Abstract: Techniques are discussed for modifying map elements associated with map data. Map data can include three-dimensional data (e.g., LIDAR data) representing an environment, while map elements can be associated with the map data to identify locations and semantic information associated with an environment, such as regions that correspond to driving lanes or crosswalks. A trajectory associated with the map data can be updated, such as when aligning one or more trajectories in response to a loop closure, updated calibration, etc. The transformation between a trajectory and an updated trajectory can be applied to map elements to warp the map elements so that they correspond to the updated map data, thereby providing automatic and accurate techniques for updating map elements associated with map data.

Abstract: Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include monitoring a fleet of vehicles, at least one of which is configured to autonomously transit from a first geographic region to a second geographic region, detecting data indicating an event associated with the vehicle having a calculated confidence level, receiving data representing a subset of candidate trajectories responsive to detecting the event, which is associated with a planned path for the vehicle, identifying guidance data to select from one or more of the candidate trajectories as a guided trajectory, receiving data representing a selection of a candidate trajectory, and transmitting the selection of the candidate trajectory as of the guided trajectory to the vehicle.

Abstract: A method for autonomously operating a driverless vehicle along a path between a first geographic location and a destination may include receiving communication signals from the driverless vehicle. The communication signals may include sensor data from the driverless vehicle and data indicating occurrence of an event associated with the path. The communication signals may also include data indicating that a confidence level associated with the path is less than a threshold confidence level due to the event. The method may also include determining, via a teleoperations system, a level of guidance to provide the driverless vehicle based on data associated with the communication signals, and transmitting teleoperations signals to the driverless vehicle. The teleoperations signals may include guidance to operate the driverless vehicle according to the determined level of guidance, so that a vehicle controller maneuvers the driverless vehicle to avoid, travel around, or pass through the event.

Abstract: Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. In particular, a method may include monitoring a fleet of vehicles, at least one of which is configured to autonomously transit from a first geographic region to a second geographic region, detecting data indicating an event associated with the vehicle having a calculated confidence level, receiving data representing a subset of candidate trajectories responsive to detecting the event, which is associated with a planned path for the vehicle, identifying guidance data to select from one or more of the candidate trajectories as a guided trajectory, receiving data representing a selection of a candidate trajectory, and transmitting the selection of the candidate trajectory as of the guided trajectory to the vehicle.

Abstract: Techniques are discussed for determining a data level for portions of data for processing. In some cases, a data level can correspond to a resolution level, a compression level, a bit rate, and the like. In the context of image data, the techniques can determine a region of first image data to be processed a high resolution and a region of second image data to be processed at a low resolution. The regions can be determined by a machine learned algorithm that is trained to output identifications of such regions. Training data may be determined by identifying differences in outputs based on the first and second image data. The image data associated with the determined regions and the determined resolutions can be processed to perform object detection, classification, segmentation, bounding box generation, and the like, thereby conserving processing, bandwidth, and/or memory resources in real time systems.

Abstract: Techniques are discussed for modifying map elements associated with map data. Map data can include three-dimensional data (e.g., LIDAR data) representing an environment, while map elements can be associated with the map data to identify locations and semantic information associated with an environment, such as regions that correspond to driving lanes or crosswalks. A trajectory associated with the map data can be updated, such as when aligning one or more trajectories in response to a loop closure, updated calibration, etc. The transformation between a trajectory and an updated trajectory can be applied to map elements to warp the map elements so that they correspond to the updated map data, thereby providing automatic and accurate techniques for updating map elements associated with map data.

Abstract: Systems, methods, and apparatuses described herein are directed to performing segmentation on voxels representing three-dimensional data to identify static and dynamic objects. LIDAR data may be captured by a perception system for an autonomous vehicle and represented in a voxel space. Operations may include determining a drivable surface by parsing individual voxels to determine an orientation of a surface normal of a planar approximation of the voxelized data relative to a reference direction. Clustering techniques can be used to grow a ground plane including a plurality of locally flat voxels. Ground plane data can be set aside from the voxel space, and the remaining voxels can be clustered to determine objects. Voxel data can be analyzed over time to determine dynamic objects. Segmentation information associated with ground voxels, static object, and dynamic objects can be provided to a tracker and/or planner in conjunction with operating the autonomous vehicle.

Abstract: Techniques are discussed for determining a data level for portions of data for processing. In some cases, a data level can correspond to a resolution level, a compression level, a bit rate, and the like. In the context of image data, the techniques can determine a region of first image data to be processed a high resolution and a region of second image data to be processed at a low resolution. The regions can be determined by a machine learned algorithm that is trained to output identifications of such regions. Training data may be determined by identifying differences in outputs based on the first and second image data. The image data associated with the determined regions and the determined resolutions can be processed to perform object detection, classification, segmentation, bounding box generation, and the like, thereby conserving processing, bandwidth, and/or memory resources in real time systems.

Abstract: An autonomous delivery vehicle including locking storage containers may be used for item deliveries, rejections, returns, and/or third-party fulfillment. A delivery vehicle or robot may include a number of locking storage containers, an authorization interface, and one or more sensors to receive delivery requests, detect and authorize users, and control locker access at various delivery locations to allow users to receive delivered items, and reject or return items. The vehicle may also include a passenger compartment to transport one or more passengers. The vehicle may be reconfigurable to accommodate different combinations of lockers and/or passenger seats. An item delivery system may receive delivery requests and determine routes for delivery vehicles, including centralized delivery locations and/or direct deliveries to recipients.

Abstract: Techniques are discussed for determining a data level for portions of data for processing. In some cases, a data level can correspond to a resolution level, a compression level, a bit rate, and the like. In the context of image data, the techniques can determine a region of first image data to be processed a high resolution and a region of second image data to be processed at a low resolution. The regions can be determined by a machine learned algorithm that is trained to output identifications of such regions. Training data may be determined by identifying differences in outputs based on the first and second image data. The image data associated with the determined regions and the determined resolutions can be processed to perform object detection, classification, segmentation, bounding box generation, and the like, thereby conserving processing, bandwidth, and/or memory resources in real time systems.

Abstract: A method includes receiving a first signal from a first sensor, the first signal including data representing an environment. The method also includes receiving a second signal from a second sensor, the second signal including data representing the environment. The method further includes determining a group of objects based at least in part on the received data, and identifying an error associated with data included in the first signal and/or the second signal.

Abstract: A method for autonomously operating a driverless vehicle along a path between a first geographic location and a destination may include receiving communication signals from the driverless vehicle. The communication signals may include sensor data from the driverless vehicle and data indicating occurrence of an event associated with the path. The communication signals may also include data indicating that a confidence level associated with the path is less than a threshold confidence level due to the event. The method may also include determining, via a teleoperations system, a level of guidance to provide the driverless vehicle based on data associated with the communication signals, and transmitting teleoperations signals to the driverless vehicle. The teleoperations signals may include guidance to operate the driverless vehicle according to the determined level of guidance, so that a vehicle controller maneuvers the driverless vehicle to avoid, travel around, or pass through the event.

Abstract: A driverless vehicle may include a processor, a sensor, a network interface, and a memory having stored thereon processor-executable instructions. The driverless vehicle may be configured to obtain a stream of sensor signals including sensor data related to operation of the driverless vehicle from the sensor and/or the network interface. The driverless vehicle may be configured to determine a confidence level associated with operation of the driverless vehicle from the sensor data, and store the confidence level and at least a portion of the sensor data. The driverless vehicle may also be configured to transmit via the network interface a request for teleoperator assistance, and the request may include the portion of the sensor data and the confidence level.

Abstract: Techniques are discussed for determining a data level for portions of data for processing. In some cases, a data level can correspond to a resolution level, a compression level, a bit rate, and the like. In the context of image data, the techniques can determine a region of first image data to be processed a high resolution and a region of second image data to be processed at a low resolution. The regions can be determined by a machine learned algorithm that is trained to output identifications of such regions. Training data may be determined by identifying differences in outputs based on the first and second image data. The image data associated with the determined regions and the determined resolutions can be processed to perform object detection, classification, segmentation, bounding box generation, and the like, thereby conserving processing, bandwidth, and/or memory resources in real time systems.

Abstract: Various embodiments relate generally to autonomous vehicles and associated mechanical, electrical and electronic hardware, computer software and systems, and wired and wireless network communications to provide an autonomous vehicle fleet as a service. More specifically, systems, devices, and methods are configured to initiate modification of trajectories to influence navigation of autonomous vehicles. In particular, a method may include receiving a teleoperation message via a communication link from an autonomous vehicle, detecting data from the teleoperation message specifying an event associated with the autonomous vehicle, identifying one or more courses of action to perform responsive to detecting the data specifying the event, and generating visualization data to present information associated with the event to a display of a teleoperator computing device.

Abstract: A method for autonomously operating a driverless vehicle along a path between a first geographic location and a destination may include receiving communication signals from the driverless vehicle. The communication signals may include sensor data from the driverless vehicle and data indicating occurrence of an event associated with the path. The communication signals may also include data indicating that a confidence level associated with the path is less than a threshold confidence level due to the event. The method may also include determining, via a teleoperations system, a level of guidance to provide the driverless vehicle based on data associated with the communication signals, and transmitting teleoperations signals to the driverless vehicle. The teleoperations signals may include guidance to operate the driverless vehicle according to the determined level of guidance, so that a vehicle controller maneuvers the driverless vehicle to avoid, travel around, or pass through the event.

Abstract: Systems, methods and apparatus may be configured to implement automatic semantic classification of a detected object(s) disposed in a region of an environment external to an autonomous vehicle. The automatic semantic classification may include analyzing over a time period, patterns in a predicted behavior of the detected object(s) to infer a semantic classification of the detected object(s). Analysis may include processing of sensor data from the autonomous vehicle to generate heat maps indicative of a location of the detected object(s) in the region during the time period. Probabilistic statistical analysis may be applied to the sensor data to determine a confidence level in the inferred semantic classification. The inferred semantic classification may be applied to the detected object(s) when the confidence level exceeds a predetermined threshold value (e.g., greater than 50%).

Abstract: Techniques and methods for storing data. For instance, a vehicle can receive sensor data generated by one or more sensors. The vehicle can then detect that an event is occurring and/or has occurred using the sensor data. Based on detecting the event, the vehicle can flag a first portion of the sensor data as corresponding to the event. The vehicle can then store the first portion of the sensor data according to a first storage rule and a second portion of the sensor data according to a second storage rule. In some instances, the storage rules may indicate when sensor data is to be overwritten, lengths of time that the sensor data is to be stored, formats for storing the sensor data, and/or when the sensor data is to be sent to one or more computing devices.

Abstract: Perception sensors of a vehicle can be used for various operating functions of the vehicle. A computing device may receive sensor data from the perception sensors, and may calibrate the perception sensors using the sensor data, to enable effective operation of the vehicle. To calibrate the sensors, the computing device may project the sensor data into a voxel space, and determine a voxel score comprising an occupancy score and a residual value for each voxel. The computing device may then adjust an estimated position and/or orientation of the sensors, and associated sensor data, from at least one perception sensor to minimize the voxel score. The computing device may calibrate the sensor using the adjustments corresponding to the minimized voxel score. Additionally, the computing device may be configured to calculate an error in a position associated with the vehicle by calibrating data corresponding to a same point captured at different times.

Abstract: Techniques for decimating portions of a map of an environment are discussed herein. The environment can be represented by a three-dimensional (3D) map including a plurality of polygons and semantic information associated with the polygons. In some cases, decimation operations may be based on semantic information associated with the environment. Differing decimation operations and/or levels may be applied to polygons of different semantic classifications or differing contribution levels. Boundaries between regions having different semantic information can be preserved. Meshes can be decimated using different decimation operators or decimation levels and an accuracy of localizing can be compared using the various decimated meshes. An optimal mesh can be selected and sent to vehicles for localizing the vehicles in the environment.