An autonomous driving control device is capable of starting an autonomous driving control without an operation of a driver and reducing a possibility that the driver can not start manual driving. An autonomous driving control is switched to manual driving when a determination section determines that the amount of operation by the driver is equal to or greater than a first threshold, before a predetermined time elapses since the autonomous driving control is automatically started. An autonomous driving control is switched to a manual driving when the determination section determines that the amount of operation by the driver is equal to or greater than a second threshold that is greater than the first threshold, after the predetermined time elapses.

A driving assistance device executes processing relating to a behavior model of a vehicle. Detected information from the vehicle is input to a detected information inputter. An acquirer derives at least one of a travel difficulty level of a vehicle, a wakefulness level of a driver, and a driving proficiency level of the driver on the basis of the detected information that is input to the detected information inputter. A determiner determines whether or not to execute processing on the basis of at least one information item derived by the acquirer. If the determiner has made a determination to execute the processing, a processor executes the processing relating to the behavior model. It is assumed that the processor does not execute the processing relating to the behavior model if the determiner has made a determination to not execute the processing.

System, methods, and other embodiments described herein relate to dynamically determining an appropriate responsive action for a moving vehicle that encounters a roadside pedestrian and stationary vehicle. In one embodiment the disclosed system identifies a stationary vehicle in an environment of a subject vehicle based at least in part on information from a plurality of sensors disposed on the subject vehicle and determines a classification for the stationary vehicle as valid or abandoned based at least in part on the information from the plurality of sensors. A classification of valid indicates that the subject vehicle is recommended to undertake an action relative to the stationary vehicle. The disclosed generates a score based at least in part on the classification, the score indicating a recommended trajectory modification for the subject vehicle, and modifies a trajectory of the subject vehicle based on score.

A control system for a vehicle includes a camera having a field of view exterior of the vehicle. Responsive at least in part to processing by a processor of image data captured by the camera, the control system determines traffic lanes on the road. The control system determines a leading vehicle ahead of the equipped vehicle and determines a traffic condition where two traffic lanes change to a different number of traffic lanes. The traffic lane being traveled by the equipped vehicle is one of the two traffic lanes that change to the different number of traffic lanes. Responsive to determination of the traffic condition and responsive to detection of the leading vehicle moving from the traffic lane being traveled by the equipped vehicle into another traffic lane, the control system controls the steering system of the equipped vehicle to follow the leading vehicle into the other traffic lane.

An information processing apparatus according to an embodiment of the present technology includes a detection unit, an estimation unit, and a judgment unit. The detection unit detects a target object from an input image. The estimation unit estimates a posture of the detected target object. The judgment unit judges a possibility of the target object slipping on the basis of the estimated posture.

The present invention relates to a vehicle control device provided in a vehicle and a method of controlling the vehicle. A vehicle control device according to one embodiment of the present invention includes a processor to autonomously run a vehicle using driving information that the vehicle has traveled in a manual driving mode, wherein the driving information includes a start place where the manual driving mode is started, an end place where the manual driving mode is ended, and a travel route from the start place to the end place, wherein the processor autonomously runs the vehicle along the travel route from the start place to the end place when the vehicle has moved up to the start place through manual driving.

Methods and systems for autonomous vehicle recharging or refueling are disclosed. Autonomous vehicles may be automatically refueled by routing the vehicles to available fueling stations when not in operation, according to methods described herein. A fuel level within a tank of an autonomous vehicle may be monitored until it reaches a refueling threshold, at which point an on-board computer may generate a predicted use profile for the vehicle. Based upon the predicted use profile, a time and location for the vehicle to refuel the vehicle may be determined. In some embodiments, the vehicle may be controlled to automatically travel to a fueling station, refill a fuel tank, and return to its starting location in order to refuel when not in use.

A route generator includes: a conversion unit configured to generate virtual road information in which a shape of a road has been converted into a rectilinear shape on the basis of map information including information indicating the shape of the road; a traveling path generating unit configured to generate a traveling path of a host vehicle on the road having the rectilinear shape in the virtual road information generated by the conversion unit; and an inverse conversion unit configured to generate a traveling path of the host vehicle in the shape of the road which has not been converted into the rectilinear shape by the conversion unit by performing inverse conversion of the conversion performed by the conversion unit on the traveling path of the host vehicle generated on the road having the rectilinear shape by the traveling path generating unit.

A method for adaptive risk modeling for an autonomous vehicle, the method comprising: retrieving parameters of an identified driving mission of the autonomous vehicle; in response to the parameters of the identified driving mission, generating values of: a comparative autonomous parameter, a mix model parameter, a surrounding risk parameter, a geographic operation parameter, and a security risk parameter upon evaluating situational inputs associated with the identified driving mission with a comparative autonomous model, a mix model, a sensor-surrounding model, a geography-dependent model, and a security risk model generated using sensor and supplementary data extraction systems associated with the autonomous vehicle; upon generating values, generating a risk analysis with a rule-based algorithm; and contemporaneously with execution of the identified driving mission, implementing a response action associated with control of the autonomous vehicle, based upon the risk analysis.

A system for operating an automated vehicle in a crowd of pedestrians includes an object-detector, optionally, a signal detector, and a controller. The object-detector detects pedestrians proximate to a host-vehicle. The signal-detector detects a signal-state displayed by a traffic-signal that displays a stop-state that indicates when the host-vehicle should stop so the pedestrians can cross in front of the host-vehicle, and displays a go-state that indicates when the pedestrians should stop passing in front of the host-vehicle so that the host-vehicle can go forward. The controller is in control of movement of the host-vehicle and in communication with the object-detector and the signal-detector. The controller operates the host-vehicle to stop the host-vehicle when the stop-state is displayed, and operates the host-vehicle to creep-forward after a wait-interval after the traffic-signal changes to the go-state when the pedestrians fail to stop passing in front of the host-vehicle.