A vehicle includes a navigation device, a sensor device, and a control device configured to identify an entry of the vehicle into a predetermined section of a road through the navigation device. In particular, the control device may identify at least one of a curvature of the road, a speed of the vehicle, or an acceleration of the vehicle through the sensor device in response to the entry of the vehicle into the predetermined section, and perform a lateral control of the vehicle based on at least one of the curvature of the road, the speed of the vehicle, or the acceleration of the vehicle.

An information processing apparatus acquires vehicle state information for each of a plurality of vehicles. The information processing apparatus estimates road surface state information of a road surface on which each of the vehicles has traveled, based on the acquired vehicle state information for each of the vehicles. The information processing apparatus estimates the road surface state information by inputting the acquired vehicle state information to a trained model that outputs the road surface state information in a case where the vehicle state information is input and that has been trained in advance based on training data in which the vehicle state information and the road surface state information are associated with each other.

An embodiment driving assistance system for a vehicle includes a driving information provision unit configured to acquire and provide driving information of a traveling vehicle, a control unit configured to generate and output a control signal for driving assistance when it is determined the vehicle travels on a rough road based on the driving information of the vehicle provided by the driving information provision unit and it is determined that the vehicle is currently in a rough road traveling state, and a steering actuator configured to generate and apply a steering assistance force according to a control value of the control signal for the driving assistance output by the control unit to a steering wheel.

Disclosed herein is an apparatus for predicting movement of a user of a vehicle. The apparatus may include an acceleration sensor that senses an acceleration of the vehicle, a braking controller that automatically controls a deceleration of the vehicle, a steering controller that automatically controls a direction of the vehicle, and a control circuit electrically connected to the acceleration sensor, the braking controller, and the steering controller, where the control circuit may monitor an operation of the braking controller, and predict a movement of the user of the vehicle based on a longitudinal acceleration of the vehicle sensed by the acceleration sensor and a first predetermined parameter when braking by the braking controller is detected.

The present disclosure relates to a control system for selectively controlling a vehicle, ensuring high integrity of decisions taken by the control system when controlling the vehicle. The present disclosure also relates to a corresponding computer implemented method and to a computer program product.

A method for generating a vehicle trajectory by optimizing a performance measure J. The trajectory may include a sequence of states x=(x.sub.k).sub.k=1.sup.N of the vehicle. The optimization is subject to predefined vehicle dynamics x.sub.k+1=f(x.sub.k, u.sub.k), where u.sub.k is a control input to the vehicle, and a condition that each position of the vehicle shall be close to a reference path X.sup.r. The vehicle's position is constrained inside a variable-width corridor around the reference path. A quantity r controlling the width of the corridor is included as an additional optimization variable and the performance measure includes a penalty on the corridor width. To define the corridor, each point of the reference path may be associated with a pair of laterally spaced ellipses and requiring each vehicle position to be outside the ellipses.

A lateral virtual boundary for a host vehicle is identified based on a lateral distance between the host vehicle and a target vehicle, a longitudinal distance between the host vehicle and the target vehicle, and a speed of the target vehicle relative to the host vehicle. A forward virtual boundary for the host vehicle is identified based on the longitudinal distance between the host vehicle and the target vehicle. A lateral constraint value of the lateral virtual boundary and a forward constraint value of the forward virtual boundary are determined. A longitudinal acceleration and a steering angle are determined based on the lateral and forward virtual boundaries and the lateral and forward constraint values. One or both of a steering component or a brake are actuated based on the longitudinal acceleration and the steering angle.

An embodiment of the present invention provides an artificial intelligence apparatus for controlling an auto stop function, including: an input unit configured to receive brake information and velocity information of a vehicle; a storage unit configured to store a control model for the auto stop function; and a processor configured to: acquire driving information comprising the brake information and the velocity information through at the input unit, acquire base data used for determining a control of the auto stop function from the driving information, determine a control mode for the auto stop function by using the base data and the control model for the auto stop function, and control the auto stop function according to the determined control mode, wherein the control mode is one of an activation mode which activates the auto stop function or a deactivation mode which deactivates the auto stop function.

The invention is a method of characterizing a road for at least one route travelled by at least one road user, with sensors: a three-axis accelerometer (ACC) and a geolocation sensor (GPS). The method comprises a measurement step (MES), a measurement processing step for disregarding (AFF) the effects related to the road user's speed in order to determine vibrations due to the road roughness, and an analysis of the vibrations to characterize (CAR) the condition of the road.

A vehicle travel control device executes trajectory following control to make the vehicle follow a target trajectory. A delay time represents control delay of the trajectory following control. A delay compensation time is at least a part of the delay time. The trajectory following control includes: displacement estimation processing that estimates a displacement of the vehicle in the delay compensation time; and delay compensation processing that corrects a deviation between the vehicle and the target trajectory based on the estimated displacement to compensate the control delay. The displacement estimation processing is effective in an effective period and ineffective in an ineffective period. When the ineffective period is included in the delay time of the trajectory following control, the displacement estimation processing is executed in a temporary mode by using sensor-detected information in the effective period without using the sensor-detected information in the ineffective period.