Abstract: This application provides a traffic shaping method and apparatus. The method includes: A packet marking apparatus receives a first packet; the packet marking apparatus determines an enqueuing queue of the first packet; and the packet marking apparatus marks a queue identifier of the first packet as a queue identifier of the enqueuing queue of the first packet, and then sends the queue identifier of the first packet to a packet output apparatus, where the packet output apparatus is configured to send, based on the queue identifier of the first packet, the first packet to a corresponding queue for outputting. Therefore, packet output time after traffic shaping can be determined.

Abstract: Techniques for determining a probability of a false negative associated with a location of an environment are discussed herein. Data from a sensor, such as a radar sensor, can be received that includes point cloud data, which includes first and second data points. The first data point has a first attribute and the second data point has a second attribute. A difference between the first and second attributes is determined such that a frequency distribution may be determined. The frequency distribution may then be used to determine a distribution function, which allows for the determination of a resolution function that is associated with the sensor. The resolution function may then be used to determine a probability of a false negative at a location in an environment. The probability can be used to control a vehicle in a safe and reliable manner.

Abstract: This application provides data packet scheduling methods and apparatuses. One method includes: A first network device receives, at a first moment, a data packet from a second network device in a network, the first network device determines a first reference moment based on the first moment and time information carried in the data packet, the first network device determines, based on the first reference moment, a target queue from a plurality of queues included in a first queue system and adds the data packet to the target queue, and the first network device processes the target queue according to a scheduling rule of the plurality of queues.

Abstract: Embodiments of this application provide a method for sending a data packet. The method includes: A second network device may determine a remaining processing time of a data packet on a first network device based on a predefined first delay. When the remaining processing time is greater than 0, a moment at which the data packet enters a queue on the second network device serves as a start moment, and after a period of time, the remaining processing time ends at a second reference moment corresponding to the data packet. The remaining processing time is consumed on the second network device.

Abstract: This application discloses a packet transmission method, an apparatus, a device, and a readable storage medium, and relates to the field of communication technologies. The method applied to a second network device includes: First, a first packet sent by a first network device is received, and then a second packet, a first dwell time period, and a second dwell time period are obtained based on the first packet. Then, a time difference between the second dwell time period and the first dwell time period is determined. Then, a third dwell time period and a fourth dwell time period are determined, to encapsulate the third dwell time period, the fourth dwell time period, and the second packet to obtain a third packet.

Abstract: Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.

Abstract: Techniques are discussed herein for analyzing radar data to determine that radar noise from one or more target detections potentially conceals additional objects near the target detection. Determining whether an object may be concealed can be based at least in part on a radar noise level based on a target detection, as well as distributions of radar cross sections and/or doppler data associated with particular object types. For a location near a target detection, a radar system may determine estimated noise levels, and compare the estimated noise levels to radar cross section probabilities associated with object types to determine the likelihood that an object of the object type could be concealed at the location. Based on the analysis, the system may determine a vehicle trajectory or otherwise may control a vehicle based on the likelihood that an object may be concealed at the location.

Abstract: A leakage detection method for a long petroleum pipeline based on AFPSO-K-means includes: initializing a particle swarm, and defining an initial velocity and an initial position of each particle in the particle swarm; determining a fitness value of the each particle; traversing the fitness value of the each particle to obtain an optimal position corresponding to the fitness value of the each particle and a global optimal position in optimal fitness values of the particle swarm; iteratively updating a velocity and a position of the each particle based on the optimal position corresponding to the fitness value of the each particle; and when a distance from the position of the each particle to the global optimal position is less than a preset threshold or a number of iterations reaches a preset value, outputting a current iteration updated result to determine an actual leakage position of the petroleum pipeline.

Abstract: Techniques relating to monitoring map consistency are described. In an example, a monitoring component associated with a vehicle can receive sensor data associated with an environment in which the vehicle is positioned. The monitoring component can generate, based at least in part on the sensor data, an estimated map of the environment, wherein the estimated map is encoded with policy information for driving within the environment. The monitoring component can then compare first information associated with a stored map of the environment with second information associated with the estimated map to determine whether the estimated map and the stored map are consistent. Component(s) associated with the vehicle can then control the object based at least in part on results of the comparing.

Abstract: Embodiments of this application provide a period mapping method and a network device. The method includes: receiving, by a downstream first network device, first information sent by an upstream second network device, where the first information carries a number of the 1st period of the second network device, the number is referred to as a first number, and the first number includes a first label number and a first group number; and establishing, by the first network device, mapping relationships between numbers of a plurality of periods of the first network device and numbers of a plurality of periods of the second network device based on a mapping relationship between a second label number and a second group number that are included in a number of a first period that can be used to send the first information and a first label number and a first group number.

Abstract: Techniques for estimating a height range of an object in an environment are discussed herein. For example, a sensor, such as a lidar sensor, can capture three-dimensional data of an environment. The sensor data can be associated with a two-dimensional representation. A ground surface can be removed from the sensor data, and clustering techniques can be used to cluster remaining sensor data provided in a two-dimensional representation to determine object(s) represented therein. A height of a sensor object can be represented as a first height based on an extent of the sensor data associated with the object and can be represented as a second height based on beam spreading aspects of the sensor data and/or sensor data associated with additional objects. Thus, a minimum and/or maximum height of an object can be determined in a robust manner. Such height ranges can be used to control an autonomous vehicle.

Abstract: Some radar sensors may provide a Doppler measurement indicating a relative velocity of an object to a velocity of the radar sensor. Techniques for determining a two-or-more-dimensional velocity from one or more radar measurements associated with an object may comprise determining a data structure that comprises a yaw assumption and a set of weights to tune the influence of the yaw assumption. Determining the two-or-more-dimensional velocity may further comprise using the data structure as part of regression algorithm to determine a velocity and/or yaw rate associated with the object.

Abstract: Techniques for determining a safety area for a vehicle are discussed herein. In some cases, a first safety area can be based on a vehicle travelling through an environment and a second safety area can be based on a steering control or a velocity of the vehicle. A width of the safety areas can be updated based on a position of a bounding box associated with the vehicle. The position can be based on the vehicle traversing along a trajectory. Sensor data can be filtered based on the sensor data falling within the safety area(s).

Abstract: Sensors, including radar sensors, may be used to detect objects in an environment. In an example, a vehicle may include one or more radar sensors that sense objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. A plurality of radar points from one or more radar scans are associated with a sensed object and a representation of the sensed object is determined from the plurality of radar points. The representation may be compared to track information of previously-identified, tracked objects. Based on the comparison, the sensed object may be associated with one of the tracked objects, and, alternatively, the track information may be updated based on the representation. Conversely, the comparison may indicate that the sensed object is not associated with any of the tracked objects. In this instance, the representation may be used to generate a new track, e.g., for the newly-sensed object.

Abstract: Sensors, including radar sensors, may be used to detect objects in an environment. In an example, a vehicle may include one or more radar sensors that sense objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. A plurality of radar points from one or more radar scans are associated with a sensed object and a representation of the sensed object is determined from the plurality of radar points. The representation may be compared to track information of previously-identified, tracked objects. Based on the comparison, the sensed object may be associated with one of the tracked objects, and, alternatively, the track information may be updated based on the representation. Conversely, the comparison may indicate that the sensed object is not associated with any of the tracked objects. In this instance, the representation may be used to generate a new track, e.g., for the newly-sensed object.

Abstract: Techniques are discussed for determining reflected returns in radar sensor data. In some instances, pairs of radar returns may be compared to one another. For example, a reflection point may be determined from a first position of a first radar return and a second position of a second radar return. Additional data, e.g., sensor data and/or map data, may be used to determine the presence of objects in the environment. The first return or the second return may be a reflected return if an object is disposed at the reflection point. In some instances, a vehicle, such as an autonomous vehicle, may be controlled at the exclusion of information from reflected returns.

Abstract: Techniques for updating data operations in a perception system are discussed herein. A vehicle may use a perception system to capture data about an environment proximate to the vehicle. The perception system may receive state data stored in cyclic buffer of globally registered detection and occasionally converted to gridded point cloud in a local reference frame. The two-dimensional gridded point cloud may be processed using one or more neural networks to generate semantic data associated with a scene or physical environment surrounding the vehicle such that the vehicle can make environment aware operational decisions, which may improve reaction time(s) and/or safety outcomes of the autonomous vehicle.

Abstract: Navigation systems can identify objects in an environment and generate representations of those objects. A representation of an articulated vehicle can include two segments rotated relative to each other about a pivot, with a first segment corresponding to a first portion of the articulated vehicle and the second segment corresponding to a second portion of the articulated vehicle. The articulated object can be tracked in the environment by generating estimated updated states of the articulated agent based on previous states and/or measured states of the object using differing motion model updates for the differing portions. The estimated updated states may be determined using one or more filtering algorithms, which may be constrained using pseudo-observables.

Abstract: Techniques for accurately predicting and avoiding collisions with objects detected in an environment of a vehicle are discussed herein. A vehicle safety system can implement a model to output data indicating an intersection probability between the object and a portion of the vehicle in the future. The model may employ a rear collision filter, a distance filter, and a time to stop filter to determine whether a predicted collision may be a false positive, in which case the techniques may include refraining from reporting such predicted collision to other another vehicle computing device to control the vehicle.

Abstract: A collision avoidance system may validate, reject, or replace a trajectory generated to control a vehicle. The collision avoidance system may comprise a secondary perception component that may receive sensor data, receive and/or determine a corridor associated with operation of a vehicle, classify a portion of the sensor data associated with the corridor as either ground or an object, determine a position and/or velocity of at least the nearest object, determine a threshold distance associated with the vehicle, and control the vehicle based at least in part on the position and/or velocity of the nearest object and the threshold distance.