A method to personalize the operation of a self-driving automobile is disclosed that improves the applicability and user appreciation of a self-driving automobile by acquiring and applying the user preference data set and the user profile data set, incorporating individual user choice of preferred driving behaviors on different scenarios and user preferred driving styles, and/or the moral or ethics into the control of the automobile operation.

A system and method for autonomous vehicle driving behavior modification include: receiving passenger driving behavior preferences for setting a driving behavior of an autonomous vehicle; generating driving behaviors controls based on the passenger driving behavior preferences; setting an initial driving behavior of the autonomous vehicle using the driving behavior controls, wherein setting the initial driving behavior comprises providing the driving behavior controls to be implemented by the autonomous vehicle during one or more routes involving the passenger; aggregating vehicle behavior feedback relating to a driving behavior of the autonomous vehicle during the one or more routes; and modifying the driving behavior of the autonomous vehicle based on the vehicle behavior feedback.

An in-vehicle processing apparatus includes: a storage unit configured to store point group data, which is created based on output of a sensor for acquiring information about surroundings of a vehicle, including an environmental condition which is a condition for an ambient environment when the output of the sensor is acquired, and including a plurality of coordinates of points indicating parts of objects in a first coordinate system; a sensor input unit configured to acquire the output of the sensor; a current environment acquisition unit configured to acquire the environmental condition; a movement information acquisition unit configured to acquire information about movements of the vehicle; a local peripheral information creation unit configured to generate local peripheral information including a position of the vehicle in a second coordinate system and a plurality of coordinates of points indicating parts of objects in the second coordinate system on the basis of the information acquired by the sensor input unit and the movement information acquisition unit; and a position estimation unit configured to estimate a relationship between the first coordinate system and the second coordinate system on the basis of the point group data, the local peripheral information, the environmental condition included in the point group data, and the environmental condition acquired by the current environment acquisition unit and estimate the position of the vehicle in the first coordinate system.

The present invention relates to an autonomous driving control apparatus and an autonomous driving control method, and an exemplary embodiment of the present invention provides the autonomous driving control apparatus including: a driver seating sensor configured to recognize whether or not the driver is seated; a driver monitoring device configured to recognize whether a driver is looking ahead; and a processor configured to determine whether control authority is switchable for activating an autonomous driving control function based on sensing results of the driver seating sensor and the driver monitoring device.

Enclosed are embodiments for sampling driving scenarios for training machine learning models. In an embodiment, a method comprises: assigning, using at least one processor, a set of initial physical states to a set of objects in a map for a set of simulated driving scenarios, wherein the set of initial physical states are assigned according to one or more outputs of a random number generator; generating, using the at least one processor, the set of simulated driving scenarios in the map using the initial physical states of the objects in the set of objects; selecting, using the at least one processor, samples of the simulated driving scenarios; training, using the at least one processor, a machine learning model using the selected samples; and operating, using a control circuit, a vehicle in an environment using the trained machine learning model.

The present disclosure relates to an on-board control system (200) for operating a vehicle (100, 102, 104), the control system (200) comprising processing circuitry (202) and a plurality of sensors (204, 206, 208) arranged with the vehicle (100). The control system (200) employs e.g. a general vehicle control model (M) for operating the vehicle (100, 102, 104), where the vehicle control model (M) is specifically adapted for the vehicle (100, 102, 104) based on an ongoing operation of the vehicle (100, 102, 104). The present disclosure also relates to a corresponding computer implemented method and to a computer program product.

Through-the-road (TTR) hybrid designs using control strategies such as an equivalent consumption minimization strategy (ECMS) or an adaptive ECMS are implemented at the supplemental torque delivering electrically-powered drive axle (or axles) in a manner that follows operational parameters or computationally estimates states of the primary drivetrain and/or fuel-fed engine, but does not itself participate in control of the fuel-fed engine or primary drivetrain. BSFC type data particular to the paired-with fuel-fed engine allows an ECMS implementation (or other similar control strategy) to adapt to efficiency curves for the particular fuel-fed engine and to improve overall efficiencies of the TTR hybrid configuration.

Examples of the disclosure relate to example devices and methods for generating a map of a vehicle’s passenger compartment and controlling features of the vehicle based on the map. An example vehicle with a passenger compartment mapping system includes one or more sensors to capture sensor data, which includes depth data for a passenger compartment of the vehicle. The system also includes one or more processors to generate a passenger compartment map from the sensor data, and control one or more subsystems of the vehicle based on the passenger compartment map.

The present invention relates to an autonomous driving control apparatus and an autonomous driving control method, and an exemplary embodiment of the present invention provides the autonomous driving control apparatus including: a driver seating sensor configured to recognize whether or not the driver is seated; a driver monitoring device configured to recognize whether a driver is looking ahead; and a processor configured to determine whether control authority is switchable for activating an autonomous driving control function based on sensing results of the driver seating sensor and the driver monitoring device.

A method for controlling a preparation function for an operation of a motor vehicle includes determining an expected time of use of the motor vehicle based on a previous journey of the motor vehicle. The motor vehicle includes a time switch configured to control the preparation function. The method also includes outputting the specified time of use and detecting a confirmation of the time of use. The method also includes setting the time switch to prepare the motor vehicle at the confirmed time of use.