Data fusion method, apparatus and non-transitory computer-readable storage medium to park a vehicle
AssigneeNanning FuLian FuGui Precision Industrial Co., Ltd.
InventorsBing Tan, Chia-Hung Wen, Lin-Fen Tang
Life of this patent
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Abstract
A data fusion method, apparatus and non-transitory computer-readable storage medium to park a vehicle, the method comprising; using a weather classification model to obtain a weather condition probability based on weather information of a current location of a vehicle and an extracted video clip of the current environment, and obtaining a corresponding weather impact factor based on the weather condition probability. Finally, determining the fusion area where the visual image data and the radar point cloud data are to be fused based on the length of the body of the vehicle, the weather condition probability and the weather impact factor to generate a parking scene image.
Claims9
A data fusion method to park a vehicle, executed in a computing device, the method comprising:
- obtaining weather information of a current location of the vehicle;
- capturing a video segment of a surrounding environment of the vehicle;
- inputting the weather information and the video segment into a weather classification model to obtain a weather condition probability;
- obtaining a weather impact factor according to the weather condition probability;
- determining a fusion area between visual image data and radar point cloud data according to a length of a body of the vehicle, the weather condition probability, and the weather impact factor, wherein the determining the fusion area further comprises:
- representing the length of the body of the vehicle as d, the weather condition probability as Penv, and the weather impact factor as αenv;
- calculating a first area radius R1 for distinguishing the fusion area from an observation area using a first formula:
- R1=2d×(1+Penv×αenv), 0≤αenv<1;
- calculating a second area radius R2 using a second formula:
- R2=3d×(1+Penv×αenv), 0≤αenv<1;
- and defining the fusion area as a first coverage range centered on the vehicle with the first area radius R1 and the observation area as a second coverage range centered on the vehicle with the second area radius R2 excluding the fusion area;
- and
- determining whether to perform data fusion between the visual image data and the radar point cloud data to generate a parking scene image based on the relationship between distance information of the radar point cloud data and the fusion area.
A data fusion apparatus configured for parking a vehicle, the apparatus comprising:
- a non-transitory memory storage storing processor-executable instructions;
- and
- at least one processor coupled to the memory to receive the processor-executable instructions, wherein, upon execution of the processor executable instructions, the at least one processor:
- obtaining weather information of a current location of the vehicle;
- capturing a video segment of a surrounding environment of the vehicle;
- inputting the weather information and the video segment into a weather classification model to obtain a weather condition probability;
- obtaining a weather impact factor according to the weather condition probability;
- determining a fusion area between visual image data and radar point cloud data according to a length of a body of the vehicle, the weather condition probability, and the weather impact factor, wherein the determining the fusion area further comprises:
- representing the length of the body of the vehicle as d, the weather condition probability as Penv, and the weather impact factor as αenv;
- calculating a first area radius R1 for distinguishing the fusion area from an observation area using a first formula:
- R1=2d×(1+Penv×αenv), 0≤αenv<1;
- calculating a second area radius R2 using a second formula:
- R2=3d×(1+Penv×αenv), 0≤αenv<1;
- and defining the fusion area as a first coverage range centered on the vehicle with the first area radius R1 and the observation area as a second coverage range centered on the vehicle with the second area radius R2 excluding the fusion area;
- and
- determining whether to perform data fusion between the visual image data and the radar point cloud data to generate a parking scene image based on the relationship between distance information of the radar point cloud data and the fusion area.
A non-transitory computer readable storage medium storing processor-executable instructions which, when executed by at least one processor, cause the at least one processor to perform a data fusion method to park a vehicle, the data fusion method comprising:
- obtaining weather information of a current location of the vehicle;
- capturing a video segment of a surrounding environment of the vehicle;
- inputting the weather information and the video segment into a weather classification model to obtain a weather condition probability;
- obtaining a weather impact factor according to the weather condition probability;
- determining a fusion area between visual image data and radar point cloud data according to a length of a body of the vehicle, the weather condition probability, and the weather impact factor, wherein the determining the fusion area further comprises:
- representing the length of the body of the vehicle as d, the weather condition probability as Penv, and the weather impact factor as αenv;
- calculating a first area radius R1 for distinguishing the fusion area from an observation area using a first formula:
- R1=2d×(1+Penv×αenv), 0≤αenv<1;
- calculating a second area radius R2 using a second formula:
- R2=3d×(1+Penv×αenv), 0≤αenv<1;
- and defining the fusion area as a first coverage range centered on the vehicle with the first area radius R1 and the observation area as a second coverage range centered on the vehicle with the second area radius R2 excluding the fusion area;
- and
- determining whether to perform data fusion between the visual image data and the radar point cloud data to generate a parking scene image based on the relationship between distance information of the radar point cloud data and the fusion area.
Dependencies are inferred from the claim text.