Provided are a vehicle driving control method and apparatus. The method includes: determining, according to driving data of a vehicle, at least one piece of candidate speed information of the vehicle, the candidate speed information is used to indicate traveling speed of the vehicle at each moment in a first preset time period; predicting, according to movement data of an obstacle on a road, an obstacle trajectory of the obstacle in the first preset time period; determining right-of-way information corresponding to the obstacle trajectory, the right-of-way information is used to indicate a priority in traveling of the vehicle and the obstacle; and determining target speed information in the at least one piece of candidate speed information according to the right-of-way information corresponding to the obstacle trajectory and the obstacle trajectory, and controlling the vehicle to travel according to a route corresponding to the vehicle and the target speed information.

A vehicle control device recognizes an intersection present in front of a vehicle proceeding in a first direction on a first road, a first another vehicle proceeding in a second direction opposite to the first direction on the first road to approach the intersection, and a second another vehicle traveling after the first another vehicle, controls the vehicle based on a first relative relation between the first another vehicle and the vehicle and a second relative relation between the second another vehicle and the vehicle, determines, when the first and second another vehicles are expected to enter the second road, whether the vehicle enters the second road after the first another vehicle and before the second another vehicle or after the second another vehicle based on relative relations between a basis position and the vehicle, the first and second another vehicles, and controls the vehicle based on a determining result.

Provided is a vehicle control device capable of suitably controlling the driving (autonomous driving or driving assistance) of a host vehicle when the host vehicle turns left from a side road into a main road (to merge into traffic). When the host vehicle is about to turn left into the main road, if the host vehicle recognizes that another vehicle is traveling along the main road behind the point [the intersection region of the side road and the main road] where the host vehicle enters the main road, then the host vehicle determines a risk that is associated with the other vehicle and that is dependent on the traveling position of the other vehicle, and determines whether or not to enter the main road, on the basis of the determined risk associated with the other vehicle.

A computer is programmed to identify a target vehicle to be monitored and to identify first and second virtual boundaries on a roadway based on a position of the target vehicle. The computer is further programmed to determine a first constraint value based on (1) a first boundary approach velocity and (2) a first boundary approach acceleration and a second constraint value based on (1) a second boundary approach velocity and (2) a second boundary approach acceleration. The computer is further programmed to identify a maneuver of the target vehicle based on whether the first and second constraint values violate respective thresholds or a position of the target vehicle relative to the first and second virtual boundaries violates a threshold and to adjust a path of a host vehicle according to the identified maneuver.

Upon detecting a mobile object approaching a target region, a target speed profile for a vehicle, a position and a speed for the vehicle, and a position and a speed for the mobile object are input to a first neural network that outputs an entry probability distribution of predicted entry times at which the mobile object will enter the target region. The target speed profile, the position and the speed for the vehicle, the position and the speed for the mobile object, and a predicted entry time at which the mobile object entered the target region are input to a second neural network that outputs an exit probability distribution of predicted exit times at which the mobile object will exit the target region. An optimized speed profile is determined based on the entry and exit probability distributions by executing a model predictive control algorithm in a rolling horizon. A vehicle is operated based on the optimized speed profile.

A vehicle control method of an autonomous vehicle for a right and left turn at a crossroad includes: determining whether a second vehicle intends to change a lane while passing a front or a rear of a first vehicle in order to move to a target lane for the right and left turn at the crossroad; controlling the first vehicle to decelerate when it is determined that the second vehicle intends to change the lane while passing the front of the first vehicle; determining whether the second vehicle is entering the first lane toward the front or the rear of the first vehicle; calculating a steering amount of the second vehicle when it is determined that the second vehicle is entering the first lane toward the front of the first vehicle; and controlling the first vehicle to decelerate according to the steering amount.

An object of the present invention is to provide a vehicle control device configured, regardless of the number of travel lanes of a road, to generate a travel route based on a road width of the road or the like, so as to improve a continuous automatic driving system. The present invention provides a vehicle control device configured to perform vehicle control of a self vehicle based on information of a position of the self vehicle, information of a map, and information of a route. The vehicle control device determines, at a specific spot of the route, whether or not a travel lane (311) of the self vehicle has a lane width (Wr1) that allows a plurality of virtual vehicles (303 and 304) to travel parallel to each other, and on determination that the travel lane has the lane width, the vehicle control device generates, on the travel lane, a plurality of virtual lanes and virtual lane center lines (332 and 333) of the plurality of virtual lanes.

A vehicle configured to operate in an autonomous mode may operate a sensor to determine an environment of the vehicle. The sensor may be configured to obtain sensor data of a sensed portion of the environment. The sensed portion may be defined by a sensor parameter. Based on the environment of the vehicle, the vehicle may select at least one parameter value for the at least one sensor parameter such that the sensed portion of the environment corresponds to a region of interest. The vehicle may operate the sensor, using the selected at least one parameter value for the at least one sensor parameter, to obtain sensor data of the region of interest, and control the vehicle in the autonomous mode based on the sensor data of the region of interest.

An autonomous vehicle can be navigated through an intersection. Topological information about the intersection can be obtained. The topological information can include, for example, a count of a number of lanes on roads associated with the intersection, information about an entrance point to the intersection, or information about an exit point from the intersection. Based on the topological information, context information about a candidate trajectory through the intersection can be obtained. For example, the context information can be based on a current time or information about a position of an object with respect to the topological information. Based on the context information, an existence of an advantage to change an actual trajectory of the autonomous vehicle can be determined. In response to a determination of the existence of the advantage, a change to the actual trajectory of the autonomous vehicle can be caused to occur.

A method for predicting a driving maneuver in a driver assistance system of a motor vehicle. Based on dynamic data of the vehicle detected with the aid of sensors, a history of a driving maneuver, which is not yet completed, is recorded and, based on the history, a future course of the driving maneuver is predicted. Multiple hypotheses for potentially occurring driving maneuvers are represented by respective associated sets of parameters. A theoretical history of the driving maneuver is calculated based on the parameters for each hypothesis. The recorded history is compared to at least one of the theoretical histories. Based on a similarity degree, the hypothesis which describes the presently occurring driving maneuver with the highest probability is determined and output as the prediction result.