Certain aspects of the present disclosure provide a method for lane marker detection, including: receiving an input image; providing the input image to a lane marker detection model; processing the input image with a shared lane marker portion of the lane marker detection model; processing output of the shared lane marker portion of the lane marker detection model with a plurality of lane marker-specific representation layers of the lane marker detection model to generate a plurality of lane marker representations; and outputting a plurality of lane markers based on the plurality of lane marker representations.

A method for locating an object of interest using offset. The object may be a mobile platform, or portion of same, associated with a vehicle, or a pavement segment or feature of or on a pavement segment on which the mobile platform is located. The vehicle includes first and second fixed points having a known offset from each other. An image sensor whose field of view includes the second fixed point and a portion of the mobile platform provides image data which is used with the known offset to calculate the precise location of the object of interest.

A control method of a vehicle may include generating navigation information based on destination information and current location information; determining whether a lane to be driven is a merge lane based on the generated navigation information, map information, and the current location information; recognizing a lane in an image acquired by an imaging device; recognizing a driving first lane based on the recognized location information of the lane; dividing a certain area including the merge lane into an entry section, a merge section, and a stabilization section when the first lane converges with a second lane; generating a driving route by performing curve fitting for route points in the entry section and the merge section; and controlling autonomous driving based on the generated driving route.

A method, apparatus and computer program product are provided to estimate the location of a vehicle based at least in part upon two or more road signs that are depicted by one or more images captured by one or more image capture devices onboard the vehicle. By relying at least in part upon the two or more road signs, the location of the vehicle may be refined or otherwise estimated with enhanced accuracy, such as in instances in which there is an inability to maintain a line-of-sight with the satellites of a satellite positioning system or otherwise in instances in which the location estimated based upon reliance on satellite or radio signals is considered insufficient. As a result, the vehicle may be navigated in a more informed and reliable manner and the relationship of the vehicle to other vehicles may be determined with greater confidence.

A vehicle position estimation device is configured to acquire a distance from a road edge to a subject vehicle using at least one of an imaging device or a distance measuring sensor, acquire position information of lane boundary lines detected by analyzing the images captured by the imaging device; acquire map information including a lane quantity of a traveling road of the subject vehicle from a map storage, calculate, as a roadside area width, a lateral direction distance between an outermost detection line, which is an outermost boundary line among the detected boundary lines, and the road edge, and specify a traveling lane of the subject vehicle based on (i) the distance from the road edge to the subject vehicle, (ii) the roadside area width, and (iii) the lane quantity included in the acquired map information.

A process for identifying a road feature in an image includes receiving an image at a controller, identifying a region of interest within the image and converting the region of interest from red-green-blue (RGB) to a single color using the controller. A set edges is detected within the region of interest, and at least one line within the set of edges is identified using the controller. The at least one line is compared with a set of known and expected road marking features, and the set of at least one first line in the at least one line is identified as corresponding to a road feature in response to the at least the first line matching the set of known and expected road marking features.

An object detection device includes a processor. The processor acquires or generates a first disparity image generated on the basis of an output of a stereo camera mounted in a mobile object. In the first disparity image, pixels representing disparities are arranged on a two-dimensional plane formed by a first direction corresponding to a base-length direction of the stereo camera and a second direction intersecting the first direction. The processor approximates a relationship between a coordinate, in the second direction, of a road surface in a direction of travel of the mobile object and a disparity representing the road surface with two straight lines, the relationship being included in the first disparity image.

A method and an apparatus for generating a high-precision map includes sampling road data for generating the high-precision map, and obtaining a sampling trajectory, wherein the sampling trajectory comprises sampling location points and road data corresponding to the sampling location points, obtaining a navigation map comprising first road elements, and generating a target high-precision map by associating the first road elements with the high-precision map based on the sampling trajectory.

The disclosure provides methods and apparatuses for determining a height of a road target in a driving process. One example method includes determining a target object on a road, and determining a height threshold of the road based on the target object, to obtain a maximum allowed height of the road.

A processing device includes a first processor configured to detect a bounding box of a distant vehicle, in an input image generated by imaging the distant vehicle, and extract at least one feature of the distant vehicle. A second processor is configured to estimate a geometry of a road on which the distant vehicle is located, based on a position of at least one feature relative to at least a portion of the bounding box.