Vehicle manipulation is performed using crowdsourced data. A camera within a vehicle is used to collect cognitive state data, including facial data, on a plurality of occupants in a plurality of vehicles. A first computing device is used to learn a plurality of cognitive state profiles for the plurality of occupants, based on the cognitive state data. The cognitive state profiles include information on an absolute time or a trip duration time. Voice data is collected and is used to augment the cognitive state data. A second computing device is used to capture further cognitive state data on an individual occupant in an individual vehicle. A third computing device is used to compare the further cognitive state data with the cognitive state profiles that were learned. The individual vehicle is manipulated based on the comparing of the further cognitive state data.

An apparatus and a method for controlling vehicle driving are provided. The apparatus includes a sensor that detects a first steering angle and a lane of a first vehicle and senses a second vehicle traveling in an opposite lane. A communication device receives a second steering angle of the second vehicle and a controller determines whether the first steering angle and the second steering angle are changed. The controller determines which of the vehicles enters a curved road based on a determination result and operates the vehicle which does not enter the curved road to decelerate.

Systems and methods are disclosed for collecting driving data from simulated autonomous driving vehicle (ADV) driving sessions and real-world ADV driving sessions. The driving data is processed to exclude manual (human) driving data and to exclude data corresponding to the ADV being stationary (not driving). Data can further be filtered based on driving direction: forward or reverse driving. Driving data records are time stamped. The driving data can be aligned according to the timestamp, then a standardized set of metrics is generated from the collected, filtered, and time-aligned data. The standardized set of metrics are used to grade the performance the control system of the ADV, and to generate an updated ADV controller, based on the standardized set of metrics.

Systems and methods for automatically establishing a gap between a concave and a rotor of a rotary crop processing system are disclosed. Establishing the gap may include displacing the concave towards the rotor until contact is detected therebetween. Contact may be detected using a sensor configured to detect contact between the rotor and the concave. The sensor may be a knock sensor. The concave is displaced away from the rotor when contact is detected until contact between the rotor and the concave is no longer detected. One or more actuators may be coupled to the concave to move the concave relative to the rotor. In some implementations, the actuators may be operated in sequence to form the gap between the rotor and the concave.

A method for a follower vehicle following a lead vehicle, comprising establishing, in a first control mode of the follower vehicle, a path for the follower vehicle to follow the lead vehicle, characterized by generating environmental data which is related to the environment of the lead vehicle, determining, based on the generated environmental data, an expected behaviour of an operational parameter of the lead vehicle, determining an actual behaviour of the lead vehicle operational parameter, comparing the determined expected behaviour of the lead vehicle operational parameter and the determined actual behaviour of the lead vehicle operational parameter, determining based on said comparison whether to continue in first control mode of the follower vehicle, or in a second control mode of the follower vehicle, differing from the first control mode.

A control system and method for controlling a vehicle subsystem are provided. The control system includes a remote parameter sensor configured to generate a remote parameter signal indicative of a value of a universal parameter associated with an environment in which a vehicle is operating. The system further includes a local parameter sensor configured to generate a local parameter signal indicative of the value of the universal parameter and a local controller. The controller is configured to receive the local parameter signal along a first signal path, receive the remote parameter signal and a command signal configured for controlling a function of the vehicle subsystem along a second signal path, compare the local and remote parameter signals and implement the function of the vehicle subsystem responsive to the command signal if the remote parameter signal meets a predetermined condition relative to the local parameter signal.

A map information system includes: an in-vehicle device that executes automated driving control of a vehicle; and an external device having external map information used for the automated driving control. The in-vehicle device includes: a memory device in which map information is stored; and a control device configured to execute the automated driving control based on the map information stored in the memory device. The control device is further configured to: determine whether or not a takeover occurs during the automated driving control; set an upload target area including the takeover occurrence position, in a case where the takeover occurs during the automated driving control; and upload the map information regarding the upload target area to the external device. The external device updates the external map information based on the map information uploaded from the in-vehicle device.

A driver assistance method for a vehicle includes the steps of establishing an energy prediction for a route on the basis of an anticipated driver behavior, determining a driving behavior which is optimized with regard to the energy prediction, and outputting an action recommendation on the basis of the optimized driving behavior.

A system and method for learning naturalistic driving behavior based on vehicle dynamic data that include receiving vehicle dynamic data and image data and analyzing the vehicle dynamic data and the image data to detect a plurality of behavioral events. The system and method also include classifying at least one behavioral event as a stimulus-driven action and building a naturalistic driving behavior data set that includes annotations that are based on the at least one behavioral event that is classified as the stimulus-driven action. The system and method further include controlling a vehicle to be autonomously driven based on the naturalistic driving behavior data set.

A vehicle controller device including: a communication section configured to receive operation information to operate a vehicle from an operation device located externally to the vehicle; a first memory; and a first processor, the first processor being configured to: acquire peripheral information regarding a periphery of the vehicle from a peripheral information detection section; generate a travel plan for the vehicle based on the peripheral information of the vehicle; control autonomous driving in which the vehicle travels based on the generated travel plan and also control remote driving in which the vehicle travels based on the received operation information; predict that a compromised state in which autonomous driving of the vehicle becomes compromised will arise based on environmental information including meteorological information received by the communication section; and notify the operation device of the compromised state in a case in which the compromised state has been predicted to arise.