Disclosed herein is a driver assistance apparatus including a camera which is installed in a vehicle, has a field of view around the vehicle, and is configure to acquire image data, and a controller including a processor configured to process the image data. The controller is configured to identify a gesture corresponding to a predesignated reference gesture based on the image data, and change a gear state of the vehicle and control a driving device of the vehicle to move the vehicle, based on identifying the gesture.

A cruise control method for a hybrid vehicle is provided. The method includes detecting a preceding vehicle and estimating the speed of the preceding vehicle from the information input from a preceding vehicle detecting unit in the on state of a cruise mode and a PnG mode. An upper limit target vehicle speed and a lower limit target vehicle speed are determined from the estimated speed of the preceding vehicle. The driving source of the vehicle is operated to alternately repeat the acceleration (pulse phase) and deceleration (glide phase) of the vehicle between the determined upper limit target vehicle speed and lower limit target vehicle speed.

A cruise control method and system, and a vehicle, the method being applied to a first vehicle. When the first vehicle is driving in a first lane, before the first vehicle enters from the first lane into a preset recognition region to thereby complete vehicle identity recognition in the preset recognition region, the cut-in probability of a second vehicle cutting into the first lane can be predicted according to a movement parameter of the second vehicle that is in an adjacent second lane, and it is determined, according to the cut-in probability and the stopping time of the first vehicle, whether to brake to stop the first vehicle; therefore, during the process of controlling the first vehicle to pass through the preset recognition region, the driver is not required to temporarily take over control of the first vehicle.

The present disclosure relates to an apparatus and a method for controlling a vehicle, and, particularly, an apparatus and a method for controlling a vehicle capable of preventing a collision accident in advance by expanding a detection area of a vehicle by monitoring a lower end of a parked/stopped vehicle by adjusting a rotation angle of a sensor attached to the lower end of the vehicle on a front side and predicting a situation having a risk for a collision that may occur due to a blind spot in advance can be provided.

There is provided a method for preventing car collision, the method comprising: obtaining information about a vehicle stopped in front of a preceding vehicle acquired by the preceding vehicle; calculating a deceleration amount for preventing a collision with the stopped vehicle based on the information about the stopped vehicle; predicting a preset event related to the preceding vehicle based on a change in the position of the preceding vehicle; and controlling an autonomous driving vehicle based on the deceleration amount according to a prediction result.

Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise at least one processor. The processor may be programmed to receive images representative of an environment of the host vehicle and analyze the images to identify a first object and a second object. The processor may determine a first predefined navigational constraint implicated by the first object and a second predefined navigational constraint implicated by the second object, wherein the first and second predefined navigational constraints cannot both be satisfied, and the second predefined navigational constraint has a priority higher than the first predefined navigational constraint. The processor may determine a navigational action for the host vehicle satisfying the second predefined navigational constraint, but not satisfying the first predefined navigational constraint and, cause an adjustment of a navigational actuator of the host vehicle in response to the determined navigational action.

A personalized adaptive cruise control (P-ACC) system and associated algorithm are disclosed for determining a driver's preferred following gap in relation to vehicle speed based on periods of steady-state operation of a vehicle. While the P-ACC system is activated, vehicle transition states initiated by driver manual interventions such as takeover or overwrite events are used to identify subsequent periods of vehicle steady-state operation. Vehicle dynamics data captured during periods of steady-state operation is stored as steady-state data, which is then used to train a machine learning model to learn the driver's preferred following gap. This learned relationship is fed into second-order vehicle dynamics to determine a target acceleration for achieving the desired following gap while the P-ACC system is activated. Upon achieving the desired following gap, the vehicle speed may be held constant to maintain the following gap unless a change in lead vehicle speed necessitates updating the following gap.

Systems and methods for situational behavior of an autonomous vehicle are disclosed. In one aspect, an autonomous vehicle includes at least one perception sensor configured to generate perception data indicative of at least one other vehicle on a roadway, a non-transitory computer readable medium, and a processor. The processor is configured to determine that the other vehicle is violating one or more rules of the roadway based on the perception data, tag the other vehicle as a non-compliant driver, and modify control of the autonomous vehicle in response to tagging the other vehicle as a non-compliant driver.

Determination of a trajectory for a first vehicle (1) by model predictive control (MPC) is provided. Trajectory information about a second vehicle (18) traveling in the area ahead of the first vehicle (1) is utilized. In particular, discretization points (P.sub.1, P.sub.2, P.sub.3) and arrival times of the vehicles (1, 18) at the discretization points (P.sub.1, P.sub.2, P.sub.3) are utilized to generate constraints for the model predictive control of the first vehicle (1).

The present invention relates to a collision distance estimation device and a driver assistance system using the same. The collision distance estimation device includes an image acquisition unit configured to acquire images of surroundings of a vehicle to generate image information, an image reading unit configured to detect and identify an object present around the vehicle from the image information to generate object recognition information, a travel detection unit configured to generate movement distance information on the basis of wheel sensing information, steering information, and the image information, and a collision distance calculation unit configured to calculate collision distance information on the basis of the object recognition information and the movement distance information.