An arithmetic unit includes circuitry configured to recognize a vehicle exterior environment, set a route, determine a target motion to follow a set route, and output amounts to control traveling devices in accordance with the target motion. The circuitry is configured to calculate a steering amount for achieving the target motion, generate a control signal for controlling a steering device based on the steering amount, and directly output the control signal to the steering device. The circuitry is configured to calculate a driving force for achieving the target motion and in accordance with the steering amount, and output the drive force to a first microcomputer controlling a driving device. The circuitry is configured to calculate a braking force for achieving the target motion and in accordance with the steering amount, and output the braking force to a second microcomputer controlling a braking device.

A method for controlling a longitudinal position of a vehicle involves a longitudinal positioning control system generating a longitudinal acceleration control signal from a longitudinal dynamic feedforward set point and from longitudinal dynamic control error quantities for a subordinate acceleration control unit acting on a drive device and braking device of the vehicle. A current control reference point corresponding to a current time point and at least one forward control reference point corresponding to a presettable look-ahead time point are determined as control-relevant time points, current or predicted actual/required deviations of a longitudinal position, of a driving speed and of acceleration are determined for each of the control reference points and provide the basis for forming the longitudinal dynamic control error quantities, and required values of an acceleration are determined for each of the control reference points and provide the basis for forming the longitudinal dynamic feedforward set point.

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 method of determining a target lateral acceleration of a vehicle for use in autonomous control of the vehicle to drive along a road, comprising: evaluating each of a plurality of scalar velocity functions at a plurality of key lateral positions predefined with respect to a model of the road to generate a respective set of scalar velocity values; combining the velocity values calculated for each key lateral position to generate a respective target lateral velocity value, the velocity values calculated for each of the key lateral positions being combined by adding the greatest of zero and the velocity values, to the smallest of zero and the velocity values; generating a lateral velocity field by interpolating between the target lateral velocity values; and determining the target lateral acceleration of the vehicle using the lateral velocity field and a measured lateral velocity of the vehicle.

A method for determining the direction of travel of a vehicle comprises providing a first sensor for measuring a longitudinal acceleration of the vehicle and at least one second sensor for establishing the rotational movement of a wheel of the vehicle, An acceleration signal containing acceleration information from the first sensor is received by the system. The acceleration signal is filtered resulting in a modified acceleration signal. The direction of travel of the vehicle is determined based on the modified acceleration signal and based on the output signal of the second sensor.

A vehicle and a method of controlling turning thereof are provided. The turning control method of a vehicle includes calculating first compensation torque based on a lateral acceleration variation during turning, determining first compensated demanded torque by applying the first compensation torque to demanded torque, determining second compensation torque for preventing wheel slip of a driving wheel based on the first compensated demanded torque and an actual vehicle behavior, and determining second compensated demanded torque input to a driving source controller by applying the second compensation torque to the first compensated demanded torque.

A control method for an electrically driven road vehicle comprising the steps of: providing an electric powertrain system; determining a plurality of first virtual gears for a boost configuration and/or a plurality of second virtual gears for a release configuration; detecting, following actuation of an interface system by the driver, while driving, a first selection for one of the first virtual gears and/or a second selection for one of the second virtual gears; and delivering, in the boost configuration, a drive torque to the at least two wheels as a function of the first selection; or delivering, in the release configuration, an anti-motive torque to the at least two wheels according to the second selection.

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.

Techniques and methods associated with performing monitoring associated with operations of autonomous vehicles. For instance, the vehicle may capture sensor data using one or more sensors. The vehicle may then analyze the sensor data using systems in order to determine estimated locations associated with the vehicle and estimated locations associated with situations that may result in a potential unsafe scenario represented by the sensor data. Additionally, the vehicle may determine a distribution of estimated locations associated with the vehicle and using the distributions of estimated locations, the vehicle may determine risk probabilities associated with operations of the vehicle. The vehicle may then redefine or update a route or maneuver to perform when the risk probability exceeds a threshold.

An autonomous driving evaluation apparatus includes: an initial traffic scene setting unit configured to set an initial traffic scene, an initial state of the moving object model, and a road environment in which the autonomous driving vehicle model and the moving object model are disposed; a past traffic scene calculation unit configured to calculate a past traffic scene in which the autonomous driving vehicle model and the moving object model are involved at a past time point back traced from a time point of the initial traffic scene; and a performance evaluation unit configured to evaluate a performance of the autonomous driving algorithm.