Distance prediction method, model training method, planning-control system and related apparatuses

A distance prediction apparatus, wherein the apparatus comprises:

• one or more processors;
• the processors are coupled with a storage apparatus, the storage apparatus is used for storing one or more programs;
• when the one or more programs are executed by the one or more processors, the distance prediction apparatus is caused to:
• acquire a first global pose, a first expanded navigation path and first landmark information of a first vehicle, wherein the first global pose is a global pose of the first vehicle at a current moment, the first expanded navigation path comprises an expanded path of a first vehicle navigation path determined based on the first global pose, and the first landmark information comprises landmark information comprised in a first road environment image collected by the first vehicle at the first global pose;
• process the first global pose, the first expanded navigation path and the first landmark information based on a distance prediction model to obtain a farthest reachable distance on each lane in the first landmark information;
• acquire a distance training sample set before the first global pose, the first expanded navigation path and the first landmark information are processed based on the distance prediction model, wherein each training sample in the distance training sample set comprises:
• a second expanded navigation path, second landmark information, a second global pose and a truth value of a farthest reachable distance, wherein the second expanded navigation path comprises an expanded path of a second vehicle navigation path, the second landmark information comprises landmark information comprised in a second road environment image collected by a second vehicle on the second expanded navigation path, the second global pose comprises a global pose of the second vehicle when collecting the second road environment image, and the truth value of the farthest reachable distance comprises a truth value of a farthest reachable distance of each lane in the second landmark information;
• perform training by using the distance training sample set to obtain the distance prediction model;
• wherein the distance prediction apparatus is further caused to:
• acquire input data of a landmark perception model in a case that target landmark information comprises the first landmark information and/or the second landmark information, wherein the input data comprises the target road environment image at the target moment and a target local pose at the target moment, wherein the target local pose comprises an offset of a global pose of the target vehicle when collecting the target road environment image relative to a global pose at a target starting point, and the target moment is any moment when the target vehicle collects the target road environment image on a target expanded navigation path;
• when the target landmark information is the first landmark information, the target vehicle is the first vehicle, the target road environment image is the first road environment image, the target expanded navigation path is the first expanded navigation path, and the target local pose is a first local pose;
• when the target landmark information is the second landmark information, the target vehicle is the second vehicle, the target road environment image is the second road environment image, the target expanded navigation path is the second expanded navigation path, and the target local pose is a second local pose;
• process the input data based on the landmark perception model to obtain the target landmark information comprised in the target road environment image at the target moment.