Abstract: Disclosed a device scheduling method. A Task Description (TD) in a task queue is read and parsed, to acquire task information of a task corresponding to the TD; and when it is determined that the task has met a starting condition and the task is a task with a highest priority among tasks which currently meet the starting condition, a preset parameter is acquired according to the task information, and the parameter is configured to a device intended to complete the task. A task manager and a storage medium is also disclosed.

Abstract: A vehicle guidance system facilitates maneuvering of an autonomous vehicle with respect to an object in a scene. The vehicle includes a steering apparatus having a range of angular positions and a multitude of actuators for controlling the dynamics of the vehicle. The system includes a steering angle sensor, a camera device, and a video processing module. The sensor is configured to monitor the angular position of the wheel. The device is configured to capture an original image of a scene having the object. The module is configured to receive and process the original image from the device, detect the object in the original image, receive and process the angular position from the sensor, generate a dynamic trajectory based on at least the angular position, orientate the dynamic trajectory with regard to the object, and operate at least one of the actuators to guide the vehicle along the dynamic trajectory.

Abstract: A vehicle guidance system assists a driver in maneuvering a vehicle with respect to an object in a scene. The system includes a steering angle sensor, a camera device, a video processing module (VPM), and a human-machine interface (HMI). The sensor is configured to monitor the angular position of a vehicle wheel. The device is configured to capture an original image of a scene having the object. The VPM is configured to receive and process the original image from the device, detect the object in the original image, receive and process the angular position from the sensor, generate a vehicle trajectory based on the angular position, and orientate the trajectory with regard to the object. The HMI is configured to display a processed image associated with the original image and a trajectory overlay associated with the trajectory from the VPM. Together, the object is displayed in relation to the overlay.

Abstract: A system and method are provided for detecting remote vehicles relative to a host vehicle using wheel detection. The system and method include tracking wheel candidates based on wheel detection data received from a plurality of object detection devices, comparing select parameters relating to the wheel detection data for each of the tracked wheel candidates, and identifying a remote vehicle by determining if a threshold correlation exists between any of the tracked wheel candidates based on the comparison of select parameters.

Abstract: Techniques for road scene primitive detection using a vehicle camera system are disclosed. In one example implementation, a computer-implemented method includes receiving, by a processing device having at least two parallel processing cores, at least one image from a camera associated with a vehicle on a road. The processing device generates a plurality of views from the at least one image that include a feature primitive. The feature primitive is indicative of a vehicle or other road scene entities of interest. Using each of the parallel processing cores, a set of primitives are identified from one or more of the plurality of views. The feature primitives are identified using one or more of machine learning and classic computer vision techniques. The processing device outputs, based on the plurality of views, result primitives based on the plurality of identified primitives from multiple views based on the plurality of identified entities.

Abstract: Examples of techniques for road feature detection using a vehicle camera system are disclosed. In one example implementation, a computer-implemented method includes receiving, by a processing device, an image from a camera associated with a vehicle on a road. The computer-implemented method further includes generating, by the processing device, a top view of the road based at least in part on the image. The computer-implemented method further includes detecting, by the processing device, lane boundaries of a lane of the road based at least in part on the top view of the road. The computer-implemented method further includes detecting, by the processing device, a road feature within the lane boundaries of the lane of the road using machine learning.

Abstract: A method to equip a vehicle to perform object detection and tracking and a surround view camera system to perform the object detection and tracking involve two or more cameras arranged respectively at two or more locations of the vehicle. The cameras capture images within a field of view of the two or more cameras. A processing system obtains the images from the two or more cameras and performs image processing to detect and track objects in the field of view of the two or more cameras.

Abstract: A system and method are provided for detecting and identifying elongated objects relative to a host vehicle. The method includes detecting objects relative to the host vehicle using a plurality of object detection devices, identifying patterns in detection data that correspond to an elongated object, wherein the detection data includes data fused from at least two of the plurality of object detection devices, determining initial object parameter estimates for the elongated object using each of the plurality of object detection devices, calculating object parameter estimates for the elongated object by fusing the initial object parameter estimates from each of the plurality of object detection devices, and determining an object type classification for the elongated object by fusing the initial object parameter estimates from each of the plurality of object detection devices.

Abstract: A system and method are provided for detecting remote vehicles relative to a host vehicle using wheel detection. The system and method include tracking wheel candidates based on wheel detection data received from a plurality of object detection devices, comparing select parameters relating to the wheel detection data for each of the tracked wheel candidates, and identifying a remote vehicle by determining if a threshold correlation exists between any of the tracked wheel candidates based on the comparison of select parameters.

Abstract: The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems, for smoothing seismic data. One computer-implemented method includes obtaining, by a hardware data processing apparatus, a plurality of seismic data samples; forming, by the hardware data processing apparatus, guiding vectors using the plurality of seismic data samples and a plurality of guiding structure attributes; generating, by the hardware data processing apparatus, a structure guided directional weighted vector filter using the guiding vectors and a plurality of weighting factors; filtering, by the hardware data processing apparatus, the seismic data samples using the structure guided directional weighted vector filter to generate smoothed seismic data; and initiating output of the smoothed seismic data.

Abstract: A method for autonomously aligning a tow hitch ball on a towing vehicle and a trailer drawbar on a trailer through a human-machine interface (HMI) assisted visual servoing process. The method includes providing rearview images from a rearview camera. The method includes touching the tow ball on a display to register a location of the tow ball in the image and touching the drawbar on the display to register a location of a target where the tow ball will be properly aligned with the drawbar. The method provides a template pattern around the target on the image and autonomously moves the vehicle so that the tow ball moves towards the target. The method predicts a new location of the target as the vehicle moves and identifies the target in new images as the vehicle moves by comparing the previous template pattern with an image patch around the predicted location.

Abstract: A method for determining a wet surface condition of a road. Capturing an image of a wheel of a remote vehicle traveling in an adjacent lane by an image capture device of a host vehicle. Identifying in the captured image, by processor of a host vehicle, a region of interest relative to the wheel where the region of interest is representative of where precipitation dispersion occurs. A determination is made whether precipitation is present in the region of interest. A wet road surface signal is generated in response to the identification of precipitation in the adjacent lane.

Abstract: A method for determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and captures an image in a downward direction. Identifying in the captured image, by a processor, a region of interest. The region of interest is in a region sideways to a face of the wheel. The region of interest is representative of where sideways splash as generated by the wheel occurs. A determination is made whether water is present in the region of interest. A wet road surface signal is generated in response to the identification of water in the region of interest.

Abstract: A method of determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and an image is captured in a downward direction. A region of interest is identified in the captured image by a processor. The region of interest is in a region rearward of a tire of a host vehicle. The region of interest is representative of where a tire track as generated by the tire rotating on the road when the road surface is wet. A determination is made whether water is present in the region of interest as a function of identifying the tire track. A wet road surface signal is generated in response to the identification of water in the region of interest.

Abstract: A method for determining a wet surface condition of a road. An image of a road surface is captured by an image capture device of the host vehicle. The image capture device is mounted on a side of the host vehicle and captures an image in a downward direction. A region of interest rearward of the wheel of the host vehicle is identified in the captured image by a processor. The region of interest is representative of where rearward splash as generated by the wheel occurs. A determination is made whether precipitation is present in the region of interest by applying a filter to the image. A wet road surface signal is generated in response to the identification of precipitation in the region of interest.

Abstract: A method for determining a snow covered surface condition of a path of travel. A beam of light is emitted at a surface of the path of travel by a light emitting source. An image of a path of travel surface is captured by an image capture device. The image capture device is mounted on the vehicle and captures an image in a downward direction. The captured image captures the beam of light emitted on the path of travel surface. Analyzing a subsurface scattering of the light generated on the path of travel surface by a processor. A determination is made whether snow is present on the path of travel. A snow covered path of travel surface signal is generated in response to the identification of snow on the path of travel.

Abstract: An embodiment contemplates a method of calibrating multiple image capture devices of a vehicle. A plurality of image capture devices having different poses are provided. At least one of the plurality of image capture devices is identified as a reference device. An image of a patterned display exterior of the vehicle is captured by the plurality of image capture devices. The vehicle traverses across the patterned display to capture images at various instances of time. A processor identifies common landmarks of the patterned display between each of the images captured by the plurality of image capture devices and the reference device. Images of the patterned display captured by each of the plurality of image devices are stitched using the identified common landmarks. Extrinsic parameters of each image capture device are adjusted relative to the reference device based on the stitched images for calibrating the plurality of image capture devices.

Abstract: A method of distinguishing between daytime lighting conditions and nighttime lighting conditions based on a captured image by a vision-based imaging device along a path of travel. An image is captured by a vision-based imaging device. A region of interest is selected in the captured image. A light intensity value is determined for each pixel within the region of interest. A cumulative histogram is generated based on light intensity values within the region of interest. The cumulative histogram including a plurality of category bins representing the light intensity values. Each category bin identifies an aggregate value of light intensity values assigned to each respective category bin. An aggregate value is compared within a predetermined category bin of the histogram to a first predetermined threshold. A determination is made whether the image is captured during the daytime lighting conditions as a function of the aggregate value within the predetermined category bin.

Abstract: A reconfigurable clear path detection system includes an image capture device and a primary clear path detection module for determining corresponding probability values of identified patches within a captured image representing a likelihood of whether a respective patch is a clear path of the road. A plurality of secondary clear path detection modules each are used to assist in identifying a respective clear path of the traveled road in the input image. One or more of the secondary clear path detection modules are selectively enabled for identifying the clear path. The selectively enabled secondary clear path detection modules are used to identify the clear path of the road of travel in the input image. A fusion module collectively analyzes the clear path detection results of the primary clear path detection module and the selectively enabled secondary clear path detection modules for identifying the clear path in the input image.

Abstract: A system and method for estimating dynamics of a mobile platform by matching feature points in overlapping images from cameras on the platform, such as cameras in a surround-view camera system on a vehicle. The method includes identifying overlap image areas for any two cameras in the surround-view camera system, identifying common feature points in the overlap image areas, and determining that the common feature points in the overlap image areas are not at the same location. The method also includes estimating three-degree of freedom vehicle dynamic parameters from the matching between the common feature points, and estimating vehicle dynamics of one or more of pitch, roll and height variation using the vehicle dynamic parameters.