Abstract: A method including determining a direct distance between the vehicle position and a position of the roadside unit and a planar distance between the vehicle position in a horizontal plane containing the vehicle position, and a projection of the position of the roadside unit onto the plane. The method further includes calculating a height of a roadside unit relative to the vehicle position using the direct distance and the planar distance determined for the vehicle position. The method also includes setting a value indicative of the height of the roadside unit relative to the vehicle position as a height correction value and storing the height correction value of the vehicle position in association with information indicative of the vehicle position as the height correction function.

Abstract: One aspect relates to a computer-implemented method for determining a quasi-rotation-invariant shape descriptor of point cloud data in an automotive system for monitoring the environment of a vehicle. In order to determine the shape descriptor, a method of determining a central point of a point cloud data is performed multiple times, each time using different values for one or more starting parameters. The method may yield different central points depending on the selected values for the one or more starting parameters. A regression line is calculated for the plurality of central points such that the shape descriptor is determined based on a base form of the plurality of central points.

Abstract: One aspect relates to a computer-implemented method for determining a skeleton of point cloud data in an automotive system for monitoring the environment of a vehicle. In order to determine the skeleton, a method of determining a central point of a point cloud data is performed multiple times, each time using different values for one or more starting parameters. The method may yield different central points depending on the selected values for the one or more starting parameters. The resulting plurality of central points is interpreted as the skeleton of the point cloud data.

Abstract: One aspect relates to a computer-implemented method for determining a quasi-rotation-invariant shape descriptor of point cloud data in an automotive system for monitoring the environment of a vehicle. In order to determine the shape descriptor, a method of determining a central point of a point cloud data is performed multiple times, each time using different values for one or more starting parameters. The method may yield different central points depending on the selected values for the one or more starting parameters. A regression line is calculated for the plurality of central points such that the shape descriptor is determined based on a base form of the plurality of central points.

Abstract: The present disclosure relates to a computer-implemented method for determining a central point of point cloud data in an automotive system for monitoring the environment of a vehicle. The point cloud data is generated by one or more sensors of the vehicle with respect to a reference coordinate system. The point cloud data defines a connected subspace of the reference coordinate system. The method comprises a step a) of determining a bounding box of the point cloud data, a step b) of selecting a starting agent position of an agent within the bounding box, and a step c) of selecting a coordinate system relative to the bounding box. In a further step d) a plurality of agent moving operations are performed. Each agent moving operation comprises moving the agent from the current agent position to a new agent position parallel to a coordinate axis of the selected coordinate system. The new agent position is determined based on an intersecting line through the current agent position parallel to the coordinate axis.

Abstract: One aspect relates to a computer-implemented method for determining a skeleton of point cloud data in an automotive system for monitoring the environment of a vehicle. In order to determine the skeleton, a method of determining a central point of a point cloud data is performed multiple times, each time using different values for one or more starting parameters. The method may yield different central points depending on the selected values for the one or more starting parameters. The resulting plurality of central points is interpreted as the skeleton of the point cloud data.

Abstract: A method of processing an image of a scene including a road acquired by a vehicle-mounted camera to generate boundary data indicative of a boundary of an image region which represents an unoccupied area of the road, comprising: generating an LL sub-band image of an Nth level of an (N+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image N times, where N is an integer equal to or greater than one; generating a sub-band image of an (N+1)th level by high-pass filtering the LL sub-band image of the Nth level, and down-sampling a result of the high-pass filtering, such that the sub-band image of the (N+1)th level has a pixel region having substantially equal pixel values representing the unoccupied area of the road in the image; and generating the boundary data by determining a boundary of the pixel region.

Abstract: Disclosed are aspects of methods performed by an onboard unit of a vehicle and aspects of apparatuses that include an onboard unit of a vehicle to perform the methods. An example of the method includes, for each of a plurality of vehicle positions on a road, determining a direct distance between the vehicle position and a position of a roadside unit. The method also includes determining, based on the determined direct distance, a planar distance between the vehicle position in a horizontal plane containing the vehicle position and a projection of a position of the roadside unit onto a plane of the road. The determining of the planar distance is performed based on a height of the roadside unit relative to a ground level of the road.

Abstract: A method including determining a direct distance between the vehicle position and a position of the roadside unit and a planar distance between the vehicle position in a horizontal plane containing the vehicle position, and a projection of the position of the roadside unit onto the plane. The method further includes calculating a height of a roadside unit relative to the vehicle position using the direct distance and the planar distance determined for the vehicle position. The method also includes setting a value indicative of the height of the roadside unit relative to the vehicle position as a height correction value and storing the height correction value of the vehicle position in association with information indicative of the vehicle position as the height correction function.

Abstract: An apparatus for determining a speed of a vehicle along a road by processing a first image and a second image of the road captured by a camera on the vehicle and comprising respective road marker images of a road marker, the apparatus arranged to: determine a location of the road marker in the first image; predict a location of the road marker in the second image based on the determined location, an estimate of the vehicle speed, and a time period between capture of the images; detect the road marker in a portion of the second image at the predicted location; estimate a distance moved by the vehicle during the time period based on the determined location, and a location of the detected road marker in the portion of the second image; and calculate the speed based on the estimated distance and the time period.

Abstract: A method of processing an image of a road having a road marker acquired by a vehicle-mounted camera to generate boundary data indicating a boundary of the road marker region of the image which represents the road marker, comprising: generating an LL sub-band image of an Mth level of an (M+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image M times; generating a sub-band image of an (M+1)th level of the (M+1) level DWT decomposition by high-pass filtering the LL sub-band image and down-sampling a result of the high-pass filtering; and determining a boundary of a region of pixels of the sub-band image of the (M+1)th level, the region being surrounded by pixels having pixel values substantially different to the pixel values of the pixels in the region, the determined boundary indicating the boundary of the road marker region.

Abstract: A method of processing an image of a scene including a road acquired by a vehicle-mounted camera to generate boundary data indicative of a boundary of an image region which represents an unoccupied area of the road, comprising: generating (S10) an LL sub-band image of an Nth level of an (N+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image N times, where N is an integer equal to or greater than one; generating (S20) a sub-band image of an (N+1)th level by high-pass filtering the LL sub-band image of the Nth level, and down-sampling a result of the high-pass filtering, such that the sub-band image of the (N+1)th level has a pixel region having substantially equal pixel values representing the unoccupied area of the road in the image; and generating (S30) the boundary data by determining a boundary of the pixel region.

Abstract: A computer implemented method for object detection includes the following steps carried out by computer hardware components: acquiring an image; determining a pixel of the image as a base pixel; determining coordinates of a plurality of sets of target pixels, each set of target pixels including a plurality of pixels in a respective pre-determined relationship to the base pixel; for each of the sets of target pixels, determining information representing values of the pixels in the respective set of target pixels; and determining whether a pre-determined object is shown in the image based on the determined information.

Abstract: A computer implemented method for object detection includes the following steps carried out by computer hardware components: acquiring an image; determining a pixel of the image as a base pixel; determining coordinates of a plurality of sets of target pixels, each set of target pixels including a plurality of pixels in a respective pre-determined relationship to the base pixel; for each of the sets of target pixels, determining information representing values of the pixels in the respective set of target pixels; and determining whether a pre-determined object is shown in the image based on the determined information.