CN112967522A - Intelligent vehicle calling method and system based on deep learning and planning algorithm - Google Patents
Intelligent vehicle calling method and system based on deep learning and planning algorithm Download PDFInfo
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- CN112967522A CN112967522A CN202110129286.3A CN202110129286A CN112967522A CN 112967522 A CN112967522 A CN 112967522A CN 202110129286 A CN202110129286 A CN 202110129286A CN 112967522 A CN112967522 A CN 112967522A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/14—Traffic control systems for road vehicles indicating individual free spaces in parking areas
- G08G1/145—Traffic control systems for road vehicles indicating individual free spaces in parking areas where the indication depends on the parking areas
- G08G1/148—Management of a network of parking areas
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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Abstract
The invention relates to the technical field of intelligent driving, in particular to an intelligent vehicle calling method and system based on deep learning and planning algorithms; the method comprises the following steps: step 1): creating an underground parking lot map; step 2): a whole underground parking static map component; step 3): parking: sending a parking instruction on the personal mobile communication equipment, and enabling the vehicle to automatically drive into a designated parking space; step 4): a step of calling a vehicle; the intelligent vehicle calling system based on the deep learning and planning algorithm comprises personal mobile communication equipment, vehicle-mounted communication equipment and a cloud server.
Description
Technical Field
The invention relates to the technical field of intelligent driving, in particular to an intelligent vehicle calling method and system based on deep learning and planning algorithms.
Background
A lot of existing intelligent driving technologies are widely applied to various fields of vehicles, intelligent vehicle calling is a very hot technology at present, a vehicle owner can call out a vehicle in a parking lot very conveniently, and intelligent driving of the vehicle is achieved.
The current intelligent vehicle calling has the technical difficulty that the position of the vehicle cannot be accurately positioned, and the fact that the vehicle cannot avoid obstacles, pedestrians and the like in the calling process is a practical problem which hinders the technical development.
Disclosure of Invention
The invention aims to provide an intelligent vehicle calling method and system based on a deep learning and planning algorithm.
In order to solve the technical problems, the invention adopts the following technical scheme:
the intelligent vehicle calling method based on the deep learning and planning algorithm comprises the following steps:
step 1): creating an underground parking lot map;
step 2): a whole underground parking static map component;
step 3): parking: sending a parking instruction on the personal mobile communication equipment, enabling the vehicle to drive into a designated parking space independently,
step 4): a vehicle calling step: sending a signal for calling a vehicle through the personal mobile communication equipment, designating the position of a vehicle owner in the map and sending the signal to the cloud server, reading the parking space and the coordinates of the vehicle owner after the cloud server receives a vehicle calling request, transmitting the signal to the vehicle-mounted communication equipment, inputting a 2d grid graph and the current coordinates of the vehicle,
it is determined whether to change the route,
if so, judging whether the path can be planned from the newly planned route, if so, tracking, reaching the end point, judging whether the path is finished or not after reaching the end point, if so, finishing the operation, successfully taking the vehicle by the vehicle owner, if not, stopping the vehicle, and returning to the step of inputting the 2d grid map and the current coordinates of the vehicle;
if not, tracking, reaching the end point, judging whether the operation is finished or not after reaching the end point, if so, finishing the operation, successfully getting the vehicle by the vehicle owner, and if not, returning to the step of inputting the 2d grid map and the current coordinate of the vehicle.
Further, the processes of step 3) and step 4) further comprise: the real-time map component of the surrounding environment in the vehicle motion detects the surrounding environment of the vehicle body according to deep learning, and the environment information around the vehicle body can be updated into a static map according to the real-time laser radar point cloud map in the vehicle motion process, and the planning module can change the path according to the real-time map information.
Further, the surrounding environment includes pedestrians, vehicles, obstacles, and the like.
Further, the vehicle calling step also comprises that the vehicle owner clicks to drive away from the parking lot through the personal mobile communication equipment after getting on the vehicle, and the vehicle automatically drives to the exit.
Further, the entire underground parking lot static map component passes through the Slam component.
Further, the constructed static map of the underground parking lot comprises two parts: one is a binary gray scale map for marking a travelable region and a non-travelable region; the other is a configuration file, and the reference numbers of the parking spaces and the map coordinates of the parking spaces at the map coordinate and the entrance and the exit are listed in the file.
The invention also discloses an intelligent vehicle calling system based on the deep learning and planning algorithm, which comprises personal mobile communication equipment, a cloud server and a control system, wherein the personal mobile communication equipment is used for sending position information of a request end, parking and calling requests to the cloud server and receiving vehicle information fed back according to the parking and calling requests;
the vehicle-mounted communication equipment is used for providing position information of the vehicle to the cloud server and checking parking and calling requests and position information of passengers sent by the cloud server;
and the cloud server is used for receiving the position information of the personal mobile communication equipment and the vehicle-mounted communication equipment, comparing the position information in a map, sending the position information of the passenger to the vehicle-mounted communication equipment, receiving the parking and calling request and the position information of the passenger by the vehicle-mounted communication equipment, and completing the pairing of parking and calling.
Further, the personal mobile communication device and the vehicle-mounted communication device are provided with a data communication module and a GPS positioning module.
Compared with the prior art, the invention has the beneficial effects that: the method and the system can effectively solve the problems of positioning accuracy and incapability of avoiding in the intelligent vehicle calling, and solve the problems that the vehicle is difficult to position and the obstacle cannot be avoided in the intelligent calling scene through a deep learning and path planning algorithm.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic view of an underground parking garage according to the present invention;
FIG. 3 is a static map constructed by Slam;
fig. 4 is a path diagram for parking in garage No. 4 plan;
FIG. 5 is a diagram of a real-time trajectory change based on point cloud information.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings and embodiments, as shown in fig. 1-5. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Examples
The intelligent vehicle calling method based on the deep learning and planning algorithm comprises the following steps:
step 1): underground parking lot map creation, as shown in fig. 2;
step 2): the whole underground parking lot static map component, as shown in fig. 3;
step 3): parking: sending a parking instruction on the personal mobile communication equipment, enabling the vehicle to drive into a designated parking space independently,
step 4): a vehicle calling step: sending a signal for calling a vehicle through the personal mobile communication equipment, designating the position of a vehicle owner in the map and sending the signal to the cloud server, reading the parking space and the coordinates of the vehicle owner after the cloud server receives a vehicle calling request, transmitting the signal to the vehicle-mounted communication equipment, inputting a 2d grid graph and the current coordinates of the vehicle,
it is determined whether to change the route,
if so, judging whether the path can be planned from the newly planned route, if so, tracking, reaching the end point, judging whether the path is finished or not after reaching the end point, if so, finishing the operation, successfully taking the vehicle by the vehicle owner, if not, stopping the vehicle, and returning to the step of inputting the 2d grid map and the current coordinates of the vehicle;
if not, tracking, reaching the end point, judging whether the operation is finished or not after reaching the end point, if so, finishing the operation, successfully getting the vehicle by the vehicle owner, and if not, returning to the step of inputting the 2d grid map and the current coordinate of the vehicle.
As an optional implementation manner, during the steps 3) and 4), the method further includes: the real-time map component of the surrounding environment in the vehicle motion detects the surrounding environment of the vehicle body according to deep learning, and the environment information around the vehicle body can be updated into a static map according to the real-time laser radar point cloud map in the vehicle motion process, and the planning module can change the path according to the real-time map information.
As an alternative embodiment, the surroundings comprise pedestrians, vehicles, obstacles, etc.
In an optional implementation mode, the step of calling the vehicle further comprises that after the vehicle owner gets on the vehicle, the vehicle owner clicks to drive out of the parking lot through the personal mobile communication device, and the vehicle is driven to the exit.
As an alternative embodiment, the entire underground parking garage static map member passes through the Slam member.
As an alternative embodiment, the constructed static map of the underground parking lot comprises two parts: one is a binary gray scale map for marking a travelable region and a non-travelable region; the other is a configuration file, and the reference numbers of the parking spaces and the map coordinates of the parking spaces at the map coordinate and the entrance and the exit are listed in the file.
More specifically, as shown in fig. 4-5, the parking space No. 4 is used as a specific parking space, the parking trajectory and the environment around the vehicle body are detected according to deep learning after encountering an obstacle, the environment information around the vehicle body can be updated into a static map according to a real-time laser radar cloud image in the vehicle motion process, the planning module can change the path according to the real-time map information,
the method and the system can effectively solve the problems of positioning accuracy and incapability of avoiding in the intelligent vehicle calling, and solve the problems that the vehicle is difficult to position and the obstacle cannot be avoided in the intelligent calling scene through a deep learning and path planning algorithm.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think of the changes or substitutions within the technical scope disclosed in the present application, and all the technical solutions falling within the scope of the present invention defined by the claims fall within the scope of the present invention.
Claims (8)
1. The intelligent vehicle calling method based on the deep learning and planning algorithm is characterized in that: the method comprises the following steps:
step 1): creating an underground parking lot map;
step 2): a whole underground parking static map component;
step 3), parking: sending a parking instruction on the personal mobile communication equipment, enabling the vehicle to drive into a designated parking space independently,
step 4), vehicle calling step: sending a signal for calling a vehicle through the personal mobile communication equipment, designating the position of a vehicle owner in the map and sending the signal to the cloud server, reading the parking space and the coordinates of the vehicle owner after the cloud server receives a vehicle calling request, transmitting the signal to the vehicle-mounted communication equipment, inputting a 2d grid graph and the current coordinates of the vehicle,
it is determined whether to change the route,
if so, judging whether the path can be planned from the newly planned route, if so, tracking, reaching the end point, judging whether the path is finished or not after reaching the end point, if so, finishing the operation, successfully taking the vehicle by the vehicle owner, if not, stopping the vehicle, and returning to the step of inputting the 2d grid map and the current coordinates of the vehicle;
if not, tracking, reaching the end point, judging whether the operation is finished or not after reaching the end point, if so, finishing the operation, successfully getting the vehicle by the vehicle owner, and if not, returning to the step of inputting the 2d grid map and the current coordinate of the vehicle.
2. The intelligent vehicle summoning method based on deep learning and planning algorithm of claim 1, wherein: the steps 3) and 4) further comprise: the real-time map component of the surrounding environment in the vehicle motion detects the surrounding environment of the vehicle body according to deep learning, and the environment information around the vehicle body can be updated into a static map according to the real-time laser radar point cloud map in the vehicle motion process, and the planning module can change the path according to the real-time map information.
3. The intelligent vehicle summoning method based on the deep learning and planning algorithm of claim 2, wherein: the surrounding environment includes pedestrians, vehicles, obstacles, and the like.
4. The intelligent vehicle summoning method based on deep learning and planning algorithm of claim 1, wherein: and the step of calling the vehicle further comprises that the vehicle owner clicks to drive away from the parking lot through the personal mobile communication equipment after getting on the vehicle, and the vehicle automatically drives to the exit.
5. The intelligent vehicle summoning method based on deep learning and planning algorithm of claim 1, wherein: the entire underground parking lot static map component passes through the Slam component.
6. The intelligent vehicle summoning method based on deep learning and planning algorithm according to claim 1 or 5, wherein: the constructed static map of the underground parking lot comprises two parts: one is a binary gray scale map for marking a travelable region and a non-travelable region; the other is a configuration file, and the reference numbers of the parking spaces and the map coordinates of the parking spaces at the map coordinate and the entrance and the exit are listed in the file.
7. Intelligent vehicle calling system based on deep learning and planning algorithm, its characterized in that:
the system comprises personal mobile communication equipment, a cloud server and a server, wherein the personal mobile communication equipment is used for sending position information of a request end and parking and car calling requests to the cloud server and receiving vehicle information fed back according to the parking and car calling requests;
the vehicle-mounted communication equipment is used for providing position information of the vehicle to the cloud server and checking parking and calling requests and position information of passengers sent by the cloud server;
and the cloud server is used for receiving the position information of the personal mobile communication equipment and the vehicle-mounted communication equipment, comparing the position information in a map, sending the position information of the passenger to the vehicle-mounted communication equipment, receiving the parking and calling request and the position information of the passenger by the vehicle-mounted communication equipment, and completing the pairing of parking and calling.
8. The intelligent vehicle summoning system based on deep learning and planning algorithm of claim 4, wherein: the personal mobile communication equipment and the vehicle-mounted communication equipment are provided with a data communication module and a GPS positioning module.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114373331A (en) * | 2022-01-11 | 2022-04-19 | 浙江吉利控股集团有限公司 | Cloud splicing method for learned parking lot data of vehicle |
| CN114422950A (en) * | 2022-01-21 | 2022-04-29 | 重庆长安汽车股份有限公司 | Vehicle calling system and method based on ultra-wideband signal positioning and automobile |
| CN116153083A (en) * | 2023-04-20 | 2023-05-23 | 江铃汽车股份有限公司 | Vehicle calling method and system applied to underground parking garage |
| CN116363890A (en) * | 2021-12-27 | 2023-06-30 | 比亚迪股份有限公司 | Vehicle calling method, terminal, server, system and medium |
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