Abstract: Method for improving global positioning performance of a first road vehicle (10), the method comprising, by means of a data server (3, 4, 4?): acquiring data from onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g) arranged on the first road vehicle (10) and on at least two neighbouring road vehicles (10?, 10?, 10??), the data comprising data on relative positions and data on heading angle and velocity of the road vehicles (10, 10?, 10?, 10??), and acquiring global positioning data of at least two of the road vehicles (10, 10?, 10?, 10??), processing (102) data comprising the global positioning data, the data, with corresponding timestamp, acquired from the onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g), and a motion model for each of the first road vehicle (10) and the at least two neighbouring road vehicles (10?, 10?, 10??) using a data fusion algorithm, calculating adjusted global positioning data for the first road vehicle (10) and communicating (104) the adjusted global positioning data to a positioning system (6

Abstract: An automated map generation and map positioning solution for vehicles is disclosed. The solution includes a method for map generation based on the vehicle's sensory perception of the surrounding environment. Moreover, presented map generating method utilizes the inherent advantages of trained self-learning models (e.g. trained artificial networks) to efficiently collect and sort sensor data in order to generate high definition (HD) maps of a vehicle's surrounding environment “on-the-go”. In more detail, the automated map generation method utilizes two self-learning models are used, one general, low-level, feature extraction part and one high-level feature fusion part. The automated positioning method is based on similar principles as the automated map generation, where two self-learning models are used, one “general” feature extraction part and one “task specific” feature fusion part for positioning in the map.

Abstract: Method for improving global positioning performance of a first road vehicle (10), the method comprising, by means of a data server (3, 4, 4?): acquiring data from onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g) arranged on the first road vehicle (10) and on at least two neighbouring road vehicles (10?, 10?, 10??), the data comprising data on relative positions and data on heading angle and velocity of the road vehicles (10, 10?, 10?, 10??), and acquiring global positioning data of at least two of the road vehicles (10, 10?, 10?, 10??), processing (102) data comprising the global positioning data, the data, with corresponding timestamp, acquired from the onboard sensors (2a, 2b, 2c, 2d, 2e, 2f, 2g), and a motion model for each of the first road vehicle (10) and the at least two neighbouring road vehicles (10?, 10?, 10??) using a data fusion algorithm, calculating adjusted global positioning data for the first road vehicle (10) and communicating (104) the adjusted global positioning data to a positioning system (6

Abstract: A digital map enhancement system and a method performed by the system are disclosed for improving accuracy of pre-stored digital map data of a digital map to be utilized by a vehicle. The system determines a current position of the vehicle, and identifies, in the pre-stored digital map data, a mapped digital landmark representing a stationary landmark predicted to be in the vicinity of the current position of the vehicle, which mapped digital landmark includes a pre-stored position of the stationary landmark. The system detects the stationary landmark by one or more sensor devices on-board the vehicle, which are adapted for observing the surroundings of the vehicle, and determines a detected position of the stationary landmark based on the current position of the vehicle and the detection of the stationary landmark. The system updates the pre-stored position of the mapped digital landmark with the detected position of the stationary landmark.

Abstract: A digital map enhancement system and a method performed by the system are disclosed for improving accuracy of pre-stored digital map data of a digital map to be utilized by a vehicle. The system determines a current position of the vehicle, and identifies, in the pre-stored digital map data, a mapped digital landmark representing a stationary landmark predicted to be in the vicinity of the current position of the vehicle, which mapped digital landmark includes a pre-stored position of the stationary landmark. The system detects the stationary landmark by one or more sensor devices on-board the vehicle, which are adapted for observing the surroundings of the vehicle, and determines a detected position of the stationary landmark based on the current position of the vehicle and the detection of the stationary landmark. The system updates the pre-stored position of the mapped digital landmark with the detected position of the stationary landmark.