Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

The disclosure describes embodiments for modifying a whether an ego vehicle changes lanes to a target lane at a target time based on a payload of a Vehicle-to-Everything (V2X) message originated by a remote vehicle. In some embodiments, a method includes determining, based on the payload, whether the remote vehicle is changing lanes to the target lane at the target time. The method includes determining that the ego vehicle is changing lanes to the target lane at approximately the target time. The method includes estimating that the ego vehicle and the remote vehicle will collide at the target lane at the target time. The method includes modifying an operation of a vehicle component of the ego vehicle so that the ego vehicle does not change lanes to the target lane at the target time.

Systems and methods are directed to an autonomy computing system of an autonomous vehicle. The autonomy computing system can include first functional circuitry configured to generate a first output associated with a first autonomous compute function of the autonomous vehicle based on sensor data using first neural networks. The autonomy computing system can include second functional circuitry configured to generate a second output associated with the first autonomous compute function of the autonomous vehicle based on the sensor data and neural networks. The autonomy computing system can include monitoring circuitry configured to determine a difference between the first output of the first functional circuitry and the second output of the second functional circuitry. The autonomy computing system can include a vehicle control system configured to generate vehicle control signals for the autonomous vehicle based on the outputs.

A method for evaluating contextual risk profiles at a computing device (110, 212, 312) in a vehicle (310), the method including obtaining information about a proximate vehicle (320, 330); utilizing the information to create a risk profile for the proximate vehicle (320, 330); and based on the risk profile, initiating an action at computing device (110, 212, 312).

A method for operating an assistance system for a vehicle, including an object sensor device for determining an object characteristic value representative of coordinates of an object in the environment of the vehicle and a viewing sensor device for determining a direction characteristic value representative of a viewing direction of a driver of the vehicle and for determining a position characteristic value representative of a head position of the driver. According to the method, a projection characteristic value representative of a vector that connects the head position to the object is determined; a first region characteristic value representative of an estimated primary field of view of the driver is determined; an attention characteristic value representative of the probability that the object is at least partially in the field of view of the driver is determined; and a warning function of the vehicle is activated.

A vehicle control apparatus controls movement of a vehicle in a lateral direction intersecting a direction in which the vehicle travels based on a movement trajectory of a preceding vehicle. The vehicle control apparatus includes a detection unit configured to detect a surrounding environment of the vehicle, and a preceding vehicle which travels ahead in the same lane in which the vehicle travels, a determination unit configured to determine whether or not the preceding vehicle straddles lanes or approaches within a set distance predetermined for the lanes based on lateral movement information of the preceding vehicle detected by the detection unit, and a control unit configured to control lateral movement of the vehicle based on a determination result of the determination unit and detection information of the detection unit.

Techniques for determining a vehicle action and controlling a vehicle to perform the vehicle action for navigating the vehicle in an environment can include determining a vehicle action, such as a lane change action, for a vehicle to perform in an environment. The vehicle can detect, based at least in part on sensor data, an object associated with a target lane associated with the lane change action sensor data. In some instances, the vehicle may determine attribute data associated with the object and input the attribute data to a machine-learned model that can output a yield score. Based on such a yield score, the vehicle may determine whether it is safe to perform the lane change action.

A control device controlling the movement route of a transportation vehicle calculates a movement route to a target position of the transportation vehicle and, in a case where an obstacle exists on the calculated movement route, counts the number of detours indicating the magnitude of an influence of the obstacle on movement route calculation, and calculates the movement route again so as to avoid the obstacle.

A device receives location data that identifies geographic locations of a vehicle at corresponding times, and speed data that identifies respective speeds of the vehicle at the set of corresponding times. The device identifies, based on the location data, speed limit data that identifies respective speed limits at the geographic locations. The device determines, based on the respective speeds and the respective speed limits, a set of respective relative speed values associated with the vehicle. The device identifies respective relative speed values, of the set of respective relative speed values, that exceed a threshold. The device determines, based on the respective relative speed values, a univocal speed indicator that represents an overall degree to which the respective relative speed values exceed the threshold, during a time interval that includes the corresponding times. The device performs a set of actions based on the univocal speed indicator.

Systems and methods for detection of people are disclosed. In some exemplary implementations, a robot can have a plurality of sensor units. Each sensor unit can be configured to generate sensor data indicative of a portion of a moving body at a plurality of times. Based on at least the sensor data, the robot can determine that the moving body is a person by at least detecting the motion of the moving body and determining that the moving body has characteristics of a person. The robot can then perform an action based at least in part on the determination that the moving body is a person.