A vehicle cruise control device controls traveling of a vehicle, the vehicle cruise control device includes arithmetic circuitry, and device processing circuitry to control actuation of one or more traveling devices mounted in the vehicle, based on an arithmetic result from the arithmetic circuitry. The arithmetic circuitry is configured to recognize a vehicle external environment based on an output from information acquisition circuitry that acquires information of the vehicle external environment; set a route to be traveled by the vehicle, in accordance with the recognized vehicle external environment; determine a target motion of the vehicle to follow the route that was set; and set operations of one or more body-related devices of the vehicle, based on the target motion of the vehicle, and generate control signals that control the one or more body-related devices.

Systems and methods are disclosed for identifying time-latency and subsystem control actuation dynamic delay due to second order dynamics that are neglected in control systems of the prior art. Embodiments identify time-latency and subsystem control actuation delays by developing a discrete-time dynamic model having parameters and estimating the parameters using a least-squares method over selected crowd-driving data. After estimating the model parameters, the model can be used to identify dynamic actuation delay metrics such as time-latency, rise time, settling time, overshoot, bandwidth, and resonant peak of the control subsystem. Control subsystems can include steering, braking, and throttling.

A model of a system of an intelligent vehicle is trained and optimized using system operation data of the intelligent vehicle in a normal running state. The system operation data of the intelligent vehicle in a running state is collected in real time. Sensor data of the system operation data is de-noised, and feature extraction and screening are performed for a fatal sensor fault to reconstruct the system operation data. The reconstructed system operation data is inputted into the trained model to output system state data of the intelligent vehicle in the running state. The system state data is compared with a set threshold. If the system state data exceeds the set threshold, an actuator corresponding to the system state data is determined to have a fault. In addition, a system for a fault diagnosis of the intelligent vehicle is further provided.

Systems and methods for vehicle motion control are provided. The method includes: calculating a correction factor using one of three different sets of operations when the vehicle is performing a limit handling maneuver, wherein the correction factor is calculated using a first set of operations when the vehicle is operating in an understeer state, calculated using a second set of operations when the vehicle is operating in an oversteer state, and calculated using a third set of operations when the vehicle is operating in a neutral steer state; adjusting a desired lateral acceleration and a desired yaw rate by applying the correction factor to account for a reduced level of friction experienced by the vehicle when traveling on a non-ideal friction surface; calculating optimal control actions based on the adjusted desired lateral acceleration and adjusted desired yaw rate; and applying the optimal control actions with vehicle actuators during vehicle operations.

A vehicular lane centering system is enabled responsive to speed of the vehicle exceeding a threshold speed and includes a camera and a processor. Based on processing of captured image data, the system determines position of a left lane delimiter and a right lane delimiter on the road. The system establishes a left safe zone delimiter based on the determined position of the left lane delimiter and a right safe zone delimiter based on the determined position of the right lane delimiter. With the system enabled, the system takes corrective action responsive to the vehicular lane centering system determining that the vehicle is at risk of unintentionally crossing the left safe zone delimiter or the right safe zone delimiter. When the system does not determine position of the left lane delimiter or the right lane delimiter for a first period of time, corrective action taken by the system is reduced.

A vehicle and control method include a traction battery, a temperature sensor, current sensor, and voltage sensor associated with the traction battery, an electric machine powered by the traction battery to provide propulsive power to the vehicle, and a controller configured to control at least one of the electric machine and the traction battery in response to a battery state of charge (SOC) estimated using a battery model having parameters including a first resistance in series with a second resistance and a capacitance in parallel to the second resistance. The battery model parameters are adjusted during vehicle operation using a Kalman filter and reinitialized to new values in response to a vehicle key-on, in response to a change in the battery current exceeding a corresponding threshold, and/or in response to any of the parameter values crossing an associated limit.

An exemplary method generally involves determining a hazard parameter for a tracked vehicle including a ground interface assembly. The ground interface assembly generally includes a track and a drive wheel operable to move the track to thereby propel the tracked vehicle. A load sensor senses a load carried by the tracked vehicle, and a speed sensor senses a vehicle speed of the tracked vehicle. A control system in communication with the load sensor, the speed sensor, and a temperature sensor determines the hazard parameter based upon the load, the vehicle speed, and an ambient temperature in a vicinity of the tracked vehicle. The control system compares the hazard parameter to a threshold parameter, and performs an action based upon the comparison.

Systems and methods are disclosed for reducing second order dynamics delays in a control subsystem (e.g. throttle, braking, or steering) in an autonomous driving vehicle (ADV) and increasing control system bandwidth by accounting for time-latency in a control subsystem actuation system. A control input is received from an ADV's autonomous driving system. The control input is translated into a control command of the control subsystem of the ADV. A reference actuation output and a predicted actuation output are generated corresponding to a by-wire (“real”) actuation action for the control subsystem. A control error is determined between the reference actuation action and the by-wire actuation action. A predicted control error is determined between the predicted actuation action and the between the by-wire actuation action. Adaptive gains are determined and applied to the by-wire actuation action to generate a second by-wire actuation action.

A method for managing a powertrain (3) of a motor vehicle (1) comprises the following steps: (a) determining a predictive rolling resistance coefficient (Crr) for at least one tyre (10) of the motor vehicle (1); and (b) adapting the operation of the powertrain (3) according to the predictive rolling resistance coefficient (Crr) in order notably to optimize the energy consumption of the motor vehicle (1).

A method for controlling a plurality of driving functions in an automated or autonomous vehicle, a control unit designed to carry out the method, a computer program, and a machine-readable memory medium on which the computer program is stored are provided. In the method, the plurality of driving functions is described in each case by finite state machines. At least one finite state machine is of the Moore type, and includes a structure with a finite set of states. The states are linked to one another via edges. An edge defines from the finite set of states a transition from a starting state to a target state, in that an associated edge condition is true or false. The finite state machine is accessible during runtime based on the structure, so that an access to the states and the edges is made possible to change the states and/or the edges.