A driving support control device for a vehicle includes an acquisition unit configured to acquire, from a first detector which detects change of a brightness value of an object which occurs in accordance with displacement of the object, information indicating change of a brightness value of a partially shielded object partially shielded by an obstacle which occurs in accordance with displacement of the partially shielded object, as a first detection signal, and a control unit configured to, in a case where it is determined, by using the first detection signal, that the partially shielded object is moving, cause a driving support execution device to execute collision prevention support for preventing a collision with the partially shielded object.

In one approach, a method includes: displaying, to a user of a first vehicle, image data obtained using a first field of view of a camera of the first vehicle, where the camera collects the image data for objects located outside of the first vehicle; detecting, by at least one processing device of the first vehicle, a second vehicle; determining, by the one processing device, whether the second vehicle is within a predetermined region relative to the first vehicle; and in response to determining that the second vehicle is within the predetermined region, displaying image data obtained using a second field of view of the camera.

The present disclosure relates to a method for identifying an obstacle on a driving ground and a robot for implementing the same, and according to one embodiment of the present disclosure, the method for identifying an obstacle on a driving ground comprises the steps of allowing: a sensing module of the robot to sense the depths of objects in a driving direction so as to generate first depth information; a plane analysis unit of the sensing module to calibrate second depth information by using ground information stored by a map storage of the robot so as to generate the first depth information; a control unit to identify an obstacle from the second depth information; and the control unit to store position information of the identified obstacle in a temporary map of the map storage.

A technique is described for controlling an autonomous vehicle such as an unmanned aerial vehicle (UAV) using objective-based inputs. In an embodiment, the underlying functionality of an autonomous navigation system is via an application programming interface (API). In such an embodiment, the UAV can be controlled trough specifying a behavioral objective, for example, using a call to the API to set parameters for the behavioral objective. The autonomous navigation system can then incorporate perception inputs such as sensor data from sensors mounted to the UAV and the set parameters using a multi-objective motion planning process to generate a proposed trajectory that most closely satisfies the behavioral objective in view of certain constraints. In some embodiments, developers can utilize the API to build customized applications for utilizing the UAV to capture images. Such applications, also referred to as “skills,” can be developed, shared, and executed to control the behavior of an autonomous UAV and to aid in overall system improvement.

A computer vision system is provided that includes an image generation device configured to generate consecutive two dimensional (2D) images of a scene, and a dense optical flow engine (DOFE) configured to determine a dense optical flow map for pairs of the consecutive 2D images, wherein, for a pair of consecutive 2D images, the DOFE is configured to perform a predictor based correspondence search for each paxel in a current image of the pair of consecutive 2D images, wherein, for an anchor pixel in each paxel, the predictor based correspondence search evaluates a plurality of predictors to select a best matching pixel in a reference image of the pair of consecutive 2D images, and determine optical flow vectors for each pixel in a paxel based on the best matching pixel selected for the anchor pixel of the paxel.

System and methods for automated incident identification and reporting while operating a vehicle on the road using a device. The device identifies incidents using artificial intelligence neural networks trained to detect, classify, segment, and/or extract other information pertaining to objects of interest representing incidents. Additionally, a system and method for further storing, transmitting, processing, organizing and accessing the information graphically with respect to incident type, location, date and time during operation of the vehicle along the road.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using a surfel map to generate long range localization. One of the methods includes obtaining, for a particular location of a vehicle having a camera and a detection sensor, surfel data including a plurality of surfels. Each surfel in the surfel data has a respective location and corresponds to a different respective detected surface in an environment. Image data captured by the camera is obtained. It is determined that a region of interest for detecting objects for a vehicle planning process is outside a detectable region for the detection sensor. In response, it is determined that the image data for the region of interest matches surfel color data for the surfels corresponding to the region of interest. In response, the vehicle planning process is performed with the region of interest designated as having no unexpected objects.

Embodiments pertain to a system that can be employed or that is included in a platform for detecting an obstacle to the platform in a scene. The system comprises, in an embodiment, a plurality of illuminators arranged at different locations of the platform; at least one imager; a processor; and a memory configured to store data and software code. The software code is executable by the processor to perform the following: illuminating the scene from at least two different directions by the plurality of illuminators; acquiring, by the at least one imager, a plurality of images of the illuminated scene; comparing at least one image of the scene illuminated from a first direction with at least one image of the scene illuminated from a second direction which is different from the first direction; and determining, based on the comparing, at least one shadow-related characteristic of the scene.

An active sensor for performing active measurements of a scene is presented. The active sensor includes at least one transmitter configured to emit light pulses toward at least one target object in the scene, wherein the at least one target object is recognized in an image acquired by a passive sensor; at least one receiver configured to detect light pulses reflected from the at least one target object; a controller configured to control an energy level, a direction, and a timing of each light pulse emitted by the transmitter, wherein the controller is further configured to control at least the direction for detecting each of the reflected light pulses; and a distance measurement circuit configured to measure a distance to each of the at least one target object based on the emitted light pulses and the detected light pulses.

Embodiments of the present disclosure provide a method and device for detecting a speed of an obstacle, a computer device, and a storage medium. The method includes: calculating at least two real-time speeds corresponding to the obstacle by using a multi-frame difference algorithm according to multi-frame data acquired by a sensor in a preset time window; calculating at least two speed statistic values corresponding to the obstacle according to the at least two real-time speeds; mapping each of the at least two speed statistic values to a corresponding static probability according to a mapping relationship between speed statistic values and static probabilities, to obtain at least two static probabilities; and fusing the at least two static probabilities to obtain a fused static probability of the obstacle, and determining the speed of the obstacle according to the fused static probability.