A computer-implemented method for determining a set of dynamic objects based on sensor data acquired by a sensor mounted on a vehicle, the method being executable by an electronic device, the electronic device being communicatively coupled to the sensor for collection of the sensor data therefrom, the electronic device executing a first machine learning algorithm, MLA, having been trained for object detection based on the sensor data, the method comprising: receiving the sensor data representative of a surrounding area of the vehicle; generating, by the first MLA, based on the sensor data, a set of feature vectors of the surrounding area; receiving vehicle data indicative of a localization of the vehicle on a map; generating, by the first MLA, based on the set of feature vectors and the vehicle data, a tensor, the tensor including a grid representation of the surrounding area, wherein the tensor is a 3D tensor representation, and the generating the 3D tensor representation comprises generating a 4D tensor representation based on the set of feature vectors, and stacking features of the set of feature vectors in the 4D tensor representation to obtain the 3D tensor representation; generating, by the first MLA, based on the tensor: a segmentation mask indicative of grid cells occupied by at least one potential object in the grid, and a velocity mask indicative of a velocity associated with the at least one potential object in the grid; and determining, by the first MLA, based on the segmentation mask and the velocity mask, the set of dynamic objects in the surrounding area of the vehicle, wherein the electronic device is configured to execute a second MLA, the second MLA being a recurrent neural network, RNN, the vehicle data includes a set of odometer parameters, and the method further comprises correcting, by the second MLA, the 3D tensor representation based on the set of odometer parameters to compensate for an ego-motion of the sensor to obtain a corrected 3D tensor representation. receiving the sensor data representative of a surrounding area of the vehicle; generating, by the first MLA, based on the sensor data, a set of feature vectors of the surrounding area; receiving vehicle data indicative of a localization of the vehicle on a map; generating, by the first MLA, based on the set of feature vectors and the vehicle data, a tensor, the tensor including a grid representation of the surrounding area, wherein the tensor is a 3D tensor representation, and the generating the 3D tensor representation comprises generating a 4D tensor representation based on the set of feature vectors, and stacking features of the set of feature vectors in the 4D tensor representation to obtain the 3D tensor representation; generating, by the first MLA, based on the tensor: a segmentation mask indicative of grid cells occupied by at least one potential object in the grid, and a velocity mask indicative of a velocity associated with the at least one potential object in the grid; and a segmentation mask indicative of grid cells occupied by at least one potential object in the grid, and a velocity mask indicative of a velocity associated with the at least one potential object in the grid; and determining, by the first MLA, based on the segmentation mask and the velocity mask, the set of dynamic objects in the surrounding area of the vehicle, wherein the electronic device is configured to execute a second MLA, the second MLA being a recurrent neural network, RNN, the vehicle data includes a set of odometer parameters, and the method further comprises correcting, by the second MLA, the 3D tensor representation based on the set of odometer parameters to compensate for an ego-motion of the sensor to obtain a corrected 3D tensor representation.