A method of recognizing gestures of a person from at least one image from a monocular camera, e.g. a vehicle camera, includes comp the steps: a) detecting key points of the person in the at least one image, b) connecting the key points to form a skeleton-like representation of body parts of the person, wherein the skeleton-like representation represents a relative position and a relative orientation of the respective body parts of the person, c) recognizing a gesture of the person from the skeleton-like representation of the person, and d) outputting a signal indicating the gesture.

A method of recognizing gestures of a person from at least one image from a monocular camera, e.g. a vehicle camera, includes comp the steps: a) detecting key points of the person in the at least one image, b) connecting the key points to form a skeleton-like representation of body parts of the person, wherein the skeleton-like representation represents a relative position and a relative orientation of the respective body parts of the person, c) recognizing a gesture of the person from the skeleton-like representation of the person, and d) outputting a signal indicating the gesture.

A vehicle position and velocity estimation based on camera and LIDAR data are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device; determining a two-dimensional (2D) position of a proximate object near the autonomous vehicle using the image data received from the image generating device; tracking a three-dimensional (3D) position of the proximate object using the distance data received from the distance measuring device over a plurality of cycles and generating tracking data; determining a 3D position of the proximate object using the 2D position, the distance data received from the distance measuring device, and the tracking data; determining a velocity of the proximate object using the 3D position and the tracking data; and outputting the 3D position and velocity of the proximate object relative to the autonomous vehicle.

A vehicle position and velocity estimation based on camera and LIDAR data are disclosed. A particular embodiment includes: receiving input object data from a subsystem of an autonomous vehicle, the input object data including image data from an image generating device and distance data from a distance measuring device; determining a two-dimensional (2D) position of a proximate object near the autonomous vehicle using the image data received from the image generating device; tracking a three-dimensional (3D) position of the proximate object using the distance data received from the distance measuring device over a plurality of cycles and generating tracking data; determining a 3D position of the proximate object using the 2D position, the distance data received from the distance measuring device, and the tracking data; determining a velocity of the proximate object using the 3D position and the tracking data; and outputting the 3D position and velocity of the proximate object relative to the autonomous vehicle.

Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, loose-fit cuboids overlaid on 3D graphs so as to each encompass LiDAR data points associated with a given object; defining, by the computing device, an amodal cuboid based on the loose-fit cuboids; using, by the computing device, the amodal cuboid to train a machine learning algorithm to detect objects of a given class using sensor data generated by sensors of the autonomous vehicle or another vehicle; and causing, by the computing device, operations of the autonomous vehicle to be controlled using the machine learning algorithm.

Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, loose-fit cuboids overlaid on 3D graphs so as to each encompass LiDAR data points associated with a given object; defining, by the computing device, an amodal cuboid based on the loose-fit cuboids; using, by the computing device, the amodal cuboid to train a machine learning algorithm to detect objects of a given class using sensor data generated by sensors of the autonomous vehicle or another vehicle; and causing, by the computing device, operations of the autonomous vehicle to be controlled using the machine learning algorithm.

An object identification system on a mobile work machine receives an object detection sensor signal from an object detection sensor, along with an environmental sensor signal from an environmental sensor. An object identification system generates a first object identification based on the object detection sensor signal and the environmental sensor signal. Object behavior is analyzed to determine whether the object behavior is consistent with the object identification, given the environment. If an anomaly is detected, meaning that the object behavior is not consistent with the object identification, given the environment, then a secondary object identification system is invoked to perform another object identification based on the object detection sensor signal and the environmental sensor signal. A control signal generator can generate control signals to control a controllable subsystem of the mobile work machine based on the object identification or the secondary object identification.

An object identification system on a mobile work machine receives an object detection sensor signal from an object detection sensor, along with an environmental sensor signal from an environmental sensor. An object identification system generates a first object identification based on the object detection sensor signal and the environmental sensor signal. Object behavior is analyzed to determine whether the object behavior is consistent with the object identification, given the environment. If an anomaly is detected, meaning that the object behavior is not consistent with the object identification, given the environment, then a secondary object identification system is invoked to perform another object identification based on the object detection sensor signal and the environmental sensor signal. A control signal generator can generate control signals to control a controllable subsystem of the mobile work machine based on the object identification or the secondary object identification.

A travel support system includes a server configured to support the travel of a vehicle. The server comprises a recognition unit configured to recognize an obstacle on a travel path of the vehicle, an obtainment unit configured to obtain, upon detecting an approaching vehicle which is approaching the obstacle, a blind spot region which occurs due to the obstacle recognized by the recognition unit, and a notification unit configured to notify the approaching vehicle of information of the blind spot region obtained by the obtainment unit. The server is arranged in an apparatus other than the approaching vehicle.

A travel support system includes a server configured to support the travel of a vehicle. The server comprises a recognition unit configured to recognize an obstacle on a travel path of the vehicle, an obtainment unit configured to obtain, upon detecting an approaching vehicle which is approaching the obstacle, a blind spot region which occurs due to the obstacle recognized by the recognition unit, and a notification unit configured to notify the approaching vehicle of information of the blind spot region obtained by the obtainment unit. The server is arranged in an apparatus other than the approaching vehicle.