A method for estimating cardan shaft moments in a vehicle includes performing a state space modelling of a physical model for force transmission in at least one drive train The at least one drive train is formed with at least one drive machine, at least one axle and at least two axle shafts each with a respective wheel. The method further includes selecting the physical model as a torsional oscillator chain in which a respective drive machine inertia moment is assigned to the respective drive train and a respective wheel inertia moment is assigned to the respective wheel. The respective drive machine inertia moment is connected by a respective spring-damper element to the respective wheel inertia moment of the respective wheel which is connected to the respective axle shaft. A vehicle mass is connected by a respective spring-damper element to the respective wheel inertia moment of the respective wheel.

An arithmetic model generation system includes a sensor information acquisition unit, a tire force calculator, and an arithmetic model update unit. The sensor information acquisition unit acquires acceleration of a tire. The tire force calculator includes an arithmetic model for calculating tire force F based on the acceleration, and calculates the tire force F by inputting the acceleration acquired by the sensor information acquisition unit. The arithmetic model update unit compares tire axial force measured by the tire and the tire force F calculated by the tire force calculator, and updates the arithmetic model.

A method for controlling autonomous driving in an autonomous vehicle includes detecting a situation in which autonomous driving is impossible while the vehicle operates in an autonomous driving mode, outputting a control-right handover request warning alarm and then activating a minimal risk maneuver driving mode, determining a human driver gaze validity based on the detected situation, determining a human driver intervention validity upon determination that the human driver gaze is valid, and determining control-right handover of the autonomous vehicle based on the human driver intervention validity. Thus, the control-right may be reliably transferred from a system to a human driver.

A method includes identifying a path to be followed by an ego vehicle. The method also includes determining a desired yaw rate and a desired yaw acceleration for the ego vehicle based on the identified path. The method further includes determining a desired yaw moment for the ego vehicle based on the desired yaw rate and the desired yaw acceleration. In addition, the method includes distributing the desired yaw moment to multiple wheels of the ego vehicle such that the distributed desired yaw moment creates lateral movement of the ego vehicle during travel along the identified path. In some cases, the desired yaw rate and the desired yaw acceleration for the ego vehicle may be determined based on nonlinear kinematics of the ego vehicle, and the desired yaw moment for the ego vehicle may be determined based on a single-track dynamic model of the ego vehicle.

Systems and methods are provided for controlling a hybrid powertrain of a hybrid vehicle, and may include: determining a value of a drive request for a combustion engine of the hybrid vehicle; determining electrical loading on batteries of the hybrid vehicle; adjusting operation of an accessory of the hybrid vehicle to reduce the electrical load of that accessory on the batteries of the hybrid vehicle when the drive request value is above a determined drive request threshold amount and the electrical loading on batteries of the hybrid vehicle is above a power loading threshold; and directing at least some of the power saved by adjusting operation of the accessory from the batteries of the hybrid vehicle to a drive motor of the hybrid vehicle to provide motive force for the vehicle.

A vehicle for traversing an area with a minimal environmental impact is described. The vehicle includes a first component that creates a first environmental impact when the vehicle is traversing in the area. The vehicle further includes a second component configured to reduce the first environmental impact.

The present disclosure provides a method and a device for vehicle parking control. The method includes following steps performed according to a predetermined time period until the vehicle stops at an end point: determining (101) a target position and a target speed when the vehicle arrives at the target position based on a current speed of the vehicle and a distance between a current position and the end point, the target position being on a road where the vehicle is located and in front of the vehicle; determining (102) a deceleration motion mode for the vehicle based on the current speed of the vehicle and the target speed; and performing (103) braking control for the vehicle in accordance with a vehicle braking strategy corresponding to the deceleration motion mode. The method can solve the problem in the related art associated with inaccurate vehicle parking control and uncomfortable experience.

A method for adaptive estimation of a road surface adhesion coefficient for a vehicle with complex excitation conditions taken into consideration comprises the following steps: 1) designing an estimator according to a single-wheel dynamics model of a vehicle, and estimating a longitudinal tire force and a road surface peak adhesion coefficient under longitudinal excitation; 2) designing an estimator according to a two-degree-of-freedom kinematic model of the vehicle, and estimating a tire aligning moment and a road surface peak adhesion coefficient under excitation of a lateral force; and 3) determining an excitation condition met by the vehicle according to a vehicle state parameter, performing fuzzy inference to obtain limits achievable by current longitudinal and lateral tire forces, and designing a fusion observer to fuse estimation results. The method achieves favorable robustness, improves real-time capability, and can be performed quickly and accurately.

Methods and systems are provided for controlling and diagnosing a mechanical vehicle component. In one example, a method may include determining a vehicle speed and a plurality of clutch position settings at a diagnostic controller, and identifying unauthorized conditions based on these determinations. Further, the diagnostic controller may trigger an active fault state of the mechanical vehicle component in order to avoid unauthorized conditions that may lead to unwanted or unanticipated changes in vehicle motion.

The technology employs a model structure for motion control in a vehicle configured to operate in an autonomous driving mode. The model structure has components including a vehicle dynamics system module, a column dynamics module, a rack dynamics module, and an actuation control module. A virtual validation and verification model is configurable based on the components of the model structure. Configuration is performed according to a set of operational requirements based on at least one of a vehicle type, occupant loading information, a center of gravity, or tire pressure as per a cold nominal setpoint. The virtual validation and verification model can be executed so that an electric power steering (EPS) module of the model structure components is configured for at least one of: a software-in-loop model, functional EPS assist, angle control, or to emulate an EPS controller.