A method for processing an obstacle detection result of an ultrasonic sensor array, the method comprising: obtaining obstacle coordinates collected by ultrasonic sensors in the ultrasonic sensor array; inputting the obstacle coordinates collected by ultrasonic sensors in the ultrasonic sensor array into a pre-trained neural network model, to obtain true or false labels for the obstacle coordinates collected by the ultrasonic sensors output by the pre-trained neural network model; processing the obstacle coordinates collected by the ultrasonic sensors in the ultrasonic sensor array according to the true or false labels, characterized in that the neural network model is trained by the following steps: building an obstacle test scenario; obtaining and unifying the obstacle coordinates collected by the ultrasonic sensors in the ultrasonic sensor array and the coordinates of the same obstacle collected by a LiDAR into a reference coordinate system; performing true or false labelling for the obstacle coordinates collected by the ultrasonic sensors according to the obstacle coordinates collected by the unified LiDAR, and generating training samples; training the neural network model according to the training samples, wherein the performing true or false labelling for the obstacle coordinates collected by the ultrasonic sensors according to the obstacle coordinates collected by the unified LiDAR comprises: if an error between the obstacle coordinates collected by the LiDAR and the obstacle coordinates collected by the ultrasonic sensors is within a preset threshold range, labelling the obstacle coordinates as true; if the error is greater than the preset threshold range, labelling the obstacle coordinates as false. obtaining obstacle coordinates collected by ultrasonic sensors in the ultrasonic sensor array; inputting the obstacle coordinates collected by ultrasonic sensors in the ultrasonic sensor array into a pre-trained neural network model, to obtain true or false labels for the obstacle coordinates collected by the ultrasonic sensors output by the pre-trained neural network model; processing the obstacle coordinates collected by the ultrasonic sensors in the ultrasonic sensor array according to the true or false labels, characterized in that the neural network model is trained by the following steps: building an obstacle test scenario; obtaining and unifying the obstacle coordinates collected by the ultrasonic sensors in the ultrasonic sensor array and the coordinates of the same obstacle collected by a LiDAR into a reference coordinate system; performing true or false labelling for the obstacle coordinates collected by the ultrasonic sensors according to the obstacle coordinates collected by the unified LiDAR, and generating training samples; training the neural network model according to the training samples, wherein the performing true or false labelling for the obstacle coordinates collected by the ultrasonic sensors according to the obstacle coordinates collected by the unified LiDAR comprises: if an error between the obstacle coordinates collected by the LiDAR and the obstacle coordinates collected by the ultrasonic sensors is within a preset threshold range, labelling the obstacle coordinates as true; if the error is greater than the preset threshold range, labelling the obstacle coordinates as false.