A vehicle and method for controlling a vehicle having a traction battery and an internal combustion engine include adapting a power limitation of the internal combustion engine by sensing a currently supplied power level of the internal combustion engine and a current velocity of the vehicle, sensing an ambient temperature of the vehicle and determining an associated ambient-temperature-related weighting factor, sensing an ambient air pressure and determining an associated air-pressure-related weighting factor, determining a thermal load indicator as a function of a ratio of the sensed currently supplied power and the sensed current velocity as well as of the ambient-temperature-related weighting factor, the air-pressure-related weighting factor, and a vehicle-bodywork-related weighting factor, and limiting a maximum supplied power level of the internal combustion engine as a function of the determined thermal load indicator.

A hybrid electric vehicle includes an intelligent vehicle controller, an electric motor, a battery, an internal combustion engine (ICE), and an electrical generator coupled to the ICE configured to provide electricity to the battery and the electric motor. The intelligent vehicle controller receives ICE power level shifting data from the electrical generator, ICE, battery, and electric motor. The intelligent vehicle controller determines a desirable torque and/or a desirable revolutions per minute (RPM) for the ICE based on the received ICE power level shifting data by utilizing an efficiency map that includes fuel efficiency contours and noise, vibration, and/or harshness (NVH) level lines for the hybrid electric vehicle. The intelligent vehicle controller may have first and second vehicle operation modes, and may derive first and a second desirable power levels for the ICE in the first and second operation modes, based on the ICE power level shifting data.

A method for determining a current state vector describing location and heading of an ego-vehicle with respect to a lane boundary of a road comprises a step of obtaining road sensor data from at least one road sensor of the ego-vehicle detecting the lane boundaries of the road. In another step, a measured state vector of the ego-vehicle is calculated from the road sensor data. Furthermore, motion state data related to current heading and velocity of the ego-vehicle is obtained and a predicted state vector of the ego-vehicle is calculated based on the motion state data of the ego-vehicle and a previous state vector of the ego-vehicle. Finally a current state vector is determined by calculating a weighted average of the measured state vector and the predicted state vector of the ego-vehicle. The weights are determined based on characteristics of an upcoming section of the road.

An autonomous valet parking method includes: activating an automated valet parking procedure; determining, by a parking infrastructure, a plurality of guide route candidates leading from a pickup zone to a target position; determining, by the parking infrastructure, weighting factors for the plurality of guide route candidates; selecting, by the parking infrastructure, one guide route candidates among the plurality of guide route candidates as a guide route to the target position; transmitting the target position and the guide route to the vehicle; performing, by the vehicle, autonomous driving to the target position along the guide route; performing, by the vehicle, autonomous parking at the target position, and finishing the automated valet parking procedure.

A method for predicting whether another vehicle in the driving-environment of an ego-vehicle will execute a lane-change, based on observations of the driving-environment of the ego-vehicle, including: the observations are supplied to individual classificators; based on at least a portion of the observations, each individual classificator, in accordance with an individual instruction, ascertains an individual probability that the other vehicle will change lanes; the driving situation in which the ego-vehicle finds itself is classified as a whole by a situation classificator into one of several discrete classes; a record of weighting factors, assigned to the class into which the situation-classificator has classified the driving-situation, is ascertained, that indicates the relative weighting of the individual classificators for this driving situation; the individual probabilities are set off against the weighting-factors to form an overall probability that the other vehicle will change lanes. A method for training weighting-factors and related computer-program are described.

An apparatus for controlling autonomous driving of a vehicle includes a communication device configured to receive a first determination value, which is calculated based on information obtained as surroundings are sensed, from a surrounding vehicle or configured to receive a second determination value from a server. The apparatus includes a sensor configured to sense surroundings of a subject vehicle. The apparatus includes a controller configured to: calculate a third determination value based on information sensed by the sensor; to calculate a final determination value based on at least one of the first determination value and the second determination value, and the third determination value; and to control the autonomous driving by using the final determination value.

A motor control apparatus and method for damping engine vibration are provided. The apparatus includes a data collection device that collects engine information and motor information and a processor that determines a magnitude of engine torque vibration using the engine information and the motor information. A weighting value is determined by determining a position of a piston in a cylinder of an engine, ignition timing, an engine angular acceleration, and an engine velocity. A motor arti-phase torque is calculated by reflecting the determined weighting value in the magnitude of the engine torque vibration and a motor is operated based on the motor anti-phase torque to cancel out the engine torque vibration.

A method evaluates a degree of fatigue of a vehicle occupant in a vehicle. A number of first fatigue indicators is provided which are determined according to computation rules from a plurality of first sensor values and each represent a degree of fatigue of the vehicle occupant. The first sensor values represent measured values of the vehicle and/or measured values relating to a current journey. A first metadata record is associated with each of the number of first fatigue indicators, wherein the first metadata records represent information about the characteristics of the sensors. The first sensor values are processed in the respective first fatigue indicators. A number of second fatigue indicators is provided which are determined according to computation rules from one or more second sensor values and each represent a degree of fatigue of the vehicle occupant. The second sensor values represent physiological and/or physical parameters of the vehicle occupants. A second metadata record is associated with each of the number of second fatigue indicators. The second metadata records represent information about the characteristics of the sensors. The second sensor values are processed in the respective second fatigue indicators. An overall fatigue indicator is determined which represents the degree of fatigue of the vehicle occupant by weighting the number of first fatigue indicators and the number of second fatigue indicators. The fatigue indicators are weighted according to the information about the characteristics of the sensors contained in the first metadata record and the second metadata record.

Techniques for generating trajectories and drivable areas for navigating a vehicle in an environment are discussed herein. The techniques can include receiving a reference trajectory representing an initial trajectory for a vehicle, such as an autonomous vehicle, to traverse the environment. A point density can be determined for various portions of the reference trajectory. In some cases, the point density can be based at least in part on a cost associated with a curvature value associated the reference trajectory or a cost associated with a distance between the reference trajectory and an obstacle in the environment. Further, the techniques can include evaluating a cost function at points on the reference trajectory to generate a target trajectory with respect to the reference trajectory, and controlling the autonomous vehicle to traverse the environment based at least in part on the target trajectory.

Provided is a travel evaluation method of making an evaluation related to travel of a vehicle capable of traveling in a leaning position, the method including: obtaining a tire force which is an external force exerted on a wheel of the vehicle from a ground surface; and deriving an evaluation index related to travel of the vehicle. The evaluation index includes a positive evaluation index as a rating of a positive evaluation related to travel of the vehicle. In deriving the evaluation index, the positive evaluation index is set higher as the tire force increases, and the evaluation index is corrected based on an influential parameter other than the tire force.