Method and system for extracting road data and method and system for controlling self-driving car

A method for controlling a self-driving car comprising performing following steps by a processing device:

• predicting the self-driving car's position at time of actuation of a given chassis control command, using an estimate of a processing delay time, an estimate of a chassis control delay time, a current position and a current velocity of the self-driving car;
• obtaining target road data corresponding to a estimated position from a map database;
• and
• generating a chassis control command according to the target road data to control the self-driving car;
• wherein the map database comprises a plurality of pieces of pre-stored road data of a detection region, the estimated position is in the detection region, the target road data is included in the plurality of pieces of pre-stored road data and the plurality of pieces of pre-stored road data are obtained by a road data extracting procedure, and the road data extracting procedure comprises:
• obtaining ground data from first environment detection data of the detection region, wherein the first environment detection data is a point cloud;
• obtaining a first ground model using the ground data;
• filling in missing regions of the first ground model to obtain a second ground model;
• obtaining a plurality of road attributes of the detection region according to the second ground model;
• performing road damage identification using second environment detection data of the detection region and the second ground model;
• and
• storing the plurality of road attributes and a result of the road damage identification as a plurality of pieces of pre-stored road data of the detection region,
• wherein obtaining the ground data from the first environment detection data of the detection region comprises:
• dividing the point cloud into a plurality of original voxels, wherein the plurality of original voxels comprise a plurality of voxel columns;
• obtaining a plurality of target voxel groups from the plurality of original voxels, wherein the plurality of target voxel groups are respectively included in a plurality of voxel columns, a vertical spread parameter of a plurality of points in each of the plurality of target voxel groups is less than or equal to a preset value, and the vertical spread parameter is a height difference between a highest point and a lowest point in each of the plurality of target voxel groups;
• calculating a plurality of height values of the plurality of target voxel groups;
• and
• generating the ground data using the plurality of height values.

A system for extracting road data comprising:

• a data input device configured to receive first environment detection data and second environment detection data, wherein the first environment detection data is a point cloud;
• a storage device;
• and
• a processing device connected to the data input device and the storage device and configured to perform following steps:
• obtaining ground data from the first environment detection data;
• obtaining a first ground model using the ground data;
• filling in missing regions of the first ground model to obtain a second ground model;
• obtaining a plurality of road attributes of a detection region according to the second ground model;
• performing road damage identification using the second environment detection data and the second ground model;
• and
• storing the plurality of road attributes and a result of the road damage identification as a plurality of pieces of road data of the detection region,
• wherein obtaining the ground data from the first environment detection data of the detection region comprises:
• dividing the point cloud into a plurality of original voxels, wherein the plurality of original voxels comprise a plurality of voxel columns;
• obtaining a plurality of target voxel groups from the plurality of original voxels, wherein the plurality of target voxel groups are respectively included in a plurality of voxel columns, a vertical spread parameter of a plurality of points in each of the plurality of target voxel groups is less than or equal to a preset value, and the vertical spread parameter is a height difference between a highest point and a lowest point in each of the plurality of target voxel groups;
• calculating a plurality of height values of the plurality of target voxel groups;
• and
• generating the ground data using the plurality of height values.

A system for controlling a self-driving car comprising:

• a positioning module configured to obtain a current position of the self-driving car;
• a car dynamic state sensing module configured to obtain a current velocity of the self-driving car;
• a chassis control module configured to control the self-driving car according to a chassis control command;
• a processing module connected to the positioning module, the car dynamic state sensing module and the chassis control module and configured to perform following steps:
• predicting the self-driving car's estimated position at time of actuation of a given chassis control command, using the self-driving car's current position, the car's current velocity, an estimate of processing delay time and an estimate of chassis control delay time;
• obtaining target road data corresponding to the estimated position from a map database;
• and
• generating a chassis control command, according to the target road data to control the self-driving car;
• wherein the map database comprises a plurality of pieces of pre-stored road data of a detection region, the estimated position is in the detection region, and the target road data is included in the plurality of pieces of pre-stored road data and the plurality of pieces of pre-stored road data are obtained by a road data extracting procedure, and the road data extracting procedure comprises:
• obtaining ground data from first environment detection data of a detection region, wherein the first environment detection data is a point cloud;
• obtaining a first ground model using the ground data;
• filling in missing regions of the first ground model to obtain a second ground model;
• obtaining a plurality of road attributes of the detection region according to the second ground model;
• performing road damage identification using second environment detection data of the detection region and the second ground model;
• and
• storing the plurality of road attributes and a result of the road damage identification as a plurality of pieces of pre-stored road data of the detection region,
• wherein obtaining the ground data from the first environment detection data of the detection region comprises:
• dividing the point cloud into a plurality of original voxels, wherein the plurality of original voxels comprise a plurality of voxel columns;
• obtaining a plurality of target voxel groups from the plurality of original voxels, wherein the plurality of target voxel groups are respectively included in a plurality of voxel columns, a vertical spread parameter of a plurality of points in each of the plurality of target voxel groups is less than or equal to a preset value, and the vertical spread parameter is a height difference between a highest point and a lowest point in each of the plurality of target voxel groups;
• calculating a plurality of height values of the plurality of target voxel groups;
• and
• generating the ground data using the plurality of height values.