Abstract: A system and method to perform autonomous operation of a vehicle include obtaining one or more image frames for a time instance t from corresponding one or more sensors. Processing the one or more image frames includes performing convolutional processing to obtain a multi-dimensional matrix xt. The method includes operating on the multi-dimensional matrix xt to obtain output ht. The operating includes using an output ht-1 of the operating for a previous time instance t?1. The method also includes post-processing the output ht to obtain one or more control signals to affect operation of the vehicle.

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 method for vehicle-to-vehicle communication is disclosed. The method includes establishing a communication connection between the first vehicle and the second vehicle, receiving one or more communications from the second vehicle, determining, by the first vehicle, vehicle parameter targets based on the one or more communications, communicating the vehicle parameter targets and one or more vehicle device commands to the second vehicle, and determining at least one vehicle control signal for the second vehicle based on the vehicle parameter targets and the one or more vehicle device commands.

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: Methods and systems are provided for determining a vehicle position. In one embodiment, a method includes: receiving, by a processor of a rover vehicle, vehicle position data from one or more parked vehicles; receiving, by the processor of the rover vehicle, global positioning system data from a GPS receiver of the rover vehicle; and processing, by the processor of the rover vehicle, the vehicle position data and the global position system data to determine a position of the rover vehicle.

Abstract: A control system for an autonomous vehicle includes at least one controller. The controller is programmed to receive first sensor readings from a first group of sensors, provide a first sensor fusion output based on the first sensor readings, the first sensor fusion output including a first detected state of a detected object, receive second sensor readings from a second group of sensors, and provide a second sensor fusion output based on the second sensor readings, the second sensor fusion output including a second detected state of the detected object. The controller is additionally programmed to, in response to the first detected state being outside a predetermined range of the second detected state, generate a diagnostic signal.

Abstract: A control system for an autonomous vehicle includes at least one controller. The controller is programmed to receive first sensor readings from a first group of sensors, provide a first sensor fusion output based on the first sensor readings, the first sensor fusion output including a first detected state of a detected object, receive second sensor readings from a second group of sensors, and provide a second sensor fusion output based on the second sensor readings, the second sensor fusion output including a second detected state of the detected object. The controller is additionally programmed to, in response to the first detected state being outside a predetermined range of the second detected state, generate a diagnostic signal.

Abstract: A control system for a vehicle includes at least one controller. The controller is programmed to receive first sensor readings from a first group of sensors and provide a first vehicle pose based on the first sensor readings. The first vehicle pose includes a first location and a first orientation of the vehicle. The controller is also programmed to receive second sensor readings from a second group of sensors and provide a second vehicle pose based on the second sensor readings. The second vehicle pose includes a second location and a second orientation of the vehicle. The controller is further programmed to, in response to the first vehicle pose being outside a predetermined range of the second vehicle pose, generate a diagnostic signal.

Abstract: A control system for a vehicle includes at least one controller. The controller is programmed to receive first sensor readings from a first group of sensors and provide a first vehicle pose based on the first sensor readings. The first vehicle pose includes a first location and a first orientation of the vehicle. The controller is also programmed to receive second sensor readings from a second group of sensors and provide a second vehicle pose based on the second sensor readings. The second vehicle pose includes a second location and a second orientation of the vehicle. The controller is further programmed to, in response to the first vehicle pose being outside a predetermined range of the second vehicle pose, generate a diagnostic signal.

Abstract: A method for vehicle-to-vehicle communication is disclosed. The method includes establishing a communication connection between the first vehicle and the second vehicle, receiving one or more communications from the second vehicle, determining, by the first vehicle, vehicle parameter targets based on the one or more communications, communicating the vehicle parameter targets and one or more vehicle device commands to the second vehicle, and determining at least one vehicle control signal for the second vehicle based on the vehicle parameter targets and the one or more vehicle device commands.

Abstract: An automotive vehicle includes a vehicle steering system, an actuator configured to control the steering system, and first and second controllers. The first controller is in communication with the actuator, and is configured to communicate an actuator control signal based on a primary automated driving system control algorithm. The second controller is in communication with the actuator and with the first controller. The second controller is configured to, in response to a first predicted vehicle path based on the actuator control signal passing within a first threshold distance of a detected obstacle, control the actuator to maintain a current actuator setting. The second controller is also configured to in response to the first predicted vehicle path not passing within the first threshold distance of a detected obstacle, control the actuator according to the actuator control signal.

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: 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: 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 for predicting whether a driver of a host vehicle or a remote vehicle intends to make a left or right turn or travel straight through an intersection before the host vehicle or remote vehicle reaches the intersection that relies on a probability model that employs a dynamic Bayesian network. The method includes obtaining a plurality of environmental cues that identify external parameters at or around the intersection, where the environmental cues include position and velocity of the remote vehicle, and obtaining a plurality of host vehicle cues that define operation of the host vehicle. The method then predicts the turning intent of the host vehicle and/or remote vehicle at the intersection using the model based on both the external cues and the vehicle cues using the model. The model can use learned information about previous driver turns at the intersection.

Abstract: A system and method for determining whether a driver of a host vehicle intends to make a left or right turn with a certain level of confidence. The method obtains a plurality of turning cues that identify external parameters around the host vehicle and/or define operating conditions of the host vehicle, and determines a confidence level that the host vehicle will make a left or right turn based on the turning cues, where determining the confidence level includes weighting each of the cues based on current vehicle operating conditions.