A data training method for an autonomous driving vehicle (101, 102, 103), the method comprising: acquiring sensor data of the autonomous driving vehicle (101, 102, 103) and a correction sample, the correction sample being used for representing driving behavior data of a driver when the autonomous driving vehicle (101, 102, 103) 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 (101, 102, 103) and correcting the end-to-end model using the sensor data; and training a preset brain model of the autonomous driving vehicle (101, 102, 103) using the correction sample and a negative sample, wherein the brain model of the autonomous driving vehicle (101, 102, 103) 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 (101, 102, 103), and the brain model of the autonomous driving vehicle (101, 102, 103) 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 (101, 102, 103) and a correction sample, the correction sample being used for representing driving behavior data of a driver when the autonomous driving vehicle (101, 102, 103) 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 (101, 102, 103) and correcting the end-to-end model using the sensor data; and training a preset brain model of the autonomous driving vehicle (101, 102, 103) using the correction sample and a negative sample, wherein the brain model of the autonomous driving vehicle (101, 102, 103) 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 (101, 102, 103), and the brain model of the autonomous driving vehicle (101, 102, 103) 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 (101, 102, 103) and correcting the end-to-end model using the sensor data; and training a preset brain model of the autonomous driving vehicle (101, 102, 103) using the correction sample and a negative sample, wherein the brain model of the autonomous driving vehicle (101, 102, 103) 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 (101, 102, 103), and the brain model of the autonomous driving vehicle (101, 102, 103) 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.