The disclosure includes embodiments for selecting Vehicle-to-Everything (V2X) data to be shared among vehicles in a cooperative vehicular system based on an uncertainty criterion. In some embodiments, a method for an ego vehicle includes: determining, by a sensor set of the ego vehicle, ego V2X data describing ego object information about a set of objects included in a roadway environment; receiving remote V2X data provided by a remote vehicle, where the remote V2X data describes remote object information about the set of objects from a perspective of the remote vehicle; determining uncertainty data describing an uncertainty about the ego object information based at least in part on an analysis of the ego object information relative to the remote object information; and updating the ego V2X data to form updated ego V2X data that describes ego object information that satisfies the uncertainty criterion.

The present invention relates to a method comprising obtaining validation sensor data from a sensor measurement at a validation observation time; generating a validation finding based on the validation sensor data; obtaining first sensor data from a sensor measurement at an observation time preceding the validation observation time; generating a first finding based on the first sensor data; and testing the first finding based on the validation finding. The present invention also relates to a corresponding method and a corresponding use.

An electronic device is disclosed. The electronic device comprises: a communication interface; a memory in which a learning network model for predicting next data by learning temporally continuous data is stored; and a processor for acquiring prediction data that is to replace data received from the learning network model, when the occurrence of an error in data received from a sensor device through the communication interface is identified, performing an autonomous driving function on the basis of the acquired prediction data, counting the number of error occurrences, and providing information informing that a sensing state of the sensor device is abnormal, when the counting frequency is greater than or equal to a threshold value.

Methods and systems for controlling a vehicle. The system includes a localization system, a memory storing a digital map, at least one sensor, and an electronic processor. The electronic processor is configured to receive, from the localization system, a current location of the vehicle and determine a future driving segment of the vehicle based on the current location of the vehicle. The electronic processor is further configured to determine at least one performance limitation based on the future driving segment of the vehicle and artificially falsify data of the at least one sensor to initiate a safety action for the vehicle.

Methods and systems for controlling a vehicle. The system includes a localization system, a memory storing a digital map, and an electronic processor. The electronic processor is configured to receive, from the localization system, a current location of the vehicle and determine a future driving segment of the vehicle based on the current location of the vehicle. The electronic processor is further configured to determine at least one performance limitation based on the future driving segment of the vehicle and modify a driving behavior of the vehicle based upon the determined at least one performance limitation.

A sensor calibration system for a vehicle includes one or more processors. The system also includes a memory communicably coupled to the one or more processors and storing a sensor calibration module including instructions that when executed by the one or more processors cause the one or more processors to, for an environment sensor of the vehicle, determine if the sensor can detect at least a predetermined number of viable reference features in an environment of the vehicle. The module also includes processor-executable instructions to, if the sensor cannot not detect at least a predetermined number of viable reference features, determine at least one suggested positional arrangement of viable reference features with respect to a current position the sensor. The module also includes processor-executable instructions to generate instructions to arrange viable reference features in the at least one suggested positional arrangement.

Provided are a vehicle control device and a computer program capable of simplifying design of state transition. An intermediate layer constituting an ECU divides a state of a lower-layer state machine for each function of a vehicle system in association with the state of the lower-layer state machine, and outputs the state to an upper-layer state machine, a state transition table of the upper-layer state machine includes, as a condition of state transition of the upper-layer state machine, a current state of a lower-layer state machine or a state to transition, and the upper-layer state machine receives the state of the lower-layer state machine input from the intermediate layer, refers to the state transition table, and outputs a signal for controlling the vehicle system.

Provided is a vehicle control device configured to: recognize a surrounding situation of a vehicle; control steering and acceleration/deceleration of the vehicle; detect operation states of a plurality of external recognition sensors; determine a driving mode of the vehicle as any one of a plurality of driving modes including a first driving mode, a second driving mode, and a third driving mode; change the third driving mode to either one of the first driving mode and the second driving mode when determining that a failure has occurred in one of the plurality of external recognition sensors that implement: a function of causing the vehicle to follow a preceding vehicle; and a function of assisting the vehicle in keeping a lane; and change the third driving mode to the second driving mode when determining that degradation in performance has occurred in one of the plurality of external recognition sensors.

A vehicle recognition device includes: an acquisition unit configured to acquire a value of a parameter relating to a degree of dirtiness of an in-vehicle LIDAR device detecting objects using light or a radiowave close to light; and a judgment unit configured to judge a state of the in-vehicle LIDAR device to be an abnormal state in a case in which the value of the parameter continuously exceeds a threshold during a first period, and the amount of change of the value of the parameter in a second period including a part or whole of the first period is within a reference range.

A vehicle state estimation device for a vehicle provided with an inertial measurement sensor and a wheel speed sensor includes: a vehicle state estimation unit that estimates a vehicle state including a vehicle velocity based on an acceleration and an angular velocity acquired by the inertial measurement sensor and a wheel speed acquired by the wheel speed sensor; and a determination unit that determines whether a wheel is slipping. The estimation unit estimates a steady-state vehicle velocity based on the wheel speed and calculates a transient vehicle velocity by time integration based on the acceleration and the angular velocity. When the wheel is slipping, the estimation unit decides an estimated value of the vehicle velocity to be close to the transient vehicle velocity, and when the wheel is not slipping, the estimation unit decides the estimated value of the vehicle velocity to be close to the steady-state vehicle velocity.