A simulator and a method for testing a control device function of a control device of a vehicle. The vehicle includes various environmental sensors, such as radar, a camera, and a radio receiver, which serve as inputs to the control device function of the control device. A corresponding simulation utilizing a vehicle model sensor models, and an environmental model is executed in a distributed fashion via a plurality of computing units and a memory of a simulator. The simulation utilizing the vehicle model, the sensor models, and the environmental model provides inputs to the control device function. Moreover, the simulation utilizing these models is started synchronously on the computing units, wherein data exchange occurs amongst the memory and the multiple computing units.

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).

The present disclosure provides a driverless vehicle testing method and apparatus, a device and a storage medium, wherein the method comprises: obtaining traffic scenario data of a traffic accident happening on a real road; constructing sensor data needed by travel of the driverless vehicle according to the traffic scenario data; performing simulation of a testing scenario according to the traffic scenario data; performing test for the driverless vehicle's capability of dealing with the traffic accident according to the sensor data and testing scenario. The solution of the present disclosure may be applied to improve accuracy of testing results.

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 movement distance calculation device includes: a first movement distance calculation unit which calculates a first movement distance of a movable body based on plural rotation speeds of plural wheels of the movable body and a steering angle of the movable body; a vector detection unit which detects a movement vector of an object included in the acquired images as defined herein; a second movement distance calculation unit which calculates a second movement distance of the movable body based on the movement vector; a first reliability determining unit which determines reliability of the calculated first movement distance as defined herein; a second reliability determining unit which determines reliability of the calculated second movement distance as defined herein; and a movement distance determining unit which determines a movement distance using at least one of the calculated first movement distance and the calculated second movement distance as defined herein.

A method for checking a vehicle dynamics model of a vehicle, with which a value of an output variable is ascertainable from a value of a variable input variable, for multiple values of the input variable respectively associated model-based values of the output variable being ascertained with the aid of vehicle dynamics model, for the multiple values of the input variable at the vehicle respectively associated vehicle-based values of the output variable being ascertained, difference values being ascertained from mutually corresponding model-based values and vehicle-based values, respectively, an updated dataset of the ascertained difference values being compared with a comparison dataset with the aid of a comparison method and a concordance measure being ascertained in the process, and the vehicle dynamics model being determined to be valid if the concordance measure meets a predefined concordance criterion.

A braking control system includes control circuitry configured to control first and second brakes in a vehicle. The control circuitry is configured to calculate a target braking force in accordance with the operation amount of a brake pedal by a driver, determine a first braking force and a second braking force based on the target braking force, and control each of the first and second brakes such that each of the determined braking forces is generated in the vehicle. The first and second braking forces are determined such that a sum of the first and second braking forces becomes the target braking force and a pitch behavior specified by a preset pitch behavior model occurs in the vehicle.

In various embodiments, methods, systems, and vehicle apparatuses are provided. A method for implementing adaptive cruise control (ACC) established by Reinforcement Learning (RL) including executing, by a processor, adaptive cruise control to receive a set of vehicle inputs about a host vehicle's operating environment and current operations; identify, by the processor, a target vehicle operating in the host vehicle environment and quantifying a set of target vehicle parameters about the target vehicle derived from sensed inputs; modeling a state estimation of the host vehicle and the target vehicle by generating a set of speed and torque calculations about each vehicle; generating a set of results from at least one reward function based on one or more modeled state estimations of the host and target vehicle; processing the set of results with driver behavior data established by RL to correlate one or more control actions to the driver behavior data.

An information processing device including: a detecting section detecting an observed amount relating to a change in acceleration of a vehicle due to a driving operation; an inertial measurement section detecting an actually measured value of acceleration of the vehicle; and an estimating section that determines that the vehicle has collided in a case in which an acceleration difference, which is a difference between an actually measured value of acceleration of the vehicle detected by the inertial measurement section and an estimated value of acceleration of the vehicle derived on the basis of the observed amount, is greater than or equal to a predetermined threshold value.

Systems and methods for predicting an optimal use level of a driver assist system are provided. The system may collect historical driver behavior and road condition data, and train an optimization prediction model using the historical data, e.g., via machine learning or artificial intelligence. Moreover, the system may collect real-time driver behavior and road condition data, and predict an optimal use level of the driver assist system based on the real-time data using the trained optimization prediction model. The system may then send feedback to the driver assist system when the optimal use level falls outside a predetermined threshold, such that the driver assist system may be unavailable or have reduced functionality until the optimal use level improves.