A host vehicle position estimation device includes a target recognition unit configured to recognize a relative position of the vertical position target relative to a host vehicle on the basis of a detection result of an external sensor of the host vehicle, an amount-of-position error calculation unit configured to calculate an amount of position error, an amount-of-steady error calculation unit configured to calculate an amount of steady error from a distribution of the amount of position error when it is determined that the distribution of the amount of position error satisfies a steady condition, and a host vehicle position estimation unit configured to perform estimation of a vertical position of the host vehicle using the amount of steady error when the relative position of the vertical position target relative to the host vehicle is not recognized.

An object detection device includes a processor configured to calculate, for each of a plurality of regions in a detection range of the sensor represented in the newest sensor signal among a plurality of sensor signals in time-series acquired by a sensor, a confidence indicating a degree of certainty that an object to be detected is represented in the region; track a first object which has been detected, to detect, in the newest sensor signal, a passed region through which the first object has passed; control, for each of the plurality of regions in the newest sensor signal, a confidence threshold according to whether or not the region is included in the passed region, and detect a second object in a region, among the plurality of regions, with respect to which the confidence for the second object is equal to or higher than the confidence threshold.

An imaging abnormality diagnosis device includes one or more processors configured to: detect a lane line in an image of a road surface captured by a camera mounted on a vehicle; estimate a shape of a road on which the vehicle is traveling, based on an output of a detector mounted on the vehicle, the detector being other than the camera; and detect as a distorted area an area in the image where the shape of the road and a shape of the lane line do not coincide with each other.

The disclosure provides a method for operating an autonomous vehicle. To operate the autonomous vehicle, a plurality of lane segments that are in an environment of the autonomous vehicle is determined and a first object and a second object in the environment are detected. A first position for the first object is determined in relation to the plurality of lane segments, and particular lane segments that are occluded by the first object are determined using the first position. According to the occluded lane segments, a reaction time is determined for the second object and a driving instruction for the autonomous vehicle is determined according to the reaction time. The autonomous vehicle is then operated based on the driving instruction.

A lane-line recognizing apparatus for a vehicle includes an edge-point detector and an approximate-line calculation processor. The edge-point detector is configured to detect edge points on the basis of brightness variation within a detection region for a lane line. The approximate-line calculation processor is configured to calculate an approximate line of a point group including the edge points. The lane-line recognizing apparatus has: a first mode in which the lane-line recognizing apparatus is configured to mainly search for standard edge candidate points having brightness relatively high; and a second mode in which the lane-line recognizing apparatus is configured to mainly search for opposite edge candidate points having brightness relatively low. The lane-line recognizing apparatus is configured to selectively perform switching between the first and the second modes in accordance with the number of the detected opposite edge candidate points.

A method for providing data for creating a digital map. The method includes: detecting surroundings sensor data of the surroundings during a measuring run of a physical system, preferably a vehicle, the surroundings sensor data capturing the surroundings in an at least partially overlapping manner, first surroundings sensor data including three-dimensional information, and second surroundings sensor data including two-dimensional information; extracting, with the aid of a first neural network situated in the physical system, at least one defined object from the first and second surroundings sensor data into first extracted data; and extracting, with the aid of a second neural network situated in the physical system, characteristic features including descriptors from the first extracted data into second extracted data, the descriptors being provided for a defined alignment of the second extracted data in a map creation process.

A controller is provided that includes a bit plane generation component and a processor configured to receive one or more headlight commands and to configure the bit plane generation component to generate bit planes of a headlight frame responsive to the one or more headlight commands, wherein the bit plane generation component includes bit generation pipelines configured to operate in parallel to generate respective bits of consecutive bits of a bit plane of the headlight frame.

Systems and methods involve obtaining observation points of a lane line using one or more sensors of a vehicle. Each observation point indicates a location of a point on the lane line. A method includes obtaining uncertainty values, each uncertainty value corresponding with one of the observation points. A lane model is generated or updated using the observation points. The lane model indicates a path of the lane line. An uncertainty model is generated or updated using the uncertainty values corresponding with the observation points. The uncertainty model indicates uncertainty associated with each portion of the lane model.

An apparatus, system, method, and/or non-transitory computer readable media of a distance estimation apparatus including at least one camera includes obtaining a bounding box corresponding to a target vehicle on the basis of an image obtained through the at least one camera, obtaining a first rectilinear distance to the target vehicle, obtaining a first world width on the basis of the first rectilinear distance and a width of the bounding box, obtaining a second ratio of a region, corresponding to a rear surface of the target vehicle, of a region of the bounding box on the basis of a first ratio, and calculating a second world width of the target vehicle on the basis of the second ratio, wherein the first ratio represents a ratio of the rear surface and a side surface of the target vehicle.

Techniques are discussed for determining a location of a vehicle in an environment using a feature corresponding to a portion of an image representing an object in the environment which is associated with a frequently occurring object classification. For example, an image may be received and semantically segmented to associate pixels of the image with a label representing an object of an object type (e.g., extracting only those portions of the image which represent lane boundary markings). Features may then be extracted, or otherwise determined, which are limited to those portions of the image. In some examples, map data indicating a previously mapped location of a corresponding portion of the object may be used to determine a difference. The difference (or sum of differences for multiple observations) are then used to localize the vehicle with respect to the map.