A processor unit (3) is configured to execute an autonomous driving function of the motor vehicle (1) during a first instance such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function. The processor unit (3) is further configured to store a driver intervention, the driver intervention being performed by a driver of the motor vehicle (1) during the first instance while the motor vehicle (1) travels autonomously based on the execution of the autonomous driving function. Additionally, the processor unit (3) is configured to execute the autonomous driving function during a second instance, subsequent to the first instance, based at least in part on the stored driver intervention such that the motor vehicle (1) travels autonomously based at least in part on the execution of the autonomous driving function according to the stored driver intervention.

A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.

A computer-implemented method of adaptively controlling an autonomous operation of a vehicle is provided. The method includes steps of (a) in a critic network in a computing system configured to autonomously control the vehicle, determining, using samples of passively collected data and a state cost, an estimated average cost, and an approximated cost-to-go function that produces a minimum value for a cost-to-go of the vehicle when applied by an actor network; and (b) in an actor network in the computing system and operatively coupled to the critic network, determining a control input to apply to the vehicle that produces the minimum value for the cost-to-go, wherein the actor network is configured to determine the control input by estimating a noise level using the average cost, a cost-to-go determined from the approximated cost-to-go function, a control dynamics for a current state of the vehicle, and the passively collected data.

The disclosure relates to a method for generating a reference trajectory within a lane for a vehicle. The method comprises receiving at least one vehicle current state parameter describing a current state of the vehicle (S11). The current state of the vehicle comprises at least a current position of the vehicle. Furthermore, a destination parameter describing a destination to be reached by the vehicle (S12), and at least one route parameter describing a route for reaching the destination (S13) are received. Moreover, the method comprises estimating a power loss being caused when traveling from the current position of the vehicle to the destination (S14). The reference trajectory within the lane is determined such that it minimizes the power loss and leads to the destination (S15). Additionally, a method for operating a vehicle is presented. According to this method, a reference trajectory is generated in accordance with the above method (S21) and at least one control signal is provided for controlling a motion of the vehicle along the reference trajectory (S22). Furthermore, a data processing apparatus, a vehicle and a computer-readable medium are presented.

A processor unit is configured for determining target torque values (21), which lie within a prediction horizon (20), and target speed values (19), which lie within the prediction horizon (20), by executing an MPC algorithm, which includes a longitudinal dynamics model of a drive train of the motor vehicle. An autonomous driving function of the motor vehicle is carried out in a torque specification operating mode or in a speed specification operating mode as a function of the level of the target torque values (21). In the torque specification operating mode, a prime mover of the drive train is controlled by an open-loop system based on the target torque values (21). In the speed specification operating mode, a speed governor of the drive train is controlled by an open-loop system based on the target speed values (19).

Provided are methods for vehicle state estimation based on sensor data, which can include receiving the sensor data generated by one or more sensors, calculating a cornering stiffness value associated with the vehicle, predicting a lateral velocity value associated with the vehicle based on the cornering stiffness value, and outputting a set of vehicle state variables indicative of a current state of the vehicle at least by inputting the lateral velocity value into a recursive filter. Some methods described also include updating the cornering stiffness value based on the set of vehicle state variables, updating the lateral velocity value based on the updated cornering stiffness value, and updating the set of vehicle state variables based on the updated lateral velocity value. Systems and computer program products are also provided.

Provided are autonomous vehicles and methods of controlling autonomous vehicles through topological planning with bounds, including receiving map data and sensor data, expanding a topological tree by adding a plurality of nodes to represent a plurality of actions associated with the plurality of constraints, generating a bound based on a constraint in the geographic area, the bound associated with an action for navigating the autonomous vehicle relative to the at least one constraint, storing the bound in a central bound storage, linking a set of bounds of a tree node to the bound via a bound identifier, wherein the first bound is initially linked as an active bound, or alternatively, as an inactive bound after determining it is not the most restrictive bound at any sample index, and control the autonomous vehicle based on the topological tree, to navigate the plurality of constraints.

A driving assistance system for automated driving of a vehicle includes at least one processor unit, which is designed to perform the following when the driving assistance system is carrying out a maneuver of turning onto a street: to determine, on the basis of environment data of an environment sensor system of the vehicle, whether another, on-coming road user is blocking a lane of the street which corresponds to the vehicle; and when it is determined that the other road user is blocking the lane of the street which corresponds to the vehicle to carry out a situation evaluation in order to determine whether the vehicle should back up in order to allow the other road user to exit the street or whether the other road user should be requested to clear the lane by backing up.

A method for generating a vehicle trajectory by optimizing a performance measure J. The trajectory may include a sequence of states x=(x.sub.k).sub.k=1.sup.N of the vehicle. The optimization is subject to predefined vehicle dynamics x.sub.k+1=f(x.sub.k, u.sub.k), where u.sub.k is a control input to the vehicle, and a condition that each position of the vehicle shall be close to a reference path X.sup.r. The vehicle's position is constrained inside a variable-width corridor around the reference path. A quantity r controlling the width of the corridor is included as an additional optimization variable and the performance measure includes a penalty on the corridor width. To define the corridor, each point of the reference path may be associated with a pair of laterally spaced ellipses and requiring each vehicle position to be outside the ellipses.

A control unit that controls a travelling state and an air conditioning state of a vehicle includes: a drive control unit performing a vehicle speed control and a power train control, the vehicle speed control selectively executing an acceleration operation where an engine mounted on the vehicle is operated and a deceleration operation where the engine is stopped to allow the vehicle to coast, the power train control selectively executing activation or deactivation of the engine; m and an air conditioning control unit that controls an air conditioning system provided in the vehicle to execute an air conditioning control. A content of control is changed for at least one of the vehicle speed control, the power train control and the air-conditioning control while the air conditioning system is operating, compared to a case where the air conditioning system is not operating.