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: Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. For instance, a first location corresponding to a location where the vehicle is to pick up or drop off a passenger is received. A first cost for the vehicle to reach the first location is determined. A second location based on the first location is identified, and a second cost is determined based on a cost for the vehicle to reach the second location and a cost for the passenger to reach the second location. The first cost is compared to the second cost, and a notification is sent based on the notification. In response to sending the notification, instructions to proceed to the second location are received, and in response to receiving the instructions, the vehicle is controlled in the autonomous driving mode to the second location to pick up or drop off the passenger.

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: 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: The technology relates to actively looking for an assigned passenger prior to a vehicle reaching a pickup location. For instance, information identifying the pickup location and client device information for authenticating the assigned passenger is received. Sensor data is received from a perception system of the vehicle identifying objects in an environment of the vehicle. When the vehicle is within a predetermined distance of the pickup location, authenticating a client device using the client device information is attempted. When the client device has been authenticated, the sensor data is used to determine whether a pedestrian is within a first threshold distance of the vehicle. When a pedestrian is determined to be within the first threshold distance of the vehicle, the vehicle is stopped prior to reaching the pickup location, to wait for the pedestrian within the first threshold distance of the vehicle to enter the vehicle.

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 route is selected for travel by an autonomous vehicle based on at least a level of difficulty of traversing the driving environment along that route. Vehicle signals, provided by one or more autonomous vehicles, indicating a difficulty associated with traveling a portion of a route are collected and used to predict a most favorable driving route for a given time. The signals may indicate a probability of disengaging from autonomous driving mode, a probability of being stuck for an unduly long time, traffic density, etc. A difficulty score may be computed for each road segment of a route, and then the scores of all of the road segments of the route are added together. The scores are based on number of previous disengagements, previous requests for remote assistance, unprotected left or right turns, whether parts of the driving area are occluded, etc. The difficulty score is used to compute a cost for a particular route, which may be compared to costs computed for other possible routes.

Abstract: Aspects of the disclosure relate to controlling a vehicle in an autonomous driving mode. For instance, a first location corresponding to a location where the vehicle is to pick up or drop off a passenger is received. A first cost for the vehicle to reach the first location is determined. A second location based on the first location is identified, and a second cost is determined based on a cost for the vehicle to reach the second location and a cost for the passenger to reach the second location. The first cost is compared to the second cost, and a notification is sent based on the notification. In response to sending the notification, instructions to proceed to the second location are received, and in response to receiving the instructions, the vehicle is controlled in the autonomous driving mode to the second location to pick up or drop off the passenger.

Abstract: Aspects of the disclosure relate to assigning a fleet of driverless vehicles to a plurality of parking locations for parking vehicles of the fleet. For instance, locations of the vehicles of the fleet as well as a number of available spaces at each of the plurality of parking location locations may be tracked. A subset of the fleet not already located at one of the plurality of parking locations is identified. At least one assignment assigning each vehicle of the subset to a respective parking location of the plurality of parking locations is determined according to the numbers of available spaces and the identified locations of the subset. For the at least one assignment, a total cost is determined by determining a cost value for each of a plurality of factors. The given assignment is sent to the fleet based on the total cost and the cost value.

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 provide systems and methods for providing suggested locations for pick up and destination locations. Pick up locations may include locations where an autonomous vehicle can pick up a passenger, while destination locations may include locations where the vehicle can wait for an additional passenger, stop and wait for a passenger to perform some task and return to the vehicle, or for the vehicle to drop off a passenger. As such, a request for a vehicle may be received from a client computing device. The request may identify a first location. A set of one or more suggested locations may be selected by comparing the predetermined locations to the first location. The set may be provided to the client computing device.

Abstract: Aspects of the disclosure relate to maneuvering a vehicle in an autonomous driving mode. For instance, that the vehicle needs to pullover may be determined based on a first input. A severity level corresponding to the first input may be determined. A set of requirements may be determined based on the severity level. A search may be performed over a map to identify a location for the vehicle to pull over that meets the set of requirements. The vehicle may then be maneuvered to pull over at the location.

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 present disclosure relate to context aware stopping of a vehicle without a driver. As an example, after a passenger has entered the vehicle, the vehicle is maneuvered by one or more processors in an autonomous driving mode towards a destination location along a route. The route is divided into two or more stages. A signal is received by the one or more processors. The signal indicates that the passenger is requesting that the vehicle stop or pull over. In response to the signal, the one or more processors determine a current stage of the route based on a current distance of the vehicle from a pickup location where the passenger entered the vehicle or a current distance of the vehicle from the destination location. The one or more processors then stop the vehicle in accordance with the determined current stage.

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: Aspects of the disclosure provide systems and methods for providing suggested locations for pick up and destination locations. Pick up locations may include locations where an autonomous vehicle can pick up a passenger, while destination locations may include locations where the vehicle can wait for an additional passenger, stop and wait for a passenger to perform some task and return to the vehicle, or for the vehicle to drop off a passenger. As such, a request for a vehicle may be received from a client computing device. The request may identify a first location. A set of one or more suggested locations may be selected by comparing the predetermined locations to the first location. The set may be provided to the client computing device.

Abstract: The technology relates to actively looking for an assigned passenger prior to a vehicle reaching a pickup location. For instance, information identifying the pickup location and client device information for authenticating the assigned passenger is received. Sensor data is received from a perception system of the vehicle identifying objects in an environment of the vehicle. When the vehicle is within a predetermined distance of the pickup location, authenticating a client device using the client device information is attempted. When the client device has been authenticated, the sensor data is used to determine whether a pedestrian is within a first threshold distance of the vehicle. When a pedestrian is determined to be within the first threshold distance of the vehicle, the vehicle is stopped prior to reaching the pickup location, to wait for the pedestrian within the first threshold distance of the vehicle to enter the vehicle.

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: 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.