Disclosed embodiments include systems, vehicles, and computer-implemented methods to adjust a threshold level of force to be applied to a steering wheel to disengage an autosteering system. In an illustrative embodiment, a system includes a computing device operably coupled with a vehicle that includes a processor and a computer-readable media configured to store computer-executable instructions configured to cause the processor to: detect that an autosteering system of the vehicle is engaged; detect a level of force applied by an operator to a steering wheel of the vehicle; and disengage the autosteering system when the level of force surpasses a threshold chosen from a default threshold and an adjusted threshold, wherein the adjusted threshold is applied upon detecting a predetermined condition..

Surrounding situation information of a vehicle is acquired. A steering torque applied by using a steering mechanism of the vehicle is detected. A steering angle and a steering direction of the vehicle are detected. Traveling control involving steering assist control is executed based on those pieces of information. In a case where a steering torque amount or the steering angle is detected, a new target lane keeping traveling path or a predetermined target lane departure prevention traveling path of the vehicle is created based on the steering torque amount, the steering angle, and the steering direction of the vehicle. In a case where the steering torque amount or the steering angle is detected again within a predetermined period, the new target lane keeping traveling path or the predetermined target lane departure prevention traveling path is set and traveling control is executed along the set traveling path.

Techniques for cognitive analysis for directed control transfer with autonomous vehicles are described. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.

A vehicle control device recognizing a runway area in which a vehicle is to travel, generating a target trajectory such that the target trajectory follows a center of the recognized runway area in a lateral direction, causing the vehicle to travel along the target trajectory, and changing a drive mode of the vehicle from a second drive mode to a first drive mode in a case in which a distance between the center and a reference point of the vehicle is equal to or longer than a reference distance.

A vehicle control device recognizing a runway area in which a vehicle is to travel, generating a target trajectory such that the target trajectory follows a center of the recognized runway area in a lateral direction, causing the vehicle to travel along the target trajectory, and changing a drive mode of the vehicle from a second drive mode to a first drive mode in a case in which a distance between the center and a reference point of the vehicle is equal to or longer than a reference distance.

Disclosed is a vehicle path control apparatus including a memory provided to store danger driving vehicle data, a communication device provided to receive road information from an external server, and a processor configured to identify a danger driving vehicle driving on a road based on the road information and the danger driving vehicle data, select an unmanned vehicle driving within a preset distance from the danger driving vehicle based on the road information, generate a driving path of the unmanned vehicle for obstructing a course of the danger driving vehicle based on an expected path of the danger driving vehicle, and control the communication device to transmit information on the driving path to the unmanned vehicle.

A system includes a first vehicle configured to generate first data, and an infrastructure configured to acquire high-definition (HD) map data, to receive the first data, and to generate first electronic horizon data of a specified region in terms of the first vehicle based on the HD map data and the first data and to transmit the first electronic horizon data to the first vehicle when receiving a request signal from the first vehicle.

An evaluation apparatus for evaluating a predefined trajectory hypothesis for a vehicle includes a calculation unit, a detection unit and an assigning unit. The calculation unit is configured to calculate at least one necessary driving parameter for the vehicle to follow the predefined trajectory hypothesis. The detection unit is configured to detect a current driving parameter of the vehicle. The assigning unit is configured to assign a probability value for the vehicle to follow the predefined trajectory hypothesis using the current driving parameter and the necessary driving parameter.

A sensor information fusion method of an embodiment includes selecting target tracks having distances from a reference track within a predetermined distance from among tracks obtained by a plurality of sensors, and selecting a target fusion track to be fused with the reference track on the basis of sums of first calculation values calculated according to distances between the selected target tracks and the reference track and second calculation values calculated according to overlapping areas of the target tracks and the reference track.

In a vehicle control device, a switching hyperplane generation unit generates a switching hyperplane based on a travel state of a vehicle and cornering stiffness dependent on a travel surface state as a state of a road surface on which the vehicle travels. A deviation computation unit calculates a deviation between a target trajectory and an actual trajectory of the vehicle. A state estimation unit estimates a state to be controlled of the vehicle based on the deviation calculated by the deviation computation unit. A target steering angle and acceleration/deceleration computation unit calculates a target steering angle and a target acceleration/deceleration rate of the vehicle based on the switching hyperplane generated by the switching hyperplane generation unit and an estimated state as the state estimated by the state estimation unit.