Abstract: Disclosed herein are systems, methods and computer program products for object state prediction. For example, the methods include: obtaining, by a computing device, LiDAR datasets from a LiDAR system; and generating, by the computing device, a most likely sequence of states for an object using (i) velocity and acceleration values that are each functions of center point coordinates of amodal cuboids encompassing at least some points in LiDAR point clouds comprising the LiDAR datasets and (ii) a score that is a function of a first geometrical distance of a given point in the LiDAR datasets to an edge of an amodal cuboid and a second geometrical distance from the given point to a center point of the amodal cuboid.

Abstract: Systems and methods for operating a mobile platform. The methods comprise, by a computing device: obtaining a LiDAR point cloud; using the LiDAR point cloud to generate a track for a given object in accordance with a particle filter algorithm by generating states of a given object over time (each state has a score indicating a likelihood that a cuboid would be created given an acceleration value and an angular velocity value); using the track to train a machine learning algorithm to detect and classify objects based on sensor data; and/or causing the machine learning algorithm to be used for controlling movement of the mobile platform.

Abstract: Systems and methods for operating a mobile platform. The methods comprise, by a computing device: obtaining a LiDAR point cloud; using the LiDAR point cloud to generate a track for a given object in accordance with a particle filter algorithm by generating states of a given object over time (each state has a score indicating a likelihood that a cuboid would be created given an acceleration value and an angular velocity value); using the track to train a machine learning algorithm to detect and classify objects based on sensor data; and/or causing the machine learning algorithm to be used for controlling movement of the mobile platform.

Abstract: Disclosed herein are systems, methods and computer program products for object state prediction. For example, the methods include: obtaining, by a computing device, LiDAR datasets from a LiDAR system; and generating, by the computing device, a most likely sequence of states for an object using (i) velocity and acceleration values that are each functions of center point coordinates of amodal cuboids encompassing at least some points in LiDAR point clouds comprising the LiDAR datasets and (ii) a score that is a function of a first geometrical distance of a given point in the LiDAR datasets to an edge of an amodal cuboid and a second geometrical distance from the given point to a center point of the amodal cuboid.

Abstract: Systems/methods for controlling an Autonomous Vehicle (“AV”). The methods comprise: generating LiDAR datasets measuring a distance from AV to an object at different times; plotting each LiDAR dataset on a 3D graph; defining an amodal cuboid on each 3D graph; selecting different positions for amodal cuboid in 3D graphs; determining coordinates for an amodal cuboid center point at different positions within 3D graphs; using Center Point Coordinates (“CPCs”) to compute velocity and acceleration values for the object at each time; using CPCs to compute Single Frame Score (“SFS”) values; using velocity values, acceleration values and SFS values to compute scores for different cuboid positions in 3D graphs; using the scores as inputs to a Viterbi algorithm for determining a most likely sequence of states for the object given the LiDAR datasets; and using the most likely sequence of states for the object by AV to facilitate autonomous driving operation(s).

Abstract: Systems/methods for controlling an Autonomous Vehicle (“AV”). The methods comprise: generating LiDAR datasets measuring a distance from AV to an object at different times; plotting each LiDAR dataset on a 3D graph; defining an amodal cuboid on each 3D graph; selecting different positions for amodal cuboid in 3D graphs; determining coordinates for an amodal cuboid center point at different positions within 3D graphs; using Center Point Coordinates (“CPCs”) to compute velocity and acceleration values for the object at each time; using CPCs to compute Single Frame Score (“SFS”) values; using velocity values, acceleration values and SFS values to compute scores for different cuboid positions in 3D graphs; using the scores as inputs to a Viterbi algorithm for determining a most likely sequence of states for the object given the LiDAR datasets; and using the most likely sequence of states for the object by AV to facilitate autonomous driving operation(s).