Abstract: Apparatus, method and system for hybrid multi-mode data fusion to fuse data of multiple different formats by a centralized data fusion processing and send the fused data by a distributed data processing to a set of heterogeneous sensor devices. The hybrid multi-mode data fusion apparatus comprises a multi-mode fusion module configured to operate in a first mode to receive fused data of multiple different formats sensed from a set of heterogeneous devices and configured to operate in a second mode to send the fused data to each of the heterogeneous devices. An association module to fuse data of multiple different formats sensed from each of the heterogeneous devices and to send the fused data to the multi-mode fusion module. A fusion track data file to store prior fused data to enable the multi-mode fusion module to send cueing information based upon the stored prior fused data to the heterogeneous devices.

Abstract: An autonomic vehicle control system is described, and includes a vehicle spatial monitoring system including a subject spatial sensor that is disposed to monitor a spatial environment proximal to the autonomous vehicle. A controller is in communication with the subject spatial sensor, and the controller includes a processor and a memory device including an instruction set. The instruction set is executable to evaluate the subject spatial sensor, which includes determining first, second, third, fourth and fifth SOH (state of health) parameters associated with the subject spatial sensor, and determining an integrated SOH parameter for the subject spatial sensor based thereupon.

Abstract: A system and method to perform detection based on sensor fusion includes obtaining data from two or more sensors of different types. The method also includes extracting features from the data from the two or more sensors and processing the features to obtain a vector associated with each of the two or more sensors. The method further includes concatenating the two or more vectors obtained from the two or more sensors to obtain a fused vector, and performing the detection based on the fused vector.

Abstract: Described herein are systems, methods, and computer-readable media for generating and training a high precision low bit convolutional neural network (CNN). A filter of each convolutional layer of the CNN is approximated using one or more binary filters and a real-valued activation function is approximated using a linear combination of binary activations. More specifically, a non-1×1 filter (e.g., a k×k filter, where k>1) is approximated using a scaled binary filter and a 1×1 filter is approximated using a linear combination of binary filters. Thus, a different strategy is employed for approximating different weights (e.g., 1×1 filter vs. a non-1×1 filter). In this manner, convolutions performed in convolutional layer(s) of the high precision low bit CNN become binary convolutions that yield a lower computational cost while still maintaining a high performance (e.g., a high accuracy).

Abstract: An adaptive parallel imaging processing system in a vehicle is provided. The system may include, but is not limited to, a plurality of processors and a resource management system including, but not limited to, an execution monitor, the execution monitor configured to calculate an average utilization of each of the plurality of processors over a moving window, and a service scheduler controlling a request queue for each of the plurality of processors, the service scheduler scheduling image processing tasks in the respective request queue for the each of the plurality of processors based upon the average utilization of each of the plurality of processors, the capabilities of each of the plurality of processors, and a priority associated with each image processing task, wherein an autonomous vehicle control system is configured to generate the instructions to control the at least one vehicle system based upon the processed image processing tasks.

Abstract: In various embodiments, methods, systems, and vehicles are provided for calibrating vehicle cameras. In certain embodiments, a vehicle includes a camera, a memory, and a processor. The camera is disposed onboard the vehicle, and is configured to generate a camera image in which an object is detected onboard the vehicle. The memory is configured to store map data relating to the detected object. The processor is disposed onboard the vehicle, and is configured to perform an initial projection using the map data and initial values of calibration parameters for the camera; and update values of the calibration parameters using a comparison of the initial projection with the camera image.

Abstract: A crowdsourced virtual sensor generator is provided which could be generated by a vehicle or provided to the vehicle as, for example, a service. The crowdsourced virtual sensor generator may include, but is not limited to, a communication system configured to receive contributing vehicle sensor data from one or more contributing vehicles, a location of the one or more contributing vehicles and a target vehicle, and a processor, the processor configured to filter the received contributing vehicle sensor data based upon the location of the one or more contributing vehicles and the location of the target vehicle, aggregate the filtered contributing vehicle sensor data into at least one of a data-specific dataset and an application-specific data set, and generate a virtual sensor for the target vehicle, the virtual sensor processing the filtered and aggregated contributing vehicle sensor data to generate output data relative to the location of the target vehicle.

Abstract: Systems and methods are provided for controlling an autonomous vehicle (AV). A feature map generator module generates a feature map (FM). Based on the FM, a perception map generator module generates a perception map (PM). A scene understanding module selects from a plurality of sensorimotor primitive modules (SPMs), based on the FM, a particular combination of SPMs to be enabled and executed for the particular driving scenario (PDS). Each SPM maps information from either the FM or the PM to a vehicle trajectory and speed profile (VTSP) for automatically controlling the AV to cause the AV to perform a specific driving maneuver. Each one of the particular combination of the SPMs addresses a sub-task in a sequence of sub-tasks that address the PDS. Each of the particular combination of the SPMs are retrieved from memory and executed to generate a corresponding VTSP.

Abstract: Systems and methods are provided for controlling an autonomous vehicle (AV). A map generator module processes sensor data to generate a world representation of a particular driving scenario (PDS). A scene understanding module (SUM) processes navigation route data, position information and a feature map to define an autonomous driving task (ADT), and decomposes the ADT into a sequence of sub-tasks. The SUM selects a particular combination of sensorimotor primitive modules (SPMs) to be enabled and executed for the PDS. Each one of the SPMs addresses a sub-task in the sequence. A primitive processor module executes the particular combination of the SPMs such that each generates a vehicle trajectory and speed (VTS) profile.

Abstract: Systems and methods are provided for controlling an autonomous vehicle (AV). A scene understanding module of a high-level controller selects a particular combination of sensorimotor primitive modules to be enabled and executed for a particular driving scenario from a plurality of sensorimotor primitive modules. Each one of the particular combination of the sensorimotor primitive modules addresses a sub-task in a sequence of sub-tasks that address a particular driving scenario. A primitive processor module executes the particular combination of the sensorimotor primitive modules such that each generates a vehicle trajectory and speed profile. An arbitration module selects one of the vehicle trajectory and speed profiles having the highest priority ranking for execution, and a vehicle control module processes the selected one of vehicle trajectory and speed profiles to generate control signals used to execute one or more control actions to automatically control the AV.

Abstract: A system and method for determining an object location by using map information is disclosed. The method includes receiving, by a controller, an image data of a scene. An object is located within the scene. The method also includes determining, by the controller, a location of the object based on the image data of the scene. The method can also include receiving, by the controller, a map information. The map information includes at least one path information. The method also includes determining an association between the location of the object based on the image data and the map information of the scene. The method also includes determining a 3-dimensional location of the object based on the determined association.

Abstract: Methods and systems are provided for detecting an object. In one embodiment, a method includes: receiving, by a processor, image data from an image sensor; receiving, by a processor, radar data from a radar system; processing, by the processor, the image data from the image sensor and the radar data from the radar system using a deep learning method; and detecting, by the processor, an object based on the processing.

Abstract: A system and method of controlling operation of a host device in real-time, the host device operatively connected to an optical device and a radar device. The optical device is configured to obtain visual data of at least one object. The object is located at an incline, relative to the host device, the incline being characterized by an elevation angle (?) and an azimuth angle (?). The radar device is configured to obtain radar data, including a radial distance (r) of the object from the host device, the azimuth angle (?), and a range rate (dr/dt). The controller is programmed to determine a time-to-contact for the host device and the object based at least partially on a 3-D position and 3-D velocity vector. The operation of the host device is controlled based at least partially on the time-to-contact.

Abstract: A system and method to determine trailer pose includes imaging first and second telltales affixed to a trailer to provide trailer image data. A controller is operable to determine trailer pose from the trailer image data. The trailer pose information may be provided to one or more controllers of a vehicle towing the trailer.

Abstract: Methods and apparatus are provided for controlling an autonomous vehicle. A sensor fusion system with a sensor system for providing environment condition information and a convolutional neural network (CNN) is provided. The CNN includes a receiving interface configured to receive the environment condition information from the sensor system, a common convolutional layer configured to extract traffic information from the received environment condition information, and a plurality of fully connected layers configured to detect objects belonging to different object classes based on the extracted traffic information, wherein the object classes include at least one of a road feature class, a static object class, and a dynamic object class.

Abstract: A system and method of operating a vehicle. The system includes a two-dimensional imager, a three-dimensional imager, and at least one processor. The two-dimensional imager obtains a two-dimensional image of an environment surrounding the vehicle, wherein the environment includes an object. The three-dimensional imager obtains a three-dimensional (3D) point cloud of the environment. The at least one processor identifies the object from the 2D image and assigns the identification of the object to a selected point of the 3D point cloud.

Abstract: A system and method to perform detection based on sensor fusion includes obtaining data from two or more sensors of different types. The method also includes extracting features from the data from the two or more sensors and processing the features to obtain a vector associated with each of the two or more sensors. The method further includes concatenating the two or more vectors obtained from the two or more sensors to obtain a fused vector, and performing the detection based on the fused vector.

Abstract: A system and method to perform inter-sensor learning obtains a detection of a target based on a first sensor. The method also includes determining whether a second sensor with an overlapping detection range with the first sensor also detects the target, and performing learning to update a detection algorithm used with the second sensor based on the second sensor failing to detect the target.

Abstract: A method of identifying a condition of a road surface includes capturing at least a first image of the road surface with a first camera, and a second image of the road surface with a second camera. The first image and the second image are tiled together to form a combined tile image. A feature vector is extracted from the combined tile image using a convolutional neural network, and a condition of the road surface is determined from the feature vector using a classifier.

Abstract: Technical solutions are described for controlling an automated driving system of a vehicle. An example method includes computing a complexity metric of an upcoming region along a route that the vehicle is traveling along. The method further includes, in response to the complexity metric being below a predetermined low-complexity threshold, determining a trajectory for the vehicle to travel in the upcoming region using a computing system of the vehicle. Further, the method includes in response to the complexity metric being above a predetermined high-complexity threshold, instructing an external computing system to determine the trajectory for the vehicle to travel in the upcoming region. If the trajectory cannot be determined by the external computing system a minimal risk condition maneuver of the vehicle is performed.