The present invention is configured to include an other-vehicle situation determination circuitry to estimate traveling situations of each of other vehicles traveling ahead of one's own vehicle and to determine that an alert situation is present when any of the other vehicles is estimated to be in a situation where a vehicle should slow down or change traffic lanes to a traffic lane in which one's own vehicle is traveling in order to avoid a collision with an object or a separate vehicle on a road; and a prediction function diagnosis circuitry, when receiving a determination result that the alert situation is present, diagnoses whether an other-vehicle behavior prediction function is sound or not based on whether or not an other-vehicle behavior prediction corresponding to the alert situation was able to be received from the other-vehicle behavior prediction function.

A vehicle travel assistance method executed by a processor comprises: setting a travel lane in which a subject vehicle travels based on detection information of a sensor equipped in the subject vehicle, identifying a preceding vehicle traveling ahead of a subject vehicle based on detection information of a sensor, calculating, based on the detection information, a first evaluation value indicating a possibility that the subject vehicle can return to the travel lane from an adjacent lane adjacent to the travel lane after overtaking the preceding vehicle, calculating a shortening width of travel time shortened by overtaking the preceding vehicle based on the vehicle speed of the subject vehicle and the vehicle speed of the preceding vehicle, and determining whether or not to overtake the preceding vehicle based on the first evaluation value and the shortening width.

A method for predicting a future action of an object for a driving assistance system for a highly automated mobile vehicle. At least one sensor signal from at least one vehicle sensor of the vehicle is read in, the sensor signal representing at least one piece of kinematic object information concerning the object that is detected by the vehicle sensor at an instantaneous point in time. A planner signal from a planner of the autonomous driving assistance system is read in, the planner signal representing at least one piece of semantic information concerning the object or the surroundings of the object at a point in time in the past. The kinematic object information is fused with the semantic information to obtain a fusion signal. A prediction signal is determined using the fusion signal, the prediction signal representing the future action of the object.

A vehicle includes a controller that identifies a target around the vehicle and calculates a danger range of the identified target, based on processing surrounding data obtained by sensor devices; calculates a danger range of the vehicle based on processing driving data obtained by sensor devices; determines a danger of collision based on the danger range of the target and the danger range of the vehicle, and control a driving apparatus based on the determined danger of collision. Such a vehicle and a control method thereof can make it possible to avoid a collision based on a danger range by calculating the danger range between the vehicle and a surrounding object of the vehicle depending on a factor causing uneasiness of a user.

Disclosed herein are an autonomous driving method for avoiding a stopped vehicle and an apparatus for the same. The autonomous driving method for avoiding a stopped vehicle is performed by an autonomous driving control apparatus provided in an autonomous vehicle, and includes obtaining taillight recognition information for a stopped vehicle identified ahead of the autonomous vehicle, determining whether the stopped vehicle is to be avoided in consideration of the taillight recognition information, when it is determined that the stopped vehicle is to be avoided, setting an avoidance method in consideration of whether lane returning is to be performed, which is determined based on an autonomous driving task, and setting an avoidance time point corresponding to the avoidance method and controlling the autonomous vehicle to avoid the stopped vehicle by traveling along an avoidance path generated in conformity with the avoidance time point.

The present application discloses autonomous driving methods and apparatuses that may be applied to an intelligent vehicle and an autonomous driving vehicle. An example method includes: obtaining a motion status of an obstacle; determining a location of a towing point and a speed of the towing point based on the motion status of the obstacle, where both the location of the towing point and the speed of the towing point are continuous functions of the motion status of the obstacle; and obtaining expected acceleration of an ego vehicle based on the location of the towing point and the speed of the towing point.

The present disclosure provides a method and apparatus for determining an attribute value of an obstacle in vehicle infrastructure cooperation. The method includes: acquiring vehicle-end data collected by at least one sensor of an autonomous driving vehicle; acquiring vehicle wireless communication V2X data transmitted by a roadside device; and fusing, in response to determining that an obstacle is at an edge of a blind spot of the autonomous driving vehicle, the vehicle-end data and the V2X data to obtain an attribute estimated value of the obstacle.

Determining classification(s) for object(s) in an environment of autonomous vehicle, and controlling the vehicle based on the determined classification(s). For example, autonomous steering, acceleration, and/or deceleration of the vehicle can be controlled based on determined pose(s) and/or classification(s) for objects in the environment. The control can be based on the pose(s) and/or classification(s) directly, and/or based on movement parameter(s), for the object(s), determined based on the pose(s) and/or classification(s). In many implementations, pose(s) and/or classification(s) of environmental object(s) are determined based on data from a phase coherent Light Detection and Ranging (LIDAR) component of the vehicle, such as a phase coherent LIDAR monopulse component and/or a frequency-modulated continuous wave (FMCW) LIDAR component.

An assistance system for a vehicle capable of automated operation has a controller having a processor and tangible, non-transitory memory on which instructions are recorded. The vehicle is located on a first lane in a vicinity of one or more neighboring vehicles, the first lane merging with a second lane at a merging trajectory location. The controller is adapted to selectively execute a leader determination module when a distance of the vehicle to the merge starting point is less than a threshold value. This includes determining an estimated arrival time of the vehicle to a merge starting point of the merging trajectory location. The controller is adapted to select a leader vehicle from the neighboring vehicles based in part on their respective estimated arrival times to the merge starting point. Operation of the vehicle is controlled based in part on the leader vehicle.

Behavior information input unit receives stop-behavior information about vehicle from automatic-driving control device. Image-and-sound output unit outputs inquiry information for inquiring of an occupant whether a possibility of collision between an obstacle and vehicle is to be excluded from a determination object in automatic-driving control device to notification device, when a distance from one point on a predictive movement route of the obstacle to the obstacle is greater than or equal to a first threshold, and a speed of the obstacle is less than or equal to a second threshold. Operation signal input unit receives a response signal for excluding the collision possibility from the determination object. Command output unit outputs a command to exclude the collision possibility from the determination object to automatic-driving control device.