Abstract: A method in an autonomous vehicle (AV) is provided. The method includes determining, from vehicle sensor data and road geometry data, a plurality of range measurements and obstacle velocity data; determining vehicle state data wherein the vehicle state data includes a velocity of the AV, a distance to a stop line, a distance to a midpoint of an intersection, and a distance to a goal; determining, based on the plurality of range measurements, the obstacle velocity data and the vehicle state data, a set of discrete behavior actions and a unique trajectory control action associated with each discrete behavior action; choosing a discrete behavior action and a unique trajectory control action to perform; and communicating a message to vehicle controls conveying the unique trajectory control action associated with the discrete behavior action.

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 processing attention data. In one embodiment, a method includes: receiving, by a processor, object data associated with at least one object of an exterior environment of the vehicle; receiving upcoming behavior data determined from a planned route of the vehicle; receiving gaze data sensed from an occupant of the vehicle; processing, by the processor, the object data, the upcoming behavior data, and the gaze data to determine an attention score associated with an attention of the occupant of the vehicle; and selectively generating, by the processor, signals to at least one of notify the occupant and control the vehicle based on the attention score.

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: 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 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 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 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.

Abstract: An autonomous vehicle, a system and method of operating the autonomous vehicle. An environmental sensor obtains one or more parameters of external agents of the vehicle. A processor of the vehicle obtains a route having a destination at the autonomous vehicle, builds a Markov state model of the route that includes a plurality of states for the autonomous vehicle and one or more parameters of the external agents, generates a plurality of driving policies for navigating the route, selects a policy for navigating the route from the plurality of driving policies using a Markov Decision Process, and executes the selected policy at the autonomous vehicle to navigate the vehicle along the route towards the destination.

Abstract: Methods and systems are provided for processing attention data. In one embodiment, a method includes: receiving, by a processor, object data associated with at least one object of an exterior environment of the vehicle; receiving upcoming behavior data determined from a planned route of the vehicle; receiving gaze data sensed from an occupant of the vehicle; processing, by the processor, the object data, the upcoming behavior data, and the gaze data to determine an attention score associated with an attention of the occupant of the vehicle; and selectively generating, by the processor, signals to at least one of notify the occupant and control the vehicle based on the attention score.

Abstract: A method for controlling an operating vehicle includes: (a) determining, via a controller, a confidence level that the light of the other vehicle is ON based on images captured by a camera of the operating vehicle; and (b) controlling, via the controller, an alarm of the operating vehicle based on the confidence level that the light of the other vehicle is ON.

Abstract: The present application generally relates to a method and apparatus for generating an action policy for controlling an autonomous vehicle. In particular, the system performs a deep learning algorithm in order to determine the action policy and an automatically generated curriculum system to determine a number of increasingly difficult tasks in order to refine the action policy.

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: 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: An electric power system includes an energy recovery system that is operable to convert kinetic energy into electric energy at a first voltage. A primary energy storage device is electrically connected to the energy recovery system at the first voltage. A first voltage autonomous driving system load is disposed in a parallel circuit with the energy recovery system and the primary energy storage device. A bi-directional DC-DC converter is electrically connected to the energy recovery system and the primary energy storage device for converting the electric energy between the first voltage and a second voltage. A secondary energy storage device is electrically connected to the bi-directional DC-DC converter at the second voltage. A second voltage autonomous driving system load is disposed in a parallel circuit with the secondary energy storage device.

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.

Abstract: Systems and method are provided for evaluating control features of an autonomous vehicle for development or validation purposes. A real-world sensor data set is generated by an autonomous vehicle having sensors. A sensing and perception module generates perturbations of the real-world sensor data set. A generator module generates a 3-dimensional object data set from the real-world sensor data set. A planning and behavior module generates perturbations of the 3-dimensional object data set. A testing module tests a control feature such as an algorithm or software using the 3-dimensional object data set. A control module executes command outputs from the control feature for evaluation.

Abstract: A system and method for generating a range image using sparse depth data is disclosed. The method includes receiving, by a controller, image data of a scene. The image data includes a first set of pixels. The method also includes receiving, by the controller, a sparse depth data of the scene. The sparse depth data includes a second set of pixels, and the number of the second set of pixels is less than the number of first set of pixels. The method also includes combining the image data and the sparse depth data into a combined data. The method also includes generating a range image using the combined data.

Abstract: A vehicle, system and method of autonomous navigation of the vehicle. A reference trajectory for navigating a training traffic scenario along a road section is received at a processor of the vehicle. The processor determines a coefficient for a cost function associated with a candidate trajectory that simulates the reference trajectory. The determined coefficient is provided to a neural network to train the neural network. The trained neural network generates a navigation trajectory for navigating the vehicle using a cost coefficient determined by the neural network. The vehicle is navigated along the road section using the navigation trajectory.