A method includes receiving image data associated with an image of a roadway including a crosswalk, generating a plurality of different characteristics of the image based on the image data, determining a position of the crosswalk on the roadway based on the plurality of different characteristics, the position including a first boundary and a second boundary of the crosswalk in the roadway, and providing map data associated with a map of the roadway, the map data including the position of the crosswalk on the roadway in the map. The plurality of different characteristics include a classification of one or more elements of the image, a segmentation of the one or more elements of the image, and one or more angles of the one or more elements of the image with respect to a line in the roadway.

A device for identifying water on a surface, including an optical sensor and a processor. The optical sensor is configured to produce a first image of the surface which has a first optical bandwidth within which the water has a first absorption rate, and a second image of the surface which has a second optical bandwidth within which the water has a second absorption rate that is higher than the first absorption rate. The processor is configured to combine the first image and the second image to produce a combined image in which the surface is reduced or eliminated as compared to the water. In addition, the processor is configured to detect water in the combined image.

A lane detection method integratedly using image enhancement and a deep convolutional neural network. On the assumption that lanes have similar widths in a local region of an image and a lane can be segmented into several image blocks, each of which contains lane marking in the center, a method based on a deep convolutional neural network is provided to detect lane marking blocks in the image. Input to the model includes road images captured by a camera as well as a set of enhanced images generated by the contrast limited adaptive histogram equalization (CLAHE) algorithm. The method according to the present disclosure can effectively overcome difficulties of lane detection under complex imaging conditions, such as poor image quality, and small lane marking targets, so as to achieve better robustness.

A system for navigating a host vehicle may include memory and at least one processor configured to receive a plurality of images acquired by a camera onboard the host vehicle; generate, based on analysis of the plurality of images, a road geometry model for a segment of road forward of the host vehicle; determine, based on analysis of at least one of the plurality of images, one or more indicators of an orientation of the host vehicle; and generate, based on the one or more indicators of orientation of the host vehicle and the road geometry model for the segment of road forward of the host vehicle, one or more output signals configured to cause a change in a pointing direction of a movable headlight onboard the host vehicle.

Processes for automatically identifying road surfaces and related features such as roadside poles, trees, road dividers and walls from mobile LiDAR point cloud data. The processes use corresponding image data to improve feature identification.

A system for quantitively determining quality for pavement markings disposed along pavement on a roadway includes one or more controllers in wireless communication with a plurality of vehicles. The one or more controllers receive image data represents the pavement markings disposed along the pavement collected by the plurality of vehicles. The one or more controllers execute instructions to determine at least one of a color distance measurement between a mean color space value of the pavement markings and an ideal marking color space value and a marking intensity contrast ratio between the pavement markings and the pavement.

A method and device for determining an ego-motion of a vehicle are disclosed. Respective sequences of consecutive images are obtained from a front view camera, a left side view camera, a right side view camera and a rear view camera and merged. A virtual projection of the images to a ground plane is provided using an affine projection. An optical flow is determined from the sequence of projected images, an ego-motion of the vehicle is determined from the optical flow and the ego-motion is used to predict a kinematic state of the car.

In some examples, one or more processors may receive at least one first visible light image and a first thermal image. Further, the processor(s) may generate, from the at least one first visible light image, an edge image that identifies edge regions in the at least one first visible light image. At least one of a lane marker or road edge region may be determined based at least in part on information from the edge image. In addition, one or more first regions of interest in the first thermal image may be determined based on at least one of the lane marker or the road edge region. Furthermore, a gain of a thermal sensor may be adjusted based on the one or more first regions of interest in the first thermal image.

Provided are systems and methods for detecting a vehicle with sensors that are not calibrated properly and calibrating such sensor in real-time. In one example, a method may include iteratively capturing sensor data of a road while the vehicle is travelling on the road; monitoring a calibration of the sensors of the vehicle based on the sensor data, determining that the sensors of the vehicle are not calibrated properly based on the monitoring, generating a calibration target of an object on the road based on the sensor data, and adjusting a calibration parameter of the one or more sensors of the vehicle based on the generated calibration target.

A three-dimensional object detection device includes an image capturing unit, an image conversion unit, a three-dimensional object detection unit and a light source detection unit. The image conversion unit converts a viewpoint of the images obtained by the image capturing unit to create bird's-eye view images. The three-dimensional object detection unit detects a presence of a three-dimensional object within the adjacent lane. The three-dimensional object detection unit determines the presence of the three-dimensional object within the adjacent lane-when the difference waveform information is at a threshold value or higher. The three-dimensional object detection unit set a threshold value lower so that the three-dimensional object is more readily detected in a rearward area than forward area with respect to a line connecting the light source and the image capturing unit.