A lane mark recognition device detects an edge located in a proximal area in front of the vehicle, and determines a lane mark candidate on the basis of the detected edge and an edge that is located in a distal area farther than the proximal area in front of the vehicle and that is continuous with the edge. Therefore, an edge of another vehicle (leading vehicle) or the like located in the distal area in a captured image is not detected as a lane mark candidate (is excluded as a non lane mark candidate).

A system and method for large-scale lane marking detection using multimodal sensor data are disclosed. A particular embodiment includes: receiving image data from an image generating device mounted on a vehicle; receiving point cloud data from a distance and intensity measuring device mounted on the vehicle; fusing the image data and the point cloud data to produce a set of lane marking points in three-dimensional (3D) space that correlate to the image data and the point cloud data; and generating a lane marking map from the set of lane marking points.

A road shape estimation device includes processing circuitry to detect, from received signals of radio waves reflected by an object present around a vehicle, reflection points each indicating a reflection position of each radio wave on the object, to perform classification of reflection points of an object present in a left side area of the vehicle into a first group, and of reflection points of an object present in a right side area of the vehicle into a second group, to perform translation of each reflection point classified into the first group to a right direction of the vehicle, and perform translation of each reflection point classified into the second group to a left direction of the vehicle, and to calculate an approximate curve representing a point cloud including all reflection points after the translation and perform estimation of a shape of the road from the approximate curve.

A hybrid approach for using reference frames is presented in which a series of anchor frames is used, effectively resetting a global frame upon a trigger event. With each new anchor frame, parameter values for lane boundary estimates (known as lane boundary states) can be recalculated with respect to the new anchor frame. Triggering events may a based on a length of time, distance traveled, and/or an uncertainty value.

An apparatus and method automatically detects and positions structure faces. After receiving data points describing a geographical area, neighborhoods are defined based on the data points and classified as linear, planar, or volumetric. Neighborhoods are merged into at least one cluster based on local surface normals. At least one bounding frame is fit to the at least one cluster and modified based on a field of interest.

Disclosed are methods, systems, and non-transitory computer readable media that control an autonomous vehicle via at least two sensors. One aspect includes capturing an image of a scene ahead of the vehicle with a first sensor, identifying an object in the scene at a confidence level based on the image, determining the confidence level of the identifying is below a threshold, in response to the confidence level being below the threshold, directing a second sensor having a field of view smaller than the first sensor to generate a second image including a location of the identified object, further identifying the object in the scene based on the second image, controlling the vehicle based on the further identification of the object.

A driving determination device includes an acquirer configured to acquire at least a captured image of a driving body in a driving direction and information that changes with movement of the driving body; a driving level calculator configured to calculate a driving level for evaluating a driving method for the driving body for each predetermined determination item, using at least one of the acquired captured image and the acquired information that changes with the movement of the driving body; an itemized calculator configured to calculate values based on a plurality of the calculated driving levels for each determination item; and an evaluation result calculator configured to calculate a value for comprehensively evaluating the driving method for the driving body, using the values based on the driving levels for each determination item.

An embodiment vehicle includes a camera having an external field of view with respect to the vehicle and a controller configured to process image data obtained by the camera to determine a wheel alignment state of the vehicle and detect an abnormality in wheel alignment of the vehicle by comparing an initial wheel alignment state of the vehicle with a driving wheel alignment state obtained while the vehicle is being driven.

An apparatus for ascertaining, from at least one image, a delimited region and/or a motion path of at least one object includes at least one artificial neural network (ANN) made up of several successive layers. The first layer of the ANN receives as input the at least one image or a part thereof. The second layer supplies as output a boundary line of the delimited region, a linear course of the motion path, or a portion of the boundary line or motion path. The dimensionality of the second layer is lower than the dimensionality of the first layer.

Systems and methods of tracking a road boundary are provided. According to one aspect, a method of tracking a road boundary may include capturing an image from a camera, identifying a pair of regions of interest (ROI) in the image on each side of a candidate boundary position, extracting a color profile from each of the ROIs, generating a weighted color difference score by comparing the color profiles and weighting a difference between the color profiles based on a color similarity between colors in the color profiles, and outputting a determination of a detected boundary based upon the weighted color difference score.