Embodiments of the disclosed systems and methods provide techniques for dynamically allocating processing tasks between in-vehicle and remote processing resources. In various embodiments, aspects of the disclosed systems and methods may advantageously use relatively low latency edge cloud and/or cloud processing resources accessed via higher speed wireless networks to enhance processing resources available to a vehicle for use in a variety of control and/or operation decisions. Consistent with various disclosed embodiments, processing tasks may be dynamically allocated based on relative impact and/or importance to safe vehicle operation, network latency between a vehicle and remote processing resources, available network bandwidth between a vehicle and remote processing resources, network traffic, processing complexity, processing resource availability, and/or the like.

An arithmetic operation device for automobiles includes an arithmetic element state detection unit that detects a parameter indicating a state of an arithmetic element, an external device detection unit that detects a use state of a device that affects the parameter, and a mode selection unit that selects one of a normal mode in which both a basic traveling function unit that can execute control related to a basic traveling function and an automatic driving function unit that can execute control related to an automatic driving function and a degeneration mode in which only the basic traveling function unit is operated, and the mode selection unit selects the degeneration mode when the parameter exceeds a threshold or when it is predicted that the parameter exceeds the threshold.

A computer resource disparity is detected. The computer resource disparity is related to performing a computing task. The computer resource disparity is located proximate to a first location. A set of one or more autonomous vehicles capable of being adjacent to the first location is identified. An autonomous vehicle computing inquiry is generated. The inquiry is generated based on the first location and based on the computer resource disparity. The autonomous vehicle computing inquiry is transmitted based on the first location. An autonomous vehicle status is received in response to the autonomous vehicle computing inquiry that includes a set of one or more computing resources of the set of autonomous vehicles. A first autonomous vehicle of the set of autonomous vehicles is assigned to perform the computing task. The assignment is based on the set of computing resources of the set of autonomous vehicles.

Technologies and techniques for the at least the partially automated driving of a motor vehicle. A first application and at least one redundant second application provide output data depending on motor vehicle operating data and/or environmental data. Vehicle driving data for the at least partially automated driving of the motor vehicle are determined depending on the output data. Vehicle operating data from another vehicle are received, and, depending on the vehicle operating data, the at least one redundant second application switches from an active state to a standby state in which a computer instance of a computer unit used by the at least one redundant second application is at least executed at a lower frequency than in the active state.

A plurality of candidate estimated paths (301) for a vehicle (100) to travel to an intermediate destination (300) while avoiding a moving object present by estimation time is generated depending on cost information of lanes (200, 201), and an estimated path selected from the plurality of candidate estimated paths (301) is set as a path of the vehicle (100) for each estimation time.

An apparatus for controlling an imaging sensor in a vehicle includes an Ethernet transceiver, a sensor interface and a local processor. The Ethernet transceiver is configured to communicate over an in-vehicle Ethernet network with a remote processor. The sensor interface is configured to communicate with the imaging sensor. The local processor that is local to the apparatus and remotely located from the remote processor is configured to receive from the imaging sensor, via the sensor interface, image data and auxiliary data related to the image data, to send at least the image data to the remote processor via the Ethernet transceiver, to generate locally, based on the auxiliary data, and independently from the remote processor, control commands to control an operational aspect of the imaging sensor, and to send the control commands to the imaging sensor via the sensor interface.

In one embodiment, a computing system receives sensor data from one or more sensors of a vehicle. The computing system determines a metric associated with the vehicle based on the received sensor data. The computing system determines, based on the metric, a length of a transmission cycle of a communication network of the vehicle. The transmission cycle comprises one or more scheduled time periods dedicated for transmitting data from respective first nodes in the communication network. The computing system configures the communication network of the vehicle based at least in part on the length of the transmission cycle to adjust respective occurrence frequencies of the scheduled time periods over multiple instances of the transmission cycle.

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.

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.

An autonomous driving system includes a plurality of sensor combinations and a plurality of edge nodes, each of the plurality of sensor combinations includes at least one sensor; the plurality of edge nodes are in a one-to-one correspondence with the plurality of sensor combinations; and a first edge node in the plurality of edge nodes is configured to determine first perception information of a vehicle based on data collected by a first sensor combination corresponding to the first edge node. A central node is connected to the plurality of edge nodes. The central node is configured to delegate a first state decision function to the first edge node so that the first edge node determines that the vehicle enters a first state or determines a first driving policy of the vehicle in the first state by executing the first state decision function.