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 system and method for fusing the outputs from multiple LiDAR sensors on a vehicle. 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 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 method of associating targets from at least two object detection systems. An initial prior correspondence matrix is generated based on prior target data from a first object detection system and a second object detection system. Targets are identified in a first field-of-view of the first object detection system based on a current time step. Targets are identified in a second field-of-view of the second object detection system based on the current time step. The prior correspondence matrix is adjusted based on respective targets entering and leaving the respective fields-of-view. A posterior correspondence matrix is generated as a function of the adjusted prior correspondence matrix. A correspondence is identified in the posterior correspondence matrix between a respective target of the first object detection system and a respective target of the second object detection system.

Abstract: A method and system may operate a coupled range imager and intensity imager to obtain a sequence of at least two automatically registered images, each automatically registered image including concurrently acquired range data and intensity data with regard to an imaged scene. The registered range and intensity data is analyzed to yield an estimated motion of an element of an imaged object of the imaged scene.

Abstract: A vehicle having an on-board computer, vehicle sensors, a satellite-positioning unit, a database storing a lane-level map performs a method to determine a new pose of the vehicle using map matching. The method includes the on-board computer of the vehicle receiving new data from at least one of the vehicle sensors and collecting measurements from the vehicle sensors. The method also includes the on-board computer of the vehicle computing propagation of vehicle pose with respect to consecutive time instances and performing a curve-fitting process. The method further includes the on-board computer of the vehicle performing a sub-routine of updating at least one observation model based on results of the curve-fitting process, performing a tracking sub-routine including using a probability distribution to update the vehicle pose in terms of data particles, and performing a particle filtering sub-routine based on the data particles to compute the new vehicle pose.

Abstract: A lane tracking system for tracking the position of a vehicle within a lane includes a camera configured to provide a video feed representative of a field of view and a video processor configured to receive the video feed from the camera and to generate latent video-based position data indicative of the position of the vehicle within the lane. The system further includes a vehicle motion sensor configured to generate vehicle motion data indicative of the motion of the vehicle, and a lane tracking processor. The lane tracking processor is configured to receive the video-based position data, updated at a first frequency; receive the sensed vehicle motion data, updated at a second frequency; estimate the position of the vehicle within the lane from the sensed vehicle motion data; and fuse the video-based position data with the estimate of the vehicle position within the lane using a Kalman filter.

Abstract: An active vision system includes an image capture device for capturing images in a region exterior of a vehicle and a headlamp control unit for controlling a vehicle headlamp beam for illuminating an environment exterior of a vehicle. The headlamp control unit is configured to selectively illuminate between making a path of travel of a road visible to a driver of the vehicle and making the region exterior of the vehicle visible for capturing images by the image capture device. The headlamp control unit utilizes a duty cycle for controlling a first cycle time that the headlamp beam illuminates the path of travel for making the road visible to the driver and for controlling a second cycle time that the headlamp beam makes the captured region visible for capturing images by the image capture device.

Abstract: A system and method for controlling a vehicle sun visor that uses commonly existing onboard sensors and systems to provide all necessary inputs. The method uses GPS and solar almanac data to determine the location of the sun relative to the vehicle, driver side view mirror angle data to determine the position of the driver's eyes within the vehicle, and an existing outside light metering device to determine whether the sun is actually shining on the vehicle, and uses this information to calculate the optimum position of the sun visor. If a forward-looking camera is available on the vehicle, camera images can be used to improve the estimate of the sun position relative to the vehicle.

Abstract: A method of autonomously convoying vehicles traveling along a route with a leader vehicle being in communication with at least one follower vehicle. The at least one follower vehicle receives a communication relating to a target offset position and route data. Tracking data is generated and derived from on-board sensing devices of the at least one follower vehicle that includes a traveled path of the leader vehicle sensed by the at least one follower vehicle. The route data is compared to the tracking data for identifying accuracy between the route data relative to the tracking data. An adjusted target offset position and a set of trajectory points that provides a trajectory path of travel from a current position of the at least one follower vehicle to the adjusted target offset position are determined based on the accuracy between the route data and the tracking data.

Abstract: A method and system may determine the estimated location of a vehicle, measure a location of an object relative to the vehicle using a sensor associated with the vehicle, and determine an updated vehicle location using the measured relative object location in conjunction with previously stored object locations. The estimated vehicle location may be determined using a system different from that associated with the sensor, for example a GPS system. The object location may be measured relative to a sub-map corresponding to the location of the vehicle.

Abstract: A method and tools for virtually aligning object detection sensors on a vehicle without having to physically adjust the sensors. A sensor misalignment condition is detected during normal driving of a host vehicle by comparing different sensor readings to each other. At a vehicle service facility, the host vehicle is placed in an alignment target fixture, and alignment of all object detection sensors is compared to ground truth to determine alignment calibration parameters. Alignment calibration can be further refined by driving the host vehicle in a controlled environment following a leading vehicle. Final alignment calibration parameters are authorized and stored in system memory, and applications which use object detection data henceforth adjust the sensor readings according to the calibration parameters.

Abstract: A method and system may determine a location of a vehicle, collect an image using a camera associated with the vehicle, analyze the image in conjunction with the location of the vehicle and/or previously collected information on the location of traffic signals or other objects (e.g., traffic signs), and using this analysis locate an image of a traffic signal within the collected image. The position (e.g., a geographic position) of the signal may be determined, and stored for later use. The identification of the signal may be used to provide an output such as the status of the signal, such as green light.

Abstract: An object detection tracking system for a vehicle includes a first modulated light source for transmitting a first light signal with a first propagation pattern into a target region and a second modulated light source for transmitting a second light signal with a second propagation pattern into the target region. A sensor measures light reflected off objects in the target region. A controller demodulates the measured light to detect when the first or second light signals are received. The controller determines respective ranges for generating a first set of range data for the first light signal and a second set of range data for the second light signal. The controller maintains an object tracking list that includes a position of detected objects relative to the vehicle. The position of each object is determined using trilateration of the first and second sets of range data.

Abstract: A method and system for detecting and tracking objects near a vehicle using a three dimensional laser rangefinder. The method receives points from the laser rangefinder, where the points represent locations in space where the rangefinder senses that some object exists. An algorithm first estimates the location of a ground plane, based on a previous ground plane location, data from onboard sensors, and an eigenvector calculation applied to the point data. Next, a plan view occupancy map and elevation map are computed for stationary objects, based on point data in relation to the ground plane. Finally, dynamic objects are detected and tracked, sensing objects which are moving, such as other vehicles, pedestrians, and animals. The output of the method is a set of stationary and dynamic objects, including their shape, range, and velocity. This output can be used by downstream applications, such as collision avoidance or semi-autonomous driving systems.

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 cross traffic collision alert system is provided that includes an image-based detection device for capturing motion data of objects in a region of a direction of travel. A motion analysis module monitors the captured motion data. A salient region detection module identifies regions of interest within the captured motion data. A predictive object detection module is responsive to the outputs of the motion analysis module and the salient region detection module for generating at least one object candidate. Non-imaging object detection sensors sense a region for detecting the at least one object candidate. An object tracking module tracks the at least one object candidate and fuses the at least one object candidate transitioning between the regions. A threat assessment module determines a threat assessment of a potential collision between a tracked object candidate and the driven vehicle and determines whether a warning should be provided.

Abstract: A vehicle awareness system for monitoring remote vehicles relative to a host vehicle. The vehicle awareness system includes at least one object sensing device and a vehicle-to-vehicle communication device. A data collection module is provided for obtaining a sensor object data map and vehicle-to-vehicle object data map. A fusion module merges the sensor object data map and vehicle-to-vehicle object data map for generating a cumulative object data map. A tracking module estimates the relative position of the remote vehicles to the host vehicle.

Abstract: A system and method for automatically calibrating the aiming direction of a camera onboard a vehicle. The method uses GPS and solar almanac data to estimate the location of the sun relative to the vehicle, and compares this to the solar position in an image from the camera. The comparison is performed with an ongoing recursive calculation which yields an improved estimate of the sun position relative to the vehicle, while simultaneously calibrating the aiming direction of the camera. The aiming calibration data is used to improve the accuracy of the camera's images as used for all of the camera's other functions.

Abstract: A vehicle having an on-board computer, vehicle sensors, a satellite-positioning unit, a database storing a lane-level map performs a method to determine a new pose of the vehicle using map matching. The method includes the on-board computer of the vehicle receiving new data from at least one of the vehicle sensors and collecting measurements from the vehicle sensors. The method also includes the on-board computer of the vehicle computing propagation of vehicle pose with respect to consecutive time instances and performing a curve-fitting process. The method further includes the on-board computer of the vehicle performing a sub-routine of updating at least one observation model based on results of the curve-fitting process, performing a tracking sub-routine including using a probability distribution to update the vehicle pose in terms of data particles, and performing a particle filtering sub-routine based on the data particles to compute the new vehicle pose.