A vehicle rear wheel steering assist control system is provided where a wheel deflection angle measuring instrument is mounted at a front wheel for measuring a steering angle of the front wheel, a rotational velocity measuring instrument is also mounted at the front wheel for testing a velocity of the front wheel, output ends of the wheel deflection angle measuring instrument and the rotational velocity measuring instrument are electrically connected to the data acquisition module, the data acquisition module is electrically connected to the controller, the controller is electrically connected to a data execution module, the rear wheel active steering device is mounted at a rear wheel of the automobile, the rear wheel active steering device is electrically connected to the data execution module, the controller is electrically connected to an ECU of the automobile, each automobile seat is provided with a weight sensor, which are electrically connected to the controller.

A system and method are provided for identifying a potential road hazard in a host vehicle based on a remote vehicle. The host vehicle has a host vehicle-to-vehicle (V2V) module and a host advanced driver assistant system (ADAS) module, such as a system employing the ADASIS standard. The remote vehicle also has a remote V2V module that provides position data, and one or more of, longitudinal acceleration data, steering angle change rate data, braking system data, anti-lock braking status and stability control system status of the remote vehicle. The host vehicle receives the position data of the remote vehicle using the host V2V module and determines if the remote vehicle is in the main path zone (MPZ) of the host vehicle. The system determines a potential road hazard when it receives a signal indicating any of the following are true, the longitudinal acceleration data and/or the steering angle change rate data of the remote vehicle exceeds a predetermined threshold, the anti-lock braking system of the remote vehicle is activated, or the stability control system of the remote vehicle is activated. The system indicates the potential road hazard to a driver of the host vehicle when a potential road hazard is identified and the remote vehicle is in the MPZ of the host vehicle.

The techniques and systems herein are configured for dynamically calculating lane change trajectories. An offset that corresponds to an amount of asymmetry of a lane change trajectory (a zero-offset may be a symmetrical lane change trajectory) is determined for a lane change based on vehicle/driver preferences, road curvature, and/or road conditions. The offset is then used to calculate the lane change trajectory for the lane change, which is then provided to a vehicle system to execute the lane change according to the lane change trajectory. By dynamically calculating the lane change trajectory (e.g., actively determining the offset), lane change trajectories may be tailored to driver preferences, including asymmetry preferences and actions for different types of curves, as well as for road and weather conditions. Doing so, may not only improve driver experience due to better emulation of their behavior but also increase safety by decreasing unsafe lane change trajectories.

An HEV-CU determines whether a traveling state requires giving priority to a front and rear driving force control (e.g., whether a vehicle is cornering) on the basis of a traveling state of the vehicle. If the traveling state requires giving priority to the front and rear driving force control (e.g., the vehicle is cornering), the HEV-CU-preferentially controls of an engine and a motor generator as well as engagement force of a differential limiting clutch in accordance with friction force between front and rear wheels and a road surface. If the traveling state does not require giving priority to the front and rear driving force control (e.g., the vehicle is traveling straight), the HEV-CU preferentially controls the driving of the motor generator to cause a charged state (SOC) of a high-voltage battery that supplies electric power to the motor generator to fall within a predetermined range.

A device for distributing a driving force includes a first sensor for collecting first information related to an obstacle located around a vehicle, a second sensor for collecting second information related to a travel state of the vehicle, a driving control device that distributes a driving force to wheels of the vehicle, and a processor electrically connected to the first sensor, the second sensor, and the driving control device, and the processor determines whether an avoidance travel situation of avoiding collision of the vehicle with the obstacle is detected based on the first information and the second information, and controls a posture of the vehicle by adjusting the distributing of the driving force to the wheels when the avoidance travel situation is detected.

A method is for determining a performance discrepancy of a vehicle actuator and includes determining a target degree of freedom of movement value; determining an expected manipulated variable value; determining an actual degree of freedom of movement value; determining an actual manipulated variable value; and obtaining a maximum manipulated variable value. The method includes determining the performance discrepancy of the vehicle actuator using the actual degree of freedom of movement value, the target degree of freedom of movement value, the actual manipulated variable value, the expected manipulated variable value, and the maximum manipulated variable value; and, determining an updated maximum degree of freedom of movement value of the vehicle based on the determined performance discrepancy. An actuator monitoring system is for monitoring a performance characteristic of a vehicle actuator configured to influence at least one degree of freedom of movement of a vehicle. A vehicle has the actuator monitoring system.

A method for approximating a friction value includes: determining a load characteristic; determining a setpoint variable of the vehicle; determining a manipulated variable expected value specifying a predicted value of a manipulated variable to be provided to set the setpoint variable, wherein the determination of the manipulated variable expected value is performed using the load characteristic; determining an actual variable corresponding to the setpoint variable; determining a manipulated variable actual value, which is provided at the steering system, in order to modulate the actual variable; determining a manipulated variable deviation between the manipulated variable expected value and the manipulated variable actual value; and/or determining a setpoint-actual deviation between the setpoint variable and the corresponding actual variable; approximating the friction value based on the determined load characteristic and based on the determined manipulated variable deviation and/or the determined setpoint-actual deviation. A driver assistance system is configured to carry out the method.

The present disclosure provides a driving assistance apparatus of a host vehicle including a sensor configured to detect a steering angle or a steering angular velocity of a steering wheel of the host vehicle and a controller connected to the sensor, and the controller is configured to steer the host vehicle with a biased brake torque when a steering system fails and control a damping force of a damper provided on each wheel of the host vehicle when the host vehicle starts turning.

A system and method for operating a hybrid vehicle including hybrid powertrain a controller for controlling the hybrid powertrain. The hybrid powertrain includes a motor-generator unit, a battery electrically connected to a motor-generator unit, an engine, and a transmission engageably connected to the engine and the motor-generator unit. The controller is configured to (1) detect if a triggering event occurs, (2) determine if a boost drive mode is enabled, (3) determine a state of charge of the battery, and (4) determine an amount of excess torque or power available from the motor-generator unit. The controller is configured to signal the motor-generator unit to apply additional motor torque to the hybrid powertrain when (1) the triggering event occurs, (2) the boost drive mode is enabled, (3) a state of charge of the battery is above a threshold, and (4) excess torque or power from the motor-generator unit is available.

A autonomous driving system detects information related to at least one of driving conditions and surrounding conditions of a vehicle using one or more sensors, implements vehicle control using a machine learning model based on the information, presents the detection results of the sensor when a detection performance of the sensor is lower than a predetermined level, and presents the detection results of the sensor corresponding to a situation when a situation with a risk higher than a specified value occurs in the vehicle.