Abstract: An embodiment collision avoidance control method with an autonomous driving control system includes generating a grid map by storing vehicle information including position and velocity vectors of a vehicle and object information including position and velocity vectors of an object sensed around the vehicle in a grid cell corresponding to an area occupied by the vehicle and the object and determining, as a collision risk area, an area where a grid cell occupied by the vehicle for each reference time set based on the vehicle information overlaps a grid cell occupied by the object for each reference time predicted based on the object information, to control avoidance of the collision risk area.

Abstract: An object detection method includes determining an error parameter associated with an amount of movement of a vehicle through a predetermined regression method based on positioning information of the vehicle and dynamics information of the vehicle with respect to a predetermined center of gravity of the vehicle, determining a velocity of a predetermined point of the vehicle, based on a fixed error parameter stored in a memory or a corrected fixed error parameter, through a comparison between the error parameter and the fixed error parameter, generating a local map in consideration of the amount of movement of the vehicle based on the determined velocity, and detecting an object around the vehicle based on the local map.

Abstract: An embodiment method for classifying objects around a vehicle includes generating a dynamic occupancy grid map including a plurality of cells including point data corresponding to each of a plurality of objects located around the vehicle and velocity vector information of the point data, based on LiDAR data received from a LiDAR sensor of the vehicle and information related to movement of the vehicle, determining a cluster corresponding to each of the plurality of objects on the dynamic occupancy grid map using a clustering technique, and classifying an object corresponding to the cluster into a static object or a dynamic object, based on a region size of the cluster and velocity vector information included in cells belonging to the cluster.

Abstract: A method of tracking an object includes obtaining a likelihood of a free behavior model of the object and a likelihood of a constant speed model of the object using the position, the speed, and the type of the object determined at a previous time point and the position, the speed, and the type of the object determined at the current time point, and correcting the type of the object at the current time point using the likelihood of the free behavior model, the likelihood of the constant speed model, and the measured type of the object.