A driving assistance device comprises an information acquisition unit that acquires movement information including a vehicle category indicating that the other vehicle is of four- or two-wheeled type, and information relating to a position and a speed of the other vehicle. The information acquisition unit determines whether the other vehicle traveling behind the vehicle is of the four- or two-wheeled type on the basis of the vehicle category. If the other vehicle is a four-wheeled vehicle, the information acquisition unit acquires speed information of the four-wheeled vehicle from four-wheeled vehicles located within a first width range in a vehicle width direction of the vehicle. If the other vehicle is a two-wheeled vehicle, the information acquisition unit acquires speed information of the two-wheeled vehicle from two-wheeled vehicles located within a second width range narrower than the first width range in the vehicle width direction of the vehicle.

In an embodiment, a method comprises: receiving ambient sound; determining if the ambient sound includes a siren; in accordance with determining that the ambient sound includes a siren, determining a first location associated with the siren; receiving a camera image; determining if the camera image includes a flashing light; in accordance with determining that the camera image includes a flashing light, determining a second location associated with the flashing light; 3D data; determining if the 3D data includes an object; in accordance with determining that the 3D data includes an object, determining a third location associated with the object; determining a presence of an emergency vehicle based on the siren, detected flashing light and detected object; determining an estimated location of the emergency vehicle based on the first, second and third locations; and initiating an action related to the vehicle based on the determined presence and location.

A data communication acquires a map image, determines high- and low-risk areas in the map, determines whether to transmit data related to the high- or low-risk areas, detects objects around the system, determines a position in the map image for each of the objects detected, determines whether the objects belongs to the high- or low-risk areas, determines a data compression ratio for each of the objects detected, compresses data related to each of the objects, compresses data related to each of the objects belonging to the high-risk area when data related to the high-risk area is determined to be transmitted, compresses data related to each of the objects belonging to the low-risk area when data related to the low-risk area is determined to be transmitted, receives reply data replied in association with the compression data transmitted, and makes a decision in accordance with the reply data.

A method for controlling a vehicle. In the method, data of a digital road map are read in, zones are determined for the digital road map, and possible sequences of trips along a road of the digital road map are ascertained as a function of the determined zones. Furthermore, it is ascertained, as a function of sensor data and/or current driving data of the vehicle, whether a current or predicted traffic situation is outside the possible sequences or corresponds to a possible sequence that is determined as being outside an intended operating range. If the current or predicted traffic situation is outside the possible sequences or corresponds to the possible sequence outside the intended operating range, a measure is determined and the vehicle is controlled as a function of the measure that is taken.

Sensors, including time-of-flight sensors, may be used to detect objects in an environment. In an example, a vehicle may include a time-of-flight sensor that images objects around the vehicle, e.g., so the vehicle can navigate relative to the objects. The sensor may generate first image data at a first configuration and second image data at a second configuration. An estimated depth of an object may be determined from the first image data, and an actual depth of the object may be determined from the second image data, based on the estimated depth. In examples, the first and second configurations have different modulation frequencies such that a nominal maximum depth in the first configuration is greater than the nominal maximum depth in the second configuration.

Included are: a learning history data acquiring unit for acquiring, as learning history data, driving history data obtained when a learning mobile object is operated in a risk-free environment; an imitation learning unit for performing learning for imitating driving of the learning mobile object in the risk-free environment using the learning history data as training data and generating an imitation learning model; a training history data acquiring unit for acquiring, as training history data, driving history data obtained when a mobile object for generating training data is operated in the same environment as the environment in which the learning history data has been acquired; a training data generating unit for estimating whether the training history data matches the learning history data using the training history data as input to the imitation learning model and assigning a label related to risks; and a cognitive learning unit for learning a model for inferring a result for controlling a mobile object to be controlled using at least the label related to risks as training data, on the basis of sensor information of the mobile object to be controlled.

Systems and methods are provided for vehicle navigation. In one implementation, a processing device may be configured to obtain a planned driving action for accomplishing a navigational goal of a host vehicle; receive sensor data from an environment surrounding the host vehicle; identify a target vehicle moving in the environment and a velocity of the target vehicle; calculate a stopping distance and a predicted trajectory for the target vehicle; calculate a planned trajectory for the host vehicle corresponding to the planned driving action; identify an intersection of the planned trajectory for the host vehicle with the predicted trajectory for the target vehicle; determine a braking action of the host vehicle to comply with a safety requirement; and cause the braking action to be applied to decelerate the host vehicle to change the planned trajectory, until the changed trajectory does not intersect the predicted trajectory of the target vehicle.

A method is for controlling the positioning of a motor vehicle on a road including several traffic lanes defined laterally by road marking lines. The method includes receiving a signal originating from a camera equipping the vehicle, analyzing the signal in order to detect the road marking lines, identifying, according to a predefined classification of the types of road marking lines, the type of lines detected defining the vehicle's current traffic lane, determining characteristics of the current traffic lane depending on the type of road marking lines defining it and, if the current traffic lane is one of the left-most lane or the right-most lane in the vehicle's direction of travel, shifting the vehicle towards the distal edge of the road relative to the central axis of the current traffic lane.

Methods of determining which kinematic model an autonomous vehicle (AV) should use to predict motion of a detected moving actor are disclosed. One or more sensors of the AV sensors will detect a moving actor. The AV will assign one or more probable classes to the actor, and it will process the information to determine a kinematic state of the actor. The system will query a library of kinematic models to return one or more kinematic models that are associated with each of the probable classes. The system will apply each of the returned kinematic models to predict trajectories of the actor. The system will then evaluate each of the forecasted trajectories of the actor against the kinematic state of the actor to select one of the returned kinematic models to predict a path for the actor. The system will then use the predicted path to plan motion of the AV.

A driving support apparatus (10) executes collision prevention control for avoiding collision with the object when a possibility of a vehicle (VA) colliding with an object based on object information (e.g., distance, direction, and relative speed) acquired by a millimeter wave radar device (21) and a camera device (22) is high. Further, the driving support apparatus does not execute the collision prevention control when an accelerator pedal operation amount is equal to or larger than a stop threshold value. However, the driving support apparatus executes the collision prevention control even when the accelerator pedal operation amount is equal to or larger than the stop threshold value within a specific period of from a start point at which a predetermined erroneous operation condition is satisfied, to an end point, which is a time point after a predetermined consideration period has elapsed since the erroneous operation condition has no longer been satisfied.