The present disclosure provides a rear cross collision detection system and method. The rear cross collision detection system includes an obstacle detection unit configured to receive an electromagnetic wave reflected from a reflection point of an obstacle in order to detect the position of the obstacle, an identity determination unit configured to, when a plurality of obstacles is detected by the obstacle detection unit, determine whether the plurality of obstacles is the same object based on the positions or speeds of the detected obstacles, and a collision determination unit configured to determine the possibility of a collision with the plurality of obstacles detected by the obstacle detection unit based on the result of the determination by the identity determination unit.

Provided is An apparatus for assisting driving of a host vehicle, the apparatus comprising: a camera mounted to the host vehicle and having a field of view in front of the host vehicle, the camera configured to acquire image data; and a controller including a processor configured to process the image data, and configured to receive a map on which the host vehicle travels from a server, compare a speed limit included in the map with a speed limit included in the image data, and control a display of the host vehicle so as to display the speed limit based on the image data.

A vehicle control apparatus includes an electric motor, an engine, and a control system. The control system executes a first speed mode or a second speed mode as a speed mode of the transmission on the basis of a driving operation performed by a driver, sets a speed ratio on a lower side in the second speed mode than in the first speed mode in a case where an accelerator operation performed by the driver is cancelled, executes a first assist mode or a second assist mode as an assist mode in which the electric motor is brought into a power-running state, and switches the assist mode to the second assist mode in a case where the amount of the accelerator operation is increased greater than a starting threshold while the second speed mode is being executed.

An information processing device is configured to acquire first data related to a driving operation performed in a first vehicle and second data related to a surrounding condition of the first vehicle, and perform driving evaluation for the first vehicle based on the first data and the second data.

A computer includes a processor and a memory, the memory storing instructions executable by the processor to predict a heading angle of a target vehicle, determine a distance between a host vehicle and a center line of the target vehicle based on the predicted heading angle, and perform a threat assessment for the target vehicle when the distance is below a threshold.

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 system of linearizing a trajectory of an autonomous vehicle about a reference path includes a computing device and a computer-readable storage medium. The computer-readable storage medium includes one or more programming instructions that, when executed, cause the computing device to receive a reference path for an autonomous vehicle, where the reference path defines a proposed trajectory for the autonomous vehicle in a Cartesian reference frame, identify an objective based on the received reference path, where the objective comprises a longitudinal component and a lateral component, project the objective into a curvilinear coordinate frame described by the received reference path, decouple the longitudinal component and the lateral component, linearize the lateral component about the reference path, generate a new reference path for the autonomous vehicle by fusing the linearized longitudinal component and the linearized lateral component, and map the new reference path back to the Cartesian reference frame.

A control system and a method for vehicle control in geographical control zones is provided. The control system receives traffic information, including a plurality of image frames of a group of moving objects in a geographical control zone and generates a set of images frames of a first moving object of the group of moving objects based on application of a trained Neural Network (NN) model on the received traffic information. The generated set of image frames corresponds to a set of likely positions of the first moving object at a future time instant. The control system predicts the unsafe behavior of the first moving object based on the generated set of image frames and generates first control information, including an alternate route for a first vehicle in the geographical control zone based on the predicted unsafe behavior. The first vehicle is controlled based on the generated first control information.

An autonomous vehicle (AV) includes features that allows the AV to comply with applicable regulations and statues for performing safe driving operation. An example method for operating the AV includes determining a trajectory related information of a vehicle operating on a roadway on which the AV is operating; receiving sensor data of a first area that includes the vehicle; determining an additional trajectory related information for the AV by comparing the trajectory related information of the vehicle to a current trajectory related information of the AV, wherein the additional trajectory related information is based on a category to which the vehicle belongs, and wherein the additional trajectory related information allows the AV to maintain at least a distance between the AV and the vehicle; and causing the AV to operate in accordance with the additional trajectory related information.

Techniques for vehicle deceleration planning are discussed. The techniques include determining a first location and a first velocity of a vehicle. The techniques further include determining a second location and a second velocity of an object. Based on the first location, the second location, the first velocity, and the second velocity, a relative stopping distance between the vehicle and the object can be determined. If the relative stopping distance is less than a threshold distance, the first maximum deceleration value can be increased to a second maximum deceleration value, and the techniques determine a trajectory for the vehicle based at least in part on the second maximum deceleration value.