A method for a trajectory prediction for a vehicle. The method includes receiving trajectory data of a travel trajectory of a further vehicle driving in a traffic lane within a surroundings of a vehicle; detecting at least one control action performed by the further vehicle based on the trajectory data, the control action representing at least part of a driving maneuver, by which the further vehicle is controlled along the travel trajectory; ascertaining a future travel trajectory of the vehicle by taking into account the detected control action executed by the further vehicle; and providing the future travel trajectory of the further vehicle.

A method for modeling the surroundings of an automated vehicle in which environment information is continuously received from currently available information sources. Each information source provides pieces of environment information. A formal assumption and a formal guarantee is associated with each piece of environment information in such a way that it is guaranteed, if the formal assumption associated with the respective piece of environment information is fulfilled, that the piece of environment information fulfills the formal guarantee associated with it. Each information source provides the associated formal assumptions and formal guarantees for the pieces of environment information it supplies. A piece of environment information is used for calculating the world model at a given point in time only if the formal assumption associated with this piece of environment information is fulfilled at this point in time.

A vehicle control device of an embodiment recognizes that a first other lane is present at a position of a first other vehicle on the basis of a first relative distance between an own vehicle and the first other vehicle, recognizes an advancing direction of the first other lane on the basis of a first relative speed between the own vehicle and the first other vehicle, in a case where the recognized advancing direction of the first other lane is opposite to an advancing direction of an own lane, determines whether or not an adjacent lane of the own lane is an oncoming lane on the basis of at least two of the first relative distance, a second relative speed between the own vehicle and a second other vehicle, and map information including an advancing direction of a lane and the number of lanes, and in a case where it is determined that the adjacent lane is an oncoming lane, continues driving assistance or automated driving.

In various examples, systems and methods are disclosed that perform sensor fusion using rule-based and learned processing methods to take advantage of the accuracy of learned approaches and the decomposition benefits of rule-based approaches for satisfying higher levels of safety requirements. For example, in-parallel and/or in-serial combinations of early rule-based sensor fusion, late rule-based sensor fusion, early learned sensor fusion, or late learned sensor fusion may be used to solve various safety goals associated with various required safety levels at a high level of accuracy and precision. In embodiments, learned sensor fusion may be used to make more conservative decisions than the rule-based sensor fusion (as determined using, e.g., severity (S), exposure (E), and controllability (C) (SEC) associated with a current safety goal), but the rule-based sensor fusion may be relied upon where the learned sensor fusion decision may be less conservative than the corresponding rule-based sensor fusion.

A system for controlling a subject vehicle includes a front detection unit to detect a driving situation of a target vehicle located in front of the subject vehicle; a determination unit to detect reversing of the target vehicle or predict a collision between the target vehicle and the subject vehicle through the front detection unit; and a control unit to, when the reversing of the target vehicle is detected or the collision between target vehicle and the subject vehicle is predicted through the determination unit, generate a warning signal of the subject vehicle or control driving of the subject vehicle so that the subject vehicle avoids the collision with the target vehicle.

In a vehicle measuring device unit, a data processing device includes a plurality of input parts each connected to a different one of the plurality of detectors, an output part connected to a control device provided in a vehicle, and an integrated data generation part that generates integrated data using detection data input from the plurality of detectors via the plurality of input parts and outputs the generated integrated data via the output part. The number of wires connecting the control device with the integrated data generation part is smaller than the number of wires connecting the integrated data generation part with the plurality of detectors.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for agent trajectory prediction using context-sensitive fusion.

A cruise control system for a motor vehicle. The system includes a setpoint value generator for generating a setpoint value determining the vehicle speed, and an actuating device that controls the speed of the vehicle to a setpoint speed through intervention into the drive and/or braking system. The system automatically adapts the setpoint speed to a predicted roadway curvature. The setpoint value generator includes at least two modules working independently from one another, including a base module for generating a base setpoint value and a curve module for generating a curve setpoint value that represents a maximum speed at which a curve having a given curvature may be negotiated without exceeding a predefined limiting value for the lateral acceleration of the vehicle, and includes a fusion module that forms a final setpoint value for the actuating device from the base setpoint value and the curve setpoint value through minimum selection.

A driving assistance device for a vehicle includes an acquisition module configured to acquire information comprising at least one of vehicle state information and environment information surrounding the vehicle; a decision module configured to determine a driving behavior for the vehicle in an autonomous driving mode based on the acquired information; a pre-processing module configured to process the acquired information to identify a respective information category among a plurality of predefined information categories the acquired information belongs to; and a determining module configured to retrieve one or more traffic rules related to the information category the acquired information belongs to from a traffic rule database pre-stored in the driving assistance device; determine whether the driving behavior violates any of the retrieved traffic rules; and determine risks of said driving behavior if it is determined the driving behavior violates at least one of the retrieved traffic rules, said risks comprising one or more of the legal penalty, property damages and personal injury caused by the driving behavior.

According to one embodiment, an apparatus includes an interface to receive sensor data from a plurality of sensors of an autonomous vehicle. The apparatus also includes processing circuitry to apply a sensor abstraction process to the sensor data to produce abstracted scene data, and to use the abstracted scene data in a perception phase of a control process for the autonomous vehicle. The sensor abstraction process may include one or more of: applying a Sensor data response normalization process to the sensor data, applying a warp process to the sensor data, and applying a filtering process to the sensor data.