GPS receiver and an antenna;
RTK involves building a satellite-augmented base station on the ground that knows its exact location, but also measures its location via GPS, knowing the offset between the ground location and the GPS-measured location as an error in the GPS measurement, and then passing this error to the GPS receiver on the autonomous vehicle for it to adjust its position calculations. With the help of RTK, GPS can limit the positioning error to within 10 cm.
2) Feature-based local localization
The laser radar is used for positioning through point cloud matching, the detection data of the laser radar is used for being continuously matched with the information pre-stored in the high-precision map, and the global position and the driving direction of the automobile on the high-precision map can be obtained through comparison. The high-precision map is divided into two levels, wherein the bottom layer is a static high-precision map, and the upper layer is a dynamic high-precision map. The static high-precision map comprises lane models, road components, road attributes and other positioning layers, and the high-precision map is required to meet lane-level automatic driving navigation, so that road detail information such as lane lines, lane center lines, lane attribute changes and the like is required to be contained, for example, an automobile can know which areas are dotted lines and can change lanes. In addition, the lane model also needs to contain mathematical parameters of the curvature, the gradient, the course, the cross slope and the like of the road, so that the vehicle can accurately steer, brake, climb and the like. This information constitutes a lane model. It is also necessary to include road components such as traffic signs and pavement markers, and to mark special points such as areas where GPS disappears, road construction conditions, and the like. The storage information of the high-precision map is rich, the position of the automobile, which is about to reach a tunnel, can be detected by comparing the front position and the rear position of the GPS, the positioning of the GPS is weak, the commercial vehicle automatically receives the high-precision map information, and the positioning is mainly based on an inertial navigation system.
Firstly, sensors such as a GPS (global positioning system) and an IMU (inertial measurement unit) give an initial (approximate) position, secondly, feature extraction is carried out on local point cloud information of the laser radar, vector features under a global coordinate system are obtained by combining the initial position, and finally, feature information in a vector feature root high-precision map is matched to obtain accurate centimeter-level global positioning.
3) Positioning effect
In an open scene without high buildings, tunnels and valley barriers, the automatic driving positioning depends on GPS positioning, meanwhile, the IMU inertial navigation positioning is used for overcoming the defect of low GPS updating frequency, the GPS corrects the movement error of the IMU, and the GPS and the IMU are combined to realize better positioning;
under the weak scene of corridor bridge GPS in the tunnel, the front road is monitored in real time through a binocular camera carried by an automatic driving vehicle, the distance between the tunnel, the corridor bridge and an unmanned vehicle is monitored, a message is sent to an automatic driving control system, the automatic driving system receives corridor bridge tunnel scene information sent by the camera, high-precision map positioning can be called in advance, and the system is based on the high-precision map positioning information.
The tunnel and corridor bridge scenes are detected in advance through the binocular camera, an instruction is sent to the whole vehicle control system in real time, a high-precision map laser point cloud positioning mode is switched, and the full-scene high-precision positioning of the commercial vehicle is guaranteed.
The commercial vehicle automatic driving system solves the problem of unmanned positioning under the condition that GPS is weak in a tunnel and a corridor bridge for commercial use by fusing GPS positioning, IMU inertial navigation positioning and high-precision map positioning.
Under the scene without a high-rise tunnel corridor bridge, the positioning is carried out in a mode of combining a GPS (global positioning system) and an IMU (inertial measurement unit), the IMU can provide near-real-time position information, the updating frequency is near 1000Hz, main components of the IMU are an accelerometer and a gyroscope, but the method has the defect that the error is increased along with the increase of time and can be positioned in a short time range only by means of inertial navigation. The GPS may correct for motion errors of the IMU. The IMU makes up the defect of low GPS updating evaluation rate.
Even if the GPS is combined with inertial navigation, the positioning problem cannot be completely solved, and the positioning fails without GPS updating for a long time under a tunnel and a gallery bridge. The binocular camera that carries through the automatic driving car monitors the place ahead road in real time to the distance of monitoring tunnel and corridor bridge and unmanned vehicle sends the message for automatic driving control system, and automatic driving system receives the corridor bridge tunnel scene information of the sending of camera, can call high accuracy map location in advance, and the system uses high accuracy map location information as the standard. And updating the vehicle positioning information in real time, so that the unmanned logistics vehicle detects the scene of weak GPS by means of a camera under the condition of weak GPS in the tunnel, and meanwhile, the positioning of inertial navigation is utilized, so that the unmanned logistics vehicle keeps the positioning accuracy and updates in real time under the condition of the whole scene of logistics transportation, and the positioning of the unmanned vehicle is confirmed to reach the centimeter level.
While the foregoing is directed to the preferred embodiment of the present invention, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (8)
1. A full scene positioning method for automatic driving of a commercial vehicle is characterized by comprising the following steps:
detecting a scene in front of the vehicle through a camera, judging whether the scene in front of the vehicle is a tunnel scene or a gallery bridge scene or not, sending a judgment result to an automatic driving control system, and if so, positioning by adopting a high-precision map; if not, simultaneously positioning by adopting GPS positioning and inertial navigation positioning.
2. The automatic driving full-scene positioning method of the commercial vehicle according to claim 1, wherein the high-precision map positioning specifically comprises:
the laser radar generates a high-precision map through an SLAM technology, and the laser radar compares, matches and preprocesses the point cloud data with landmarks on the high-precision map in a pre-existing system to acquire the global position and the driving direction of the vehicle in the high-precision map.
3. The method for automatically driving a full scene of a commercial vehicle as claimed in claim 1, wherein the camera in the method is a binocular camera.
4. The method according to claim 1, wherein the method corrects the motion error of the inertial navigation positioning by the GPS positioning, and overcomes the defect of low update frequency of the GPS positioning by the inertial navigation positioning.
5. The method according to claim 4, wherein the GPS positioning is realized by an autopilot GPS receiver, a cloud system, a campus satellite reference station, and a GPS satellite.
6. The method as claimed in claim 5, wherein the cloud system receives an error value sent from a campus satellite reference station and sends the error value to the autopilot GPS receiver.
7. The method as claimed in claim 6, wherein the autopilot GPS receiver receives GPS position information from GPS satellites, and adjusts the GPS position information according to the error value to obtain the precise actual position.
8. The method as claimed in claim 7, wherein the campus satellite reference station receives GPS position from GPS satellites, and calculates an error value according to the actual position of the reference station.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111649957A (en) * | 2020-06-08 | 2020-09-11 | 山东省交通规划设计院有限公司 | Tunnel environment automatic driving vehicle driving capability test system and test method |
| CN112954192A (en) * | 2021-01-27 | 2021-06-11 | 惠州华阳通用电子有限公司 | Camera shooting mode control method |
| CN113514863A (en) * | 2021-03-23 | 2021-10-19 | 重庆兰德适普信息科技有限公司 | Multi-sensor fusion positioning method |
| CN113554890A (en) * | 2021-06-30 | 2021-10-26 | 东风汽车集团股份有限公司 | Navigation enhancement system and method based on 5G communication under tunnel working condition |
| CN113587937A (en) * | 2021-06-29 | 2021-11-02 | 阿波罗智联(北京)科技有限公司 | Vehicle positioning method and device, electronic equipment and storage medium |
| GB2598717A (en) * | 2020-08-28 | 2022-03-16 | Drone Defence Services Ltd | A method of calculating the position of an unmanned aerial vehicle |
| CN114342456A (en) * | 2020-06-28 | 2022-04-12 | 北京小米移动软件有限公司 | Measurement method, base station, multimode terminal, communication device and storage medium |
| CN114821522A (en) * | 2022-03-29 | 2022-07-29 | 东南大学 | Urban road cross slope and super height value calculation method based on vehicle-mounted laser point cloud data |
| CN115435770A (en) * | 2022-08-08 | 2022-12-06 | 重庆长安汽车股份有限公司 | Method for constructing tunnel road section map based on high-precision map |
| CN116562601A (en) * | 2023-07-11 | 2023-08-08 | 昆明理工大学 | Operation scheduling method suitable for automatic logistics vehicle to enter and exit from room and outside |
| CN117091619A (en) * | 2023-10-19 | 2023-11-21 | 安徽蔚来智驾科技有限公司 | Vehicle navigation method, control device, readable storage medium and vehicle |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015099463A1 (en) * | 2013-12-24 | 2015-07-02 | 엘지전자 주식회사 | Vehicle driving assist device and vehicle having same |
| CN107992050A (en) * | 2017-12-20 | 2018-05-04 | 广州汽车集团股份有限公司 | Pilotless automobile local path motion planning method and device |
| CN108303721A (en) * | 2018-02-12 | 2018-07-20 | 北京经纬恒润科技有限公司 | A kind of vehicle positioning method and system |
| CN208344067U (en) * | 2018-06-29 | 2019-01-08 | 江铃汽车股份有限公司 | Intelligent driving alarm set |
| CN109455187A (en) * | 2019-01-30 | 2019-03-12 | 新誉轨道交通科技有限公司 | Train pressure wave control method, device and electronic equipment |
| CN109470240A (en) * | 2018-11-15 | 2019-03-15 | 安徽酷哇机器人有限公司 | Automatic Pilot localization method |
| CN110132280A (en) * | 2019-05-20 | 2019-08-16 | 广州小鹏汽车科技有限公司 | Vehicle positioning method, vehicle locating device and vehicle under indoor scene |
| CN110271554A (en) * | 2018-03-13 | 2019-09-24 | 奥迪股份公司 | Driving assistance system and method for vehicle |
-
2019
- 2019-11-25 CN CN201911166894.0A patent/CN110967008A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015099463A1 (en) * | 2013-12-24 | 2015-07-02 | 엘지전자 주식회사 | Vehicle driving assist device and vehicle having same |
| CN107992050A (en) * | 2017-12-20 | 2018-05-04 | 广州汽车集团股份有限公司 | Pilotless automobile local path motion planning method and device |
| CN108303721A (en) * | 2018-02-12 | 2018-07-20 | 北京经纬恒润科技有限公司 | A kind of vehicle positioning method and system |
| CN110271554A (en) * | 2018-03-13 | 2019-09-24 | 奥迪股份公司 | Driving assistance system and method for vehicle |
| CN208344067U (en) * | 2018-06-29 | 2019-01-08 | 江铃汽车股份有限公司 | Intelligent driving alarm set |
| CN109470240A (en) * | 2018-11-15 | 2019-03-15 | 安徽酷哇机器人有限公司 | Automatic Pilot localization method |
| CN109455187A (en) * | 2019-01-30 | 2019-03-12 | 新誉轨道交通科技有限公司 | Train pressure wave control method, device and electronic equipment |
| CN110132280A (en) * | 2019-05-20 | 2019-08-16 | 广州小鹏汽车科技有限公司 | Vehicle positioning method, vehicle locating device and vehicle under indoor scene |
Non-Patent Citations (1)
| Title |
|---|
| 中国卫星导航定位协会: "《卫星导航定位与北斗系统应用》", 测绘出版社, pages: 12 * |
Cited By (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111649957A (en) * | 2020-06-08 | 2020-09-11 | 山东省交通规划设计院有限公司 | Tunnel environment automatic driving vehicle driving capability test system and test method |
| CN114342456B (en) * | 2020-06-28 | 2024-04-09 | 北京小米移动软件有限公司 | Measurement method, base station, multimode terminal, communication device, and storage medium |
| CN114342456A (en) * | 2020-06-28 | 2022-04-12 | 北京小米移动软件有限公司 | Measurement method, base station, multimode terminal, communication device and storage medium |
| GB2598717A (en) * | 2020-08-28 | 2022-03-16 | Drone Defence Services Ltd | A method of calculating the position of an unmanned aerial vehicle |
| CN112954192A (en) * | 2021-01-27 | 2021-06-11 | 惠州华阳通用电子有限公司 | Camera shooting mode control method |
| CN112954192B (en) * | 2021-01-27 | 2022-06-07 | 惠州华阳通用电子有限公司 | Camera shooting mode control method |
| CN113514863A (en) * | 2021-03-23 | 2021-10-19 | 重庆兰德适普信息科技有限公司 | Multi-sensor fusion positioning method |
| CN113587937A (en) * | 2021-06-29 | 2021-11-02 | 阿波罗智联(北京)科技有限公司 | Vehicle positioning method and device, electronic equipment and storage medium |
| CN113554890A (en) * | 2021-06-30 | 2021-10-26 | 东风汽车集团股份有限公司 | Navigation enhancement system and method based on 5G communication under tunnel working condition |
| CN114821522A (en) * | 2022-03-29 | 2022-07-29 | 东南大学 | Urban road cross slope and super height value calculation method based on vehicle-mounted laser point cloud data |
| CN115435770A (en) * | 2022-08-08 | 2022-12-06 | 重庆长安汽车股份有限公司 | Method for constructing tunnel road section map based on high-precision map |
| CN115435770B (en) * | 2022-08-08 | 2024-09-10 | 重庆长安汽车股份有限公司 | Method for constructing tunnel road section map based on high-precision map |
| CN116562601A (en) * | 2023-07-11 | 2023-08-08 | 昆明理工大学 | Operation scheduling method suitable for automatic logistics vehicle to enter and exit from room and outside |
| CN116562601B (en) * | 2023-07-11 | 2023-09-12 | 昆明理工大学 | Operation scheduling method suitable for automatic logistics vehicle to enter and exit from room and outside |
| CN117091619A (en) * | 2023-10-19 | 2023-11-21 | 安徽蔚来智驾科技有限公司 | Vehicle navigation method, control device, readable storage medium and vehicle |
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