Disclosed herein is a method for controlling an autonomous driving vehicle. The vehicle control method includes detecting an eye level of an occupant adjacent to the window through a first camera which captures an image of an inside of the vehicle, setting an area of the window corresponding to the eye level of the occupant to a first area and setting the other remaining area of the window to a second area, and adjusting light transmittance of the window such that light transmittance of the first area is lower than light transmittance of the second area.

A device for assisting in the driving of a motor vehicle includes a line detector for detecting the boundary line of a traffic lane, a setup module capable of establishing a virtual lane from the line detected, a monitoring module capable of monitoring the risk of the motor vehicle leaving the virtual lane established. The device also includes a module for acquiring an image representing a relevant object. The setup module includes a first map containing values of a position of a virtual-lane boundary according to a lateral shift of the relevant object in the acquired image.

Various technologies described herein pertain to generating an occupancy grid movie for utilization in motion planning for the autonomous vehicle. The occupancy grid movie can be generated for a given time and can include time-stepped occupancy grids for future times that are at predefined time intervals from the given time. The time-stepped occupancy grids include cells corresponding to regions in an environment surrounding the autonomous vehicle. Probabilities can be assigned to the cells specifying likelihoods that the regions corresponding to the cells are occupied at the future times. Moreover, cached query objects that respectively specify indices of cells of a grid occupied by a representation of an autonomous vehicle at corresponding orientations are described herein. An occupancy grid for the environment surrounding the autonomous vehicle can be queried to determine whether cells of the occupancy grid are occupied utilizing a cached query object from the cache query objects.

A system and methods are provided for determining a vehicle action during a phantom projection attack, including processing a received image to identify a traffic object, and creating from the received image multiple processed images that are applied to respective neural network (NN) models. Latent representations of the multiple processed images from each of the NN models are then fed to a combiner model trained to determine whether the latent representations indicate a phantom projection attack, and, responsively to a determination of a phantom projection attack, issuing a phantom projection indicator.

An autonomous driving system acquires information concerning a vehicle density in an adjacent lane that is adjacent to a lane on which an own vehicle is traveling, when the own vehicle travels on a road having a plurality of lanes. The autonomous driving system selects the adjacent lane as an own vehicle travel lane, when the vehicle density in the adjacent lane that is calculated from the acquired information is lower than a threshold density that is determined in accordance with relations between the own vehicle and surrounding vehicles. The autonomous driving system performs lane change to the adjacent lane autonomously, or propose lane change to the adjacent lane to a driver, when the adjacent lane is selected as the own vehicle travel lane.

A vehicle and control method include: a seat; an image acquirer to acquire an image of the seat; a first type of roof airbag module in a fixed position in a first area of a headlining; a rail member in a second area of the headlining in a left-right direction of a vehicle body; a second type of roof airbag module in the rail member and movable in left and right directions along the rail member; an angle detector to detect a rotation angle of the seat; and a controller. The controller identifies the seat rotation angle based on the image of the seat and controls activation of at least the first type of roof airbag module or the second type of roof airbag module based on at least the seat rotation angle based on the image of the seat or detected by the angle detector.

A graph representation of a tactical map representing a plurality of static components of an environment of a vehicle is generated. Nodes of the graph represent static components, and edges represent relationships between multiple static components. Different edge types are used to indicate respective relationship semantics among the static components. Individual nodes are represented as having the same number and types of edges in the graph. Using the graph as input to a neural network based model, a set of results is obtained. A motion control directive based at least in part on the results is transmitted to a motion-control subsystem of the vehicle.

A movable object for responding to an object includes a first passive infrared sensor having a first detection range and a first field of view, and one or more second passive infrared sensors each having a second detection range and a second field of view. The second detection range is longer than the first detection range and the second field of view is smaller than the first field of view. The movable object further includes one or more processors configured to recognize the object based on one or more heat signals received from at least one of the first passive infrared sensor or the one or more second passive infrared sensors, and perform a flight response measure to control the movable object based on the recognized object.

The present invention relates to a method for providing a route stipulation for a route system of a vehicle, comprising the following steps: providing a plurality of detected trajectories of further vehicles in a route section to be used, ascertaining a trajectory stipulation from the detected trajectories, ascertaining a deviation zone from the detected trajectories, wherein the deviation zone is determined on the basis of a deviation of at least individual detected trajectories from the trajectory stipulation, determining the route stipulation at least on the basis of the trajectory stipulation and the deviation zone.

A modular mobility base for a modular autonomous bot apparatus transporting an item being shipped including a mobile base platform, a component alignment interface, a mobility controller, a propulsion and steering system, and sensors. The component alignment interface provides an alignment channel into which another modular component can be placed and secured on the platform. The mobility controller generates propulsion control signals for controlling speed of the modular mobility base and steering control signals for navigation of the modular mobility base. The propulsion system is connected to the platform and responsive to the propulsion control signal. The steering system is connected to the mobile base platform and is responsive to the steering control signal to cause changes to directional movement of the modular mobility base. The sensors are disposed on the platform provide feedback sensor data to the mobility controller about a condition of the modular mobility base.