Abstract: A method of generating a high dynamic range (HDR) image is provided that includes capturing a long exposure image and a short exposure image of a scene, computing a merging weight for each pixel location of the long exposure image based on a pixel value of the pixel location and a saturation threshold, and computing a pixel value for each pixel location of the HDR image as a weighted sum of corresponding pixel values in the long exposure image and the short exposure image, wherein a weight applied to a pixel value of the pixel location of the short exposure image and a weight applied to a pixel value of the pixel location in the pixel long exposure image are determined based on the merging weight computed for the pixel location and responsive to motion in a scene of the long exposure image and the short exposure image.

Abstract: Autonomous ground vehicles that are outfitted with onboard cameras and configured to travel within an area or region are programmed with a previously generated visual map of the area or region. The visual map includes a plurality of map points from which images were previously captured and processed to determine visual features of such images. In the event of a loss of one or more position signals, such as GPS signals, the autonomous ground vehicles capture images using one or more of the cameras, and process the images to determine visual features from such images. The visual features determined from such images are compared to the visual map. Positions of one or more of the map points are identified as most likely corresponding to a position of the autonomous ground vehicle where visual features associated with such map points are similar to the visual features determined from such images.

Abstract: A sensor fusion system and method are disclosed. One or more processors are operable to receive a plurality of object detection measurements from a plurality of sensors. Each of the plurality of object detection measurements are associated with a potential object detection track. A plurality of sensor confidence values associated with each of the plurality of sensors are received. A track confidence value is determined for each of the potential object detection tracks based on the received plurality of object detection measurements and the received plurality of sensor confidence values. An object detection for a potential object detection track that has a determined track confidence value meeting a predetermined detection threshold is then determined, or confirmed, and stored in a memory for subsequent use, and is relatively unaffected by a measurement from a sensor that has a field of view that omits or is occluded with respect to the given object detection track.

Abstract: The present invention is related to systems and methods for detecting an occluded object based on the shadow of the occluded object. In some examples, a vehicle of the present invention can capture one or more images while operating in an autonomous driving mode, and detecting shadow items within the captured image. In response to detecting a shadow item moving towards the direction of vehicle travel, the vehicle can reduce its speed to avoid a collision, should an occluded object enter the road. The shadow can be detected using image segmentation or a classifier trained using convolutional neural networks or another suitable algorithm, for example.

Abstract: A vehicle control system for moving a vehicle to a target location is disclosed. According to examples of the disclosure, a camera captures one or more images of a known object corresponding to the target location. An on-board computer having stored thereon information about the known object can process the one or more images to determine vehicle location with respect to the known object. The system can use the vehicle's determined location and a feedback controller to move the vehicle to the target location.

Abstract: A method of generating a high dynamic range (HDR) image is provided that includes capturing a long exposure image and a short exposure image of a scene, computing a merging weight for each pixel location of the long exposure image based on a pixel value of the pixel location and a saturation threshold, and computing a pixel value for each pixel location of the HDR image as a weighted sum of corresponding pixel values in the long exposure image and the short exposure image, wherein a weight applied to a pixel value of the pixel location of the short exposure image and a weight applied to a pixel value of the pixel location in the pixel long exposure image are determined based on the merging weight computed for the pixel location and responsive to motion in a scene of the long exposure image and the short exposure image.

Abstract: Systems and methods for asynchronous late-fusion of measurements. State information and intermediate values may be calculated as measurements arrive and are stored. When late sensor measurements arrive out of the temporal order in which the measurements were generated, the late measurements are stored in temporal order rather than the order in which measurements arrive. State information is then recalculated to account for the late-arriving sensor measurement, with state outputs propagated forward in temporal order using the previously computed intermediate values to speed up computation. In this manner, more accurate revised state information is efficiently generated, accounting for any late-arriving measurements. This modular processing framework also enables sensors to be added or changed, which may cause measurements to arrive asynchronously, without having to reprogram the processing framework.

Abstract: A sensor fusion system and method are disclosed. One or more processors are operable to receive a plurality of object detection measurements from a plurality of sensors. Each of the plurality of object detection measurements are associated with a potential object detection track. A plurality of sensor confidence values associated with each of the plurality of sensors are received. A track confidence value is determined for each of the potential object detection tracks based on the received plurality of object detection measurements and the received plurality of sensor confidence values. An object detection for a potential object detection track that has a determined track confidence value meeting a predetermined detection threshold is then determined, or confirmed, and stored in a memory for subsequent use, and is relatively unaffected by a measurement from a sensor that has a field of view that omits or is occluded with respect to the given object detection track.

Abstract: Described are systems and methods to utilize images to determine the position and/or orientation of a vehicle (e.g., an autonomous ground vehicle) operating in an unstructured environment (e.g., environments such as sidewalks which are typically absent lane markings, road markings, etc.). The described systems and methods can determine the vehicle's position and orientation based on an alignment of annotated images captured during operation of the vehicle with a known annotated reference map. The translation and rotation applied to obtain alignment of the annotated images with the known annotated reference map can provide the position and the orientation of the vehicle.

Abstract: A multi-processor architecture for automated driving systems can be used to improve performance and provide design flexibility. For example, a multi-processor architecture can be used to implement command generation and safety functionality in different processors. The command generation processor can be a high performing processor compared with the safety processor. The safety processor can verify the safety of commands output from the command generation processor and provide additional I/O channels that are typically absent on high performing processors. Additionally, processing of some sensor data can be moved to expansion modules with additional processors to reduce bottlenecks and provide design flexibility for systems with different sensing requirements.

Abstract: Various disclosed embodiments include methods of locating an intended passenger of an automotive vehicle and vehicle-based systems for locating an intended passenger of an automotive vehicle. In an illustrative embodiment, a method of locating an intended passenger of an automotive vehicle includes: moving an automotive vehicle to an approximate location of an intended passenger based on a location signal from the intended passenger received via a wireless network; after moving to the approximate location of the intended passenger, receiving, by the vehicle, an image from an electronic device of the intended passenger, the image being an image of the intended passenger or an image of local physical surroundings of the intended passenger; processing the image to determine additional location information of the intended passenger based on matching the image against one or more reference images; and accepting by the vehicle, the intended passenger responsive to the image processing.

Abstract: A system for use in a vehicle, the system comprising one or more sensors, one or more processors operatively coupled to the one or more sensors, and a memory including instructions, which when executed by the one or more processors, cause the one or more processors to perform a method. The method comprising capturing a current point cloud with the one or more sensors, determining an estimated location and an estimated heading of the vehicle, selecting one or more point clouds based on the estimated location and heading of the vehicle, simplifying the current point cloud and the one or more point clouds, correlating the current point cloud to the one or more point clouds, and determining an updated estimate of the location of the vehicle based on correlation between the current point cloud and the one or more point clouds.

Abstract: The present invention is related to systems and methods for detecting an occluded object based on the shadow of the occluded object. In some examples, a vehicle of the present invention can capture one or more images while operating in an autonomous driving mode, and detecting shadow items within the captured image. In response to detecting a shadow item moving towards the direction of vehicle travel, the vehicle can reduce its speed to avoid a collision, should an occluded object enter the road. The shadow can be detected using image segmentation or a classifier trained using convolutional neural networks or another suitable algorithm, for example.

Abstract: Various disclosed embodiments include methods of locating an intended passenger of an automotive vehicle and vehicle-based systems for locating an intended passenger of an automotive vehicle. In an illustrative embodiment, a method of locating an intended passenger of an automotive vehicle includes: moving an automotive vehicle to an approximate location of an intended passenger based on a location signal from the intended passenger received via a wireless network; after moving to the approximate location of the intended passenger, receiving, by the vehicle, an image from an electronic device of the intended passenger, the image being an image of the intended passenger or an image of local physical surroundings of the intended passenger; processing the image to determine additional location information of the intended passenger based on matching the image against one or more reference images; and accepting by the vehicle, the intended passenger responsive to the image processing.

Abstract: A vehicle control system for moving a vehicle to a target location is disclosed. According to examples of the disclosure, a camera captures one or more images of a known object corresponding to the target location. An on-board computer having stored thereon information about the known object can process the one or more images to determine vehicle location with respect to the known object. The system can use the vehicle's determined location and a feedback controller to move the vehicle to the target location.

Abstract: The present invention is related to systems and methods for detecting an occluded object based on the shadow of the occluded object. In some examples, a vehicle of the present invention can capture one or more images while operating in an autonomous driving mode, and detecting shadow items within the captured image. In response to detecting a shadow item moving towards the direction of vehicle travel, the vehicle can reduce its speed to avoid a collision, should an occluded object enter the road. The shadow can be detected using image segmentation or a classifier trained using convolutional neural networks or another suitable algorithm, for example.

Abstract: A vehicle control system for moving a vehicle to a target location is disclosed. According to examples of the disclosure, a camera captures one or more images of a known object corresponding to the target location. An on-board computer having stored thereon information about the known object can process the one or more images to determine vehicle location with respect to the known object. The system can use the vehicle's determined location and a feedback controller to move the vehicle to the target location.

Abstract: Described are systems and methods to utilize images to determine the position and/or orientation of a vehicle (e.g., an autonomous ground vehicle) operating in an unstructured environment (e.g., environments such as sidewalks which are typically absent lane markings, road markings, etc.). The described systems and methods can determine the vehicle's position and orientation based on an alignment of annotated images captured during operation of the vehicle with a known annotated reference map. The translation and rotation applied to obtain alignment of the annotated images with the known annotated reference map can provide the position and the orientation of the vehicle.

Abstract: A system that performs a method is disclosed. The system receives information about a map, which includes information about one or more zones in the map. While navigating a vehicle along a driving path within the map, the system receives information about the location of the vehicle in the map. The system estimates an error bounds of the location of the vehicle, and determines in which of the one or more zones in the map the error bounds is located within. In response to the determination: in accordance with a determination that the error bounds is located within a first zone in the map, the system causes the vehicle to perform a driving operation. In accordance with a determination that the error bounds is located within a second zone of the one or more zones in the map, the system causes the vehicle to perform a different driving operation.

Abstract: A method of generating a high dynamic range (HDR) image is provided that includes capturing a long exposure image and a short exposure image of a scene, computing a merging weight for each pixel location of the long exposure image based on a pixel value of the pixel location and a saturation threshold, and computing a pixel value for each pixel location of the HDR image as a weighted sum of corresponding pixel values in the long exposure image and the short exposure image, wherein a weight applied to a pixel value of the pixel location of the short exposure image and a weight applied to a pixel value of the pixel location in the pixel long exposure image are determined based on the merging weight computed for the pixel location and responsive to motion in a scene of the long exposure image and the short exposure image.