A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a first component (18) of a motor vehicle (1) and of a second component (19) of the motor vehicle (1). The MPC algorithm (13) includes a cost function (15) to be minimized and a dynamic model (14) of the motor vehicle (1). The dynamic model (14) includes a loss model (27) of the motor vehicle (1). The loss model (27) describes an overall loss of the motor vehicle (1). The cost function (15) includes a first term, which represents the overall loss of the motor vehicle (1). The overall loss depends on a combination of operating values, which includes a first value of a first operating parameter and a second value of a second operating parameter. The processor unit (3) is also configured for determining, by executing the MPC algorithm (13) as a function of the loss model (14), that combination of operating values, by which the first term of the cost function (15) is minimized.

A device (16) for determining a discrete representation (30) of a road section ahead of a vehicle (12) includes an input interface (22) for receiving sensor data (20) of a sensor (14) with information about the road section ahead of the vehicle, a setting unit (24) for ascertaining a control distance at which a property of the road section ahead of the vehicle that is relevant for an open-loop control of the vehicle changes based on the sensor data and for setting a support point in a discrete representation of the road section corresponding to the control distance. The setting unit is configured for setting a lower predefined second number (n2) of support points based on a predefined first number (n1) of support points. The device also includes an output interface (26) for outputting the lower predefined second number of support points to an optimizer (52) in order to determine a profile of at least one control parameter for the open-loop control of an open-loop system, a vehicle function based on the second number (n2) of support points.

There is provided an adaptive cruise control method for autonomously adapting the speed of an ego vehicle (300) to maintain a target headway, headway being distance from the ego vehicle to a forward vehicle (302), the ego vehicle equipped with a perception system (100) for measuring a current headway and a current speed and acceleration of the forward vehicle relative to ego vehicle, the method comprising: in response to detecting that the current headway is below the target headway, determining and implementing a deceleration strategy for increasing to the target headway; wherein the deceleration strategy is determined so as to selectively optimize for comfort in dependence on a predicted headway, the predicted headway computed for a future time instant based on the current speed and acceleration of the forward vehicle relative to the ego vehicle.

A processor unit (3) for model-based predictive control of a vehicle (1) taking into account an arrival time factor is configured to calculate a trajectory for the vehicle (1) based at least in part on at least one arrival time factor, with the trajectory including an entire route (20) to a specified destination (19) at which the vehicle (1) is to arrive, and with the at least one arrival time factor influencing an arrival time of the vehicle (1) at the specified destination (19). Additionally, the processor unit (3) is configured to optimize a section of the trajectory for the vehicle (1) for a sliding prediction horizon by executing a model-based predictive control (MPC) algorithm (13), where the MPC algorithm (13) includes a longitudinal dynamic model (14) of a drive train (7) of the vehicle (1) and a cost function (15) to be minimized.

A vehicle control apparatus includes an overlapping-prediction determination portion configured to determine whether or not it is predicted that, during execution of a synchronous control for placing a clutch, which is provided between an engine and an electric motor, into an engaged state, a synchronization-completion time point of the clutch overlaps with an inertia phase period in process of a shift control of a transmission, and a torque limitation portion configured, when the overlapping-prediction determination portion determines that it is predicted that the synchronization-completion time point overlaps with the inertia phase period, to execute a torque limitation by which at least one of a torque capacity of the clutch and an output torque of the engine is made smaller than when the overlapping-prediction determination portion determines that it is not predicted that the synchronization-completion time point overlaps with the inertia phase period.

A Coordinating Apparatus and Method is provided between Adaptive Cruise Control (ACC) and Predictive Cruise Control (PCC). The ACC system is configured to provide a target acceleration or deceleration based on maintaining a target distance from vehicles ahead. The PCC system is configured to provide a target acceleration or deceleration based on upcoming changes in elevation and maximizing fuel economy. The coordinating apparatus communicates with the ACC system and with the PCC system, and applies the lesser acceleration or greater deceleration between the ACC target acceleration or deceleration and the PCC target acceleration or deceleration. The apparatus and method may apply the target acceleration or deceleration by way of a vehicle speed control apparatus, such as a vehicle engine controller.

Systems, methods, and devices are disclosed for predicting behaviors of objects (vehicles, bicycles, pedestrians, etc.) at a location. A model descriptive of a possible object behavior can be received by an autonomous vehicle, where the model provides conditional predictions about a future behavior of an object based on a position of the object in a lane. The autonomous vehicle can detect the position of a specific object in the lane, and the model can then be applied to determine probabilities of a future behavior of the specific object.

A vehicle can traverse an environment along a first region and detect an obstacle impeding progress of the vehicle. The vehicle can determine a second region that is adjacent to the first region and associated with a direction of travel opposite the first region. The vehicle can use a state machine to determine an action (e.g., an oncoming action) to utilize the second region to overtake the obstacle. By comparing a cost to a cost threshold and/or to a cost associated with another action (e.g., a “stay in lane” action), the vehicle, using the state machine, can determine a target trajectory that traverses through the second region and can traverse the environment based on the target trajectory to avoid, for example, the obstacle in the environment while maintaining a safe distance from the obstacle and/or other entities in the environment.

An apparatus for controlling a vehicle includes an object selection device configured to select an object intersecting the vehicle at an intersection existing on a driving path of the vehicle, a risk determination device configured to determine a risk during driving of the vehicle based on a predicted path of the object, and a driving control device configured to determine a driving method of the vehicle based on a risk determination result.

Controlling a prime mover of a first vehicle following a first path is based, at least in part, on a likely speed behaviour of a second vehicle ahead of the first vehicle, which is estimated based on a predicted path of the second vehicle. At least one coasting profile for the first vehicle is estimated for at least part of the first path and/or the predicted path. At least one of the coasting profiles is determined that meets at least one predetermined coasting requirement. The prime mover may be controlled to place the vehicle into a coasting mode based on the determined coasting profile. Alternatively, feedback is provided to a user to put the vehicle into a coasting mode.