A data training method for an autonomous driving vehicle , the method comprising: acquiring sensor data of the autonomous driving vehicle and a correction sample, the correction sample being used for representing driving behavior data of a driver when the autonomous driving vehicle encounters an interference during driving; building an end-to-end model using the sensor data and the correction sample, the end-to-end model being configured for outputting a control instruction corresponding to a driving behavior of the driver using the sensor data and the correction sample; wherein building the end-to-end model and correcting the end-to-end model comprises: pushing the end-to-end model to the autonomous driving vehicle and correcting the end-to-end model using the sensor data; and training a preset brain model of the autonomous driving vehicle using the correction sample and a negative sample, wherein the brain model of the autonomous driving vehicle is configured for predicting a vehicle control instruction based on vehicle data, the negative sample is used for characterizing the interference encountered by the autonomous driving vehicle , and the brain model of the autonomous driving vehicle learns from the correction sample to issue a vehicle control instruction to eliminate interference caused by a negative sample in the presence of the negative sample. acquiring sensor data of the autonomous driving vehicle and a correction sample, the correction sample being used for representing driving behavior data of a driver when the autonomous driving vehicle encounters an interference during driving; building an end-to-end model using the sensor data and the correction sample, the end-to-end model being configured for outputting a control instruction corresponding to a driving behavior of the driver using the sensor data and the correction sample; wherein building the end-to-end model and correcting the end-to-end model comprises: pushing the end-to-end model to the autonomous driving vehicle and correcting the end-to-end model using the sensor data; and training a preset brain model of the autonomous driving vehicle using the correction sample and a negative sample, wherein the brain model of the autonomous driving vehicle is configured for predicting a vehicle control instruction based on vehicle data, the negative sample is used for characterizing the interference encountered by the autonomous driving vehicle , and the brain model of the autonomous driving vehicle learns from the correction sample to issue a vehicle control instruction to eliminate interference caused by a negative sample in the presence of the negative sample. pushing the end-to-end model to the autonomous driving vehicle and correcting the end-to-end model using the sensor data; and training a preset brain model of the autonomous driving vehicle using the correction sample and a negative sample, wherein the brain model of the autonomous driving vehicle is configured for predicting a vehicle control instruction based on vehicle data, the negative sample is used for characterizing the interference encountered by the autonomous driving vehicle , and the brain model of the autonomous driving vehicle learns from the correction sample to issue a vehicle control instruction to eliminate interference caused by a negative sample in the presence of the negative sample. <EOS>