Systems and methods for determining object prioritization and predicting future object locations for an autonomous vehicle are provided. A method can include obtaining, by a computing system comprising one or more processors, state data descriptive of at least a current or past state of a plurality of objects that are perceived by an autonomous vehicle. The method can further include determining, by the computing system, a priority classification for each object in the plurality of objects based at least in part on the respective state data for each object. The method can further include determining, by the computing system, an order at which the computing system determines a predicted future state for each object based at least in part on the priority classification for each object and determining, by the computing system, the predicted future state for each object based at least in part on the determined order.

The present disclosure is generally related to a data processing system to validate vehicular functions in a voice activated computer network environment. The data processing system can improve the efficiency of the network by discarding action data structures and requests that invalid prior to their transmission across the network. The system can invalidate requests by comparing attributes of a vehicular state to attributes of a request state.

A motion planning system includes: a processor; and memory to store instructions that when executed by the processor, cause the processor to: identify a reference path between a departure point and a destination point in an environment including one or more obstacles; generate decomposition segments of a space surrounding the reference path, the decomposition segments including a first free-space segment and a second free-space segment that are devoid of the obstacles; generating a first path segment relative to the reference path for traversing the first free-space segment, and a second path segment relative to the reference path for traversing the second free-space segment; and connecting the first and second path segments to each other to generate a navigational path to traverse the environment.

An electronic control device including a sensor fusion processing unit that integrates a plurality of pieces of sensor information having been input from a plurality of sensors. The electronic control device further including a behavior prediction processing unit that obtains a future value in which a future behavior of a target object is predicted based on joint information integrated by the sensor fusion processing unit. The electronic control device further including a comparison unit that compares a future value predicted by the behavior prediction processing unit with output information of each sensor of the sensor fusion processing unit at a predicted time.

A system includes a powertrain controller operatively coupled with and configured to control operation of a powertrain of a vehicle and for bidirectional communication via a wireless communication path. An optimization engine is configured to determine optimized powertrain operation parameters for the powertrain controller and for bidirectional communication via a second communication path. A channel management engine is configured for bidirectional communication with the powertrain controller via the wireless communication path and for bidirectional communication with the optimization engine via the second communication path, the channel management engine configured to dynamically update a plurality of data channels including a first data channel storing a non-transitory dynamically-updated instance of powertrain operation data received from the powertrain controller, and a second data channel storing a non-transitory dynamically-updated instance of optimized powertrain operation parameters received from the optimization engine.

A method for selecting and executing at least one reactive action of a vehicle includes a control unit receiving sensor data from a vehicle sensor system; detecting an emergency situation based on the sensor data; performing an evaluation; and selecting and implementing a reactive action for minimizing an accident risk of the vehicle based on the evaluation, where, in the evaluation, sensors that are uninvolved in the detection of the emergency situation are not taken into account or are taken into account at a lower weighting, are operated at a reduced performance, and/or are operated with a reduced scanning range. In addition, a control unit, computer program, and machine-readable memory medium can be provided for implementing the method.

A control system for a hybrid vehicle which includes an internal combustion engine and an electric motor and whose drive mode is switchable between an electric vehicle mode and a hybrid vehicle mode includes: an on-board learning unit mounted on the hybrid vehicle and configured to perform a learning action; a position determination unit configured to determine whether the hybrid vehicle is located in a low emission area where operation of the internal combustion engine is supposed to be restricted; and a learning control unit configured to at least partially stop the learning action of the on-board learning unit when determination is made that the hybrid vehicle is located in the low emission area.

A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

A self-driving motor vehicle including numerous control units and numerous program codes for controlling the functions of the autonomous driving and other functions of the self-driving vehicle. Numerous program codes used for an autonomous driving mode are applied redundantly to at least two different control units. The self-driving motor vehicle may then be operated in an at least a partially autonomous driving mode. In this mode, the functions directly needed for satisfying a passenger's desire are determined, and weighted with regard to their importance in fulfilling the passenger's desires. At least one function of a lower order is then shut off, if the available resources in functioning control units and/or the power level in the self-driving motor vehicle are insufficient to execute program code for executing this function of the lower order.

A method for operating an autonomous driving function of a vehicle. The vehicle includes a computer unit and sensors for detecting surroundings data. The computer unit is configured to determine a setpoint trajectory for the vehicle, based on the detected surroundings data. In step a), an actual trajectory, and distances from objects in the surroundings, are detected. In step b), an ascertainment of the quality of the autonomous driving function takes place by comparing the actual trajectory to the setpoint trajectory and monitoring the detected distances from objects in the surroundings. In step c), a control of the quality to a predefined target value takes place by selecting sensors to be used for the autonomous driving function from the plurality of sensors and/or by changing a measuring rate, at which measurements are carried out, of at least one sensor from the plurality of sensors.