This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.

An adaptive cruise control system includes an information acquisition unit having a main detector and a secondary detector, a control unit, and an execution unit. The main detector detects an object located ahead of the vehicle. The control unit determines whether control of the vehicle is required depending on an actual value determined by the main detector and the threshold value of a system property characterizing the driving environment. The execution unit controls the vehicle. The secondary detector is arranged such that its field of view for detecting an object located at an angle ahead of the vehicle covers the boundary of the main detector's field of view, and which is oriented outwards in relation to the forward direction of the vehicle. The secondary detector sends an indication signal to adjust the threshold value of the system property when an object is detected.

A cruise control device 10 is applied to a vehicle in which an imaging device 21 is mounted. The cruise control device 10 includes: a white line recognition unit 11 which recognizes a white line 61 as a lane boundary that defines an own lane 63 that is a travel lane of an own vehicle 50, on the basis of images acquired by the imaging device 21; and a cutting-in/deviation determination unit 12 which performs cutting-in determination and deviation determination, in which the forward vehicle traveling on an adjacent lane 64 is determined to be a cutting-in vehicle that cuts into the own lane, and the forward vehicle traveling on the own lane is determined to be a deviating vehicle that deviates from the own lane on the basis of a relative position with respect to the white line in a vehicle width direction of a forward vehicle 51.

According to an aspect of the present disclosure, a collision warning apparatus of a vehicle may include an information acquisition device that obtains surrounding object information and vehicle information and a controller that generates collision prediction information of a surrounding object based on the surrounding object information and the vehicle information and provides a warning to an outside of the vehicle or generates control information for controlling braking of the vehicle while providing the warning to the outside of the vehicle based on the collision prediction information.

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.

The present disclosure relates to a method and system for integrated path planning and path tracking control of an autonomous vehicle. The method includes: obtaining five input control variables and eleven system state variables of an autonomous vehicle at current time; constructing a vehicle path planning-tracking integrated state model according to the obtained variables at the current time; enveloping external contours of two autonomous vehicles using elliptical envelope curves to determine elliptical vehicle envelope curves of the two autonomous vehicles, respectively; determining time to collision (TTC) between the vehicles according to elliptical vehicle envelope curves and vehicle driving states; establishing an objective function of a model prediction controller (MPC) according to the model; and solving the objective function based on the TTC, and determining input control variables to the MPC at the next time. Autonomous vehicle collision avoidance can be achieved according to the present disclosure.

A method and system for determining lane change feasibility for autonomous vehicles is disclosed. The method includes the steps of tracking, in each frame, at least one neighboring vehicle from a plurality of neighboring vehicles in a current lane being used by the AV and in a plurality of adjacent lanes. The method further includes determining, in each frame, a set of kinematic parameters associated with each of the at least one neighboring vehicle, and assigning an occupancy state from a plurality of occupancy states to each of a plurality of voxels capturing a spatial information for each of the plurality of neighboring vehicles. The method may further includes determining an effective occupancy probability for each of the plurality of adjacent lanes, and determining feasibility of lane change for the AV to at least one adjacent lane from the plurality of adjacent lanes.

A non-transitory computer readable storage medium has instructions executed by a processor to obtain the relative speed between a first traffic object and a second traffic object. The separation distance between the first traffic object and the second traffic object is received. The relative speed and the separation distance are combined to form a quantitative measure of hazard encountered by the first traffic object. The obtain, receive and combine operations are repeated to form cumulative measures of hazard associated with the first traffic object. The cumulative measures of hazard are analyzed to derive a first traffic object safety score for the first traffic object.

The present disclosure provides a method and device for predicting a motion track of an obstacle and an autonomous vehicle, and relates to the technical field of autonomous driving, so as to at least solve the technical problem of low prediction precision of a motion track of an obstacle in an interaction scene. A specific implementation solution includes: environment information in a target scene, historical state information of a target obstacle and track planning information of a target vehicle are obtained, and the target obstacle is a potential interaction object of the target vehicle; and a motion track of the target obstacle is predicted based on the environment information, the historical state information and the track planning information.

A control device for collision avoidance assistance includes a processor. The processor is configured to execute region setting processing for setting an assistance determination region indicating a particular region forward of a vehicle, and to execute accumulation processing in which the processor gives an object a determination value and accumulates the determination value. The object is located in the assistance determination region. The determination value is decided according to the position of the object. The processor is configured to perform collision avoidance assistance control of assisting in avoidance of collision of the vehicle and the object based on driving environment information indicating a driving environment of the vehicle, when a cumulative value regarding the object calculated in the accumulation processing exceeds a predetermined threshold value.