Abstract: Aspects of the disclosure relate to an autonomous vehicle that may detected other nearby vehicles and identify them as parked or unparked. This identification may be based on visual indicia displayed by the detected vehicles as well as traffic control factors relating to the detected vehicles. Detected vehicles that are in a known parking spot may automatically be identified as parked. In addition, detected vehicles that satisfy conditions that are indications of being parked may also be identified as parked. The autonomous vehicle may then base its control strategy on whether or not a vehicle has been identified as parked or not.

Abstract: A route for a trip to a destination is generated using map information. A set of no-go roadway segments, where the vehicle is not able to drive in an autonomous mode, relevant to the route from the plurality of no-go roadway segments is identified from the map information. A local region around a current location of the vehicle is determined. A local map region including roadway segments of the map information that correspond to locations within the local region is determined. The set of the plurality of no-go roadway segments is filtered from the roadway segments of the local map region. A cost value is assigned to each roadway segment of the filtered roadway segments of the local map region. Any assigned cost values are used to determining a plan for maneuvering the vehicle for a predetermined period into the future. The vehicle is maneuvered according to the plan.

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 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: 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: 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: 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: 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.

Abstract: The technology relates to facilitating transportation services between a user and a vehicle having an autonomous driving mode. For instance, one or more server computing devices having one or more processors may information identifying the current location of the vehicle. The one or more server computing devices may determine that the user is likely to want to take a trip to a particular destination based on prior location history for the user. The one or more server computing devices may dispatch the vehicle to cause the vehicle to travel in the autonomous driving mode towards a location of the user. In addition, after dispatching, the one or more server computing devices sending a notification to a client computing device associated with the user indicating that the vehicle is currently available to take the passenger to the particular destination.

Abstract: Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

Abstract: A method is provided that involves identifying a target region of an environment of an autonomous vehicle to be monitored for presence of moving objects. The method also involves operating a first sensor to obtain a scan of a portion of the environment that includes at least a portion of the target region and an intermediate region between the autonomous vehicle and the target region. The method also involves determining whether a second sensor has a sufficiently clear view of the target region based on at least the scan obtained by the first sensor. The method also involves operating the second sensor to monitor the target region for presence of moving objects based on at least a determination that the second sensor has a sufficiently clear view of the target region. Also provided is an autonomous vehicle configured to perform the method.

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: Aspects of the disclosure relate to validating autonomous control software for operating a vehicle autonomously. For instance, the autonomous control software is run through a driving scenario to observe an outcome for the autonomous control software. A validation model is run through the driving scenario a plurality of times to observe an outcome for the model for each of the plurality of times. Whether the software passed the driving scenario is determined based on whether the outcome for the software indicates that a virtual vehicle under control of the software collided with another object during the single time. Whether the validation model passed the driving scenario is determined based on whether the outcome for the model indicates that a virtual vehicle under control of the model collided with another object in any one of the plurality of times. The software is validated based on the determinations.

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.

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: Aspects of the disclosure relate to arranging a pickup between a driverless vehicle and a passenger. For instance, dispatch instructions dispatching the vehicle to a predetermined pickup area in order to pick up the passenger are received by the vehicle which begins maneuvering to the predetermined pickup area. While doing so, the vehicle receives from the passenger's client computing device the device's location. An indication that the passenger is interested in a fly-by pickup is identified. The fly-by pickup allows the passenger to safely enter the vehicle at a location outside of the predetermined pickup area and prior to the one or more processors have maneuvered the vehicle to the predetermined pickup area. The vehicle determines that the fly-by pickup is appropriate based on at least the location of the client computing device and the indication, and based on the determination, maneuvers itself in order to attempt the fly-by pickup.

Abstract: Examples described may enable provision of remote assistance for an autonomous vehicle. An example method includes a computing system operating by default in a first mode and periodically transitioning from operation in the first mode to operation in a second mode. In the first mode, the system may receive environment data provided by the vehicle and representing object(s) having a detection confidence below a threshold, where the detection confidence is indicative of a likelihood of correct identification of the object(s), and responsive to the object(s) having a confidence below the threshold, provide remote assistance data comprising an instruction to control the vehicle and/or a correct identification of the object(s). In the second mode, the system may trigger user interface display of remote assistor alertness data based on pre-stored data related to an environment in which the pre-stored data was acquired, and receive a response relating to the alertness data.

Abstract: A method is provided that involves identifying a target region of an environment of an autonomous vehicle to be monitored for presence of moving objects. The method also involves operating a first sensor to obtain a scan of a portion of the environment that includes at least a portion of the target region and an intermediate region between the autonomous vehicle and the target region. The method also involves determining whether a second sensor has a sufficiently clear view of the target region based on at least the scan obtained by the first sensor. The method also involves operating the second sensor to monitor the target region for presence of moving objects based on at least a determination that the second sensor has a sufficiently clear view of the target region. Also provided is an autonomous vehicle configured to perform the method.

Abstract: Aspects of the disclosure relate to validating autonomous control software for operating a vehicle autonomously. For instance, the autonomous control software is run through a driving scenario to observe an outcome for the autonomous control software. A validation model is run through the driving scenario a plurality of times to observe an outcome for the model for each of the plurality of times. Whether the software passed the driving scenario is determined based on whether the outcome for the software indicates that a virtual vehicle under control of the software collided with another object during the single time. Whether the validation model passed the driving scenario is determined based on whether the outcome for the model indicates that a virtual vehicle under control of the model collided with another object in any one of the plurality of times. The software is validated based on the determinations.

Abstract: The technology relates to facilitating transportation services between a user and a vehicle having an autonomous driving mode. For instance, one or more server computing devices having one or more processors may information identifying the current location of the vehicle. The one or more server computing devices may determine that the user is likely to want to take a trip to a particular destination based on prior location history for the user. The one or more server computing devices may dispatch the vehicle to cause the vehicle to travel in the autonomous driving mode towards a location of the user. In addition, after dispatching, the one or more server computing devices sending a notification to a client computing device associated with the user indicating that the vehicle is currently available to take the passenger to the particular destination.