A server includes a server communication unit, a server control unit, and a server storage unit, and is connected in a communicable manner to a first vehicle and a second vehicle via the server communication unit. The server control unit transmits an imaging start command to the second vehicle, which runs within a predetermined range from an accident site, in response to the reception of an accident notification associated with positional information on the accident site from the first vehicle, receives an imaged image from the second vehicle, and stores the imaged image into the server storage unit.

A signal recognizing apparatus detects a target traffic light from an image showing surroundings of a host vehicle and generates traffic light detecting information including a traffic light image. The apparatus recognizes a lighting state of the target traffic light and specifies a positional relationship between the host vehicle and the target traffic light on the basis of position information of the host vehicle and the traffic light detecting information, and determines a center of a lighting area corresponding to a lighting portion of the target traffic light in the traffic light image, and performs a predetermined conversion process on the traffic light image on the basis of the positional relationship in order to compare with the lighting pattern information, and recognizes the lighting state by comparing the center of the lighting area in a traffic light image with the lighting pattern information.

Systems, apparatus, methods, and articles of manufacture for Artificial Intelligence (AI) driving analysis and incentives, by utilizing on-board image object analysis to classify driving events and provide driving-based awards.

Enhancing situational awareness of an advanced driver assistance system in a host vehicle can be provided by acquiring, with an image sensor, an image data stream comprising a plurality of image frames. Analyzing A vision processor can analyze the image data stream to detect objects, shadows and/or lighting in the image frames. Recognizing A situation recognition engine can recognize at least one most probable traffic situation out of a set of predetermined traffic situations taking into account the detected objects, shadows and/or lighting. A processor can then control the host vehicle taking into account the at least one most probable traffic situation.

A fully-automated computer-implemented system and method for generating a road network map from a remote sensing (RS) image in which the classification accuracy is satisfactory combines moving vehicle detection with spectral classification to overcome the limitations of each. Moving vehicle detections from an RS image are used as seeds to extract and characterize image-specific spectral roadway signatures from the same RS image. The RS image is then searched and the signatures matched against the scene to grow a road network map. The entire process can be performed using the radiance measurements of the scene without having to perform the complicated geometric and atmospheric conversions, thus improving computational efficiency, the accuracy of moving vehicle detection (location, speed, heading) and ultimately classification accuracy.

Described herein are systems and methods for determining if a vehicle is parked. In one example, a system includes a processor, a sensor system, and a memory. Both the sensor system and the memory are in communication with the processor. The memory includes a parking determination module having instructions that, when executed by the processor, cause the processor to determine, using a random forest model, when the vehicle is parked based on vehicle estimated features, vehicle learned features, and vehicle taillight features of the vehicle that are based on sensor data from the sensor system.

A deep neural network(s) (DNN) may be used to detect objects from sensor data of a three dimensional (3D) environment. For example, a multi-view perception DNN may include multiple constituent DNNs or stages chained together that sequentially process different views of the 3D environment. An example DNN may include a first stage that performs class segmentation in a first view (e.g., perspective view) and a second stage that performs class segmentation and/or regresses instance geometry in a second view (e.g., top-down). The DNN outputs may be processed to generate 2D and/or 3D bounding boxes and class labels for detected objects in the 3D environment. As such, the techniques described herein may be used to detect and classify animate objects and/or parts of an environment, and these detections and classifications may be provided to an autonomous vehicle drive stack to enable safe planning and control of the autonomous vehicle.

An apparatus, method and computer program product are provided for determining a state of visibility for a road object. In one example, the apparatus receives temporal data, calculates an orientation of a light source with respect to a road object using the temporal data, and predicts a state of visibility for the road object based on the orientation of the light source. In another example, the apparatus determines an artificial light source associated with a road object, receives attribute data associated with the artificial light source, determines a state of the artificial light source using the attribute data, and predicts a state of visibility for the road object based on the state of the artificial light source.

Automatic exposure control of an in-vehicle camera is performed under dark driving environments such as at night. An in-vehicle camera system includes a vehicle camera mounted in a vehicle configured to capture surroundings of the vehicle, and control circuitry that controls an exposure level of an image captured by the vehicle camera, the control of the exposure level being based on brightness information of a detection area set within the image, the detection area being a portion of the captured image and configured to output the image having exposure control performed thereon to a display.

The present invention relates to a method and system for automatic localisation of static objects in an urban environment. More particularly, the present invention relates to the use of noisy 2-Dimensional (2D) image data to identify and determine 3-Dimensional (3D) positions of objects in large scale urban or city environments. Aspects and/or embodiments seek to provide a method, system, and vehicle for automatically locating static 3D objects in urban environments by using a voting-based triangulation technique. Aspects and/or embodiments also provide a method for updating map data after automatically new 3D static objects in an environment.