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: 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 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 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: 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 generating a speed plan for an autonomous vehicle. As an example, the vehicle is maneuvered in an autonomous driving mode along a route using pre-stored map information. This information identifies a plurality of keep clear regions where the vehicle should not stop but can drive through in the autonomous driving mode. Each keep clear region of the plurality of keep clear regions is associated with a priority value. A subset of the plurality of keep clear regions is identified based on the route. A speed plan for stopping the vehicle is generated based on the priority values associated with the keep clear regions of the subset. The speed plan identifies a location for stopping the vehicle. The speed plan is used to stop the vehicle in the location.

Abstract: Example systems and methods enable an autonomous vehicle to request assistance from a remote operator in certain predetermined situations. One example method includes determining a representation of an environment of an autonomous vehicle based on sensor data of the environment. Based on the representation, the method may also include identifying a situation from a predetermined set of situations for which the autonomous vehicle will request remote assistance. The method may further include sending a request for assistance to a remote assistor, the request including the representation of the environment and the identified situation. The method may additionally include receiving a response from the remote assistor indicating an autonomous operation. The method may also include causing the autonomous vehicle to perform the autonomous operation.

Abstract: Aspects of the disclosure relate to detecting vehicle collisions. In one example, one or more computing devices may receive acceleration data of a vehicle and the expected acceleration data of the vehicle over a period of time. The one or more computing devices may determine a change in the vehicle's acceleration over the period of time, where the change in the vehicle's acceleration over the period of time is the difference between the expected acceleration data and the acceleration data. The one or more computing devices may detect an occurrence when the change in the vehicle's acceleration is greater than a threshold value and assign the occurrence into a collision category. Based on the assigned collision category, the one or more computing devices may perform a responsive action.

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 present disclosure relate to a vehicle having one or more computing devices that may receive instructions to pick up a passenger at a pickup location and determine when the vehicle is within a first distance of the pickup location. When the vehicle is within the first distance, the computing devices may make a first attempt to find a spot to park the vehicle and wait for the passenger. When the vehicle is unable to find a spot to park the vehicle on the first attempt, the computing devices may maneuvering the vehicle in order to make a second attempt to find a spot to park the vehicle and wait for the passenger. When the vehicle is unable to find a spot to park the vehicle on the second attempt, the computing devices may stop the vehicle in a current lane to wait for the passenger.

Abstract: Example systems and methods enable an autonomous vehicle to request assistance from a remote operator in certain predetermined situations. One example method includes determining a representation of an environment of an autonomous vehicle based on sensor data of the environment. Based on the representation, the method may also include identifying a situation from a predetermined set of situations for which the autonomous vehicle will request remote assistance. The method may further include sending a request for assistance to a remote assistor, the request including the representation of the environment and the identified situation. The method may additionally include receiving a response from the remote assistor indicating an autonomous operation. The method may also include causing the autonomous vehicle to perform the autonomous operation.

Abstract: Aspects of the disclosure relate to testing predictions of an autonomous vehicle relating to another vehicle or object in a roadway. For instance, one or more processors may plan to maneuver the autonomous vehicle to complete an action and predict that the other vehicle will take a responsive action. The autonomous vehicle is then maneuvered towards completing the action in a way that would allow the autonomous 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 using the determination of whether the second vehicle begins to take the particular responsive action.

Abstract: Aspects of the disclosure relate to providing audible indications to objects located externally to a vehicle having an autonomous driving mode. For instance, a vehicle is controlled in the autonomous driving mode along a particular path. Information identifying a characteristic and a location of an object is received. When vehicle is determined to be prevented from proceeding along the particular path because of the location of the object, a scenario is identified using the characteristic. The scenario is associated with a predetermined period of time and a type of audible indication used to identify an audible indication. After the waiting the predetermined period, when the object is determined to still be preventing the vehicle from proceeding along the particular path, the audible indication is played through a speaker of the vehicle to encourage the object to take an action to allow the vehicle to proceed along the particular path.

Abstract: Aspects of the disclosure relate to generating a speed plan for an autonomous vehicle. As an example, the vehicle is maneuvered in an autonomous driving mode along a route using pre-stored map information. This information identifies a plurality of keep clear regions where the vehicle should not stop but can drive through in the autonomous driving mode. Each keep clear region of the plurality of keep clear regions is associated with a priority value. A subset of the plurality of keep clear regions is identified based on the route. A speed plan for stopping the vehicle is generated based on the priority values associated with the keep clear regions of the subset. The speed plan identifies a location for stopping the vehicle. The speed plan is used to stop the vehicle in the location.

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: 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: 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: 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 system and method is provided for identifying an object along a road, where the object may be represented by a bounding box, and projecting a set of obstacle points within the bounding box corresponding to the identified object. In one aspect, a two-dimensional plane oriented perpendicular to a direction of the movement of the vehicle may be identified. In another aspect, the areas of the plane that may be occupied based on the set of obstacle points may be determined to generate a contour of the identified object. Thereafter, the height profiles of the identified object and the vehicle may be determined and identified, respectively. Based on the height profiles, a minimum clearance may be determined.

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.