A vehicle includes a speed profile generating device that generates a speed profile indicating an expected speed change of a vehicle with respect to a unit time, based on a current driving state of the vehicle, a time point selecting device that selects a target time point, a first time point earlier than the target time, and a second time point later than the target time point from the speed profile, a tracking acceleration generating device that generates a tracking acceleration for tracking the speed profile based on the target time point, the first time point, the second time point, and the speed profile, and a controller module that controls a driving state of the vehicle based on the tracking acceleration.

A driver command interpreter system for a vehicle includes one or more controllers that execute instructions to receive a plurality of dynamic variables, vehicle configuration information, and driving environment conditions, and determine a target vehicle state during transient driving conditions based on the plurality of dynamic variables from the one or more sensors, the vehicle configuration information, and the driving environment conditions. The one or more controllers build a transient vehicle dynamic model based on the target vehicle state during transient driving conditions, the plurality of dynamic variables, the vehicle configuration information, and the driving environment conditions, and solve for desired zeros corresponding to the target vehicle state during transient conditions.

A method and system for target detection of a vehicle is proposed. In the method and system, when the vehicle is turning, a warning signal according to a risk level of collision is generated at the right timing as a risk level of collision between the host vehicle and the other vehicle behind the host vehicle may be accurately identified by correcting a driving path of the host vehicle and position of the other vehicle on the basis of a driving state of the host vehicle.

A pre-crash control device includes target information acquisition units and an electric control unit configured to update a recognized position based on an acquired position each time target information is newly acquired, to estimate a moving direction based on history of the recognized position, to determine whether a collision probability is high based on the recognized position and the moving direction, and to perform pre-crash control if it is determined that the collision probability is high and a time to collision becomes equal to or smaller than a threshold execution time. The electric control unit is configured to update the recognized position based on a currently predicted position and on the acquired position and to update the recognized position to the acquired position when the time to collision becomes equal to or smaller than a first threshold switching time.

A control unit for a vehicle is configured to determine, during an approaching manoeuvre of a distance controller and/or cruise controller of the vehicle towards a vehicle in front, whether or not the vehicle will overtake or can overtake the vehicle in front during the approaching manoeuvre. Furthermore, during the approaching manoeuvre, the control unit is configured to adjust a behaviour of the cruise controller and/or adaptive cruise controller of the vehicle on the basis of the above determination.

A system for adaptive in-drive updating, for a vehicle travelling on a route, includes a controller having a processor and tangible, non-transitory memory. The vehicle is carrying a load. The controller is adapted to obtain one or more dynamic parameters pertaining to the load. A plurality of adaptive predictors is selectively executable by the controller at a timepoint during the route at which a completed portion of the route has been traversed by the vehicle and a remaining portion remains untraversed. The plurality of adaptive predictors includes a speed predictor configured to generate a global speed profile. The plurality of adaptive predictors includes a driving consumption predictor is configured to predict a driving consumption profile for the remaining portion of the route based in part on the dynamic parameter, the route features, the global speed profile, and a past drive consumption.

A computer implemented method for determining weights for an attention based trajectory prediction comprises the following steps carried out by computer hardware components: receiving a sequence of a plurality of captures taken by a sensor; determining an unnormalized weight for a first capture of the sequence based on the first capture of the sequence; and determining a normalized weight for the first capture of the sequence based on the unnormalized weight for the first capture of the sequence and a normalized weight for a second capture of the sequence.

Provided are methods for determining a trajectory, which can include obtaining, using the at least one processor, sensor data associated with an environment in which a vehicle is operating, wherein the environment comprises one or more agents including a first agent; determining, using the at least one processor, based on the sensor data, a first prediction associated with the first agent; determining, using at least one processor, based on the first prediction, a primary homotopy; determining, using the at least one processor, based on the primary homotopy and the first prediction, one or more contingency homotopies associated with a contingency; determining, using the at least one processor, based on the primary homotopy and the one or more contingency homotopies, a primary trajectory; and providing, using the at least one processor, operation data associated with the primary trajectory to cause the vehicle to operate based on the primary trajectory.

In one embodiment, a steering control delay is measured, where the steering delay represents the delay between the time of issuing a steering control command and the time of a response from one or more wheels of an autonomous vehicle. A speed control delay is measured between the time of issuing a speed control command and the time of a response from one or more wheels of the autonomous vehicle or the time of supplying pressure to the gas pedal or brake pedal. In response to a given route subsequently, an overall system delay is determined based on the steering control delay and the speed control delay using a predetermined algorithm. Planning and control data is generated in view of the system delay for operating the autonomous vehicle.

Described embodiments include a self-propelled vehicle, method, and system. The self-propelled vehicle includes an autonomous driving system configured to dynamically determine maneuvers operating the vehicle along a route in an automated mode without continuous input from a human driver. The vehicle includes an input device configured to receive a real-time request for a specific dynamic maneuver by the vehicle operating along the route from the human driver. The vehicle includes a decision circuit configured to select a real-time dynamic maneuver by arbitrating between (i) the received real-time request for the specific dynamic maneuver from the human driver and (ii) a real-time determination relative to the specific dynamic maneuver received from the autonomous driving system. The vehicle includes an implementation circuit configured to output the selected real-time dynamic maneuver to an operations system of the vehicle.