A system for navigating a host vehicle includes at least one electronic horizon processor to access a map representative of at least a road segment on which the host vehicle travels or is expected to travel, wherein the map includes one or more splines representative of road features associated with the road segment, localize the host vehicle relative to a drivable path for the host vehicle represented among the one or more splines, determine a set of points associated with the one or more splines based on the localization of the host vehicle relative to the drivable path for the host vehicle, and generate a navigation information packet including information associated with the one or more splines and the determined set of points relative to the one or more splines.

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.

A monitoring device receives an image captured by an image capturing device mounted on a vehicle. The monitoring device performs a monitoring process for a preset monitoring range based on the captured image received from the image capturing unit. The monitoring device displays a plurality of menus indicating different monitoring ranges on a display device, and sets a monitoring range corresponding to a menu selected by the user from the plurality of menus as the monitoring range in the monitoring process.

A feature mapping computer system configured to (i) receive a localized image including a photo depicting a driving environment and location data associated with the photo, (ii) identify, using an image recognition module, a roadway feature depicted in the photo, (iii) generate, using a photogrammetry module, a point cloud based upon the photo and the location data, wherein the point cloud comprises a set of data points representing the driving environment in a three dimensional (“3D”) space, (iv) localize the point cloud by assigning a location to the point cloud based upon the location data, and (v) generate an enhanced base map that includes a roadway feature.

Examples disclosed herein may involve (i) obtaining a first layer of map data associated with sensor data capturing a geographical area, the first layer of map data comprising an aggregated overhead-view image of the geographical area, where the aggregated overhead-view image is generated from aggregated pixel values from a plurality of images associated with the geographical area, (ii) obtaining a second layer of map data, the second layer of map data comprising label data for the geographical area derived from the aggregated overhead-view image of the geographical area, and (iii) causing the first layer of map data and the second layer of map data to be presented to a user for curation of the label data.

Systems and methods is provided for road hazard analysis. The method includes obtaining sensor data of a road environment including a road and observable surroundings, and applying labels to the sensor data. The method further includes training a first neural network model to identify road hazards, training a second neural network model to identify faded lane markings, and training a third neural network model to identify overhanging trees and blocking foliage. The method further includes implementing the trained neural network models to detect road hazards in a real road setting.

A navigation system for a host vehicle may include a processing device including circuitry and a memory storing instructions that when executed by the circuitry cause the at least one processing device to receive images acquired by a camera representative of an environment of the host vehicle, and analyze the images to identify a double merge scenario including a first flow of traffic and a second flows of traffic in a same direction that merge to form a merged flow of traffic in a merged lane. The instructions that when executed by the circuitry may further cause the processing device to cause a navigational change in the host vehicle based on a trajectory of a first target vehicle in the first flow of traffic and a trajectory of a second target vehicle in the second flow of traffic.

An image processor includes an imaging device that captures an image of a road surface around a vehicle V, and a control portion that detects a marker drawn on the road surface from the captured image. The control portion connects a plurality of broken markers to create a single marker when the detected marker is broken into plural.

A number-of-occupants detection system (1) includes an image correction unit (120) that generates a corrected image based on an image generated by an imaging unit (110) that images a vehicle traveling on a road having a plurality of lanes by executing correction processing on the image for reducing blurring of a subject captured in the image using a parameter according to a position where the vehicle travels, and a count unit (130) that counts the number of occupants of the vehicle using the corrected image generated by the image correction unit (120).

A method of processing an image of a scene including a road acquired by a vehicle-mounted camera to generate boundary data indicative of a boundary of an image region which represents an unoccupied area of the road, comprising: generating (S10) an LL sub-band image of an N.sup.th level of an (N+1)-level discrete wavelet transform, DWT, decomposition of the image by iteratively low-pass filtering and down-sampling the image N times, where N is an integer equal to or greater than one; generating (S20) a sub-band image of an (N+1).sup.th level by high-pass filtering the LL sub-band image of the N.sup.th level, and down-sampling a result of the high-pass filtering, such that the sub-band image of the (N+1).sup.th level has a pixel region having substantially equal pixel values representing the unoccupied area of the road in the image; and generating (S30) the boundary data by determining a boundary of the pixel region.