Abstract: A computing system can obtain, through one or more sensor systems onboard an autonomous vehicle, a motion input, a surface element registration input representing one or more tracked surface elements, and a lane alignment input representing one or more lane boundaries. The computing system can provide the motion input, the surface element registration input, and the lane alignment input to a pose estimation system including a localization filter. The pose estimation system can generate one or more pose states of the autonomous vehicle, including a local pose and a global pose. The computing system can determine a motion plan for the autonomous vehicle based on the one or more pose states.

Abstract: A computing system can obtain, through one or more sensor systems onboard an autonomous vehicle, a motion input, a surface element registration input representing one or more tracked surface elements, and a lane alignment input representing one or more lane boundaries. The computing system can provide the motion input, the surface element registration input, and the lane alignment input to a pose estimation system including a localization filter. The pose estimation system can generate one or more pose states of the autonomous vehicle, including a local pose and a global pose. The computing system can determine a motion plan for the autonomous vehicle based on the one or more pose states.

Abstract: An autonomous vehicle includes a first compute lane, the first compute lane including a first motion planner configured to output a first motion trajectory at a first planning cycle; a second compute lane, the second compute lane including a second motion planner configured to output a second motion trajectory; and one or more processors configured to: obtain data describing the first motion trajectory from the first motion planner; provide the data describing the first motion trajectory to the second motion planner; generate the second motion trajectory by the second motion planner at a second planning cycle based on the data describing the first motion trajectory from the first motion planner; and control an autonomous vehicle in accordance with the first motion trajectory.

Abstract: An autonomous vehicle includes a first compute lane, the first compute lane including a first motion planner configured to output a first motion trajectory at a first planning cycle; a second compute lane, the second compute lane including a second motion planner configured to output a second motion trajectory; and one or more processors configured to: obtain data describing the first motion trajectory from the first motion planner; provide the data describing the first motion trajectory to the second motion planner; generate the second motion trajectory by the second motion planner at a second planning cycle based on the data describing the first motion trajectory from the first motion planner; and control an autonomous vehicle in accordance with the first motion trajectory.

Abstract: A computing system can obtain, through one or more sensor systems onboard an autonomous vehicle, a motion input, a surface element registration input representing one or more tracked surface elements, and a lane alignment input representing one or more lane boundaries. The computing system can provide the motion input, the surface element registration input, and the lane alignment input to a pose estimation system including a localization filter. The pose estimation system can generate one or more pose states of the autonomous vehicle, including a local pose and a global pose. The computing system can determine a motion plan for the autonomous vehicle based on the one or more pose states.

Abstract: A localization system can be validated by: obtaining log data descriptive of a plurality of environmental and operational conditions associated with operation of the autonomous vehicle in an environment; augmenting the log data with one or more simulated anomalies; simulating operation of the localization system using the augmented data as input; obtaining simulation result data descriptive of a state of the localization system subsequent simulating the operation of the localization system; and determining a metric associated with the localization system based on the simulation result data.

Abstract: An apparatus for dynamically determining a displacement of a target sensor in an electronic system is disclosed. The apparatus can comprise a non-line-of-sight sensor rigidly mounted on or proximate to the target sensor and configured to measure a parameter that varies with the displacement of the target sensor. The apparatus further can comprise at least one processor coupled to the non-line-of-sight sensor and configured to compute the displacement of the target sensor based on the parameter, and to compute an adjustment value based on the computed displacement.

Abstract: A technique is described herein that employs a resource-efficient image capture system. The image capture system includes an active illumination system for emitting electromagnetic radiation within a physical environment. The image capture system also includes a camera system that includes one or more cameras for detecting electromagnetic radiation received from the physical environment, to produce image information. In one implementation, the technique involves using the same image capture system to produce different kinds of image information for consumption by different respective image processing components. The technique can perform this task by allocating timeslots over a span of time for producing the different kinds of image information. In one case, the image processing components include: a pose tracking component; a controller tracking component; and a surface reconstruction component, etc., any subset of which may be active at any given time.

Abstract: An apparatus for dynamically determining a displacement of a target sensor in an electronic system is disclosed. The apparatus can comprise a non-line-of-sight sensor rigidly mounted on or proximate to the target sensor and configured to measure a parameter that varies with the displacement of the target sensor. The apparatus further can comprise at least one processor coupled to the non-line-of-sight sensor and configured to compute the displacement of the target sensor based on the parameter, and to compute an adjustment value based on the computed displacement.

Abstract: A method and apparatus for estimating and tracking a 3D object shape and pose estimation is disclosed A plurality of 3D object models of related objects varying in size and shape are obtained, aligned and scaled, and voxelized to create a 2D height map of the 3D models to train a principle component analysis model. At least one sensor mounted on a host vehicle obtains a 3D object image. Using the trained principle component analysis model, the processor executes program instructions to estimate the shape and pose of the detected 3D object until the shape and pose of the detected 3D object matches one principle component analysis model. The output of the shape and pose of the detected 3D object is used in one vehicle control function.