A method for training a trajectory planning model, wherein the trajectory planning model is a neural network model, the method comprising: obtaining an image of a physical environment in which a vehicle is located via at least one sensor of the vehicle, wherein the obtained image comprises a plurality of objects surrounding the vehicle; inputting the image into the trajectory planning model and obtaining a feature chart from the trajectory planning model based on the image, wherein the obtained image is processed by the trajectory planning model to determine a planned trajectory of the vehicle, the feature chart indicating a plurality of initial trajectory points of the vehicle in the image; identifying the image to determine in the image a first area associated with a road object in the plurality of objects and a second area associated with a non-road object in the plurality of objects; determining planning trajectory points based on a positional relationship of the plurality of initial trajectory points with respect to the first area and the second area, wherein determining the planning trajectory points comprises: determining weights for the initial trajectory points based on the positional relationship, wherein a first set of the initial trajectory points belonging to the first area are set to have a first weight and a second set of the initial trajectory points belonging to the second area are set to have a second weight smaller than the first weight; determining the planning trajectory points based on the weight and on the feature chart by point-multiplying a matrix corresponding to the feature chart and a matrix a weight matrix corresponding to the weights; and determining the planning trajectory points by a transposed convolution and sigmoid activation layer; and training the trajectory planning model by comparing the planning trajectory points with actual trajectory points of the vehicle. obtaining an image of a physical environment in which a vehicle is located via at least one sensor of the vehicle, wherein the obtained image comprises a plurality of objects surrounding the vehicle; inputting the image into the trajectory planning model and obtaining a feature chart from the trajectory planning model based on the image, wherein the obtained image is processed by the trajectory planning model to determine a planned trajectory of the vehicle, the feature chart indicating a plurality of initial trajectory points of the vehicle in the image; identifying the image to determine in the image a first area associated with a road object in the plurality of objects and a second area associated with a non-road object in the plurality of objects; determining planning trajectory points based on a positional relationship of the plurality of initial trajectory points with respect to the first area and the second area, wherein determining the planning trajectory points comprises: determining weights for the initial trajectory points based on the positional relationship, wherein a first set of the initial trajectory points belonging to the first area are set to have a first weight and a second set of the initial trajectory points belonging to the second area are set to have a second weight smaller than the first weight; determining the planning trajectory points based on the weight and on the feature chart by point-multiplying a matrix corresponding to the feature chart and a matrix a weight matrix corresponding to the weights; and determining the planning trajectory points by a transposed convolution and sigmoid activation layer; and determining weights for the initial trajectory points based on the positional relationship, wherein a first set of the initial trajectory points belonging to the first area are set to have a first weight and a second set of the initial trajectory points belonging to the second area are set to have a second weight smaller than the first weight; determining the planning trajectory points based on the weight and on the feature chart by point-multiplying a matrix corresponding to the feature chart and a matrix a weight matrix corresponding to the weights; and determining the planning trajectory points by a transposed convolution and sigmoid activation layer; and training the trajectory planning model by comparing the planning trajectory points with actual trajectory points of the vehicle.