Abstract: A rear cross traffic avoidance system includes an object detection device sensing remote objects rearward of a host vehicle. An object classifier distinguishes a remote dynamic object from remote static objects. The object classifier identifies a shape of the dynamic object. A tracking system tracks the remote dynamic object. A processor determines the remote object being on an intersecting path to the remote vehicle. The processor determines a warning threat assessment as a function of a time to intersect between the host vehicle and the remote dynamic object. The processor determines a brake threat assessment in response to an actuated warning of a collision. A brake actuation system actuates a braking operation for mitigating the collision.

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 for providing redundant vehicle speed estimation. The method includes providing sensor output signals from a plurality of primary sensors and providing inertial measurement signals from an inertial measurement unit. The method also includes estimating the vehicle speed in a primary module using the primary sensor signals, and buffering the estimated vehicle speed values from the primary module for a predetermined period of time. The method further includes determining that one or more of the primary sensors or the primary module has failed, and if so, estimating the vehicle speed in a secondary module using the buffered vehicle speed values and the inertial measurement signals. The method can use GPS signal data and/or range data from static objects to improve the estimated vehicle speed in the secondary module if they are available.

Abstract: A method of determining velocity of a target and a fusion system on a moving platform to determine the velocity of the target are described. The method includes obtaining, using a radar system, position and radial velocity of the target relative to the moving platform, obtaining, using a vision system, optical flow vectors based on motion of the target relative to the moving platform, and estimating a dominant motion vector of the target based on the optical flow vectors. The method also includes processing the position, the radial velocity, and the dominant motion vector and determining the velocity of the target in two dimensions.

Abstract: A method and system are disclosed for tracking objects which are crossing behind a host vehicle. Target data from a vision system and two radar sensors are provided to an object detection fusion system. Salient points on the target object are identified and tracked using the vision system data. The salient vision points are associated with corresponding radar points, where the radar points provide Doppler radial velocity data. A fusion calculation is performed on the salient vision points and the radar points, yielding an accurate estimate of the velocity of the target object, including its lateral component which is difficult to obtain using radar points only or traditional vision system methods. The position and velocity of the target object are used to trigger warnings or automatic braking in a Rear Cross Traffic Avoidance (RCTA) system.

Abstract: A rear cross traffic avoidance system includes an object detection device sensing remote objects rearward of a host vehicle. An object classifier distinguishes a remote dynamic object from remote static objects. The object classifier identifies a shape of the dynamic object. A tracking system tracks the remote dynamic object. A processor determines the remote object being on an intersecting path to the remote vehicle. The processor determines a warning threat assessment as a function of a time to intersect between the host vehicle and the remote dynamic object. The processor determines a brake threat assessment in response to an actuated warning of a collision. A brake actuation system actuates a braking operation for mitigating the collision.

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: A vehicle lateral control system includes a lane marker module configured to determine a heading and displacement of a vehicle in response to images received from a secondary sensing device, a lane information fusion module configured to generate vehicle and lane information in response to data received from heterogeneous vehicle sensors and a lane controller configured to generate a collision free vehicle path in response to the vehicle and lane information from the lane information fusion module and an object map.

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 system and method for identifying the position and orientation of a vehicle. The method includes obtaining an environmental model of a particular location from, for example, a map database on the vehicle or a roadside unit. The method further includes detecting the position of the vehicle using GPS signals, determining range measurements from the vehicle to stationary objects at the location using radar sensors and detecting visual cues around the vehicle using cameras. The method includes registering the stationary objects and detected visual cues with stationary objects and visual cues in the environmental model, and using those range measurements to the stationary objects and visual cues that are matched in the environmental model to determine the position and orientation of the vehicle. The vehicle can update the environmental model based on the detected stationary objects and visual cues.

Abstract: A positioning method for a mobile platform includes acquiring GPS position data associated with the 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 received (e.g., via time-of-flight measurements). Wireless position data associated with the plurality of wireless access points is received from a server communicatively coupled to the mobile platform over a network. A corrected position of the mobile platform based on the wireless position data, the wireless range measurements, and the GPS position data.

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 method of adaptively re-generating a planned path for an autonomous driving maneuver. An object map is generated based on the sensed objects in a road of travel. A timer re-set and actuated. A planned path is generated for autonomously maneuvering the vehicle around the sensed objects. The vehicle is autonomously maneuvered along the planned path. The object map is updated based on sensed data from the vehicle-based devices. A safety check is performed for determining whether the planned path is feasible based on the updated object map. The planned path is re-generated in response to a determination that the existing path is infeasible, otherwise a determination is made as to whether the timer has expired. If the timer has not expired, then a safety check is re-performed; otherwise, a return is made to re-plan the path.

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 providing redundant vehicle speed estimation. The method includes providing sensor output signals from a plurality of primary sensors and providing inertial measurement signals from an inertial measurement unit. The method also includes estimating the vehicle speed in a primary module using the primary sensor signals, and buffering the estimated vehicle speed values from the primary module for a predetermined period of time. The method further includes determining that one or more of the primary sensors or the primary module has failed, and if so, estimating the vehicle speed in a secondary module using the buffered vehicle speed values and the inertial measurement signals. The method can use GPS signal data and/or range data from static objects to improve the estimated vehicle speed in the secondary module if they are available.

Abstract: A method and system are disclosed for tracking a remote vehicle which is driving in a lateral position relative to a host vehicle. Target data from two radar sensors are provided to an object detection fusion system. Wheels on the remote vehicle are identified as clusters of radar points with essentially the same location but substantially varying Doppler range rate values. If both wheels on the near side of the remote vehicle can be identified, a fusion calculation is performed using the wheel locations measured by both radar sensors, yielding an accurate estimate of the position, orientation and velocity of the remote vehicle. The position, orientation and velocity of the remote vehicle are used to trigger warnings or evasive maneuvers in a Lateral Collision Prevention (LCP) system. Radar sensor alignment can also be calibrated with an additional fusion calculation based on the same wheel measurement data.

Abstract: A method and system are disclosed for tracking objects which are crossing behind a host vehicle. Target data from a vision system and two radar sensors are provided to an object detection fusion system. Salient points on the target object are identified and tracked using the vision system data. The salient vision points are associated with corresponding radar points, where the radar points provide Doppler radial velocity data. A fusion calculation is performed on the salient vision points and the radar points, yielding an accurate estimate of the velocity of the target object, including its lateral component which is difficult to obtain using radar points only or traditional vision system methods. The position and velocity of the target object are used to trigger warnings or automatic braking in a Rear Cross Traffic Avoidance (RCTA) system.

Abstract: A method for localizing a vehicle in a digital map. GPS raw measurement data is retrieved from satellites. A digital map of a region traveled by the vehicle based on the raw measurement data is retrieved from a database. 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 earth-fixed coordinates. Roadside objects are sensed in the region traveled by the vehicle using distance data and bearing angle data. The sensed roadside objects are matched on the digital map. A vehicle position is determined on the traveled road by fusing raw measurement data and sensor measurements of the identified roadside objects. The position of the vehicle is represented as a function of linearizing raw measurement data and the sensor measurement data as derived by a Jacobian matrix and normalized measurements, respectively.

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.