A vehicle control device includes: a recognizer configured to recognize a surrounding situation of a vehicle; and a driving controller configured to control a speed and steering of the vehicle according to a recognition result of the recognizer. The driving controller is configured to stop following travel of following a front vehicle recognized by the recognizer according to a state of a target recognized by the recognizer in a traveling region through which the front vehicle has passed when the following travel is performed. The target is a regulation sign for regulating a part of a lane in which a vehicle is traveling, a regulation sign for regulating a lane, or a guiding sign for guiding a visual line of an occupant of the vehicle.

The inventive subject matter provides devices and methods for improving driving safety using information obtained from images taken by a camera in a device. Among other things, contemplated devices and methods can produce alarms when it appear from rates of speed and acceleration/deceleration, that a vehicle will likely go through a red light, or fail to stop or sufficiently slow down at a stop sign. A significant feature of preferred devices and methods is that they are not dependent on WI-FI, cellular, satellite and radio signals, or any other external data transmission, and are not even dependent on speed, acceleration, or other information native to the vehicle.

A method, apparatus and computer program product are provided to estimate the location of a vehicle based at least in part upon two or more road signs that are depicted by one or more images captured by one or more image capture devices onboard the vehicle. By relying at least in part upon the two or more road signs, the location of the vehicle may be refined or otherwise estimated with enhanced accuracy, such as in instances in which there is an inability to maintain a line-of-sight with the satellites of a satellite positioning system or otherwise in instances in which the location estimated based upon reliance on satellite or radio signals is considered insufficient. As a result, the vehicle may be navigated in a more informed and reliable manner and the relationship of the vehicle to other vehicles may be determined with greater confidence.

The described aspects and implementations enable efficient identification of real and image signs in autonomous vehicle (AV) applications. In one implementation, disclosed is a method and a system to perform the method that includes obtaining, using a sensing system of the AV, a combined image that includes a camera image and a depth information for a region of an environment of the AV, classifying a first sign in the combined image as an image-true sign, performing a spatial validation of the first sign, which includes evaluation of a spatial relationship of the first sign and one or more objects in the region of the environment of the AV, and identifying, based on the performed spatial validation, the first sign as a real sign.

In a system for adaptively providing auxiliary driving information, a processing unit, which is installed in a vehicle, acquires traffic sign(s) from vehicle driving images, and determines whether a driver of the vehicle is familiar with a current road section, and whether the driver has good driving habits. Based on the determinations and reference information that is related to a traffic violation history and/or a dangerous situation of the current road section, the processing unit selects prompt object(s) from the traffic sign(s), and determines a prompt order of the prompt object(s). Then, the processing unit causes an output module to perceivably output the prompt objects in the prompt order.

Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a host vehicle may comprise at least one processor. The processor may be programmed to receive from a first camera at least a first captured image representative of an environment of the host vehicle. The processor may be programmed to receive from a second camera at least a second captured image representative of the environment of the host vehicle. Both the first captured image and the second image includes a representation of the traffic light, and wherein the second camera is configured to operate in a primary mode where at least one operational parameter of the second camera is tuned to detect at least one feature of the traffic light. The processor may be further programmed cause at least one navigational action by the vehicle based on analysis of the representation of the traffic light.

A method for detecting a traffic sign using a traffic sign detection device is provided. The method includes acquiring point data from a lidar sensor detecting a vicinity of the lidar sensor; clustering a plurality of points based on positions of the plurality of points included in the point data; determining one or more plane object clusters, each of which corresponds to a plane object, among one or more clusters, each of which is formed by clustering the plurality of points; and detecting one or more traffic sign object clusters, each of which corresponds to the traffic sign, among the one or more plane object clusters based on a predetermined traffic sign detection factor.

Systems and methods of detecting a traffic object outside of a vehicle and controlling the vehicle. The systems and methods receive perception data from a sensor system included in the vehicle, determine a focused Region Of Interest (ROI) in the perception data, scale the perception data of the at least one focused ROI, process the scaled perception data of the focused ROI using a neural network (NN)-based traffic object detection algorithm to provide traffic object detection data, and control at least one vehicle feature based, in part, on the traffic object detection data.

Embodiments of the present disclosure relate to a method and apparatus for generating information. The method can include: acquiring first driving environment data of a target road segment; comparing the first driving environment data with pre-stored second driving environment data of the target road segment, and determining a difference between the first driving environment data and the second driving environment data; and generating, in response to determining the difference satisfying a preset condition, road abnormality information.

A method, data processing apparatus, and computer code for identifying road intersections includes providing location data obtained from at least one vehicle's trajectory, wherein the location data may include geographical data within a geographical perimeter. The method includes determining node vectors by applying a geographical descriptor model on a target location included in the geographical perimeter. The geographical descriptor model includes a plurality of multiscale node descriptors including a target multiscale descriptor and neighboring multiscale descriptors. Each of the plurality of multiscale node descriptors includes at least two shape descriptors of different geographical resolution. Each of the neighboring locations is at a respective geographical distance from the target location. The node vectors may be respectively determined for each of the plurality of multiscale node descriptors. The method includes inputting the node vectors into a trained multiscale classifier including a graph convolutional network to provide a probability of the target location being a road intersection.