Abstract: System, methods, and other embodiments described herein relate to coordinating and optimizing prediction by a model using intermediate data and vehicle-to-vehicle (V2V) communications that improves bandwidth utilization. In one embodiment, a method includes processing acquired data, by a subject vehicle using a prediction model, to execute a vehicle task associated with navigating a driving scene. The method also includes extracting processed data according to the acquired data from intermediate activations of layers within the prediction model according to a cooperation model. The method also includes quantifying relevancy of the processed data, for a target vehicle, to select a subset using the cooperation model. The method also includes communicating the subset to a selected vehicle having the prediction model for additional prediction according to the relevancy and available resources to transmit the subset.

Abstract: Systems and methods described herein relate to estimating risk associated with a vehicular maneuver. One embodiment acquires a geometric representation of an intersection including a lane in which a vehicle is traveling and at least one other lane; discretizes the at least one other lane into a plurality of segments; determines a trajectory along which the vehicle will travel; estimates a probability density function for whether a road agent external to the vehicle is present in the respective segments; estimates a traffic-conflict probability of a traffic conflict in the respective segments conditioned on whether an external road agent is present; estimates a risk associated with the vehicle following the trajectory by integrating a product of the probability density function and the traffic-conflict probability over the at least one other lane and the plurality of segments; and controls operation of the vehicle based, at least in part, on the estimated risk.

Abstract: A method for task-informed planning by a behavior planning system of a vehicle includes observing a previous trajectory of an agent within a distance from the vehicle. The method also includes predicting, by the behavior planning system, a set of potential trajectories for the agent and/or the vehicle based on observing the previous trajectory. The method further includes selecting, by the behavior planning system, a potential action from a set of potential actions associated with a task to be performed by the vehicle, each potential action being associated with a utility value based on the respective potential action and the set of potential trajectories, the selected potential action being associated with a highest utility value of respective utility values associated with the set of potential actions. The method still further includes controlling the vehicle to perform an action associated with the potential action selected by the behavior planning system.

Abstract: Systems and methods for predicting a trajectory of a moving object are disclosed herein. One embodiment downloads, to a robot, a probabilistic hybrid discrete-continuous automaton (PHA) model learned as a deep neural network; uses the deep neural network to infer a sequence of high-level discrete modes and a set of associated low-level samples, wherein the high-level discrete modes correspond to candidate maneuvers for the moving object and the low-level samples are candidate trajectories; uses the sequence of high-level discrete modes and the set of associated low-level samples, via a learned proposal distribution in the deep neural network, to adaptively sample the sequence of high-level discrete modes to produce a reduced set of low-level samples; applies a sample selection technique to the reduced set of low-level samples to select a predicted trajectory for the moving object; and controls operation of the robot based, at least in part, on the predicted trajectory.

Abstract: Systems and methods for parallel autonomy of a vehicle are disclosed herein. One embodiment receives input data, the input data including at least one of sensor data and structured input data; encodes the input data into an intermediate embedding space using a first neural network; inputs the intermediate embedding space to a first behavior model and a second behavior model, the first behavior model producing a first behavior output, the second behavior model producing a second behavior output; combines the first behavior output and the second behavior output using an ideal-behavior model to produce an ideal behavior for the vehicle; and controls one or more aspects of operation of the vehicle based, at least in part, on the ideal behavior.

Abstract: Systems and methods for generating driving recommendations are disclosed herein. One embodiment divides automatically a roadway into a plurality of lane-level cells; generates a graph network that represents the plurality of lane-level cells; gathers information pertaining to one or more detected road agents; projects onto the graph network the gathered information pertaining to the one or more detected road agents to update a current status of the plurality of lane-level cells; processes the graph network based on the updated current status of the plurality of lane-level cells to predict a future status of the plurality of lane-level cells, the predicted future status including at least occupancy, by a detected road agent, of the respective lane-level cells in the plurality of lane-level cells; and generates a driving recommendation based, at least in part, on the predicted future status of the plurality of lane-level cells.

Abstract: Systems and methods for estimating lane geometry are disclosed herein. One embodiment receives sensor data from one or more sensors; detects a road agent based on the sensor data; detects, based on the sensor data, that the road agent has performed a lane shift from a first lane of a roadway to a second lane of the roadway; and estimates a boundary line between the first lane of the roadway and the second lane of the roadway based, at least in part, on the detected lane shift.

Abstract: Systems and methods for generating driving recommendations are disclosed herein. One embodiment divides automatically a roadway into a plurality of lane-level cells; generates a graph network that represents the plurality of lane-level cells; gathers information pertaining to one or more detected road agents; projects onto the graph network the gathered information pertaining to the one or more detected road agents to update a current status of the plurality of lane-level cells; processes the graph network based on the updated current status of the plurality of lane-level cells to predict a future status of the plurality of lane-level cells, the predicted future status including at least occupancy, by a detected road agent, of the respective lane-level cells in the plurality of lane-level cells; and generates a driving recommendation based, at least in part, on the predicted future status of the plurality of lane-level cells.

Abstract: Systems and methods for predicting a trajectory of a road agent are disclosed herein. One embodiment receives sensor data from one or more sensors; analyzes the sensor data to generate a predicted trajectory of the road agent, wherein the predicted trajectory includes a sequence of primitives, at least one primitive in the sequence of primitives having an associated duration that is determined in accordance with a dynamic timescale; and controls one or more aspects of the operation of an ego vehicle based, at least in part, on the predicted trajectory of the road agent.

Abstract: Systems and methods described herein relate to controlling the operation of a vehicle. One embodiment generates predicted trajectories of the vehicle using first trajectory predictors based, at least in part, on first inputs; generates predicted trajectories of a road agent that is external to the vehicle using second trajectory predictors based, at least in part, on second inputs; integrates the predicted trajectories of the road agent into the first inputs to iteratively update the predicted trajectories of the vehicle and integrates the predicted trajectories of the vehicle into the second inputs to iteratively update the predicted trajectories of the road agent; and controls operation of the vehicle based, at least in part, on at least one of (1) the iteratively updated predicted trajectories of the vehicle and (2) the iteratively updated predicted trajectories of the road agent.

Abstract: Systems and methods described herein relate to predicting a trajectory of a vehicle.

Abstract: Systems and methods described herein relate to predicting a trajectory of a road agent external to a vehicle.

Abstract: Systems and methods for parallel autonomy of a vehicle are disclosed herein. One embodiment receives input data, the input data including at least one of sensor data and structured input data; encodes the input data into an intermediate embedding space using a first neural network; inputs the intermediate embedding space to a first behavior model and a second behavior model, the first behavior model producing a first behavior output, the second behavior model producing a second behavior output; combines the first behavior output and the second behavior output using an ideal-behavior model to produce an ideal behavior for the vehicle; and controls one or more aspects of operation of the vehicle based, at least in part, on the ideal behavior.

Abstract: Systems and methods for estimating lane geometry are disclosed herein. One embodiment receives sensor data from one or more sensors; detects a road agent based on the sensor data; detects, based on the sensor data, that the road agent has performed a lane shift from a first lane of a roadway to a second lane of the roadway; and estimates a boundary line between the first lane of the roadway and the second lane of the roadway based, at least in part, on the detected lane shift.

Abstract: Systems and methods described herein relate to estimating risk associated with a vehicular maneuver. One embodiment acquires a geometric representation of an intersection including a lane in which a vehicle is traveling and at least one other lane; discretizes the at least one other lane into a plurality of segments; determines a trajectory along which the vehicle will travel; estimates a probability density function for whether a road agent external to the vehicle is present in the respective segments; estimates a traffic-conflict probability of a traffic conflict in the respective segments conditioned on whether an external road agent is present; estimates a risk associated with the vehicle following the trajectory by integrating a product of the probability density function and the traffic-conflict probability over the at least one other lane and the plurality of segments; and controls operation of the vehicle based, at least in part, on the estimated risk.

Abstract: Systems and methods described herein relate to predicting a trajectory of a road agent external to a vehicle.

Abstract: Systems and methods described herein relate to controlling the operation of a vehicle. One embodiment generates predicted trajectories of the vehicle using first trajectory predictors based, at least in part, on first inputs; generates predicted trajectories of a road agent that is external to the vehicle using second trajectory predictors based, at least in part, on second inputs; integrates the predicted trajectories of the road agent into the first inputs to iteratively update the predicted trajectories of the vehicle and integrates the predicted trajectories of the vehicle into the second inputs to iteratively update the predicted trajectories of the road agent; and controls operation of the vehicle based, at least in part, on at least one of (1) the iteratively updated predicted trajectories of the vehicle and (2) the iteratively updated predicted trajectories of the road agent.

Abstract: Systems and methods described herein relate to predicting a trajectory of a vehicle.

Abstract: System, methods, and other embodiments described herein relate to dynamically adjusting a vehicle trajectory according to driver deviations. In one embodiment, a method includes generating expected inputs for controlling the vehicle along a segment of a roadway on which the vehicle is traveling by analyzing a present context of the vehicle using a driver model. The expected inputs as controls for operating the vehicle to maintain a preferred trajectory along the segment. The method includes computing a variance of received inputs from the expected inputs by comparing the expected inputs with the received inputs. The method includes controlling the vehicle based, at least in part, on the expected inputs when the deviation score satisfies a deviation threshold indicating that the received inputs are inadequate to maintain the vehicle along the preferred trajectory.

Abstract: System, methods, and other embodiments described herein relate to dynamically adjusting a vehicle trajectory according to driver deviations. In one embodiment, a method includes generating expected inputs for controlling the vehicle along a segment of a roadway on which the vehicle is traveling by analyzing a present context of the vehicle using a driver model. The expected inputs as controls for operating the vehicle to maintain a preferred trajectory along the segment. The method includes computing a variance of received inputs from the expected inputs by comparing the expected inputs with the received inputs. The method includes controlling the vehicle based, at least in part, on the expected inputs when the deviation score satisfies a deviation threshold indicating that the received inputs are inadequate to maintain the vehicle along the preferred trajectory.