Abstract: An occupancy grid determination method includes: determining a predicted occupancy grid based on a previous occupancy grid, the predicted occupancy grid comprising a plurality of first cells corresponding to sub-regions of a region, each of the plurality of first cells including a plurality of predicted indications of probability each indicative of a predicted probability of a respective possible type of occupier of the respective first cell; determining, using machine learning and based on the first sensor measurements, an observed occupancy grid comprising a plurality of second cells corresponding to the sub-regions of the region; and determining an updated occupancy grid based on the observed occupancy grid and the predicted occupancy grid.

Abstract: An occupancy grid probability prediction method includes: determining a dynamic particle probability based on a first probability that a dynamic particle will move from a donor cell of an occupancy grid to a recipient cell of the occupancy grid; and determining one or more predicted probabilities of at least one of the donor cell or the recipient cell based on the dynamic particle probability.

Abstract: An occupancy map processing method includes: obtaining an occupancy map of a region comprising a plurality of cells, corresponding to sub-regions, each including an occupancy indication indicative of occupier type of the sub-region, and the plurality of cells comprising delimiter cells and non-delimiter cells; and providing, from the apparatus, occupancy information comprising first occupancy information corresponding to the delimiter cells and either second occupancy information corresponding to fewer than all of the non-delimiter cells or no second occupancy information.

Abstract: A method for performing a sensor verification for a vehicle is disclosed. The vehicle includes a first sensor and a second sensor, wherein a first sensor coordinate system of the first sensor and a second sensor coordinate system of the second sensor are related to a vehicle coordinate system, and wherein the first sensor and the second sensor have an at least partly overlapping observable space. The method includes determining a first position of an external object located in the at least partly overlapping observable space by means of the first sensor of the vehicle, and determining a second position of the external object by means of the second sensor of the vehicle. The method includes comparing the determined first and second positions in relation to any one of the first sensor coordinate system, second sensor coordinate system or vehicle coordinate system in order to form a first comparison value.

Abstract: The present invention relates to methods and arrangements for safety monitoring of a safety zone under and in near vicinity around a parked autonomous vehicle before driving, wherein the method comprise acquiring sensor data from an active sensor system located on the undercarriage of the vehicle, wherein the active sensor system is arranged to transmit a signal and receive the signal reflected off objects within the safety zone and store sensor data representative of substantially all of the safety zone, repeatedly building a representation of the safety zone by acquiring sensor data from the active sensor system; iteratively aggregating the representations of the safety zone with a statistical model, and continuously analysing the aggregation of representations and determining if a sensitive object is located within the safety zone of the vehicle.