A method for supporting autonomous driving of an autonomous vehicle, in particular in terms of an interaction with a vulnerable road user that is in the vicinity of the autonomous vehicle, the method comprising: detecting, by an in-vehicle loT platform of the autonomous vehicle, a vulnerable road user, `VRU', having a mobile device in the vicinity of the autonomous vehicle, by, depending on availability, processing at least one of image data that is gathered by a camera of the autonomous vehicle and VRU-specific data received from a mobility application running on the mobile device of the VRU, determining a transportation mode of the VRU based on the image data, determining, by the in-vehicle loT platform, a movement intention prediction for the VRU based on the VRU-specific data received from the mobile device and based on the transportation mode of the VRU, wherein the movement intention prediction is computed by the use of a machine learning model, wherein the VRU-specific data of the mobile device and the transportation mode are employed as input data for the machine learning model, wherein said input data include information on a movement history of the VRU; performing, by the in-vehicle loT platform, an autonomous driving decision for the autonomous vehicle based on said movement intention prediction, wherein the movement intention prediction that is determined by the machine learning model represents a user action that is expected to be performed next by the VRU, and wherein a set of user actions is defined for the movement intention prediction that is determinable by the machine learning model, wherein said set of actions comprises waiting, walking straight, turning left, turning right and/or turning back. detecting, by an in-vehicle loT platform of the autonomous vehicle, a vulnerable road user, `VRU', having a mobile device in the vicinity of the autonomous vehicle, by, depending on availability, processing at least one of image data that is gathered by a camera of the autonomous vehicle and VRU-specific data received from a mobility application running on the mobile device of the VRU, determining a transportation mode of the VRU based on the image data, determining, by the in-vehicle loT platform, a movement intention prediction for the VRU based on the VRU-specific data received from the mobile device and based on the transportation mode of the VRU, wherein the movement intention prediction is computed by the use of a machine learning model, wherein the VRU-specific data of the mobile device and the transportation mode are employed as input data for the machine learning model, wherein said input data include information on a movement history of the VRU; performing, by the in-vehicle loT platform, an autonomous driving decision for the autonomous vehicle based on said movement intention prediction, wherein the movement intention prediction that is determined by the machine learning model represents a user action that is expected to be performed next by the VRU, and wherein a set of user actions is defined for the movement intention prediction that is determinable by the machine learning model, wherein said set of actions comprises waiting, walking straight, turning left, turning right and/or turning back.