Abstract: Systems/methods for operating an autonomous vehicle. The methods comprise: obtaining LiDAR data generated by a LiDAR system of the autonomous vehicle and image data generated by a camera of the autonomous vehicle; performing a first object detection algorithm to generate first object detection information using the LiDAR data, and a second object detection algorithm to generate second object detection information using both the LiDAR data and the image data; and processing the first and second object detection information to determine whether a given object was detected by both the first and second object detection algorithms. When a determination is made that the given object was detected by both the first and second object detection algorithms, the first object detection information is selectively modified based on contents of the second object detection information. The modified first object detection information is used to facilitate at least one autonomous driving operation.

Abstract: Systems and methods for object detection. The object detection may be used to control an autonomous vehicle. For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by the computing device, the LiDAR dataset and at least one image to detect an object that is in proximity to the autonomous vehicle. The object is detected by: matching points of the LiDAR dataset to pixels in the at least one image; and detecting the object in a point cloud defined by the LiDAR dataset based on the matching. The object detection may be used to facilitate at least one autonomous driving operation (e.g., autonomous driving operation comprises an object tracking operation, an object trajectory prediction operation, a vehicle trajectory determination operation, and/or a collision avoidance operation).

Abstract: This document discloses system, method, and computer program product embodiments for controlling a vehicle.

Abstract: A system may receive point cloud data that includes one or more data points associated with an object that was detected by sensors of an autonomous vehicle. The system may identify a subset of the point cloud data having data points that are associated with a likelihood of a pedestrian entering a scene with the object, determine a current probability value using a logistic function that is associated with the subset of the point cloud data, determine, based at least in part on the current probability value, a probability value representing a likelihood of the pedestrian actually being present for the subset of the point cloud data, determine whether the probability value exceeds a false alarm threshold value, and in response to the probability value exceeding the false alarm threshold value, assign data points of the subset an attribute value indicative of the pedestrian being present.

Abstract: Systems and methods for tracking an object. The method comprising: receiving, by a processor, a series of observations made over time for the object; selecting, by the processor, a plurality of sets of observations using the series of observations; causing, by the processor, the plurality of sets of observations to be used by at least one filter to generate a track for the object (wherein the at least one filter uses sensor data associated with each of a plurality of frames of sensor data only once during generation of the track); and causing, by the processor, operations of an autonomous robot to be controlled based on the track for the object.

Abstract: This document discloses system, method, and computer program product embodiments for controlling a vehicle.

Abstract: A system receives an observation probability distribution function associated with a target object that was detected by sensors of an autonomous vehicle. The system identifies a target attribute of the target object, and detects a target attribute value associated with the target object. The system determines a first probability distribution function representing a probability of the autonomous vehicle detecting an object having an object label, determines a second probability distribution function defining a probability of the autonomous vehicle detecting the target attribute, determines a third probability distribution function defining a probability of the target attribute being present for the target object based on the target attribute value, and determines an attribute probability distribution function defining a probability that the target attribute is actually present for the target object.

Abstract: Systems and methods for object detection. The object detection may be used to control an autonomous vehicle. For example, the methods comprise: obtaining, by a computing device, a LiDAR dataset generated by a LiDAR system of the autonomous vehicle; and using, by the computing device, the LiDAR dataset and at least one image to detect an object that is in proximity to the autonomous vehicle. The object is detected by: matching points of the LiDAR dataset to pixels in the at least one image; and detecting the object in a point cloud defined by the LiDAR dataset based on the matching. The object detection may be used to facilitate at least one autonomous driving operation (e.g., autonomous driving operation comprises an object tracking operation, an object trajectory prediction operation, a vehicle trajectory determination operation, and/or a collision avoidance operation).

Abstract: A system receives an observation probability distribution function associated with a target object that was detected by sensors of an autonomous vehicle. The system identifies a target attribute of the target object, and detects a target attribute value associated with the target object. The system determines a first probability distribution function representing a probability of the autonomous vehicle detecting an object having an object label, determines a second probability distribution function defining a probability of the autonomous vehicle detecting the target attribute, determines a third probability distribution function defining a probability of the target attribute being present for the target object based on the target attribute value, and determines an attribute probability distribution function defining a probability that the target attribute is actually present for the target object.

Abstract: A system may receive point cloud data that includes one or more data points associated with an object that was detected by sensors of an autonomous vehicle. The system may identify a subset of the point cloud data having data points that are associated with a likelihood of a pedestrian entering a scene with the object, determine a current probability value using a logistic function that is associated with the subset of the point cloud data, determine, based at least in part on the current probability value, a probability value representing a likelihood of the pedestrian actually being present for the subset of the point cloud data, determine whether the probability value exceeds a false alarm threshold value, and in response to the probability value exceeding the false alarm threshold value, assign data points of the subset an attribute value indicative of the pedestrian being present.

Abstract: A method of estimating a kinetic state of a rigid body is disclosed. A LiDAR sensor receives multiple point clouds. A processor determined a rigid body transform for a pair of the received point clouds and obtains a sample space of previous rigid body transforms. The processor then identifies a proposal distribution that represents the probability that any rigid body transform in the sample space best aligns a subset of points of a first point cloud of the pair to a subset of points in a second point cloud of the pair. The processor then uses importance sampling to identify a probability distribution for the rigid body transform that best aligns the subsets of points, It then uses the identified probability distribution and a history of probability distributions to estimate a kinetic state of the rigid body.