An artificial intelligence (AI) mobile robot and a method of controlling the same for learning an obstacle are configured to capture an image while traveling through an image acquirer, to store a plurality of captured image data, to determine an obstacle from image data, to set a response motion corresponding to the obstacle, and to operate the set response motion depending on the obstacle, and thus, the obstacle is recognized through the captured image data, the obstacle is easily determined by repeatedly learning an image, and the obstacle is determined before the obstacle is detected or from a time point of detecting the obstacle to perform an operation of a response motion, and even if the same detection signal is input when a plurality of different obstacles is detected, the obstacle is determined through the image and different operations are performed depending on the obstacle to respond to various obstacles, and accordingly, the obstacle is effectively avoided and an operation is performed depending on a type of the obstacle.

The present teaching relates to method, system, medium, and implementation of determining depth information in autonomous driving. Stereo images are first obtained from multiple stereo pairs selected from at least two stereo pairs. The at least two stereo pairs have stereo cameras installed with the same baseline and in the same vertical plane. Left images from the multiple stereo pairs are fused to generate a fused left image and right images from the multiple stereo pairs are fused to generate a fused right image. Disparity is then estimated based on the fused left and right images and depth information can be computed based on the stereo images and the disparity.

A method with distance estimation includes detecting a pedestrian region of a pedestrian comprised in a plurality of images received from a camera; determining a static point in the detected pedestrian region; and determining a distance between the pedestrian and the camera based on the static point in each of the images and a position of the camera corresponding to each of the images.

An apparatus, including an interface configured to receive images with measured distance information of an environment in which an autonomous agent is designed to operate; and processing circuitry that is configured to: generate distance histogram images over time, wherein the distance histogram images include the measured distance information in corresponding picture elements; perform a distribution-based outlier analysis on the distance histogram images to classify each picture element of each of the received images as either an outlier picture element representing a dynamic object or a non-outlier picture element representing a static portion of the environment; and track a dynamic object over time and cause an action by the autonomous agent if it is determined, based on a result of the distribution-based outlier analysis, a distance between the dynamic object and the autonomous agent is less than a predefined distance.

Various embodiments for systems and methods of evaluating perception systems for autonomous vehicles using a quality temporal logic are disclosed herein.

Systems and methods involve detecting objects using a radar system of a vehicle. Tracks of the objects are initiated in a track database. The tracks store data, respectively, for the objects and are updated based on additional detections of the objects. The tracks of the objects are initially unclassified tracks. Two tracks corresponding to two of the objects are selected as a candidate pair. Criteria are applied to the candidate pair to determine whether one track is of a ghost object and another track is of a true object corresponding with the ghost object. The ghost object represents detection of the true object in an incorrect location. The candidate pair is classified as tracks of a true object and ghost object pair based on determining that the one track is of the ghost object and the other track is of the true object corresponding with the ghost object.

A method of adjusting a grid spacing of a height map for autonomous driving, may include acquiring a 2D image of a region ahead of a vehicle, generating a depth map using depth information on an object present in the 2D image, converting the generated depth map into a 3D point cloud, generating the height map by mapping the 3D point cloud onto a grid having a predetermined size, and adjusting a grid spacing of the height map in consideration of the driving state of the vehicle relative to the object.

An interactive vehicle safety system having capabilities to improve peripheral vision, provide warning, and improve reaction time for operators of vehicles. For example, the interactive vehicle safety system may have capabilities for portraying objects, which are being blocked by any of the structural pillars and/or mirrors of a vehicle (such as a truck, van, train, etc.). The interactive vehicle safety system disclosed may comprise one or more image capturing devices (such as camera, sensor, laser), distance and object sensors (such as ultrasonic sensor, LIDAR radar sensor, photoelectric sensor, and infrared sensor), a real-time image processing of an object, and one or more display systems (such as LCD or LED displays). The interactive vehicle safety system may give a seamless 360-degree front panoramic view to a driver.

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.

Apparatuses, methods and storage media associated with surface traversing by robotic apparatuses with an obstacle detection system are described herein. In some instances, the obstacle detection system is mounted on the body of the apparatus, and includes one or more light sources, to illuminate the surface to be traversed; a camera, to capture one or more images of the illuminated surface; and a processing device coupled with the camera and the light source, to process the captured one or more images, to detect, or cause to be detected, an obstacle disposed on the illuminated surface, based at least in part on a result of the processing of the images. Other embodiments may be described and claimed.