Method for training artificial neural network to predict future trajectories of various types of moving objects for autonomous driving

A method for training an artificial neural network to predict future trajectories of various types of objects, the method comprising:

• a training data generation step of generating past movement trajectories for one or more objects based on location information for a predetermined time about the one or more objects existing in a predetermined distance range around an autonomous vehicle based on a specific time point, generating a driving environment context information image for the autonomous vehicle through a method of displaying road information around the autonomous vehicle and the past movement trajectories on a 2D image, and generating answer future trajectories for the one or more objects based on the location information for the predetermined time about the one or more objects after the specific time point;
• a step of generating object future trajectories by inputting the past movement trajectories, the driving environment context information image, and the answer future trajectories to a deep neural network (DNN), and calculating a loss function value based on a difference between the object future trajectories and the answer future trajectories;
• and
• a step of training the DNN so that the loss function value becomes smaller.

A method for predicting future trajectories of various types of objects, the method comprising:

• a step of collecting location information of one or more objects around an autonomous vehicle for a predetermined time, and generating past movement trajectories for the one or more objects based on the location information;
• a step of generating a driving environment context information image based on road information around the autonomous vehicle and the past movement trajectories;
• a step of generating a driving environment feature map by inputting the driving environment context information image to a first convolutional neural network;
• a step of generating a motion feature vector by using a long short-term memory (LSTM) based on the past movement trajectories;
• a step of generating an object environment feature vector by using a second convolutional neural network based on the driving environment feature map;
• and
• a step of generating future trajectories for the one or more objects by using a variational auto-encoder (VAE) and an MLP based on the motion feature vector and the object environment feature vector.