A method and system for autonomous parking are disclosed. The system employs Homography guided motion segmentation (HGMS) to determine the availability of a parking slot. A free parking slot is determined based on an occupancy status check and identified structures of parking lines. For example, motion analysis is performed on successive images captured by a camera unit to determine the occupancy status of a parking slot. Information of the free parking slot, for example, the dimensions and orientation, may be determined from identifying the structure of parking lines. This information is used by the system to guide a vehicle from its current location to the detected free parking slot.

Apparatuses, systems, and techniques to identify objects in view of a camera associated with a vehicle. In at least one embodiment, objects with which a vehicle may collide are identified, based on, for example, a difference between a size of an image of the objects detected at a first point in time and a size of an image of the objects detected at a subsequent point in time.

A system ground surface projection for autonomous driving of a host vehicle is provided. The system includes a LIDAR device of the host vehicle and a computerized device. The computerized device is operable to monitor data from the LIDAR device including a total point cloud. The total point cloud describes an actual ground surface in the operating environment of the host vehicle. The device is further operable to segment the total point cloud into a plurality of local point cloud and, for each of the local point clouds, determine a local polygon estimating a portion of the actual ground surface. The device is further operable to assemble the local polygons into a total estimated ground surface and navigate the host vehicle based upon the total estimated ground surface.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for estimating a 3-D pose of an object of interest from image and point cloud data. In one aspect, a method includes obtaining an image of an environment; obtaining a point cloud of a three-dimensional region of the environment; generating a fused representation of the image and the point cloud; and processing the fused representation using a pose estimation neural network and in accordance with current values of a plurality of pose estimation network parameters to generate a pose estimation network output that specifies, for each of multiple keypoints, a respective estimated position in the three-dimensional region of the environment.

The invention relates to a method for operating an assistance system (2), in which an object (9) is detected and the object (9) is classified for further evaluation by means of an electronic computing device (5), wherein the classification is taken as a basis for predefining a first length (L) of the object (9), and wherein additionally the object (9) is captured by means of a camera (4), evaluated and classified and a second length (L) of the object (9) is determined and the classification and the second length (L) are transmitted to the electronic computing device (5), wherein the predefined first length (L) is adapted on the basis of the second length (L) to produce a current length (L) and a limited Kalman filter (7) is used to update the current length (L), the limitation of the Kalman filter (7) being predefined by the classification determined by means of the camera (4). The invention also relates to a computer program product, a computer-readable storage medium and an assistance system (2).

An obstacle detector is installed on a forklift. The obstacle detector includes a stereo camera for detecting an obstacle, and a position detector for detecting the position of the obstacle from a detection result of the stereo camera. In a detectable area, non-detection areas are preset. The non-detection areas are set at positions where a counterweight of the forklift is located. The position detector determines that an obstacle is not present in the non-detection areas. The position detector detects the position of an obstacle in a detection area that is a different area from the non-detection areas in the detectable area

A processing system for a motorized mobile system includes at least one sensor to measure one or more kinematic states of an object and at least one processor to use at least one state estimation filter and at least one object kinematic model to predict a first kinematic state estimate of the object and output the first predicted kinematic state estimate for use by at least one other process of the motorized mobile system. The processor uses the first predicted kinematic state estimate and the one or more measured kinematic states to determine a second kinematic state estimate of the object and uses the second kinematic state estimate as an input to the state estimation filter to predict another kinematic state estimate of the object.

A view of geometry captured in image data generated by an imaging sensor is compared with a description of the geometry in a volumetric data structure. The volumetric data structure describes the volume at a plurality of levels of detail and includes entries describing voxels defining subvolumes of the volume at multiple levels of detail. The volumetric data structure includes a first entry to describe voxels at a lowest one of the levels of detail and further includes a number of second entries to describe voxels at a higher, second level of detail, the voxels at the second level of detail representing subvolumes of the voxels at the first level of detail. Each of these entries include bits to indicate whether a corresponding one of the voxels is at least partially occupied with the geometry. One or more of these entries are used in the comparison with the image data.

A system for navigating a host vehicle may include a at least one processing device. The at least one processing device may be programmed to receive, from an image capture device, at least one image representative of an environment of the host vehicle. The at least one processing device may also be programmed to analyze the at least one image to identify an object in the environment of the host vehicle. The at least one processing device may also be programmed to determine a location of the host vehicle. The at least one processing device may also be programmed to receive map information associated with the determined location of the host vehicle, wherein the map information includes elevation information associated with the environment of the host vehicle. The at least one processing device may also be programmed to determine a distance from the host vehicle to the object based on at least the elevation information. The at least one processing device may further be programmed to determine a navigational action fir the host vehicle based on the determined distance.

Systems and methods are disclosed herein for tracking a vulnerable road user (VRU) regardless of occlusion. In an embodiment, the system captures a series of images including the VRU, and inputs each of the images into a detection model. The system receives a bounding box for each of the series of images of the VRU as output from the detection model. The system inputs each bounding box into a multi-task model, and receives as output from the multi-task model an embedding for each bounding box. The system determines, using the embeddings for each bounding box across the series of images, an indication of which of the embeddings correspond to the VRU.