Abstract: A system and method for generating training data for fine-tuning an Object Detector (OD) are provided. The method comprises: receiving a given sequence of labelled 3D point clouds of the source domain; selecting, in the sequence bounding boxes, a quasi-stationary bounding box that is a most overlapped bounding box by other bounding boxes from the sequence of bounding boxes; generating a re-labelled 3D point cloud of the source domain by relocating, in the given labelled 3D point cloud, the respective bounding box of the training object to a location thereof corresponding to a location of the quasi-stationary bounding box in an other labelled 3D point cloud; and storing the re-labelled 3D point cloud of the source domain in an internal memory of the server.

Abstract: An application is launched in response to identifying a specific user in the vehicle. Upon launching the application, a historical comfort setting profile is automatically downloaded for the user. The historical profile includes individual comfort setting records. The historical profile is updated with comfort setting record(s) corresponding to each instance of an automatic climate control setting, a manual climate control setting, or an alternate climate control setting while the specific user is in the vehicle. An in-vehicle setting is dynamically predicted within respective predetermined time increments while the specific user is in the vehicle or in response to a user request. The predicted in-vehicle setting is dependent upon geographic location data points and a set of climate control related settings retrieved from the individual comfort setting records. A most recently predicted in-vehicle setting is caused to be displayed on a vehicle display while the specific user is in the vehicle.

Abstract: An application is launched in response to identifying a specific user in the vehicle. Upon launching the application, a historical comfort setting profile is automatically downloaded for the user. The historical profile includes individual comfort setting records. The historical profile is updated with comfort setting record(s) corresponding to each instance of an automatic climate control setting, a manual climate control setting, or an alternate climate control setting while the specific user is in the vehicle. An in-vehicle setting is dynamically predicted within respective predetermined time increments while the specific user is in the vehicle or in response to a user request. The predicted in-vehicle setting is dependent upon geographic location data points and a set of climate control related settings retrieved from the individual comfort setting records. A most recently predicted in-vehicle setting is caused to be displayed on a vehicle display while the specific user is in the vehicle.

Abstract: An application is automatically launched in response to identifying a specific user in the vehicle. Upon launching the application, a historical calling profile is automatically downloaded for the user. The historical calling profile includes individual vehicle call records. The historical calling profile is updated with vehicle call record(s) corresponding to each instance of an outgoing call placed while the user is in the vehicle. A probability of a telephone number being chosen for an outgoing call, within time increments while the user is in the vehicle or in response to a request, is dynamically determined. The probability is dependent upon location data points retrieved from the individual vehicle call records. A list of telephone numbers sorted by the probability is dynamically generated. A most recently generated list of telephone numbers sorted by the probability is caused to be displayed on a vehicle display while the user is in the vehicle.