Patent ID: 11935407
Assignee: SHANDONG UNIVERSITY OF SCIENCE AND TECHNOLOGY
Field: Measurement (Instruments)
Classification: CPC G | IPC G

Claim 0:
1. A dynamic loose vehicle formation method based on real-time online navigation map, comprising the following steps:
S1, a driver inputs travel information of a destination and waypoints in a GIS app;
S2, the GIS app uploads the travel information to a GIS server; the GIS server performs formation matching on formation requests and existing formations in a path, and sends formation matching information to the GIS app; the driver chooses to join a recommended formation or create a formation in the GIS app;
S3, when the driver chooses the formation and joins the formation, the GIS app updates vehicle information, and the GIS server updates formation information, displays the updated formation information on the GIS app and sends the updated formation information to other vehicles within the formation for information interaction;
S4, when the driver chooses to leave the formation and leaves the formation, the formation is adjusted and the GIS server updates the formation information;, the GIS app comprises:
an operation interface module, configured for the driver to input the travel information of the destination and the waypoints and view the formation information;
a server module, configured to record a travel description file of the driver and analyze travel preference parameters of the driver;
a communication network and internet-of-vehicles module, configured to perform information interaction with the GIS server to achieve information transmission and reception or perform voice call; and
a formation matching module, configured to match the travel information input by the driver in the GIS app and the formation information in the GIS server;, the step S2 comprises the following steps:
S2.1, the formation matching is achieved in the following manner: based on the travel information of the destination and the waypoints, searching for the formation information within a range of a distance back and forth in the path, and based on a coincidence degree between a path or the travel preference parameters of the driver and a path or speed of the formation, performing matching;
S2.2, the formation matching information is sent to the GIS app of the vehicle, and thus, the driver selects a formation to join and the GIS server sends the vehicle information to other vehicles within the formation;
S2.3, when the driver chooses not to join the formation, the driver chooses to create a formation in which the vehicle of the driver is a lead vehicle to be joined by other vehicles;, the step S2.1 comprises:
S2.1.1, reading the travel description file of the driver according to a user ID to obtain the travel preference parameters of the driver;
S2.1.2, taking the travel preference parameters of the driver at a moment as a set represented by a vector U(Ri1, Ri2, . . . , Rin), wherein Rij represents a value of a travel parameter j in the travel preference parameters stored under the user ID corresponding to the driver as user i, and n is a total number of the travel parameters, comprising distance keeping, speed, lane change and overtake;
S2.1.3, based on adjusted cosine similarity, calculating a similarity Ssim(p,q)Acosine of travel preference parameters between users in the following formula:, ∑
    
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 wherein, J represents a full set of travel preference parameters, Rpj and Rqj respectively represent values of the travel preference parameters j of the users p, q at a same moment in travel description file stored under user IDs corresponding to the users p, q, Rpj and Rqj respectively represent average values of the travel preference parameters j corresponding to the users p, q;
S2.1.4, using the formula in the step S2.1.3, calculating a similarity between the travel preference parameters of the driver to join the formation and user parameter settings of a lead vehicle of each of the existing formations within a proximity range of the driver to join the formation, then sorting the similarities in a descending order, and selecting and recommending the first N formations with the highest similarity to the driver to join the formation for formation selection.