Abstract: A vehicle control apparatus includes a first sensor, a second sensor, a processor, and a memory storing first and second neural network models. The processor is configured to obtain first object data for predicting information related to the external object based on entering the first sensor data into the first neural network model, obtain second object data for predicting information related to the external object based on entering the second sensor data into the first neural network model the first object data or the second object data as input data to be entered into the second neural network model, and output data including a final location of the external object or a final type of the external object based on entering the input data into the second neural network model.

Abstract: A vehicle control apparatus may comprise sensors obtaining raw data identifying an external object, stored neural network models, decoders, and a processor. The processor may analyze the raw data from each sensor for predicting object locations and type using neural network models and decoders. The processor may select input data based on a distance between the object locations and reliability values and enter the selected input data into an additional neural network model of the neural network models. The processor may output, based on the input data entered into the additional neural network model, data comprising a final object location or type, and control, based on the output data, operation of the vehicle.

Abstract: A structure formed from one or more bi-material cantilevers, having a first portion and a second portion positioned end-to-end, wherein the first portion comprises a first material positioned on top of a second material, and wherein the second portion comprises the second material positioned on top of the first material.