System and method for calculating object heading based on lidar and deep learning algorithm

A method performed by an apparatus of a vehicle, the method comprising:

• generating and storing, based on data about a driving speed of the vehicle, driving history information, wherein the data is stored in a queue of a specified size in a memory of the vehicle;
• determining a center point of a predicted bounding box for a target object recognized by a sensor of the vehicle and a deep learning model while the vehicle is in a stationary state,
• generating and storing first center history information comprising a center vector, wherein the first center history information is generated and stored based on tracking the determined center point and the specified size of the queue;
• generating and storing second center history information comprising a relative map vector with respect to the vehicle, wherein the second center history information is generated and stored based on tracking the determined center point and a movement of the target object while the vehicle is in a moving state;
• based on a first weight for the first center history information and a second weight for the second center history information, determining a heading vector of the target object by obtaining a weighted sum of the center vector and the relative map vector;
• outputting a signal indicating the heading vector of the target object;
• and
• controlling, based on the signal, autonomous driving of the vehicle.

An apparatus of a vehicle, the apparatus comprising:

• a processor;
• and
• a memory storing at least one instruction that, when executed by the processor communicating with the memory, is configured to cause the apparatus to:
• store a driving speed history of the vehicle and a center point history of a predicted bounding box associated with a target object, wherein the predicted bounding box is generated based on sensor data obtained from a sensor of the vehicle and a deep learning model,
• generate, based on tracking the center point history of the predicted bounding box while the vehicle is in a stationary state, a first center history comprising a center vector,
• generate, based on tracking the center point history of the predicted bounding box while the vehicle is in a moving state, a second center history comprising a map vector,
• determine a heading vector of the target object based on the center vector and the map vector,
• apply a filter to the heading vector,
• determine, based on a magnitude and a direction of the filtered heading vector, a total movement amount of the target object, and
• output, based on the filtered heading vector and the total movement amount of the target object, a signal indicating a heading of the target object and a trajectory of the target object.