A vehicle includes a speed profile generating device that generates a speed profile indicating an expected speed change of a vehicle with respect to a unit time, based on a current driving state of the vehicle, a time point selecting device that selects a target time point, a first time point earlier than the target time, and a second time point later than the target time point from the speed profile, a tracking acceleration generating device that generates a tracking acceleration for tracking the speed profile based on the target time point, the first time point, the second time point, and the speed profile, and a controller module that controls a driving state of the vehicle based on the tracking acceleration.

A vehicle control system may include a controller that obtains route information based on a driving route and a location of a vehicle, searches for an uneven road surface on the driving route based on the route information, calculates an impulse based on vehicle information and shape information about the found uneven road surface when the uneven road surface is found, and sets a target speed based on the calculated impulse and user data.

A vehicle includes: a driver assistance system; an accelerator configured to perform acceleration of the vehicle; a braking device configured to perform deceleration of the vehicle; a velocity sensor configured to detect a current velocity of the vehicle; a driver status sensor configured to acquire a driver's behavioral data; and a controller. The controller is configured to identify a carelessness status of the driver based on the driver's behavioral data and to activate a velocity control mode when the carelessness status of the driver is detected in the activation status of the driver assistance system.

Embodiments of the present disclosure relate to generating velocity profiles for an autonomous vehicle (101). An ECU (107) of the autonomous vehicle (101) receives road information from one or more sensors (106) associated with the autonomous vehicle (101). One or more parameters related to smooth movement of the autonomous vehicle on the road is determined from the road information. Further, a first velocity profile is produced using an AI model and a second velocity profile is produced using a hierarchical model, based on the one or more parameters. Furthermore, one of the first and the second velocity profile is selected by comparing the first and the second velocity profiles. The selected velocity profile has a lower value of velocity value compared to the other velocity profile. The selected velocity profile is provided to the autonomous vehicle (101) for navigating on the road (102) smoothly.

A driver assistance method for a vehicle includes the steps of establishing an energy prediction for a route on the basis of an anticipated driver behavior, determining a driving behavior which is optimized with regard to the energy prediction, and outputting an action recommendation on the basis of the optimized driving behavior.

A driving support apparatus (12) has: a setting device (122) for setting a first target position (31) on the basis of a first sign object (21), if the first sign object requesting a vehicle (1) to stop is detected; and a supporting device (123) for performing a first deceleration control for decelerating the vehicle to a first target speed before the vehicle reaches the first target position, if a second sign object (22) representing a stop position is detected during a period when the first decelerating control is performed, the setting device sets a second target position (32) on the basis of the second sign object and the supporting device performs a second decelerating control for decelerating the vehicle to a second target speed before the vehicle reaches the second target position.

An apparatus and a method for controlling a velocity of an autonomous driving vehicle is provided. The method includes steps of: obtaining information of an environment surrounding the vehicle when an obstacle is detected to be on a planning path of the vehicle; obtaining an initial reference velocity profile of the vehicle; determining a safety factor based on the initial reference velocity profile, the information of the environment and information of the vehicle, wherein the safety factor at least comprises a safety distance between the vehicle and the obstacle for the vehicle to follow the obstacle; determining an optimized reference velocity profile based on the information of the environment, the information of the vehicle and the safety factor; and performing the step of determining the safety factor by using the optimized reference velocity profile as the initial reference velocity profile and the step of determining the optimized reference velocity iteratively.

A vehicular control system determines a planned path of travel for a vehicle along a traffic lane in which the vehicle is traveling on a road. The system determines a respective target speed for waypoints along the planned path that represents a speed the vehicle should travel when passing through the respective waypoint. The system determines a speed profile for the vehicle to travel at as the vehicle travels along the planned path, with at least two different speeds being based on a difference in target speeds of at least two consecutive respective waypoints of the plurality of waypoints. The system determines an acceleration profile for the vehicle to follow as it changes from one speed to another speed of the speed profile. The system controls the vehicle to maneuver the vehicle along the planned path in accordance with the determined speed and acceleration profiles.

A processor unit (3) is configured for executing an MPC algorithm (13) for model predictive control of a motor vehicle (1). The MPC algorithm (13) includes a longitudinal dynamic model (14) of the motor vehicle (1) and a cost function (15) to be minimized. The cost function (15) includes multiple terms, a first term of which represents an output of the cooling pump (28). In addition, the processor unit (3) is configured for, by executing the MPC algorithm (13) as a function of the longitudinal dynamic model (14), ascertaining a speed trajectory of the motor vehicle (1) situated within a prediction horizon and simultaneously ascertaining a pump operating value trajectory situated within the prediction horizon such that the first term of the cost function (15) is minimized.

Implementations described and claimed herein provide systems and methods for controlling an autonomous vehicle. In one implementation, the autonomous vehicle is navigated towards a flow of traffic with a first gap between first and second vehicles and a second gap following the second vehicle. A motion plan for directing the autonomous vehicle into the flow of traffic at an interaction zone is generated based on whether an ability of the autonomous vehicle to enter the interaction zone at the second gap exceeds a confidence threshold. The autonomous vehicle is autonomously navigated into the flow of traffic at the first gap when the confidence threshold is exceeded. The motion plan forgoes navigation of the autonomous vehicle into the flow of traffic at the first and second gaps when the ability of the autonomous vehicle to enter the interaction zone at the second gap does not exceed the confidence threshold.