A method may include obtaining input information relating to an environment in which an autonomous vehicle (AV) operates, the input information describing at least one of: a state of the AV, an operation of the AV within the environment, a property of the environment, or an object included in the environment. The method may include identifying a first object in the vicinity of the AV based on the obtained input information. The method may include determining a first object rule corresponding to the first object, the first object rule indicating suggested driving behavior for interacting with the first object. The method may include determining a first decision that follows the first object rule and sending an instruction to a control system of the AV, the instruction describing a given operation of the AV responsive to the first object rule according to the first decision.

A pedestrian tracking system includes: a buffer or a memory configured to store a trajectory sequence of a pedestrian; a step attention module and a control module. The step attention module iteratively performs a step attention process to predict states of the pedestrian. Each iteration of the step attention process includes the step attention module: learning the stored trajectory sequence to provide time-dependent hidden states, reshaping each of the time-dependent hidden states to provide two-dimensional tensors; condensing the two-dimensional tensors via convolutional networks to provide convolutional sequences; capturing global information of the convolutional sequences to output a set of trajectory patterns represented by a new sequence of tensors; learning time-related patterns in the new sequence and decoding the new sequence to provide one or more of the states of the pedestrian; and modifying the stored trajectory sequence to include the predicted one or more of the states of the pedestrian.

A pedestrian tracking system includes: a buffer or a memory configured to store a trajectory sequence of a pedestrian; a step attention module and a control module. The step attention module iteratively performs a step attention process to predict states of the pedestrian. Each iteration of the step attention process includes the step attention module: learning the stored trajectory sequence to provide time-dependent hidden states, reshaping each of the time-dependent hidden states to provide two-dimensional tensors; condensing the two-dimensional tensors via convolutional networks to provide convolutional sequences; capturing global information of the convolutional sequences to output a set of trajectory patterns represented by a new sequence of tensors; learning time-related patterns in the new sequence and decoding the new sequence to provide one or more of the states of the pedestrian; and modifying the stored trajectory sequence to include the predicted one or more of the states of the pedestrian.

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.

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.

A method for the preparation of an obstacle map, wherein the obstacle map comprises cells, includes assigning each of the cells to segments of an environment of the vehicle, and assigning to each of the cells information as to whether the corresponding segment of the environment is occupied by an obstacle. The method also includes preparing an environment map that comprises the cells, and determining a threshold value specification, where the threshold value specification specifies different threshold values for the cells of the environment map. The threshold value specification is determined depending on a trajectory of the vehicle. An obstacle map is then determined on the basis of the environment map and the threshold value specification.

A method for operating a control device of a motor vehicle driving by automation. The method includes determining a location of the motor vehicle, and acquiring driving-environment data of the motor vehicle, a control characteristic of the control device of the motor vehicle being formed in such a way that a driving behavior of at least one other road user is influenced in defined manner.

A method for operating a control device of a motor vehicle driving by automation. The method includes determining a location of the motor vehicle, and acquiring driving-environment data of the motor vehicle, a control characteristic of the control device of the motor vehicle being formed in such a way that a driving behavior of at least one other road user is influenced in defined manner.

Systems, components, and methodologies are provided for improvements in operation of automotive vehicles by enabling monitoring analysis and reaction to subtle sources of information that aid in prediction and response of vehicle control systems across a range of automation levels. Such systems, components, and methodologies include wheel-turn detection equipment for detecting a wheel angle of another vehicle to trigger a vehicle control system to perform an operation based on the detected wheel angle of the other vehicle.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.