A device for detecting and tracking objects in a surrounding of a vehicle, comprising: an interface configured to receive, from at least one vehicle sensor of the vehicle, new measurement data acquired at a current measurement cycle; and a computing device configured to: determine time coordinates and spatial coordinates of newly identified candidate objects in a surrounding of the vehicle based on the new measurement data, wherein the spatial coordinates of the newly identified candidate objects are defined relative to a global coordinate system; associate each newly identified candidate object with at most one tracked candidate object of a plurality of tracked candidate objects identified at earlier measurement cycles, based on distances between the newly identified candidate object and the plurality of tracked candidate objects, wherein the distance depends on both a difference between the spatial coordinates of the newly identified candidate object and the tracked candidate object and a difference between the time coordinates of the newly identified candidate object and the tracked candidate object; update spatial coordinates of the tracked candidate objects using a Kalman filtering method, using the associated newly identified candidate objects; and detect at least one tracked real object of the tracked candidate objects based on a time behavior of the spatial coordinates of the candidate objects; characterized in that the distance D between a newly identified candidate object and a tracked candidate object is computed by the following formula: D 2 = dx 2 + dy 2 + dz 2 − vt 2, wherein dx, dy and dz denote differences between the spatial coordinates x, y, z of the newly identified candidate object and the tracked candidate object for three orthogonal axes of the global coordinate system, wherein v denotes a velocity of the vehicle, and wherein t denotes a difference between the temporal coordinate of the newly identified candidate object and the tracked candidate object. an interface configured to receive, from at least one vehicle sensor of the vehicle, new measurement data acquired at a current measurement cycle; and a computing device configured to: determine time coordinates and spatial coordinates of newly identified candidate objects in a surrounding of the vehicle based on the new measurement data, wherein the spatial coordinates of the newly identified candidate objects are defined relative to a global coordinate system; associate each newly identified candidate object with at most one tracked candidate object of a plurality of tracked candidate objects identified at earlier measurement cycles, based on distances between the newly identified candidate object and the plurality of tracked candidate objects, wherein the distance depends on both a difference between the spatial coordinates of the newly identified candidate object and the tracked candidate object and a difference between the time coordinates of the newly identified candidate object and the tracked candidate object; update spatial coordinates of the tracked candidate objects using a Kalman filtering method, using the associated newly identified candidate objects; and detect at least one tracked real object of the tracked candidate objects based on a time behavior of the spatial coordinates of the candidate objects; characterized in that the distance D between a newly identified candidate object and a tracked candidate object is computed by the following formula: D 2 = dx 2 + dy 2 + dz 2 − vt 2, wherein dx, dy and dz denote differences between the spatial coordinates x, y, z of the newly identified candidate object and the tracked candidate object for three orthogonal axes of the global coordinate system, wherein v denotes a velocity of the vehicle, and wherein t denotes a difference between the temporal coordinate of the newly identified candidate object and the tracked candidate object. determine time coordinates and spatial coordinates of newly identified candidate objects in a surrounding of the vehicle based on the new measurement data, wherein the spatial coordinates of the newly identified candidate objects are defined relative to a global coordinate system; associate each newly identified candidate object with at most one tracked candidate object of a plurality of tracked candidate objects identified at earlier measurement cycles, based on distances between the newly identified candidate object and the plurality of tracked candidate objects, wherein the distance depends on both a difference between the spatial coordinates of the newly identified candidate object and the tracked candidate object and a difference between the time coordinates of the newly identified candidate object and the tracked candidate object; update spatial coordinates of the tracked candidate objects using a Kalman filtering method, using the associated newly identified candidate objects; and detect at least one tracked real object of the tracked candidate objects based on a time behavior of the spatial coordinates of the candidate objects; characterized in that the distance D between a newly identified candidate object and a tracked candidate object is computed by the following formula: D 2 = dx 2 + dy 2 + dz 2 − vt 2, wherein dx, dy and dz denote differences between the spatial coordinates x, y, z of the newly identified candidate object and the tracked candidate object for three orthogonal axes of the global coordinate system, wherein v denotes a velocity of the vehicle, and wherein t denotes a difference between the temporal coordinate of the newly identified candidate object and the tracked candidate object.