Technologies and techniques for capturing image material for monitoring image-analyzing systems, wherein an object is monitored to determine if it has been correctly recognized by the image-analyzing system in respect of time or location. The images captured are recorded in a memory. When a discrepancy is determined, in object recognition beyond a tolerance limit over a temporal or locational reference, select images or image sections are archived for more precise inspection.

A LiDAR sensor three-dimensionally scans laser light outward of the vehicle and receives reflected light. A LiDAR data analyzer groups three-dimensional points of the reflected light acquired by the LiDAR sensor into clusters. A precise docking control start determination unit compares shapes and reflectance distributions between at least one of the clusters obtained by the LiDAR data analyzer and a reference cluster representing a stop target to determine whether or not the at least one cluster includes the stop target.

A detection apparatus detects a tilt of a LiDAR apparatus that is mounted to a vehicle. The detection apparatus includes a distance image acquiring unit, a surroundings information acquiring unit, and a detecting unit. The distance image acquiring unit acquires a distance image that is expressed by a detection point group that is detected by the LiDAR apparatus. The surroundings information acquiring unit acquires surroundings information on a periphery of a traveling route of the vehicle. The detecting unit that determines the tilt of the LiDAR apparatus based on the acquired distance image and the acquired surroundings information.

A method of processing image data in a connectionist network includes: determining, a plurality of offsets, each offset representing an individual location shift of an underlying one of the plurality of output picture elements, determining, from the plurality of offsets, a grid for sampling from the plurality of input picture elements, wherein the grid comprises a plurality of sampling locations, each sampling location being defined by means of a respective pair of one of the plurality of offsets and the underlying one of the plurality of output picture elements, sampling from the plurality of input picture elements in accordance with the grid, and transmitting, as output data for at least a subsequent one of the plurality of units of the connectionist network, a plurality of sampled picture elements resulting from the sampling, wherein the plurality of sampled picture elements form the plurality of output picture elements.

In various examples, one or more deep neural networks (DNNs) are executed to regress on control points of a curve, and the control points may be used to perform a curve fitting operation—e.g., Bezier curve fitting—to identify landmark locations and geometries in an environment. The outputs of the DNN(s) may thus indicate the two-dimensional (2D) image-space and/or three-dimensional (3D) world-space control point locations, and post-processing techniques—such as clustering and temporal smoothing—may be executed to determine landmark locations and poses with precision and in real-time. As a result, reconstructed curves corresponding to the landmarks—e.g., lane line, road boundary line, crosswalk, pole, text, etc.—may be used by a vehicle to perform one or more operations for navigating an environment.

A system and method are provided for making a probabilistic determination of the state of a lane transition which may be controlled by a traffic signal, such as a traffic cue. A determination of the state of transition of a traffic signal may be used by a vehicle to determine a course of action to take. In making the determination, the states of multiple traffic signals may be combined into one collection of elements that has values associated with the likelihood of the traffic signals being in a given state. Optionally, a determination may be made of whether a traffic signal is occluded.

A circular sign candidate extraction device includes: a memory; and a processor coupled to the memory, the processor being configured to perform processing, the processing including: detecting a circle from a captured image; specifying an annular region surrounded by the detected circle and a concentric circle, the concentric circle being a circle having a radius different from the detected circle; setting one or more pixels among pixels included in the annular region as determination pixels; and extracting a circular sign candidate from the detected circle in accordance with comparison between a color of the determination pixel and a predetermined color.

A vehicle control apparatus includes an actuator used for traveling, an output device outputting information, and a microprocessor and a memory coupled to the microprocessor. The microprocessor performs capturing an image ahead of the vehicle, recognizing a traffic sign included in an imaging range of the captured image, controlling at least one of the output device and the actuator based on information of the recognized traffic sign, and determining whether the vehicle is traveling in a restricted section in which a predetermined travel restriction including at least one of a vehicle speed limit and overtaking limit is imposed temporarily. The microprocessor performs the controlling including controlling, when it is determined that the vehicle is traveling in the restricted section, at least one of the output device and the actuator without using the information of the traffic sign even if the traffic sign is recognized.

The present invention discloses a method for updating road signs and markings on the basis of monocular images, comprising the following steps: acquiring street images of urban roads and GPS phase center coordinates and spatial attitude data corresponding to the street images; extracting coordinates of the road sign marking images; constructing a sparse three-dimensional model, and then generating a streetscape image depth map; calculating the space position of the road sign and marking according to the semantic and depth values of the image, the collinear equation and the space distance relation; if the same road sign and marking is visible in multiple views, solving the position information of the road sign; and vectorizing the obtained road sign position information, and fusing the information into the original data to realize the updating of the road sign data.

An apparatus, or corresponding method, for building or updating a map database is described. In one example, the apparatus includes an image correlation module, a training device, and a learned model or neural network. The image correlation module is configured to correlate a first aerial image and terrestrial sensor data collected at a terrestrial vehicle based on at least one control point from the terrestrial data. The learned model training device is configured to define a learned model based using at least one control point from the terrestrial sensor data as ground truth for analysis of the first aerial image. The learned model inference module is configured to receive a second aerial image and apply the learned model on the second aerial image for identification of mapping information for the map data.