Abstract: A positioning method includes acquiring global positioning system (GPS) position data associated with a mobile platform from a plurality of GPS satellites observable by the mobile platform. A set of wireless range measurements associated with the mobile platform and a plurality of wireless access points in communication with the mobile platform are acquired. The method further includes receiving, from a server communicatively coupled to the mobile platform over a network, wireless position data associated with the plurality of wireless access points. A corrected position of the mobile platform is determined based on the wireless position data, the wireless range measurements, and the GPS position data.

Abstract: A method and system for localizing a vehicle in a digital map includes generating GPS coordinates of the vehicle on the traveled road and retrieving from a database a digital map of a region traveled by the vehicle based on the location of the GPS coordinates. The digital map includes a geographic mapping of a traveled road and registered roadside objects. The registered roadside objects are positionally identified in the digital map by longitudinal and lateral coordinates. Roadside objects in the region traveled are sensed by the vehicle. The sensed roadside objects are identified on the digital map. A vehicle position on the traveled road is determined utilizing coordinates of the sensed roadside objects identified in the digital map. The position of the vehicle is localized in the road as a function of the GPS coordinates and the determined vehicle position utilizing the coordinates of the sensed roadside objects.

Abstract: A vehicle obstacle detection system includes an imaging system for capturing objects in a field of view and a radar device for sensing objects in a substantially same field of view. The substantially same field of view is partitioned into an occupancy grid having a plurality of observation cells. A fusion module receives radar data from the radar device and imaging data from the imaging system. The fusion module projects the occupancy grid and associated radar data onto the captured image. The fusion module extracts features from each corresponding cell using sensor data from the radar device and imaging data from the imaging system. A primary classifier determines whether an extracted feature extracted from a respective observation cell is an obstacle.

Abstract: Methods and systems for estimating motion of a vehicle are provided. Radar data pertaining to one or more stationary objects in proximity to the vehicle are obtained via one or more radar units of the vehicle. The motion of the vehicle is estimating using the radar data via a processor of the vehicle.

Abstract: A vehicle system and method that can determine object sensor misalignment while a host vehicle is being driven, and can do so within a single sensor cycle through the use of stationary and moving target objects and does not require multiple sensors with overlapping fields of view. In an exemplary embodiment where the host vehicle is traveling in a generally straight line, one or more object misalignment angle(s) ?o between an object axis and a sensor axis are calculated and used to determine the actual sensor misalignment angle ?.

Abstract: A method is disclosed for improved target grouping of sensor measurements in an object detection system. The method uses road curvature information to improve grouping accuracy by better predicting a new location of a known target object and matching it to sensor measurements. Additional target attributes are also used for improved grouping accuracy, where the attributes includes range rate, target cross-section and others. Distance compression is also employed for improved grouping accuracy, where range is compressed in a log scale calculation in order to diminish errors in measurement of distant objects. Grid-based techniques include the use of hash tables and a flood fill algorithm for improved computational performance of target object identification, where the number of computations can be reduced by an order of magnitude.

Abstract: Techniques and methodologies for determining a relative position between a host object and a neighboring object in proximity to the host object are presented here. An exemplary embodiment of a method operates a first wireless communication module onboard the host object to wirelessly communicate packets with a second wireless communication module onboard the neighboring object. The method processes packets wirelessly received from the second wireless communication module to obtain position information related to a position of the neighboring object relative to the host object. A range sensor system onboard the host object is operated to obtain first range information related to a range of the neighboring object relative to the host object. The relative position between the host object and the neighboring object is computed using the obtained position information and the obtained first range information.

Abstract: A method of calibrating multiple vehicle-based image capture devices of a vehicle. An image is captured by at least one image capture device. A reference object is identified in the captured image. The reference object has known world coordinates. Known features of the vehicle are extracted in the captured image. A relative location and orientation of the vehicle in world coordinates is determined relative to the reference object. Each of the multiple image capture devices is calibrated utilizing intrinsic and extrinsic parameters of the at least one image capture device as a function of the relative location and orientation of the vehicle in world coordinates.

Abstract: A system and method for fusing the outputs from multiple LiDAR sensors. The method includes providing object files for objects detected by the sensors at a previous sample time, where the object files identify the position, orientation and velocity of the detected objects. The method also includes receiving a plurality of scan returns from objects detected in the field-of-view of the sensors at a current sample time and constructing a point cloud from the scan returns. The method then segments the scan points in the point cloud into predicted clusters, where each cluster initially identifies an object detected by the sensors. The method matches the predicted clusters with predicted object models generated from objects being tracked during the previous sample time. The method creates new object models, deletes dying object models and updates the object files based on the object models for the current sample time.

Abstract: A lane centering fusion system including a primary controller determining whether a vehicle is centered within a lane of travel. The primary controller includes a primary lane fusion unit for fusing lane sensed data for identifying a lane center position. A secondary controller determines whether a vehicle is centered within a lane of travel. The secondary controller includes a secondary lane fusion unit for fusing lane sensed data for identifying the lane center position. The primary controller and secondary controller are asynchronous controllers. A lane centering control unit maintains the vehicle centered within the lane of travel. The lane centering control unit utilizes fusion data output from the primary controller for maintaining lane centering control. The lane centering control unit utilizes fusion data output from the secondary controller in response to a detection of a fault with respect to the primary controller.

Abstract: A system and method for registering range images from objects detected by multiple LiDAR sensors on a vehicle. The method includes aligning frames of data from at least two LiDAR sensors having over-lapping field-of-views in a sensor signal fusion operation so as to track objects detected by the sensors. The method defines a transformation value for at least one of the LiDAR sensors that identifies an orientation angle and position of the sensor and provides target scan points from the objects detected by the sensors where the target scan points for each sensor provide a separate target point map. The method projects the target point map from the at least one sensor to another one of the LiDAR sensors using a current transformation value to overlap the target scan points from the sensors.

Abstract: A system and method for providing target selection and threat assessment for vehicle collision avoidance purposes that employ probability analysis of radar scan returns. The system determines a travel path of a host vehicle and provides a radar signal transmitted from a sensor on the host vehicle. The system receives multiple scan return points from detected objects, processes the scan return points to generate a distribution signal defining a contour of each detected object, and processes the scan return points to provide a position, a translation velocity and an angular velocity of each detected object. The system selects the objects that may enter the travel path of the host vehicle, and makes a threat assessment of those objects by comparing a number of scan return points that indicate that the object may enter the travel path to the number of the scan points that are received for that object.

Abstract: A method for state of health estimation and misalignment correction in a vehicle lane management system. Two lane sensing systems onboard a vehicle provide lane information to a lane management system, where one of the lane sensing systems may be a dedicated forward-viewing lane sensing system, and the other may use images from a surround-view camera system. The lane information is stored in a fixed-length, moving-window circular data buffer. A correlation coefficient is recursively computed from the lane information from the two lane sensing systems and used to calculate a state of health of the lane management system. A linear regression relationship is also computed between the data from the two lane sensing systems, and the scale factor and offset value are applied to the lane information from the second lane sensing system before a fusion calculation is performed on the lane information from the two lane sensing systems.

Abstract: A flexible, printable antenna for automotive radar. The antenna can be printed onto a thin, flexible substrate, and thus can be bent to conform to a vehicle body surface with compound curvature. The antenna can be mounted to the interior of a body surface such as a bumper fascia, where it cannot be seen but can transmit radar signals afield. The antenna can also be mounted to and blended into the exterior of an inconspicuous body surface, or can be made transparent and mounted to the interior or exterior of a glass surface. The antenna includes an artificial impedance surface which is tailored based on the three-dimensional shape of the surface to which the antenna is mounted and the desired radar wave pattern. The antenna can be used for automotive collision avoidance applications using 22-29 GHz or 76-81 GHz radar, and has a large aperture to support high angular resolution of radar data.

Abstract: A method of detecting and tracking objects using multiple radar sensors. Objects relative to a host vehicle are detected from radar data generated by a sensing device. The radar data includes Doppler measurement data. Clusters are formed, by a processor, as a function of the radar data. Each cluster represents a respective object. Each respective object is classified, by the processor, as stationary or non-stationary based on the Doppler measurement data of each object and a vehicle speed of the host vehicle. Target tracking is applied, by the processor, on an object using Doppler measurement data over time in response to the object classified as a non-stationary object; otherwise, updating an occupancy grid in response to classifying the object as a stationary object.

Abstract: A system and method for providing target selection and threat assessment for vehicle collision avoidance purposes that employ probability analysis of radar scan returns. The system determines a travel path of a host vehicle and provides a radar signal transmitted from a sensor on the host vehicle. The system receives multiple scan return points from detected objects, processes the scan return points to generate a distribution signal defining a contour of each detected object, and processes the scan return points to provide a position, a translation velocity and an angular velocity of each detected object. The system selects the objects that may enter the travel path of the host vehicle, and makes a threat assessment of those objects by comparing a number of scan return points that indicate that the object may enter the travel path to the number of the scan points that are received for that object.

Abstract: A method for state of health estimation and misalignment correction in a vehicle lane management system. Two lane sensing systems onboard a vehicle provide lane information to a lane management system, where one of the lane sensing systems may be a dedicated forward-viewing lane sensing system, and the other may use images from a surround-view camera system. The lane information is stored in a fixed-length, moving-window circular data buffer. A correlation coefficient is recursively computed from the lane information from the two lane sensing systems and used to calculate a state of health of the lane management system. A linear regression relationship is also computed between the data from the two lane sensing systems, and the scale factor and offset value are applied to the lane information from the second lane sensing system before a fusion calculation is performed on the lane information from the two lane sensing systems.

Abstract: A system and method for fusing the outputs from multiple LiDAR sensors on a vehicle that includes cueing the fusion process in response to an object being detected by a radar sensor and/or a vision system. The method includes providing object files for objects detected by the LiDAR sensors at a previous sample time, where the object files identify the position, orientation and velocity of the detected objects. The method projects object models in the object files from the previous sample time to provide predicted object models. The method also includes receiving a plurality of scan returns from objects detected in the field-of-view of the sensors at a current sample time and constructing a point cloud from the scan returns. The method then segments the scan points in the point cloud into predicted scan clusters, where each cluster identifies an object detected by the sensors.

Abstract: A method for tracking a target object utilizing a binocular system includes capturing first and second images, the first image captured from a first camera device and the second image captured from a second camera device. A plurality of image patches are applied to the first and second images and a plurality of detection costs each associated with respective ones of the image patches applied to the first and second images is determined. A plurality of matching costs each corresponding to respective ones of selected matching pairs of image patches between the first and second images is determined. At least one cost flow path is determined from a source vertex of the first image to a sink vertex of the second image based on the detection costs and the matching costs and the target object is tracked based on the at least one cost flow path.

Abstract: A system and method for fusing the outputs from multiple LiDAR sensors. The method includes providing object files for objects detected by the sensors at a previous sample time, where the object files identify the position, orientation and velocity of the detected objects. The method also includes receiving a plurality of scan returns from objects detected in the field-of-view of the sensors at a current sample time and constructing a point cloud from the scan returns. The method then segments the scan points in the point cloud into predicted clusters, where each cluster initially identifies an object detected by the sensors. The method matches the predicted clusters with predicted object models generated from objects being tracked during the previous sample time. The method creates new object models, deletes dying object models and updates the object files based on the object models for the current sample time.