A driving support system includes: an acquisition portion configured to acquire visual-recognition position information on a position where a driver of a vehicle visually recognizes a traffic light; an image acquisition portion configured to acquire a forward image ahead of the vehicle; a traffic-light recognition portion configured to recognize a traffic light included in a forward image; and a notification portion configured to notify the driver of warning when the traffic light is not recognized from the forward image, in a case where the vehicle is present at a position based on the visual-recognition position information.

An image recognition apparatus includes a controller. The controller is configured to perform positional detection and identification for the target in each of the frame images, and extract a first target having an ambience change feature with priority over a second target that does not have the ambience change feature. The ambience change feature is a feature about a positional change of the target that is exhibited when the ambience is photographed from a moving object. The positional change is a positional change of the target identified in common among the frame images.

A plurality of subimages of an image are generated based on output from a random number generator. Each subimage is input to a machine learning program trained to output an object classified in the subimage. When a number of subimages with a same classified object exceeds a threshold, an object in the image is identified as the classified object.

Systems and method for machine-learning assisted road sign content prediction and machine learning training is disclosed. A sign detector model processes images or video with road signs. A visual attribute prediction model extracts visual attributes of the sign in the image. The visual attribute prediction model can communicate with a knowledge graph reasoner to validate the visual attribute prediction model by applying various rules to the output of the visual attribute prediction model. A plurality of potential sign candidates are retrieved that match the visual attributes of the image subject to the visual attribute prediction model, and the rules help to reduce the list of potential sign candidates and improve accuracy of the model.

A navigation system includes a processing unit connected to a storage device for fetching and executing a navigation program to: determine a planned route; obtain a real-time image and real-time positioning data indicating a current location of the system; perform image recognition on the image, and determine whether a specific object exists in the image; determine whether a route adjustment condition is met based on the planned route, the positioning data and result of the determination; and adjust the planned route when the route adjustment condition is met. The route adjustment condition includes that a meaning conveyed by the specific object is in conflict with the planned route.

Systems and methods for constructing and managing a unique road sign knowledge graph across various countries and regions is disclosed. The system utilizes machine learning methods to assist humans when comparing a new sign template with a plurality of stored sign templates to reduce or eliminate redundancy in the road sign knowledge graph. Such a machine learning method and system is also used in providing visual attributes of road signs such as sign shapes, colors, symbols, and the like. If the machine learning determines that the input road sign template is not found in the road sign knowledge graph, the input sign template can be added to the road sign knowledge graph. The road sign knowledge graph can be maintained to add signs templates that are not already in the knowledge graph but are found in real-world by integrating human annotator's feedback during ground truth generation for machine learning.

A driving control method is provided in which a processor configured to control driving of a vehicle acquires detection information around a vehicle on the basis of a detection condition that can be set for each point; extracts events which the vehicle encounters, on the basis of the detection information; creates a driving plan in which a driving action is defined for each of the events on the basis of the detection information acquired in the events; executes a driving control instruction for the vehicle in accordance with the driving plan; and determines the detection condition on the basis of the content of the driving action defined for each of the events.

Visual analytics tool for updating object detection models in autonomous driving applications. In one embodiment, an object detection model analysis system including a computer and an interface device. The interface device includes a display device. The computer includes an electronic processor that is configured to extract object information from image data with a first object detection model, extract characteristics of objects from metadata associated with image data, generate a summary of the object information and the characteristics, generate coordinated visualizations based on the summary and the characteristics, generate a recommendation graphical user interface element based on the coordinated visualizations and a first one or more user inputs, and update the first object detection model based at least in part on a classification of one or more individual objects as an actual weakness in the first object detection model to generate a second object detection model for autonomous driving.

A vehicular control system includes a forward viewing camera disposed at an in-cabin side of a windshield of a vehicle and viewing forward of the vehicle. Road curvature of a road along which the vehicle is traveling is determined responsive at least in part to processing by an image processor of image data captured by the camera. Responsive at least in part to processing of captured image data, a traffic lane of the road along which the vehicle is traveling is determined. Upon approach of the vehicle to a curve in the road, speed of the vehicle is reduced to a reduced speed for traveling around the curve in the road at least in part responsive to at least one selected from the group consisting of (a) processing of image data captured by the forward viewing camera and (b) data relevant to a current geographical location of the equipped vehicle.

Methods, devices, and computer-readable media for generating color-neutral representations of driving objects are disclosed. In one embodiment, a method is disclosed comprising capturing an image, the image including an object of interest; identifying the object of interest in the image based on identifying one or more colors in the image; associating the object of interest with a known traffic object; identifying a color-neutral representation of the known traffic object; and displaying the color-neutral representation to a user.