Abstract: Aspects of the present disclosure relate generally to limiting the use of an autonomous or semi-autonomous vehicle by particular occupants based on permission data. More specifically, permission data may include destinations, routes, and/or other information that is predefined or set by a third party. The vehicle may then access the permission data in order to transport the particular occupant to the predefined destination, for example, without deviation from the predefined route. The vehicle may drop the particular occupant off at the destination and may wait until the passenger is ready to move to another predefined destination. The permission data may be used to limit the ability of the particular occupant to change the route of the vehicle completely or by some maximum deviation value. For example, the vehicle may be able to deviate from the route up to a particular distance from or along the route.

Abstract: Disclosed herein are systems and methods for providing supplemental identification abilities to an autonomous vehicle system. The sensor unit of the vehicle may be configured to receive data indicating an environment of the vehicle, while the control system may be configured to operate the vehicle. The vehicle may also include a processing unit configured to analyze the data indicating the environment to determine at least one object having a detection confidence below a threshold. Based on the at least one object having a detection confidence below a threshold, the processor may communicate at least a subset of the data indicating the environment for further processing. The vehicle is also configured to receive an indication of an object confirmation of the subset of the data. Based on the object confirmation of the subset of the data, the processor may alter the control of the vehicle by the control system.

Abstract: A method and apparatus for controlling a first vehicle autonomously are disclosed. For instance, one or more processors may plan to maneuver the first vehicle to complete an action and predict that a second vehicle will take a particular responsive action. The first vehicle is then maneuvered towards completing the action in a way that would allow the first vehicle to cancel completing the action without causing a collision between the first vehicle and the second vehicle, and in order to indicate to the second vehicle or a driver of the second vehicle that the first vehicle is attempting to complete the action. Thereafter, when the first vehicle is determined to be able to take the action, the action is completed by controlling the first vehicle autonomously according to whether the second vehicle begins to take the particular responsive action.

Abstract: The technology relates to identifying sensor occlusions due to the limits of the ranges of a vehicle's sensors and using this information to maneuver the vehicle. As an example, the vehicle is maneuvered along a route that includes traveling on a first roadway and crossing over a lane of a second roadway. A trajectory is identified from the lane that will cross with the route during the crossing at a first point. A second point beyond a range of the vehicle's sensors is selected. The second point corresponds to a hypothetical vehicle moving towards the route along the lane. A distance between the first point and the second point is determined. An amount of time that it would take the hypothetical vehicle to travel the distance is determined and compared to a threshold amount of time. The vehicle is maneuvered based on the comparison to complete the crossing.

Abstract: A method and apparatus are provided for optimizing one or more object detection parameters used by an autonomous vehicle to detect objects in images. The autonomous vehicle may capture the images using one or more sensors. The autonomous vehicle may then determine object labels and their corresponding object label parameters for the detected objects. The captured images and the object label parameters may be communicated to an object identification server. The object identification server may request that one or more reviewers identify objects in the captured images. The object identification server may then compare the identification of objects by reviewers with the identification of objects by the autonomous vehicle. Depending on the results of the comparison, the object identification server may recommend or perform the optimization of one or more of the object detection parameters.

Abstract: Disclosed herein are methods and apparatus for controlling autonomous vehicles utilizing maps that include visibility information. A map is stored at a computing device associated with a vehicle. The vehicle is configured to operate in an autonomous mode that supports a plurality of driving behaviors. The map includes information about a plurality of roads, a plurality of features, and visibility information for at least a first feature in the plurality of features. The computing device queries the map for visibility information for the first feature at a first position. The computing device, in response to querying the map, receives the visibility information for the first feature at the first position. The computing device selects a driving behavior for the vehicle based on the visibility information. The computing device controls the vehicle in accordance with the selected driving behavior.

Abstract: Example systems and methods allow for reporting and sharing of information reports relating to driving conditions within a fleet of autonomous vehicles. One example method includes receiving information reports relating to driving conditions from a plurality of autonomous vehicles within a fleet of autonomous vehicles. The method may also include receiving sensor data from a plurality of autonomous vehicles within the fleet of autonomous vehicles. The method may further include validating some of the information reports based at least in part on the sensor data. The method may additionally include combining validated information reports into a driving information map. The method may also include periodically filtering the driving information map to remove outdated information reports. The method may further include providing portions of the driving information map to autonomous vehicles within the fleet of autonomous vehicles.

Abstract: Methods and devices for detecting traffic signals and their associated states are disclosed. In one embodiment, an example method includes a scanning a target area using one or more sensors of a vehicle to obtain target area information. The vehicle may be configured to operate in an autonomous mode, and the target area may be a type of area where traffic signals are typically located. The method may also include detecting a traffic signal in the target area information, determining a location of the traffic signal, and determining a state of the traffic signal. Also, a confidence in the traffic signal may be determined. For example, the location of the traffic signal may be compared to known locations of traffic signals. Based on the state of the traffic signal and the confidence in the traffic signal, the vehicle may be controlled in the autonomous mode.

Abstract: Example systems and methods enable an autonomous vehicle to request assistance from a remote operator when the vehicle's confidence in operation is low. One example method includes operating an autonomous vehicle in a first autonomous mode. The method may also include identifying a situation where a level of confidence of an autonomous operation in the first autonomous mode is below a threshold level. The method may further include sending a request for assistance to a remote assistor, the request including sensor data representative of a portion of an environment of the autonomous vehicle. The method may additionally include receiving a response from the remote assistor, the response indicating a second autonomous mode of operation. The method may also include causing the autonomous vehicle to operate in the second autonomous mode of operation in accordance with the response from the remote assistor.

Abstract: A method and apparatus for controlling a first vehicle autonomously are disclosed. For instance, one or more processors may plan to maneuver the first vehicle to complete an action and predict that a second vehicle will take a particular responsive action. The first vehicle is then maneuvered towards completing the action in a way that would allow the first vehicle to cancel completing the action without causing a collision between the first vehicle and the second vehicle, and in order to indicate to the second vehicle or a driver of the second vehicle that the first vehicle is attempting to complete the action. Thereafter, when the first vehicle is determined to be able to take the action, the action is completed by controlling the first vehicle autonomously according to whether the second vehicle begins to take the particular responsive action.

Abstract: Disclosed herein are systems and methods for providing supplemental identification abilities to an autonomous vehicle system. The sensor unit of the vehicle may be configured to receive data indicating an environment of the vehicle, while the control system may be configured to operate the vehicle. The vehicle may also include a processing unit configured to analyze the data indicating the environment to determine at least one object having a detection confidence below a threshold. Based on the at least one object having a detection confidence below a threshold, the processor may communicate at least a subset of the data indicating the environment for further processing. The vehicle is also configured to receive an indication of an object confirmation of the subset of the data. Based on the object confirmation of the subset of the data, the processor may alter the control of the vehicle by the control system.

Abstract: Example systems and methods allow for reporting and sharing of information reports relating to driving conditions within a fleet of autonomous vehicles. One example method includes receiving information reports relating to driving conditions from a plurality of autonomous vehicles within a fleet of autonomous vehicles. The method may also include receiving sensor data from a plurality of autonomous vehicles within the fleet of autonomous vehicles. The method may further include validating some of the information reports based at least in part on the sensor data. The method may additionally include combining validated information reports into a driving information map. The method may also include periodically filtering the driving information map to remove outdated information reports. The method may further include providing portions of the driving information map to autonomous vehicles within the fleet of autonomous vehicles.

Abstract: The technology relates to fine maneuver control of large autonomous vehicles that employ multiple sets of independently actuated wheels. The control is able to optimize the turning radius, effectively negotiate curves, turns, and clear static objects of varying heights. Each wheel or wheel set is configured to adjust individually via control of an on-board computer system. Received sensor data and a physical model of the vehicle can be used for route planning and selecting maneuver operations in accordance with the additional degrees of freedom provided by the independently actuated wheels. This can include making turns, moving into or out of parking spaces, driving along narrow or congested roads, construction zones, loading docks, etc. A given maneuver may include maintaining a minimum threshold distance from a neighboring vehicle or other object.

Abstract: Disclosed herein are methods and apparatus for controlling autonomous vehicles utilizing maps that include visibility information. A map is stored at a computing device associated with a vehicle. The vehicle is configured to operate in an autonomous mode that supports a plurality of driving behaviors. The map includes information about a plurality of roads, a plurality of features, and visibility information for at least a first feature in the plurality of features. The computing device queries the map for visibility information for the first feature at a first position. The computing device, in response to querying the map, receives the visibility information for the first feature at the first position. The computing device selects a driving behavior for the vehicle based on the visibility information. The computing device controls the vehicle in accordance with the selected driving behavior.

Abstract: Methods and devices for detecting traffic signals and their associated states are disclosed. In one embodiment, an example method includes a scanning a target area using one or more sensors of a vehicle to obtain target area information. The vehicle may be configured to operate in an autonomous mode, and the target area may be a type of area where traffic signals are typically located. The method may also include detecting a traffic signal in the target area information, determining a location of the traffic signal, and determining a state of the traffic signal. Also, a confidence in the traffic signal may be determined. For example, the location of the traffic signal may be compared to known locations of traffic signals. Based on the state of the traffic signal and the confidence in the traffic signal, the vehicle may be controlled in the autonomous mode.

Abstract: Aspects of the present disclosure relate to a system having a memory, a plurality of self-driving systems for controlling a vehicle, and one or more processors. The processors are configured to receive at least one fallback task in association with a request for a primary task and at least one trigger of each fallback task. Each trigger is a set of conditions that, when satisfied, indicate when a vehicle requires attention for proper operation. The processors are also configured to send instructions to the self-driving systems to execute the primary task and receive status updates from the self-driving systems. The processors are configured to determine that a set of conditions of a trigger is satisfied based on the status updates and send further instructions based on the associated fallback task to the self-driving systems.

Abstract: A vehicle configured to operate in an autonomous mode may operate a sensor to determine an environment of the vehicle. The sensor may be configured to obtain sensor data of a sensed portion of the environment. The sensed portion may be defined by a sensor parameter. Based on the environment of the vehicle, the vehicle may select at least one parameter value for the at least one sensor parameter such that the sensed portion of the environment corresponds to a region of interest. The vehicle may operate the sensor, using the selected at least one parameter value for the at least one sensor parameter, to obtain sensor data of the region of interest, and control the vehicle in the autonomous mode based on the sensor data of the region of interest.

Abstract: Aspects of the disclosure relate to detecting and responding to objects in a vehicle's environment. For example, an object may be identified in a vehicle's environment, the object having a heading and location. A set of possible actions for the object may be generated using map information describing the vehicle's environment and the heading and location of the object. A set of possible future trajectories of the object may be generated based on the set of possible actions. A likelihood value of each trajectory of the set of possible future trajectories may be determined based on contextual information including a status of the detected object. A final future trajectory is determined based on the determined likelihood value for each trajectory of the set of possible future trajectories. The vehicle is then maneuvered in order to avoid the final future trajectory and the object.

Abstract: The technology relates to fine maneuver control of large autonomous vehicles that employ multiple sets of independently actuated wheels. The control is able to optimize the turning radius, effectively negotiate curves, turns, and clear static objects of varying heights. Each wheel or wheel set is configured to adjust individually via control of an on-board computer system. Received sensor data and a physical model of the vehicle can be used for route planning and selecting maneuver operations in accordance with the additional degrees of freedom provided by the independently actuated wheels. This can include making turns, moving into or out of parking spaces, driving along narrow or congested roads, construction zones, loading docks, etc. A given maneuver may include maintaining a minimum threshold distance from a neighboring vehicle or other object.

Abstract: Aspects of the disclosure relate to determining whether a vehicle should continue through an intersection. For example, the one or more of the vehicle's computers may identify a time when the traffic signal light will turn from yellow to red. The one or more computers may also estimate a location of a vehicle at the time when the traffic signal light will turn from yellow to red. A starting point of the intersection may be identified. Based on whether the estimated location of the vehicle is at least a threshold distance past the starting point at the time when the traffic signal light will turn from yellow to red, the computers can determine whether the vehicle should continue through the intersection.