An information processing apparatus mountable on a first moving body obtains second movement information including the position and velocity of a second moving body and first movement information including the position and velocity of the first moving body. A second predicted path is determined from the second movement information, and a first predicted path is determined from the first movement information. Then, the information processing apparatus generates travel assistance information including information for directing the first moving body to take a detour from the first predicted path in a direction corresponding to a difference between arrival time periods taken for the second moving body and the first moving body to reach an intersection point of the second predicted path and the first predicted path. Finally, the generated travel assistance information is output.

A traffic signal recognition unit recognizes a traffic signal of a traffic signal machine to be next followed on the basis of outside information. A traffic participant recognition unit recognizes the motion of a traffic participant on the basis of the outside information. A prediction unit predicts a traffic signal to be followed next on the basis of the motion of traffic participant recognized by the traffic participant recognition unit. A comparison unit compares the traffic signal recognized by the traffic signal recognition unit with the traffic signal predicted by the prediction unit. An action planning unit makes an action plan of a host vehicle on the basis of the comparison result of the comparison unit. A control unit carries out prescribed control on the basis of the action plan.

Systems and method are provided for controlling a vehicle. In one embodiment, a method includes: receiving sensor data sensed from an environment associated with the vehicle; processing, by a processor, the sensor data to determine a plurality of objects within the environment of the vehicle; processing, by the processor, the sensor data to determine feature data associated with each of the plurality of objects, wherein the feature data includes current data of each object, history data of each object, and interaction data between each object and at least two other objects; processing, by the processor, the feature data associated with a first object of the plurality of objects with a model to determine a future position of the first object; and controlling, by the processor, the vehicle based on the future position.

An advanced driving assistance system (ADAS) provides collision avoidance control for a host vehicle. The system can include one or more sensors mounted to the host vehicle and configured to sense a driving lane in which the host vehicle is traveling and to sense an external vehicle partially engaged in the driving lane. A controller controls steering, braking, or acceleration of the host vehicle on the basis of sensing information received from the sensor. The controller determines the external vehicle partially engaged in the driving lane as a target vehicle having at least a part thereof overlapping with a lane mark of the driving lane, and performs longitudinal braking or acceleration control or lateral steering control on the host vehicle based on lateral and longitudinal positional relationships between the host vehicle and the target vehicle.

A method for assisting a driver of a host vehicle in avoiding a collision, the method including: detecting a target in the vicinity of the vehicle; determining that the host vehicle is travelling on a collision course with the target; detecting a set of road markers on a side of the target; detecting a free space on the side of the road markers opposite the target side of the road markers, and when a collision with the target is predicted to occur within a predetermined time period and no driver initiated steering action for avoiding the collision course has been detected, providing a steering guidance to the driver of the host vehicle including altering of a steering wheel angular orientation for momentarily steering the host vehicle towards the road markers to indicate an evasive steering action to the driver.

A computer-implemented method for controlling a host vehicle having a vehicle control system that controls motion of the host vehicle relative to a preceding vehicle that is immediately ahead of the host vehicle. The method includes determining a relative headway distance and a relative velocity between the host vehicle and the preceding vehicle, and an acceleration rate of the preceding vehicle. The method includes receiving message packets transmitted from a leading vehicle and the message packets contain parameters of the leading vehicle including an acceleration rate of the leading vehicle. Further, the method includes calculating an acceleration control rate for the host vehicle to maintain the headway reference distance between the host vehicle and the preceding vehicle, based on the relative headway distance, the relative velocity, the acceleration rate of the preceding vehicle, and the acceleration rate of the leading vehicle. The acceleration rate is output to a vehicle controller to control motion of the host vehicle.

A self-localization estimation device includes: a map-information acquisition unit that acquires map information including lane information for specifying lanes in which vehicles are enabled to travel; a position calculation unit that calculates an own-vehicle absolute position being an absolute position of an own vehicle in response to navigation signals received from a plurality of navigation satellites, the position calculation unit including a self-location measurement unit, a vehicle-momentum measurement unit, and dead reckoning unit; and a position estimation unit that estimates, based on the map information and the own-vehicle absolute position, a corrected own-vehicle position being a corrected position of the own vehicle. The position estimation unit estimates the corrected own-vehicle position by superimposing a reliability of the map information and a reliability of the own-vehicle absolute position on each other.

A self-localization estimation unit of a self-localization estimation device determines, based on mutual relationships between the in-lane position and the absolute position including the error, whether there is lane-relevant candidate information, the lane-relevant candidate information representing that one or more in-vehicle positions are each estimated to be in which of lanes identified by the lane information; and estimates, based on a result of the determination of whether there is lane-relevant candidate information, a localization of the own vehicle corresponding to the map information.

A method for determining a lane change, performed by an apparatus for determining a lane change of an object located around a driving vehicle with which is equipped a sensor, the method including, detecting a plurality of objects located around the driving vehicle using scanning information obtained repeatedly at every predetermined period of time by the sensor scanning surroundings of the driving vehicle, selecting at least one candidate object estimated to change lanes among the plurality of objects based on previously detected lane edge information and determining whether the candidate object changes lanes based on information on movement of the candidate object.

A vehicle control system includes a vehicle selector configured to select, from among other vehicles present near an own vehicle, another vehicle that the own vehicle is to follow when traveling in front of a gate as a following target vehicle, and a gate passage controller configured to cause the own vehicle to travel following the following target vehicle selected by the vehicle selector when passing through the gate.