According to one embodiment, a system for determining a position of a vehicle includes an image sensor, a top-down view component, a comparison component, and a location component. The image sensor obtains an image of an environment near a vehicle. The top-down view component is configured to generate a top-down view of a ground surface based on the image of the environment. The comparison component is configured to compare the top-down image with a map, the map comprising a top-down light LIDAR intensity map or a vector-based semantic map. The location component is configured to determine a location of the vehicle on the map based on the comparison.

To improve estimation accuracy of a self-position. Light at a predetermined wavelength is projected. An image of a reflector with a reflectance higher than a predetermined reflectance is taken by receiving reflected light of the projected light reflected by the reflector. Own orientation is estimated on the basis of the taken image of the reflector. As a result, the self-position can be highly accurately estimated on the basis of the reflector even at night. The present disclosure can be applied to an on-board system.

A system for detection and identification of objects and obstacles near, between or on railway comprise several forward-looking imagers adapted to cover each different range forward and preferably to be sensitive each to different wavelength of radiation, including visible light, LWIR, and SWIR. The substantially homogeneous temperature along the rail the image of which is included in an imager frame assists in identifying and distinguishing the rail from the background. Image processing is applied to define living creature in the image frame and to distinguish from a man-made object based on temperature of the body. Electro optic sensors (e.g. thermal infrared imaging sensor and visible band imaging sensor) are used to survey and monitor railway scenes in real time.

The present disclosure generally relates to processing visual data of a road surface that includes a vertical deviation with a lateral slope. In some embodiments, a system determines a path expected to be traversed by at least one wheel of the vehicle on a road surface. In some embodiments, a system determines, using at least two images captured by one or more cameras, a height of the road surface for at least one point along the path to be traversed by the wheel. In some embodiments, a system computes an indication of a lateral slope of the road surface at the at least one point along the path. In some embodiments, a system outputs, on a vehicle interface bus, an indication of the height of the point and an indication of the lateral slope at the at least one point along the path.

Systems and methods are provided for controlling vehicle operation. A processor may access route information for navigation of a route by the vehicle including data relating to speed along the route and calculate a speed of the vehicle along the route based on the route information. The processor may cause the vehicle to be operated at the calculated speed along the route; obtain dynamic information for the route based on data collected from one or more other vehicles on the route and indicating current conditions on the route which affect the speed of the vehicle along the route; and cause the vehicle to be operated at an updated speed along the route, based on the dynamic information.

A vehicle camera calibration apparatus and method are provided. The vehicle camera calibration apparatus includes a camera module configured to acquire an image representing a road from a plurality of cameras installed in a vehicle, an input/output module configured to receive, as an input, the acquired image from the camera module, or output a corrected image, a lane detection module configured to detect a lane and extract a feature point of the lane from an image received from the input/output module, and a camera correction module configured to estimate a new external parameter using a lane equation and a lane width based on initial camera information and external parameter information in the image received from the input/output module, and to correct the image.

A system and method for determining car to lane distance is provided. In one aspect, the system includes a camera configured to generate an image, a processor, and a computer-readable memory. The processor is configured to receive the image from the camera, generate a wheel segmentation map representative of one or more wheels detected in the image, and generate a lane segmentation map representative of one or more lanes detected in the image. For at least one of the wheels in the wheel segmentation map, the processor is also configured to determine a distance between the wheel and at least one nearby lane in the lane segmentation map. The processor is further configured to determine a distance between a vehicle in the image and the lane based on the distance between the wheel and the lane.

Systems and methods for detecting trailer angle are provided. In one aspect, an in-vehicle control system includes an optical sensor configured to be mounted on a tractor so as to face a trailer coupled to the tractor, the optical sensor further configured to generate optical data indicative of an angle formed between the trailer and the tractor. The system further includes a processor and a computer-readable memory in communication with the processor and having stored thereon computer-executable instructions to cause the processor to receive the optical data from the optical sensor, determine at least one candidate plane representative of a surface of the trailer visible in the optical data based on the optical data, and determine an angle between the trailer and the tractor based on the at least one candidate plane.

The present disclosure provides a method for pose determination. The method may include obtaining a first pose of a subject at a first time point. The method may further include retrieving one or more first features associated with a road from a database. The road may be viewable by the subject at the first pose and at the first time point. The method may further include obtaining an image captured by the subject at a second time point. The method may further include extracting one or more second features associated with the road from the image. The method may also include determining a second pose of the subject at the second time point at least based on comparing the one or more first features associated with the road with the one or more second features associated with the road.

A system and method for estimating free space including applying a machine learning model to camera images of a navigation area, where the navigation area is broken into cells, synchronizing point cloud data from the navigation area with the processed camera images, and associating probabilities that the cell is occupied and object classifications of objects that could occupy the cells with cells in the navigation area based on sensor data, sensor noise, and the machine learning model.