Abstract: An apparatus for predicting a speed of a vehicle includes storage that stores a transformer model which completes learning, and a controller that predicts the speed of the vehicle based on the transformer model. The controller may extract feature information about a state of the vehicle from past driving information of the vehicle to input the feature information to an encoder of the transformer model, and extract feature information about the state of the vehicle from future driving information of the vehicle to input the feature information to a decoder of the transformer model.

Abstract: An autonomous driving control apparatus includes a memory and at least one processor. The at least one processor identifies first input information including at least one of a characteristic of a road, event information of the road, or any combination thereof and second input information including a characteristic of the vehicle. The at least one processor further performs cross-modal alignment of a backbone model for outputting a speed profile. The at least one processor also applies an output obtained by applying the first input information to a first encoder neural network, an output obtained by applying the second input information to a feature extraction neural network of the backbone model, and a target speed to a second decoder neural network. The at least one processor further obtains the speed profile from the second decoder neural network to perform main learning of the backbone model.

Abstract: An autonomous driving control apparatus includes a memory and at least one processor. The at least one processor identifies first input information including at least one of a characteristic of a road, event information of the road, or any combination thereof and second input information including a characteristic of the vehicle. The at least one processor further performs cross-modal alignment of a backbone model for outputting a speed profile. The at least one processor also applies an output obtained by applying the first input information to a first encoder neural network, an output obtained by applying the second input information to a feature extraction neural network of the backbone model, and a target speed to a second decoder neural network. The at least one processor further obtains the speed profile from the second decoder neural network to perform main learning of the backbone model.

Abstract: An apparatus for predicting a speed of a vehicle includes storage that stores a transformer model which completes learning, and a controller that predicts the speed of the vehicle based on the transformer model. The controller may extract feature information about a state of the vehicle from past driving information of the vehicle to input the feature information to an encoder of the transformer model, and extract feature information about the state of the vehicle from future driving information of the vehicle to input the feature information to a decoder of the transformer model.