Abstract: The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may receive a plurality of images captured by an image capture device onboard the host vehicle, the plurality of images being associated with an environment of the host vehicle; analyze at least one of the plurality of images to identify the target vehicle and at least one wheel component on a side of the target vehicle; determine, based on an analysis of at least two of the plurality of images, a rotation of the at least one wheel component of the target vehicle; and cause at least one navigational change of the host vehicle based on the rotation of the at least one wheel component of the target vehicle.

Abstract: System and methods are disclosed for navigating a host vehicle. In one implementation, at least one processing device may be programmed to receive an image representative of an environment of the host vehicle, identify, based on the image analysis, a target vehicle, select a safe navigation mode among a plurality of available safe navigation, each of the safe navigation mode being associated with a corresponding safe navigation distance, determine a planned navigational action for the host vehicle that if taken would result in a next-state distance between the host vehicle and the target vehicle greater than or equal to the selected safe navigation mode safe navigation distance, and implement the planned navigational action by controlling one or more actuators associated with the host vehicle. The at least one processing device may be further programmed to select a safe navigation mode based on a detected location or condition of the host vehicle.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

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: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: An imaging system is provided for a vehicle. In one implementation, the imaging system includes an imaging module, a first camera coupled to the imaging module, a second camera coupled to the imaging module, and a mounting assembly configured to attach the imaging module to the vehicle such that the first and second camera face outward with respect to the vehicle. The first camera has a first field of view and a first optical axis, and the second camera has a second field of view and a second optical axis. The first optical axis crosses the second optical axis in at least one crossing point of a crossing plane. The first camera is focused a first horizontal distance beyond the crossing point of the crossing plane and the second camera is focused a second horizontal distance beyond the crossing point of the crossing plane.

Abstract: The present disclosure relates to navigational systems for vehicles. In one implementation, such a navigational system may receive a plurality of images captured by an image capture device onboard the host vehicle, the plurality of images being associated with an environment of the host vehicle; analyze at least one of the plurality of images to identify the target vehicle and at least one wheel component on a side of the target vehicle; determine, based on an analysis of at least two of the plurality of images, a rotation of the at least one wheel component of the target vehicle; and cause at least one navigational change of the host vehicle based on the rotation of the at least one wheel component of the target vehicle.

Abstract: A vehicle navigation system for detecting and responding to a cut-in by a target vehicle is disclosed. The vehicle navigation system includes at least one processor configured to receive image data captured by an image capture device of the host vehicle; identify, based on analysis of the image data, a target vehicle traveling in a lane adjacent to a lane in which the host vehicle is traveling; determine, based on analysis of the image data, that a predetermined cut in sensitivity change factor is present; and based on the determination that the predetermined cut in sensitivity change factor is present, cause a navigational response in the host vehicle.

Abstract: Systems and methods are provided for processing reports received from vehicles. A processing device perform operations comprising receiving a first report generated by a first vehicle, the first report generated by the first vehicle for a first hazard detected by the first vehicle; receiving a second report generated by a second vehicle, the second report generated by the second vehicle for a second hazard detected by the second vehicle; analyzing the first report and the second report to make a determination that the first report and the second report identify a related hazard; aggregating the first report and the second report into a consolidated report based on the determination that the first report and the second report identify a related hazard; and generating a map, the map indicating a location of the related hazard based on the consolidated report.

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 vehicle navigation system may comprise a memory including instructions and circuitry configured by the instructions to identify a target vehicle in an environment of a vehicle that includes the vehicle navigation system. The circuitry may receive image data of the target vehicle from an image capture device of the vehicle; identify, based on analysis of the image data, a situational characteristic of the target vehicle including an indication that the target vehicle is traveling behind an additional vehicle traveling slower than the target vehicle; and change a navigational state of the vehicle to allow an action of the target vehicle. The vehicle may be configured to cause the change in the navigational state based on a determination that the situational characteristic indicates that the target vehicle would benefit from the change in the navigational state.

Abstract: Systems and methods are provided for navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on a size of a representation of the target vehicle in the plurality of images, first and second estimated positions of the target vehicle within the time period, determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

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 navigating a host vehicle. In an embodiment, a processing device may be configured to receive images captured over a time period; analyze images to identify a target vehicle; receive map information associated including a plurality of target trajectories; determine, based on analysis of the images, first and second estimated positions of the target vehicle within the time period; determine, based on the first and second estimated positions, a trajectory of the target vehicle over the time period; compare the determined trajectory to the plurality of target trajectories to identify a target trajectory traversed by the target vehicle; determine, based on the identified target trajectory, a position of the target vehicle; and determine a navigational action for the host vehicle based on the determined position.

Abstract: Systems and methods are provided for navigating a host vehicle. A navigation system for the host vehicle may include at least one processor programmed to receive an image representative of an environment of a host vehicle; analyze the image to determine a predicted path of the host vehicle; determine, based on the image, an indicator of comfort associated with the predicted path; identify, based on the indicator of comfort, an alternative path of the host vehicle; and output a control signal configured to modify an operation of a component of the host vehicle to follow the alternative path of the host vehicle.

Abstract: Systems and methods are provided for navigating an autonomous vehicle. In one implementation, a system includes a processing device programmed to receive a plurality of images representative of an environment of the host vehicle. The environment includes a road on which the host vehicle is traveling. The at least one processing device is further programmed to analyze the images to identify a target vehicle traveling in a lane of the road different from a lane in which the host vehicle is traveling; analyze the images to identify a lane mark associated with the lane in which the target vehicle is traveling; detect lane mark characteristics of the identified lane mark; use the detected lane mark characteristics to determine a type of the identified lane mark; determine a characteristic of the target vehicle; and determine a navigational action for the host vehicle based on the determined lane mark type and the determined characteristic of the target vehicle.

Abstract: Systems and methods are provided for vehicle navigation. In one implementation, a system may comprise at least one processor. The processor may be programmed to receive an image associated with the environment of the host vehicle. The processor may analyze the image to identify a side of a parked vehicle; a first structural feature of the parked vehicle in a forward region of the side of the parked vehicle or a second structural feature of the parked vehicle in a rear region of the side of the parked vehicle; and a door feature of the parked vehicle in a vicinity of the first or the second structural features. The processor may then determine, based on a subsequent image, a change of an image characteristic of the door feature of the parked vehicle and alter a navigational path of the host vehicle based on the change of the image characteristic.

Abstract: Systems and methods are provided for processing reports received from vehicles. A processing device perform operations comprising receiving a first report generated by a first vehicle, the first report generated by the first vehicle for a first hazard detected by the first vehicle; receiving a second report generated by a second vehicle, the second report generated by the second vehicle for a second hazard detected by the second vehicle; analyzing the first report and the second report to make a determination that the first report and the second report identify a related hazard; aggregating the first report and the second report into a consolidated report based on the determination that the first report and the second report identify a related hazard; and generating a map, the map indicating a location of the related hazard based on the consolidated report.

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: Computerized methods are performable by a driver assistance system while the host vehicle is moving. The driver assistance system includes a camera connectible to a processor. First and second image frames are captured from the field of view of the camera. Corresponding image points of the road are tracked from the first image frame to the second image frame. Image motion between the corresponding image points of the road is processed to detect a hazard in the road. The corresponding image points are determined to be of a moving shadow cast on the road to avoid a false positive detection of a hazard in the road or the corresponding image points are determined not to be of a moving shadow cast on the road to verify detection of a hazard in the road.