Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A device for modeling an environment comprises: a hardware sensor interface to obtain a sequence of unrectified images representative of a road environment, the sequence of unrectified images including a first unrectified image, a previous unrectified image, and a previous-previous unrectified image; and processing circuitry to: provide the first unrectified image, the previous unrectified image, and the previous-previous unrectified image to an artificial neural network (ANN) to produce a three-dimensional structure of a scene; determine a selected homography; and apply the selected homography to the three-dimensional structure of the scene to create a model of the road environment.

Abstract: An aroma display that enables, by an easy operation, generation of scents fit for situations, at various scenes and times includes: a message receiving unit 180 for receiving an external message; a correspondence storage means for storing correspondence between an event name and aroma information related to a scent to be generated at the event; an event information extracting means for extracting event information including an event name and time information related to the event from the message received by the message receiving unit 180; aroma information reading units 185 and 186 for reading aroma information corresponding to the event name from the correspondence storage means; and an aroma generating unit 194 generating a scent in accordance with the aroma information read by aroma information reading units 185 and 186.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A time-ordered sequence of images representative of a road surface may be obtained. An image from this sequence is a current image. A data set may then be provided to an artificial neural network (ANN) to produce a three-dimensional structure of a scene. Here, the data set includes a portion of the sequence of images that includes the current image, motion of the sensor from which the images were obtained, and an epipole. The road surface is then modeled using the three-dimensional structure of the scene.

Abstract: Various systems and methods for modeling a scene. A device for modeling a scene includes a hardware interface to obtain a time-ordered sequence of images representative of a scene, the time-ordered sequence including a plurality of images, one of the sequence of images being a current image, the scene captured by a monocular imaging system; and processing circuitry to: provide a data set to an artificial neural network (ANN) to produce a three-dimensional structure of the scene, the data set including: a portion of the sequence of images, the portion of the sequence of images including the current image; and motion of a sensor that captured the sequence of images; and model the scene using the three-dimensional structure of the scene, wherein the three-dimensional structure is determined for both moving and fixed objects in the scene.

Abstract: A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A device for modeling an environment comprises: a hardware sensor interface to obtain a sequence of unrectified images representative of a road environment, the sequence of unrectified images including a first unrectified image, a previous unrectified image, and a previous-previous unrectified image; and processing circuitry to: provide the first unrectified image, the previous unrectified image, and the previous-previous unrectified image to an artificial neural network (ANN) to produce a three-dimensional structure of a scene; determine a selected homography; and apply the selected homography to the three-dimensional structure of the scene to create a model of the road environment.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A device for modeling an environment comprises: a hardware sensor interface to obtain a sequence of unrectified images representative of a road environment, the sequence of unrectified images including a first unrectified image, a previous unrectified image, and a previous-previous unrectified image; and processing circuitry to: provide the first unrectified image, the previous unrectified image, and the previous-previous unrectified image to an artificial neural network (ANN) to produce a three-dimensional structure of a scene; determine a selected homography; and apply the selected homography to the three-dimensional structure of the scene to create a model of the road environment.

Abstract: A navigation system for a host vehicle may include a processor programmed to: receive, from an entity remotely located relative to the host vehicle, a sparse map associated with at least one road segment to be traversed by the host vehicle; receive point cloud information from a LIDAR system onboard the host vehicle, the point cloud information being representative of distances to various objects in an environment of the host vehicle; compare the received point cloud information with at least one of the plurality of mapped navigational landmarks in the sparse map to provide a LIDAR-based localization of the host vehicle relative to at least one target trajectory; determine an navigational action for the host vehicle based on the LIDAR-based localization of the host vehicle relative to the at least one target trajectory; and cause the at least one navigational action to be taken by the host vehicle.

Abstract: A navigation system for a host vehicle may include a processor programmed to determine at least one indicator of ego motion of the host vehicle. A processor may be also programmed to receive, from a LIDAR system, a first point cloud including a first representation of at least a portion of an object and a second point cloud including a second representation of the at least a portion of the object. The processor may further be programmed to determine a velocity of the object based on the at least one indicator of ego motion of the host vehicle, and based on a comparison of the first point cloud, including the first representation of the at least a portion of the object, and the second point cloud, including the second representation of the at least a portion of the object.

Abstract: A navigation system for a host vehicle may include a processor programmed to: receive from a center camera onboard the host vehicle a captured center image including a representation of at least a portion of an environment of the host vehicle, receive from a left surround camera onboard the host vehicle a captured left surround image including a representation of at least a portion of the environment of the host vehicle, and receive from a right surround camera onboard the host vehicle a captured right surround image including a representation of at least a portion of the environment of the host vehicle; provide the center image, the left surround image, and the right surround image to an analysis module configured to generate an output relative to the at least one captured center image; and cause a navigational action by the host vehicle based on the generated output.

Abstract: A navigation system for a host vehicle may include a processor programmed to: receive from a camera onboard the host vehicle at least one captured image representative of an environment of the host vehicle, wherein the camera is positioned at a first location relative to the host vehicle; receive point cloud information from a LIDAR system onboard the host vehicle, wherein the LIDAR system is positioned at a second location relative to the host vehicle; analyze the at least one captured image and the received point cloud information to detect one or more objects in the shared field of view region; determine whether a vantage point difference between the first location of the camera and the second location of the LIDAR system accounts for the one or more detected objects being represented in only one of the at least one captured image or the received point cloud information.

Abstract: Various systems and methods for modeling a scene. A device for modeling a scene includes a hardware interface to obtain a time-ordered sequence of images representative of a scene, the time-ordered sequence including a plurality of images, one of the sequence of images being a current image, the scene captured by a monocular imaging system; and processing circuitry to: provide a data set to an artificial neural network (ANN) to produce a three-dimensional structure of the scene, the data set including: a portion of the sequence of images, the portion of the sequence of images including the current image; and motion of a sensor that captured the sequence of images; and model the scene using the three-dimensional structure of the scene, wherein the three-dimensional structure is determined for both moving and fixed objects in the scene.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A time-ordered sequence of images representative of a road surface may be obtained. An image from this sequence is a current image. A data set may then be provided to an artificial neural network (ANN) to produce a three-dimensional structure of a scene. Here, the data set includes a portion of the sequence of images that includes the current image, motion of the sensor from which the images were obtained, and an epipole. The road surface is then modeled using the three-dimensional structure of the scene.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a navigation system for a vehicle may comprise at least one processor. The at least one processor may be programmed to receive, from at least one sensor of the vehicle, information captured from an environment of the vehicle and determine, based on the information, a first position of the vehicle relative to a road navigation model. The at least one processor may further determine, based on at least one signal received from a satellite, a second position of the vehicle and determine, based on a comparison of the first position and the second position, error information associated with the second position. The at least one processor may cause a transmission of the error information to a server.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A time-ordered sequence of images representative of a road surface may be obtained. An image from this sequence is a current image. A data set may then be provided to an artificial neural network (ANN) to produce a three-dimensional structure of a scene. Here, the data set includes a portion of the sequence of images that includes the current image, motion of the sensor from which the images were obtained, and an epipole. The road surface is then modeled using the three-dimensional structure of the scene.

Abstract: System and techniques for vehicle environment modeling with a camera are described herein. A time-ordered sequence of images representative of a road surface may be obtained. An image from this sequence is a current image. A data set may then be provided to an artificial neural network (ANN) to produce a three-dimensional structure of a scene. Here, the data set includes a portion of the sequence of images that includes the current image, motion of the sensor from which the images were obtained, and an epipole. The road surface is then modeled using the three-dimensional structure of the scene.