A vehicle driving force control method is provided. The vehicle driving force control method includes collecting vehicle driving information, estimating speed of a driving system of a vehicle from the collected vehicle driving information and calculating speed difference between measurement speed of the driving system and the estimated speed of the driving system, obtaining torque command rate information from the calculated speed difference, limiting a variation of reference torque command determined according to the vehicle driving information based on the acquired torque command rate information to determine final torque command, and controlling operation of a vehicle driving device according to the final torque command.

A control method is provided for controlling a lateral position of a motor vehicle. The control method includes calculating a sighting distance of a detector means embedded in the vehicle, calculating a first component of a steering angle setpoint of a steered wheels of the vehicle, and calculating a second component of the steering angle setpoint. The first component is an open loop component of a control system, while the second component is a closed loop component of the control system. The first component is weighted by a gain that is a decreasing function of the sighting distance.

Embodiments described herein improve fuel economy by controlling a vehicle powertrain based on a predicted vehicle velocity. The vehicle velocity is predicted based on vehicle-to-vehicle data when a prediction horizon is a longer prediction horizon and the vehicle velocity is predicted based on historical drive cycle data when the prediction horizon is a shorter prediction horizon. A time duration of the shorter prediction horizon is shorter than the time duration of the longer prediction horizon. A plurality of drive cycles are established for both the longer and the shorter prediction horizons using a neural network. A shorter prediction horizon drive cycle uses nonlinear autoregressive exogenous model neural networks and the longer prediction horizon drive cycle uses two layer feedforward neural networks. The predicted vehicle velocity is determined from a similar drive cycle of the plurality of drive cycles of either the shorter and/or the longer prediction horizon drive cycles.

A vehicle travel control device executes trajectory following control to make the vehicle follow a target trajectory. A delay time represents control delay of the trajectory following control. A delay compensation time is at least a part of the delay time. The trajectory following control includes: displacement estimation processing that estimates a displacement of the vehicle in the delay compensation time; and delay compensation processing that corrects a deviation between the vehicle and the target trajectory based on the estimated displacement to compensate the control delay. The displacement estimation processing is effective in an effective period and ineffective in an ineffective period. When the ineffective period is included in the delay time of the trajectory following control, the displacement estimation processing is executed in a temporary mode by using sensor-detected information in the effective period without using the sensor-detected information in the ineffective period.

Systems and methods of using a common control scheme to autonomously control a vehicle during semi-autonomous and fully autonomous driving modes are provided. In particular, embodiments of the presently disclosed technology incorporate reference tracking for driving input and vehicle state into this common control scheme. In some embodiments, this common control scheme may be implemented using Model Predictive Control (“MPC”).

A start control device for a hybrid vehicle includes a battery, first and second rotary electric machines, an engine, a first determination unit configured to determine whether the battery is in a low output state, a cranking control unit configured to perform a cracking of the engine, and a second determination unit configured to perform a cranking completion determination. In a case where a maximum output of the battery is in the low output state, the cranking control unit causes the first rotary electric machine to run at a low output target rotation speed, and the second determination unit determines that the cranking is completed when a condition that an actual rotation speed of the first rotary electric machine continues to be within a target range for a predetermined time is satisfied.

A driving torque command generating apparatus and method of operating a hybrid electric vehicle can obtain torsional state observation values using an engine speed, a motor speed, and a wheel speed detected by an engine speed detector, a motor speed detector, and a wheel speed detector, respectively, together with a motor torque command generated in a previous period, and generate an engine torque command and a motor torque command of a driving torque command based on a driving input value input by a driving input detector and the torsional state observation values.

A vehicle control system (30) for controlling a behavior control device (20) that controls a behavior of a vehicle (1), comprising: a feed-forward computing unit (71) that computes a feed forward control amount of the behavior control device according to a steering angle of the vehicle; a feedback computing unit (72) that computes a feedback control amount of the behavior control device according to a difference between a target vehicle state amount computed from the steering angle and an actual vehicle state amount; a correcting unit (73) that computes a corrected feed-forward control amount by correcting the feed forward control amount according to the feedback control amount; and a target control amount computing unit (74) that computes a target control amount of the behavior control device according to the feedback control amount and the corrected feed forward control amount.

A platooning control apparatus may include a processor configured to detect the occurrence of acceleration and shifting of a vehicle in front of a host vehicle based on information received from the vehicle in front during platooning, and to set a feedforward control input value of a host vehicle to zero for controlling an inter-vehicle distance with the vehicle in front in a section in which the acceleration and the shifting of the vehicle in front occurs; and a storage configured to store data and algorithms driven by the processor.

A method for setting an anticipator module with which a control device controls the trajectory of a motor vehicle is equipped includes detecting whether the anticipator module is unsuitable during a turn by taking account of a lateral deviation with respect to an ideal trajectory and/or a contribution of a feedback module of the control device, determining primary parameters, calculating a secondary parameter by an optimization-based calculation method taking account of the determined primary parameters, and updating a bicycle model of the vehicle by taking account of the calculated secondary parameter.