Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

Abstract: Techniques for determining ground surface voxels based on multiresolution planes are discussed herein. Such multiresolution planes may be generated, validated, and used to connect voxels associated with the ground. In some examples, a vehicle may use capture or otherwise receive lidar data while traversing within a driving environment. The vehicle may associate the lidar data with a voxel space. Voxels can be associated with a cell of a multiresolution grid and the vehicle may determine multiresolution planes associated with the multiresolution space. For example, the vehicle may determine a first plane associated with a first resolution level, in addition to determining a plurality of planes associated with a second resolution level. The vehicle may determine the validity of the plane(s) at the various resolution levels. Based on the validity of the planes, the vehicle may connect the valid planes that are associated with the ground surface.

Abstract: Techniques are discussed herein for controlling autonomous vehicles within a driving environment, including generating and using bounding contours associated with objects detected in the environment. Image data may be captured and analyzed to identify and/or classify objects within the environment. Image-based and/or lidar-based techniques may be used to determine depth data associated with the objects, and a bounding contour may be determined based on the object boundaries and associated depth data. An autonomous vehicle may use the bounding contours of objects within the environment to classify the objects, predict the positions, poses, and trajectories of the objects, and determine trajectories and perform other vehicle control actions while safely navigating the environment.

Abstract: Various embodiments may relate to an optical device. The optical device may include a stacked structure having a first surface and a second surface opposite the first surface. The stacked structure may include a plurality of holes or grooves extending from the first surface towards the second surface. The stacked structure may include a transition metal dichalcogenide material (TMDC) material. A thickness of the stacked structure may be of any value less than 100 nm.

Abstract: Techniques for collision avoidance using an object contour are discussed. A trajectory associated with a vehicle may be received. Sensor data can be received from a sensor associated with the vehicle. A bounding contour may be determined and associated with an object represented in the sensor data. Based on the trajectory, a simulated position of the vehicle can be determined. Additionally, a predicted position of the bounding contour can be determined. Based on the simulated position of the vehicle and the predicted position of the bounding contour, a distance between the vehicle and the object may be determined. An action can be performed based on the distance between the vehicle and the object.

Abstract: Techniques are discussed herein for controlling autonomous vehicles within a driving environment, including generating and using bounding contours associated with objects detected in the environment. Image data may be captured and analyzed to identify and/or classify objects within the environment. Image-based and/or lidar-based techniques may be used to determine depth data associated with the objects, and a bounding contour may be determined based on the object boundaries and associated depth data. An autonomous vehicle may use the bounding contours of objects within the environment to classify the objects, predict the positions, poses, and trajectories of the objects, and determine trajectories and perform other vehicle control actions while safely navigating the environment.

Abstract: Techniques for determining an object contour are discussed. Depth data associated with an object may be received. The depth data, such as lidar data, can be projected onto a two-dimensional plane. A first convex hull may be determined based on the projected lidar data. The first convex hull may include a plurality of boundary edges. A longest boundary edge, having a first endpoint and a second endpoint, can be determined. An angle can be determined based on the first endpoint, the second endpoint, and an interior point in the interior of the first convex hull. The longest boundary edge may be replaced with a first segment based on the first endpoint and the interior point, and a second segment based on the interior point and the second endpoint. An updated convex hull can be determined based on the first segment and the second segment.

Abstract: A semiconductor wafer (1a, 1b) including a plurality of chips (2) and a separation zone (3) spacing the semiconductor chips (2) from each other in this wafer (1a, 1b), such a separation zone (3) extending from a front face (4a) to an opposite backside face (4b) of this wafer (1a, 1b), this separation zone (3) includes a scribe line (6) configured to be diced using plasma etching and an inlet area (13) of this scribe line (6), the inlet (13) being delimitated by free ends of plasma etch-resistant material layers (9) extending each from a peripheral wall (20) of a functional part (18) of a chip (2) into the scribe line (6) by overlapping a top of a seal ring (7) of this chip (2).

Abstract: In some implementations, a classification system may receive credentials associated with a data source and may receive, from the data source and using the credentials, a set of structured data including input events and output events. The classification system may filter the set of structured data by applying a first set of rules to generate a filtered set of structured data and may convert the filtered set of structured data to one or more numerical vectors, where a vector space associated with the one or more numerical vectors is infinite-dimensional. The classification system may further cluster the one or more numerical vectors using a first machine learning model to generate one or more clusters. Accordingly, the classification system may determine one or more classifications based on the set of structured data, each of the one or more classifications being associated with a corresponding frequency and a corresponding category.

Abstract: Techniques for identifying curbs are discussed herein. For instance, a vehicle may generate sensor data using one or more sensors, where the sensor data represents points associated with a driving surface and a sidewalk. The vehicle may then quantize the points into distance bins that are located laterally along the driving direction of the vehicle in order to generate spatial lines. Next, the vehicle may determine separation points for the spatial lines, where the separation points are configured to separate the points associated with the driving surface from the points associated with the sidewalk. The vehicle may then generate, using the separation points, a curve that represents the curb between the driving surface and the sidewalk. This way, the vehicle may use the curve while navigating, such as to avoid the curb and/or stop at a location that is proximate to the curb.

Abstract: Systems, methods, and apparatuses described herein are directed to performing segmentation on voxels representing three-dimensional data to identify static and dynamic objects. LIDAR data may be captured by a perception system for an autonomous vehicle and represented in a voxel space. Operations may include determining a drivable surface by parsing individual voxels to determine an orientation of a surface normal of a planar approximation of the voxelized data relative to a reference direction. Clustering techniques can be used to grow a ground plane including a plurality of locally flat voxels. Ground plane data can be set aside from the voxel space, and the remaining voxels can be clustered to determine objects. Voxel data can be analyzed over time to determine dynamic objects. Segmentation information associated with ground voxels, static object, and dynamic objects can be provided to a tracker and/or planner in conjunction with operating the autonomous vehicle.

Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

Abstract: Techniques for detecting an object in an environment and determining a probability that the object is a cloud of particulate matter. The cloud of particulate matter may include steam (e.g., emitted from a man-hole cover, a dryer exhaust port, etc.), exhaust from a vehicle (e.g., car, truck, motorcycle, etc.), environmental gases (e.g., resulting from sublimation, fog, evaporation, etc.), a cloud of dust, water splashing, blowing leaves, or other types of particulate matter that may be located in the environment of the vehicle and may not impact driving behavior (e.g., an autonomous vehicle may safely pass through the particulate matter without impact to the platform). A vehicle computing system may determine the probability that the object is a cloud of particulate matter and may control the vehicle based on the probability.

Abstract: Techniques for controlling a vehicle based on height data and/or classification data being determined utilizing multi-channel image data are discussed herein. The vehicle can capture lidar data as it traverses an environment. The lidar data can be associated with a voxel space as three-dimensional data. Semantic information can be determined and associated with the lidar data and/or the three-dimensional voxel space. A multi-channel input image can be determined based on the three-dimensional voxel space and input into a machine learned (ML) model. The ML model can output data to determine height data and/or classification data associated with a ground surface of the environment. The height data and/or classification data can be utilized to determine a mesh associated with the ground surface. The mesh can be used to control the vehicle and/or determine additional objects proximate the vehicle.

Abstract: A vehicle can include various sensors to detect objects in an environment. Sensor data can be captured by a perception system in a vehicle and represented in a voxel space. Operations may include analyzing the data from a top-down perspective. From this perspective, techniques can associate and generate masks that represent objects in the voxel space. Through manipulation of the regions of the masks, the sensor data and/or voxels associated with the masks can be clustered or otherwise grouped to segment data associated with the objects.

Abstract: Techniques for detecting and classifying objects using lidar data are discussed herein. In some cases, the system may be configured to utilize a predetermined number of prior frames of lidar data to assist with detecting and classifying objects. In some implementations, the system may utilize a subset of the data associated with the prior lidar frames together with the full set of data associated with a current frame to detect and classify the objects.

Abstract: Techniques for identifying curbs are discussed herein. For instance, a vehicle may generate sensor data using one or more sensors, where the sensor data represents points associated with a driving surface and a sidewalk. The vehicle may then quantize the points into distance bins that are located laterally along the driving direction of the vehicle in order to generate spatial lines. Next, the vehicle may determine separation points for the spatial lines, where the separation points are configured to separate the points associated with the driving surface from the points associated with the sidewalk. The vehicle may then generate, using the separation points, a curve that represents the curb between the driving surface and the sidewalk. This way, the vehicle may use the curve while navigating, such as to avoid the curb and/or stop at a location that is proximate to the curb.

Abstract: A vehicle can include various sensors to detect objects in an environment. In some cases, the object may be within a planned path of travel of the vehicle. In these cases, leaving the planned path may be dangerous to the passengers so the vehicle may, based on dimensions of the object, dimensions of the vehicle, and semantic information of the object, determine operational parameters associate with passing the object while maintaining a position within the planned path, if possible.

Abstract: Various embodiments may relate to an optical device. The optical device may include a stacked structure having a first surface and a second surface opposite the first surface. The stacked structure may include a plurality of holes or grooves extending from the first surface towards the second surface. The stacked structure may include a transition metal dichalcogenide material (TMDC) material. A thickness of the stacked structure may be of any value less than 100 nm.

Abstract: Techniques are discussed for segmenting sensor data captured by a sensor to remove a ground surface from the sensor data. A first technique includes capturing sensor data represented as multichannel image and segmenting the image according to image processing techniques. The ground surface can be removed from the sensor data, and a subset of the sensor data can be associated with a voxel space. A second technique includes capturing sensor data and unprojecting the sensor data to generate three dimensional data, which can be associated with a voxel space. Ground plane data associated with a location can be accessed or determined and voxel data that is within a threshold height of the ground plane data can be removed from the voxel space. Clustering techniques can determine objects represented in the data, and a vehicle can be controlled based on the objects.