Methods and devices for operating a Self-Driving Car (SDC) are disclosed. The method includes generating a first graph-structure having nodes and edges, ranking the edges based on a priority logic into a ranked list of edges, and generating a second graph-structure (i) by iteratively generating attributes for respective ones from the ranked list of edges beginning with a highest priority edge in the ranked list of edges and (ii) until a pre-determined limit is met. The method also includes causing operation of the SDC on the road segment using the second graph-structure.

Disclosed in the present disclosure are a method and an apparatus for determining a vehicle speed control model training sample. The method includes: a lane in map data is divided into a first lane area and a second lane area, and to-be-measured vehicle speed control features indicated by the two lane areas are different; the to-be-measured vehicle speed control features are determined as target vehicle speed control feature when a difference between a first empirical highest vehicle speed distribution and a second empirical highest vehicle speed distribution is greater than or equal to a preset difference threshold; and starting coordinates and ending coordinates of each preset lane section in the map data, and the target vehicle speed control features and a vehicle speed control label in the preset lane section are taken as a vehicle speed control model training sample.

A lane departure suppression device including a control unit that executes a lane departure suppression control (automatic steering of the steering wheel and/or a warning being issued) when it is determined that there is a possibility that a vehicle departs from a lane. The control unit does not execute the lane departure suppression control when the control unit determines that a lateral speed and a lateral acceleration of the vehicle are increased within a predetermined time from a time point at which acceleration and deceleration of the vehicle is started, and that there is an adjacent lane on a side with respect to the lane in which the lateral speed and the lateral acceleration are increased.

Provided are a method for generating a lane changing decision-making model and a method and an apparatus for lane changing decision-making of an unmanned vehicle. The method for generating a lane changing decision-making model includes: obtaining a training sample set of vehicular lane changing, wherein the training sample set includes a plurality of training sample groups, each of the training sample groups includes a training sample under each time step length in a process that the vehicle completes lane changing based on a planned lane changing trajectory, the training sample includes a group of state variables and corresponding control variables; obtaining the lane changing decision-making model by training a decision-making model based on deep reinforcement learning network by use of the training sample set, wherein the lane changing decision-making model enables the state variable of the target vehicle and the corresponding control variable to be correlated.

A method for automated control of a vehicle includes automatically navigating a vehicle within a current lane of travel with a driving assistance system, and predetermining a steering maneuver during the automatic navigation with the driving assistance system. The method also includes, prior to performing the predetermined steering maneuver, biasing the vehicle within the current lane of travel with an offset from a center of the current lane of travel using the driving assistance system, the offset in a direction of the predetermined steering maneuver, and performing the predetermined steering maneuver with the driving assistance system.

A method of determining a drivable space trajectory of an ego vehicle is described. The method includes determining a set of vehicle trajectories corresponding to a same semantic driving maneuver during motion planning of the ego vehicle. The method also includes identifying the drivable space trajectory to perform the same semantic driving maneuver. The method further includes performing a vehicle control action to maneuver the ego vehicle along the drivable space trajectory.

A vehicle control device includes a recognizer configured to recognize situations around a vehicle; a determiner configured to determine any of lanes included in a road on which the vehicle travels as a reference lane; and a driving controller configured to control at least one of a speed and steering of the vehicle according to the situations recognized by the recognizer and the reference lane determined by the determiner, wherein the determiner is configured to, when the vehicle moves from a first road to a second road different from the first road, among a plurality of lanes included in the first road, according to a relative position of a first reference lane determined at a time before the vehicle moves to the second road with respect to the plurality of lanes, determine a second reference lane on the second road.

A method of controlling coasting of an eco-friendly vehicle includes: determining at least one effective event among deceleration events configured with a target speed in a forward driving path; setting a closest effective event based on a current position among the at least one effective event as a first candidate event; determining whether at least one second candidate event corresponding to an event needed to be followed is present among remaining effective events except for the first candidate event of the at least one effective event; and, when the at least one second candidate event is present, determining a target event among the first candidate event and the second candidate event in consideration of a control start point.

A method of providing automated control of vehicle speed in a driver assist mode may include receiving an operator selection of the driver assist mode and a target speed, monitoring vehicle speed, and generating a propulsive torque request and a braking torque request based on a difference between the target speed and the vehicle speed. The method may further include, responsive to vehicle speed being in a selected range from zero to about three miles per hour, initiating a low speed correction to automatically provide a variable modification to the propulsive torque request or the braking torque request.

A control method for an autonomous driving vehicle is provided. The method includes: obtaining perception information of road conditions around a vehicle; determining a first control decision based on the perception information and autonomous driving decision logic; controlling the vehicle to perform autonomous driving according to the first control decision; determining, in response to detecting an assisted driving instruction, a second control decision that is different from the first control decision based on the perception information, the autonomous driving decision logic, and the assisted driving instruction; and controlling the vehicle to perform autonomous driving according to the second control decision.