Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer-implemented method includes obtaining data associated with an object within a surrounding environment of an autonomous vehicle. The data associated with the object is indicative of a predicted motion trajectory of the object. The method includes determining a vehicle action sequence based at least in part on the predicted motion trajectory of the object. The vehicle action sequence is indicative of a plurality of vehicle actions for the autonomous vehicle at a plurality of respective time steps associated with the predicted motion trajectory. The method includes determining a motion plan for the autonomous vehicle based at least in part on the vehicle action sequence. The method includes causing the autonomous vehicle to initiate motion control in accordance with at least a portion of the motion plan.

Abstract: A vehicle sensor calibration system for self-driving vehicles can include a turntable on which a sensor system including a LIDAR sensor is positioned. A plurality of fiducial targets can be positioned around the turntable to enable calibration of the sensor system, and a control mechanism can automatically rotate the turntable when the sensor system is positioned on the turntable. The system can include one or more computing systems that receive a data log corresponding to sensor data recorded by the sensor system as the sensor system rotates on the turntable, and analyze the sensor data to determine a set of calibration parameters to calibrate the sensor system.

Abstract: The present disclosure is directed generating candidate trajectories and selecting one of them to be implemented. In particular, a computing system can obtain an initial travel path for an autonomous vehicle. The computing system can obtain sensor data describing objects within an environment of the autonomous vehicle. The computing system can generate a plurality of trajectories for the autonomous vehicle based on the sensor data and the initial travel path. The computing system can determine whether the initial travel path, an offset profile, and a velocity profile meet flatness criteria. In response to determining that the initial travel path, the offset profile, and the velocity profile meet the flatness criteria, the computing system can combine the initial travel path, the offset profile, and the velocity profile into the respective trajectory. The computing system can determine a trajectory from the plurality of trajectories.

Abstract: Systems and methods for vehicle motion planning based on uncertainty are provided. A method can include obtaining scene data descriptive of one or more objects within a surrounding environment of the autonomous vehicle. The method can include determining one or more subproblems based at least in part on the scene data. In some implementation, each of the one or more subproblems can correspond to at least one object within the surrounding environment of the autonomous vehicle. The method can include generating one or more branching policies based at least in part on the one or more subproblems. In some implementations, each of the one or more branching policies can include scene data associated with the autonomous vehicle and one or more objects within the surrounding environment of the autonomous vehicle. The method can include determining one or more costs associated each of the one or more branching policies.

Abstract: Systems and methods for controlling the motion of an autonomous are provided. In one example embodiment, a computer-implemented method includes obtaining data associated with an object within a surrounding environment of an autonomous vehicle. The data associated with the object is indicative of a predicted motion trajectory of the object. The method includes determining a vehicle action sequence based at least in part on the predicted motion trajectory of the object. The vehicle action sequence is indicative of a plurality of vehicle actions for the autonomous vehicle at a plurality of respective time steps associated with the predicted motion trajectory. The method includes determining a motion plan for the autonomous vehicle based at least in part on the vehicle action sequence. The method includes causing the autonomous vehicle to initiate motion control in accordance with at least a portion of the motion plan.

Abstract: An autonomous vehicle software management system can distribute AV software versions to safety-driven autonomous vehicles (SDAVs) operating within a given region. The system can receive log data from the SDAVs indicating any trip anomalies of the SDAVs while executing the AV software version. When a predetermined safety standard has been met based on the log data, the system can verify the AV software version for execution on fully autonomous vehicles (FAVs) operating within the given region.

Abstract: A vehicle sensor calibration system for self-driving vehicles can include a turntable on which a sensor system including a LIDAR sensor is positioned. A plurality of fiducial targets can be positioned around the turntable to enable calibration of the sensor system, and a control mechanism can automatically rotate the turntable when the sensor system is positioned on the turntable. The system can include one or more computing systems that receive a data log corresponding to sensor data recorded by the sensor system as the sensor system rotates on the turntable, and analyze the sensor data to determine a set of calibration parameters to calibrate the sensor system.

Abstract: Systems and methods for controlling an autonomous vehicle are provided. In one example embodiment, a computer-implemented method includes obtaining data representing a first trajectory including one or more states corresponding to a motion path. The method includes determining a second trajectory based at least in part on the first trajectory, the second trajectory including a first state corresponding to the motion path, and one or more secondary states corresponding to the motion path, the one or more secondary states indicating a state of the autonomous vehicle relative to the first state. The method includes determining one or more control signals based at least in part on the second trajectory. The method includes controlling a motion of the autonomous vehicle according to the motion path, based at least in part on the one or more control signals.

Abstract: An autonomous vehicle (AV) software management system can collect historical data of harmful events of human-driven vehicles (HDVs) within an autonomy grid on which AVs operate. For each path segment of the autonomy grid, the system can determine a fractional risk value for HDVs. The system may also receive AV data from a fleet of AVs operating throughout the autonomy grid, and for each path segment of the autonomy grid, the system can evaluate AV performance against the fractional risk values for HDVs based on the received AV data.

Abstract: A LIDAR calibration module can detect a set of return signals from a plurality of fiducial targets for a set of laser scanners of an autonomous vehicle's LIDAR module. The autonomous vehicle can rest on an inclined platform of a rotating turntable to increase range variation in the return signals. Based on the set of return signals, the LIDAR calibration system can generate a set of calibration transforms to adjust a set of intrinsic parameters of the LIDAR module.

Abstract: An on-demand transportation management service can perform a selection process between a set of safety-driven autonomous vehicles (SDAVs), fully autonomous vehicles (FAVs), and human-driven vehicles (HDVs) to service transport requests based on a variety of selection parameters.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices associated with the motion prediction and operation of a device including a vehicle are provided. For example, a vehicle computing system can access state data including information associated with locations and characteristics of objects over a plurality of time intervals. Trajectories of the objects at subsequent time intervals following the plurality of time intervals can be determined based on the state data and a machine-learned tracking and kinematics model. The trajectories of the objects can include predicted locations of the objects at subsequent time intervals that follow the plurality of time intervals. Further, the predicted locations of the objects can be based on physical constraints of the objects. Furthermore, indications, which can include visual indications, can be generated based on the predicted locations of the objects at the subsequent time intervals.

Abstract: A system for monitoring a vehicle used in providing a service is disclosed. The system detects when the service is complete, and upon detecting that the service is complete, the system determines, from one or more sensors of the vehicle, that an object which should have been removed from the vehicle after completion of the transport service, remains in the vehicle. The system then automatically initiates performance of a remedial action.

Abstract: A LIDAR calibration system can detect a first set of return signals from a plurality of fiducial targets in a calibration facility for a lower set of laser scanners of the LIDAR module. The LIDAR calibration system can also detect a second set of return signals from one or more planar surfaces associated with a calibration trigger location on a road network for an upper set of laser scanners of the LIDAR module. Based on the first and second sets of return signals, the LIDAR calibration system can generate a set of calibration transforms to adjust a set of intrinsic parameters of the LIDAR module.

Abstract: A vehicle sensor calibration system for self-driving vehicles can include a turntable on which a sensor system including a LIDAR sensor is positioned. A plurality of fiducial targets can be positioned around the turntable to enable calibration of the sensor system, and a control mechanism can automatically rotate the turntable when the sensor system is positioned on the turntable. The system can include one or more computing systems that receive a data log corresponding to sensor data recorded by the sensor system as the sensor system rotates on the turntable, and analyze the sensor data to determine a set of calibration parameters to calibrate the sensor system.

Abstract: A vehicle determines a traction value for a surface of a road segment, and associates the traction value with a location of the surface. The vehicle stores the traction value and location as part of a traction map.

Abstract: A transportation management system can maintain a set of driver logs for drivers operating throughout a given region, where each driver log indicates driving characteristics of a respective driver. The system can determine a destination for the respective driver operating a vehicle from an initial location to the destination, and determine a set of routes between the initial location and the destination. Based at least in part on the driving characteristics of the respective driver, the system can determine an individualized risk value for the respective driver for each route of the set of routes, and select an optimal route from the set of routes based, at least in part, on the individualized risk value for each route.

Abstract: A system for monitoring a vehicle used in providing a service is disclosed. The system detects when the service is complete, and upon detecting that the service is complete, the system determines, from one or more sensors of the vehicle, that an object which should have been removed from the vehicle after completion of the transport service, remains in the vehicle. The system then automatically initiates performance of a remedial action.

Abstract: The present disclosure provides systems and methods that employ tolerance values defining a level of vehicle control precision for motion control of an autonomous vehicle. More particularly, a vehicle controller can obtain a trajectory that describes a proposed motion path for the autonomous vehicle. A constraint set of one or more tolerance values (e.g., a longitudinal tolerance value and/or lateral tolerance value) defining a level of vehicle control precision can be determined or otherwise obtained. Motion of the autonomous vehicle can be controlled to follow the trajectory within the one or more tolerance values (e.g., longitudinal tolerance value(s) and/or a lateral tolerance value(s)) identified by the constraint set. By creating a motion control framework for autonomous vehicles that includes an adjustable constraint set of tolerance values, autonomous vehicles can more effectively implement different precision requirements for different driving situations.

Abstract: Systems and methods for presenting virtual content are provided. In one example embodiment, a computer-implemented method includes obtaining, by a computing system including one or more computing devices, data indicative of virtual content. The method includes obtaining, by the computing system, data indicative of a planned motion of an autonomous vehicle. The method includes determining, by the computing system, a location for the virtual content within a virtual environment based at least in part on the data indicative of the planned motion of the autonomous vehicle. The method includes providing for display, by the computing system via one or more display devices, the virtual content at the location within the virtual environment.