Systems, methods, tangible non-transitory computer-readable media, and devices associated with object association and tracking are provided. Input data can be obtained. The input data can be indicative of a detected object within a surrounding environment of an autonomous vehicle and an initial object classification of the detected object at an initial time interval and object tracks at time intervals preceding the initial time interval. Association data can be generated based on the input data and a machine-learned model. The association data can indicate whether the detected object is associated with at least one of the object tracks. An object classification probability distribution can be determined based on the association data. The object classification probability distribution can indicate a probability that the detected object is associated with each respective object classification. The association data and the object classification probability distribution for the detected object can be outputted.

Provided herein is a system and method that acquires data and determines a driving action based on the data. The system comprises a sensor, one or more processors, and a memory storing instructions that, when executed by the one or more processors, causes the system to perform, determining data of interest comprising an object, feature, or region of interest, determining whether a sharpness of the data of interest exceeds a threshold, in response to determining that the sharpness does not exceed a threshold, operating the sensor to increase the sharpness of the data of interest until the sharpness exceeds the threshold, in response to the sharpness exceeding the threshold, determining a driving action of a vehicle based on the data of interest, and performing the driving action.

For the benefit of pedestrians, a method for identifying and locating positions of obstacles moving on a pedestrian sidewalk acquires an image of the sidewalk and processes the image to divide it. The divided image comprises classifications of objects in the image on a pixel by pixel basis. The classifying of objects in the divided image comprises the sidewalk classification, and classification of the obstacles appears in the image. Pixels surrounding the obstacles are acquired in terms of number and classifications. Positions of the obstacles are determined based on a preset threshold, the classifications of adjacent pixels of the obstacles, and the pixel number of the adjacent pixel in each object classification. An apparatus and a system applying the method are also disclosed.

A device for recognizing an obstacle of a vehicle includes a camera for acquiring an image, a detection device for extracting an object by applying a convolutional neural network to the image, a center calculation device for estimating a center point and a region outside the center point of the extracted object, an uncertainty determination device for determining whether the estimated center point and region outside the center point are uncertain, and a condition determination device for determining whether travel is possible based on the determined uncertainty.

A method of processing a vehicle image includes obtaining a first image from at least one vehicle exterior camera when a vehicle is in a first position. An obstructed area is identified in the first image. At least one previously captured image is obtained from the at least one vehicle exterior camera when the vehicle is in a second position different from the first position. An unobstructed area of the at least one previously captured image that corresponds to at least a portion of the obstructed area of the first image is identified. The unobstructed area is stitched into at least a portion of the obstructed area to create a corrected image that corresponds to the first image with at least a portion of the obstructed area removed. The corrected image is displayed on a display in the vehicle.

Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive a plurality of images captured by an image capture device, the plurality of images being representative of an environment of the host vehicle; analyze at least one of the plurality of images to identify a target vehicle in the environment of the host vehicle; receive map information associated with an environment of the host vehicle; determine a trajectory of the target vehicle over a time period based on analysis of the plurality of images; determine, based on the determined trajectory of the target vehicle and the map information, a position of the target vehicle relative to a road in the environment of the host vehicle; and determine a navigational action for the host vehicle based on the determined position of the target vehicle.

A method of and system for processing Light Detection and Ranging (LIDAR) point cloud data. The method is executable by an electronic device, communicatively coupled to a LIDAR installed on a vehicle, the LIDAR having a plurality of lasers for capturing LIDAR point cloud data. The method includes receiving a first LIDAR point cloud data captured by the LIDAR; executing a Machine Learning Algorithm (MLA) for: analyzing a first plurality of LIDAR points of the first point cloud data in relation to a response pattern of the plurality of lasers; retrieving a grid representation data of a surrounding area of the vehicle; determining if the first plurality of LIDAR points is associated with a blind spot, the blind spot preventing a detection algorithm of the electronic device to detect presence of at least one object surrounding the vehicle conditioned on the at least one object is present.

A method for determining a location of a moving vehicle, the method comprising processing image data to determine a direction between a camera capturing an image and the moving vehicle; processing additional data comprising at least one of map data and velocity sensor data; and combining information based on the image data and the additional data to arrive at a location of the moving vehicle. The present invention also relates to a corresponding robot configured to carry out such a method.

A visual navigation inspection and obstacle avoidance method for a line inspection robot is provided. The line inspection robot is provided with a motion control system, a visual navigation system and an inspection visual system; and the method comprises the following steps: (1) according to an inspection image, the inspection visual system determining and identifying the type of a tower for inspection; (2) the visual navigation system shooting a visual navigation image in real time to obtain the type of a target object; (3) coarse positioning; (4) accurate positioning; and (5) according to the type of the tower and the type of the target object, the visual navigation system sending a corresponding obstacle crossing strategy to the motion control system, such that the inspection robot completes obstacle crossing. The inspection and obstacle avoidance method is real-time and efficient.

A method (700) for determining the value of a size of an object (150) is described. The method (710) comprises determining (711) an occupancy grid (200) indicating evidence that individual cells (201) are vacant or occupied by the object (150). Further, the method (710) comprises determining (712) a subset of cells (201) associated with the object (150). Further, the method (710) comprises detecting (713) two opposing bounding edges of the object (150), and determining (714) a measurement value of the size of the object (150) based on the distance between the detected edges of the object (150). The method (710) further comprises determining (715) a quality measure indicating how well the two opposing edges of the object (150) could be detected, and determining (716) a measurement probability distribution of the size of the object (150) dependent on the quality measure. Further, the method (710) comprises determining (717) a cumulative probability distribution based on the measurement probability distribution, and determining (718) a value of the size of the object (150) based on the cumulative probability distribution.