Patent Description:
Autonomous vehicles, such as vehicles which do not require a human driver when operating in an autonomous driving mode, may be used to aid in the transport of passengers or items from one location to another. When an autonomous vehicle, which may not have a driver, is preparing to pull over in order to pick up a passenger certain parking maneuvers, such as parallel parking (involving pulling alongside of a parking spot and reversing into that parking spot) can be difficult and sometimes dangerous maneuvers. For example, it can take quite a long time to complete a parallel parking maneuver, during which a passenger may unknowingly attempt to enter the vehicle before the vehicle is fully parked creating a possibly uncomfortable situation. In addition, the vehicle's computing devices may not be able to pick up on social cues from drivers of other nearby vehicles who may pull very close behind the vehicle or who may try to pull into the same spot without reversing. In some instances, this can be avoided by always attempting to find very long pullover locations from which the vehicle never has to use a parallel parking maneuver or to reverse in order to pull out. Of course, this may not always be possible and may result in a vehicle making one or more loops "around the block" before being able to pull over and wait for a passenger.

<CIT> discloses systems and methods for confirming the identity of a passenger and changing destination of a vehicle. This may include receiving dispatching instructions to pick up a first passenger at a pickup location and to drop off the first passenger at a first destination as well as authentication information for authenticating a first client computing device of the first passenger. Once the client device is authenticated and a second passenger enters the vehicle, the vehicle is maneuvered towards the first destination. While doing so, a location of the vehicle is compared to location information received from the client computing devices. A notification is sent to a dispatching server based on the comparison and a second destination location is received in response. The vehicle is then maneuvered towards the second destination instead of the first destination.

<CIT> discloses a method and device for controlling an unmanned vehicle. The method particularly comprises the steps that in response to determining that a pedestrian exists in a first target area, behavior prompt information prompting the pedestrian to perform a corresponding behavior is sent; according to the acquired behavior information of the pedestrian, it is determined whether or not the behavior information meets a deceleration condition matched with the prompt information; in response to determining that the deceleration condition is met and determining that the speed of the unmanned vehicle is greater than a preset deceleration threshold, control information used for reducing the moving speed of the unmanned vehicle is transmitted. The method achieves the deceleration control of the unmanned vehicle in response to the pedestrian's response to the behavioral prompt information.

The matter for protection is set out in the appended claims. One aspect provides a method of maneuvering a vehicle having an autonomous driving mode. The method includes identifying, by one or more processors, a pullover location for the vehicle to stop and wait for a passenger; maneuvering, by the one or more processors, the vehicle in the autonomous driving mode in order to pull over by pulling forward into the pullover location; determining, by the one or more processors, whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle based on context for the pull over with respect to the passenger; and maneuvering, by the one or more processors, the vehicle in the autonomous driving mode in reverse based on the determination of whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle.

In one example, the method also includes, when maneuvering the vehicle in order to pull over by pulling forward into the pullover location, adjusting a minimum distance for the vehicle with respect to other objects in order to maneuver the vehicle within the adjusted minimum distance of another object in front of the vehicle. In another example, the method also includes the context by determining that a pedestrian is within a predetermined threshold distance of the pullover location, and wherein maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse after the passenger enters the vehicle. In another example, the method also includes determining the context by determining that no pedestrian is within a predetermined threshold distance of the pullover location, and wherein maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse before the passenger enters the vehicle.

In line with the invention, the method also includes determining the context by determining an expected amount of time for the passenger to reach the vehicle once the vehicle is ready to be maneuvered in reverse in the pullover location, and comparing the expected amount of time to a threshold value. In this example, the determination of whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle is further based on the comparison. In addition, maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse before the passenger enters the vehicle when the comparison indicates that the expected amount of time is greater than the threshold value. Alternatively, maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse after the passenger enters the vehicle when the comparison indicates that the expected amount of time is less than the threshold value. In addition or alternatively, determining the expected amount of time is based on location information for a client computing device associated with the passenger. In this example, determining the expected amount of time is further based on a predetermined walking speed for pedestrians. In addition or alternatively, determining the expected amount of time is based on historical data indicating how long it has taken the passenger to reach one or more vehicles for a pick up in the past. In addition or alternatively, determining the expected amount of time is based on historical data indicating an expected amount of time for passengers to reach one or more vehicles for a pick up. In addition or alternatively, determining the expected amount of time is based on historical data indicating an expected amount of time for passengers to reach one or more vehicles for a pick up at a predetermined time of day. In addition or alternatively, determining the expected amount of time is based on historical data indicating an expected amount of time for passengers to reach one or more vehicles for a pick up on a predetermined day of the week. In addition or alternatively, determining the expected amount of time is based on historical data indicating an expected amount of time for passengers to reach one or more vehicles for a pick up at the pullover location. In addition or alternatively, determining the expected amount of time is based on historical data indicating an expected amount of time for passengers to reach one or more vehicles for a pick up at a geographic region including the pullover location and one or more additional pullover locations.

In another example, the method also includes determining the context by determining that a pedestrian who is making progress towards the vehicle is within a predetermined distance of the pullover location, and wherein maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse after the passenger enters the vehicle. In another example, the method also includes determining the context by determining that no pedestrian who is making progress towards the vehicle is within a predetermined distance of the pullover location, and wherein maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse after the passenger enters the vehicle. In another example, the method also includes determining the context by determining that a communication link has been established between one or more computing devices of the vehicle and a client computing device associated with the passenger, and wherein maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse after the passenger enters the vehicle. In another example, the method also includes determining the context by determining that no communication link has been established between one or more computing devices of the vehicle and a client computing device associated with the passenger, and wherein maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse before the passenger enters the vehicle. In another example, the method also includes determining the context by determining that location information for a client computing device associated with the passenger is within a predetermined threshold distance of the vehicle, and wherein maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse after the passenger enters the vehicle. In another example, the method also includes determining the context by determining that location information for a client computing device associated with the passenger is not within a predetermined threshold distance of the vehicle, and wherein maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse before the passenger enters the vehicle. In another example, the method also includes maneuvering the vehicle in reverse includes maneuvering the vehicle in reverse before the passenger enters the vehicle, and the method further comprises providing a notification to indicate to the passenger that the passenger should wait for the vehicle to complete a reversing maneuver before attempting entering the vehicle.

The technology relates to parking behaviors for autonomous vehicles. For instance, when an autonomous vehicle, which may not have a driver, is preparing to pull over in order to pick up a passenger certain parking maneuvers, such as parallel parking (involving pulling alongside of a parking spot and reversing into that parking spot) can be difficult and sometimes dangerous maneuvers. For example, it can take quite a long time to complete a parallel parking maneuver, during which a passenger may unknowingly attempt to enter the vehicle before the vehicle is fully parked creating a possibly uncomfortable situation. In addition, the vehicle's computing devices may not be able to pick up on social cues from drivers of other nearby vehicles who may pull very close behind the vehicle or who may try to pull into the same spot without reversing. In some instances, this can be avoided by always attempting to find very long pullover locations from which the vehicle never has to use a parallel parking maneuver or to reverse in order to pull out. Of course, this may not always be possible and may result in a vehicle making one or more loops "around the block" before being able to pull over and wait for a passenger. To avoid such situations, rather than performing a parallel parking maneuver and/or only looking for very long places to pull over, other driving behaviors may be implemented.

For instance, before a vehicle is pulled over, for example to pick up or drop off a passenger, the vehicle's computing devices may first identify a place to pull over. This may include identifying an area where the vehicle is permitted to park, for instance by identifying a set of predetermined pullover locations in pre-stored map information or by identifying a possible spot by searching for an area on a side of a road long enough for the vehicle to pull forward into and stop. Of these set of predetermined pullover locations and/or possible pullover locations, the vehicle's computing devices may select an available pullover location.

Once a vehicle is ready to pull over into an identified pullover location, the vehicle's planning system may implement pull over behavior. This may involve pulling forward into the identified pullover location and stopping the vehicle. In some instances, this may require that the vehicle reverse (i.e. back up) before being able to pull out of the identified pullover location.

At this point or shortly before when the vehicle is approaching or pulling into the identified pullover location, the vehicle's computing devices may determine whether the vehicle should immediately reverse in order to create a gap between the vehicle and another object. In order to determine whether the vehicle should immediately reverse or wait for a passenger to enter the vehicle, the vehicle's computing devices may determine context for the pull over with respect to the passenger for the vehicle. For instance, this context may indicate, among other things, whether the actual passenger (i.e. an assigned passenger) or a potential passenger (i.e. a pedestrian who may be the passenger) is nearby. Based on this context, the vehicle's computing devices should wait for the passenger to enter the vehicle before maneuvering the vehicle in reverse. If the context indicates that the actual passenger or a potential passenger is not nearby the vehicle, the vehicle's computing devices may maneuver the vehicle in reverse before the passenger enters the vehicle. This may include reversing away from an object in front of the vehicle.

In one instance, determining the context may include determining an expected amount of time for the passenger to reach or arrive at the vehicle. In addition or alternatively, determining the context may include determining whether there are any pedestrians nearby. In addition or alternatively, determining the context may include determining whether there are any pedestrians who are actively making progress towards the vehicle. In addition or alternatively, determining the context may include determining whether the vehicle's computing devices have established communications with and/or authenticated a client computing device associated with the passenger. In addition or alternatively, determining the context may include determining whether location information for a client computing device of the passenger indicates that the passenger is nearby the vehicle.

The features described herein may enable an autonomous vehicle to implement specific parking behaviors. As noted above, these parking behaviors may improve the ability for the computing devices of a vehicle having an autonomous driving mode to find available pullover locations while doing so safely and avoiding parallel parking maneuvers. In other words, because the vehicle is able to find smaller parking locations (for instance, a <NUM>-<NUM> meter long van may be able to pull over in an <NUM> to <NUM> meter pullover location as opposed to a <NUM> or more meter pullover location), the vehicle may be more likely to find a pullover location to wait for a passenger and less likely to have to loop around one or more times before doing so. In addition, because the timing of when a vehicle reverses in a pullover location is dependent upon the context for the pull over with respect to a potential or actual passenger for the vehicle, this may avoid situations in which the vehicle stops and an unknowing passenger attempts to enter the vehicle just before or while the vehicle is reversing, which again can be dangerous for the passenger.

As shown in <FIG>, a vehicle <NUM> in accordance with one aspect of the disclosure includes various components. While certain aspects of the disclosure are particularly useful in connection with specific types of vehicles, the vehicle may be any type of vehicle including, but not limited to, cars, trucks, motorcycles, buses, recreational vehicles, etc. The vehicle may have one or more computing devices, such as computing device <NUM> containing one or more processors <NUM>, memory <NUM> and other components typically present in general purpose computing devices.

The one or more processor <NUM> may be any conventional processors, such as commercially available CPUs or GPUs. Alternatively, the one or more processors may be a dedicated device such as an ASIC or other hardware-based processor. Although <FIG> functionally illustrates the processor, memory, and other elements of computing device <NUM> as being within the same block, it will be understood by those of ordinary skill in the art that the processor, computing device, or memory may actually include multiple processors, computing devices, or memories that may or may not be stored within the same physical housing. For example, memory may be a hard drive or other storage media located in a housing different from that of computing device <NUM>. Accordingly, references to a processor or computing device will be understood to include references to a collection of processors or computing devices or memories that may or may not operate in parallel.

In one aspect the computing devices <NUM> may be part of an autonomous control system capable of communicating with various components of the vehicle in order to control the vehicle in an autonomous driving mode. For example, returning to <FIG>, the computing devices <NUM> may be in communication with various systems of vehicle <NUM>, such as deceleration system <NUM>, acceleration system <NUM>, steering system <NUM>, routing system <NUM>, planning system <NUM>, positioning system <NUM>, and perception system <NUM> in order to control the movement, speed, etc. of vehicle <NUM> in accordance with the instructions <NUM> of memory <NUM> in the autonomous driving mode.

As an example, computing devices <NUM> may interact with deceleration system <NUM> and acceleration system <NUM> in order to control the speed of the vehicle. Similarly, steering system <NUM> may be used by computing devices <NUM> in order to control the direction of vehicle <NUM>. For example, if vehicle <NUM> is configured for use on a road, such as a car or truck, the steering system may include components to control the angle of wheels to turn the vehicle.

Routing system <NUM> may be used by the computing devices <NUM> in order to generate a route to a destination. planning system <NUM> may be used by computing device <NUM> in order to follow the route. In this regard, the planning system <NUM> and/or routing system <NUM> may store detailed map information, e.g., highly detailed maps identifying the shape and elevation of roadways, lane lines, intersections, crosswalks, speed limits, traffic signals, buildings, signs, real time traffic information, pull over spots vegetation, or other such objects and information.

<FIG> is an example of map information <NUM> for a section of roadway including. The map information <NUM> may be a local version of the map information stored in the memory <NUM> of the computing devices <NUM>. In this example, the map information <NUM> includes information identifying the shape, location, and other characteristics of curbs <NUM>, <NUM>, lane lines <NUM>, <NUM>, parking spots <NUM>, <NUM>, <NUM>, as well as loading zone <NUM>. Only a few such features are depicted in <FIG>, however, the map information <NUM> may include significantly more features and details in order to enable the vehicle <NUM> to be controlled in the autonomous driving mode.

Although the map information is depicted herein as an image-based map, the map information need not be entirely image based (for example, raster). For example, the map information may include one or more roadgraphs or graph networks of information such as roads, lanes, intersections, and the connections between these features which may be represented by road segments. Each feature may be stored as graph data and may be associated with information such as a geographic location and whether or not it is linked to other related features, for example, a stop sign may be linked to a road and an intersection, etc. In some examples, the associated data may include grid-based indices of a roadgraph to allow for efficient lookup of certain roadgraph features.

Positioning system <NUM> may be used by computing devices <NUM> in order to determine the vehicle's relative or absolute position on a map or on the earth. For example, the position system <NUM> may include a GPS receiver to determine the device's latitude, longitude and/or altitude position. Other location systems such as laser-based localization systems, inertial-aided GPS, or camera-based localization may also be used to identify the location of the vehicle. The location of the vehicle may include an absolute geographical location, such as latitude, longitude, and altitude as well as relative location information, such as location relative to other cars immediately around it which can often be determined with less noise that absolute geographical location.

The positioning system <NUM> may also include other devices in communication with the computing devices of the computing devices <NUM>, such as an accelerometer, gyroscope or another direction/speed detection device to determine the direction and speed of the vehicle or changes thereto. By way of example only, an acceleration device may determine its pitch, yaw or roll (or changes thereto) relative to the direction of gravity or a plane perpendicular thereto. The device may also track increases or decreases in speed and the direction of such changes. The device's provision of location and orientation data as set forth herein may be provided automatically to the computing device <NUM>, other computing devices and combinations of the foregoing.

The perception system <NUM> also includes one or more components for detecting objects external to the vehicle such as other vehicles, obstacles in the roadway, traffic signals, signs, trees, etc. For example, the perception system <NUM> may include lasers, sonar, radar, cameras and/or any other detection devices that record data which may be processed by the computing devices of the computing devices <NUM>. In the case where the vehicle is a passenger vehicle such as a minivan, the minivan may include a laser or other sensors mounted on the roof or other convenient location. For instance, <FIG> is an example external view of vehicle <NUM>. In this example, roof-top housing <NUM> and dome housing <NUM> may include a LIDAR sensor as well as various cameras and radar units. In addition, housing <NUM> located at the front end of vehicle <NUM> and housings <NUM>, <NUM> on the driver's and passenger's sides of the vehicle may each store a LIDAR sensor. For example, housing <NUM> is located in front of driver door <NUM>. Vehicle <NUM> also includes housings <NUM>, <NUM> for radar units and/or cameras also located on the roof of vehicle <NUM>. Additional radar units and cameras (not shown) may be located at the front and rear ends of vehicle <NUM> and/or on other positions along the roof or roof-top housing <NUM>. <FIG> also depicts left and right turn signals <NUM>, <NUM>. In this example, front left turn signal 112A, rear left turn signal 112B, and front right turn signal 114A are depicted, but a right rear turn signal is not visible from the perspective of <FIG>.

The computing devices <NUM> may be capable of communicating with various components of the vehicle in order to control the movement of vehicle <NUM> according to primary vehicle control code of memory of the computing devices <NUM>. For example, returning to <FIG>, the computing devices <NUM> may include various computing devices in communication with various systems of vehicle <NUM>, such as deceleration system <NUM>, acceleration system <NUM>, steering system <NUM>, routing system <NUM>, planning system <NUM>, positioning system <NUM>, perception system <NUM>, and power system <NUM> (i.e. the vehicle's engine or motor) in order to control the movement, speed, etc. of vehicle <NUM> in accordance with the instructions <NUM> of memory <NUM>.

The various systems of the vehicle may function using autonomous vehicle control software in order to determine how to and to control the vehicle. As an example, a perception system software module of the perception system <NUM> may use sensor data generated by one or more sensors of an autonomous vehicle, such as cameras, LIDAR sensors, radar units, sonar units, etc., to detect and identify objects and their characteristics. These characteristics may include location, type, heading, orientation, speed, acceleration, change in acceleration, size, shape, etc. In some instances, characteristics may be input into a behavior prediction system software module which uses various behavior models based on object type to output a predicted future behavior for a detected object. In other instances, the characteristics may be put into one or more detection system software modules, such as a traffic light detection system software module configured to detect the states of known traffic signals, construction zone detection system software module configured to detect construction zones from sensor data generated by the one or more sensors of the vehicle as well as an emergency vehicle detection system configured to detect emergency vehicles from sensor data generated by sensors of the vehicle. Each of these detection system software modules may uses various models to output a likelihood of a construction zone or an object being an emergency vehicle. Detected objects, predicted future behaviors, various likelihoods from detection system software modules, the map information identifying the vehicle's environment, position information from the positioning system <NUM> identifying the location and orientation of the vehicle, a destination for the vehicle as well as feedback from various other systems of the vehicle may be input into a planning system software module of the planning system <NUM>. The planning system may use this input to generate trajectories for the vehicle to follow for some brief period of time into the future based on a route generated by a routing module of the routing system <NUM>. A control system software module of the computing devices <NUM> may be configured to control movement of the vehicle, for instance by controlling braking, acceleration and steering of the vehicle, in order to follow a trajectory.

The computing devices <NUM> may control the vehicle in an autonomous driving mode by controlling various components. For instance, by way of example, the computing devices <NUM> may navigate the vehicle to a destination location completely autonomously using data from the detailed map information and planning system <NUM>. The computing devices <NUM> may use the positioning system <NUM> to determine the vehicle's location and perception system <NUM> to detect and respond to objects when needed to reach the location safely. Again, in order to do so, computing device <NUM> may generate trajectories and cause the vehicle to follow these trajectories, for instance, by causing the vehicle to accelerate (e.g., by supplying fuel or other energy to the engine or power system <NUM> by acceleration system <NUM>), decelerate (e.g., by decreasing the fuel supplied to the engine or power system <NUM>, changing gears, and/or by applying brakes by deceleration system <NUM>), change direction (e.g., by turning the front or rear wheels of vehicle <NUM> by steering system <NUM>), and signal such changes (e.g., by lighting turn signals <NUM> or <NUM> of the signaling system). Thus, the acceleration system <NUM> and deceleration system <NUM> may be a part of a drivetrain that includes various components between an engine of the vehicle and the wheels of the vehicle. Again, by controlling these systems, computing devices <NUM> may also control the drivetrain of the vehicle in order to maneuver the vehicle autonomously.

<FIG> is an example representation of a section of roadway <NUM> corresponding to the map information <NUM>. In this regard, the shape, location, and other characteristics of curbs <NUM>, <NUM> may correspond to curbs <NUM>, <NUM>, lane lines <NUM>, <NUM> may correspond to lane lines <NUM>, <NUM>, parking spots <NUM>, <NUM>, <NUM> may correspond to parking spots <NUM>, <NUM>, <NUM>, and loading zone <NUM> may correspond to loading zone <NUM>. <FIG> is an example representation of vehicle <NUM> on the section of roadway <NUM> approaching a pickup location represented by the location marker <NUM>.

<FIG> is an example flow diagram <NUM> in accordance with aspects of the disclosure which may be performed by one or more processors of one or more computing devices, such as processors <NUM> of computing devices <NUM>, in order to maneuver a vehicle having an autonomous driving mode. In this example, at block <NUM>, a pullover location for the vehicle to stop and wait for a passenger is identified. In other words, before vehicle <NUM> can pull over, for example to pick up or drop off a passenger, the computing devices <NUM> may first identify a place for vehicle <NUM> to pull over. This may include identifying an area where the vehicle is permitted to park, for instance by identifying a set of predetermined pullover locations in the map information <NUM>, such as parking spots <NUM>, <NUM>, <NUM> of <FIG>, or by identifying a possible pullover location by searching for an area on a side of a road long enough for the vehicle to pull forward into and stop. In some instances, the computing devices <NUM> may also identify loading zone <NUM> as a possible pullover location either based on the map information <NUM> or by determining that the area of the loading zone <NUM> is large enough for vehicle <NUM> to safely park.

Of these set of predetermined pullover locations and/or possible pullover locations, the computing devices <NUM> may identify or select an available pullover location where the pullover location is available and still wide enough (i.e. not partially occupied) such that the vehicle is able to pull into the pullover location. For instance, returning to <FIG>, the computing devices may determine that parking spots <NUM> and <NUM> (or parking spots <NUM> and <NUM>) are occupied, here by vehicles <NUM> and <NUM>, respectively. In this regard, the computing devices <NUM> may identify parking spot <NUM> (or parking spot <NUM>) and in some instances, loading zone <NUM> (or loading zone <NUM>) as available. The computing devices <NUM> may then select from loading zone <NUM> and parking spot <NUM>. In this example, it may be preferable to identify or select parking spot <NUM> as the vehicle <NUM> may be able to wait longer for a passenger in parking spot <NUM> than in loading zone <NUM>, even though the loading zone <NUM> is closed to the location of the location marker <NUM>.

Returning to <FIG>, at block <NUM>, the vehicle is maneuvered in the autonomous driving mode in order to pull over and pull forward into the pullover location. Once the vehicle <NUM> is ready to pull over into an identified pullover location, the vehicle's planning system may implement pull over behavior. This may involve the computing devices <NUM> maneuvering vehicle <NUM> autonomously in order to cause the vehicle to pull forward into the identified pullover location and stopping the vehicle <NUM>. For instance, turning to <FIG>, the computing devices <NUM> may use the planning system <NUM> to generate trajectory <NUM>. Thereafter, the computing devices <NUM> may control the vehicle <NUM> in order to follow the trajectory <NUM> as described above. <FIG> represents the vehicle <NUM> after it has pulled into the identified pullover location (here parking spot <NUM>).

Typically, when in driving situations, the computing devices <NUM> may always attempt to maintain a minimum distance between the vehicle <NUM> and other road users. For example, when coming to a stop at a traffic intersection, the vehicle <NUM> may always stop at least <NUM> meters or more or less behind another stopped vehicle. However, when pulling into an identified pullover location, the pull over behavior may cause this minimum distance to be adjusted, for instance decreased to a much smaller distance such as. <NUM> meters (approximately <NUM> inches) or more or less. For instance, as shown in <FIG>, the distance between vehicle <NUM> and vehicle <NUM>, here D1, may be just a few inches or more or less. By allowing a vehicle to pull so close to another object, this may also require the vehicle to reverse before being able to pull out of the identified pullover location.

Returning to <FIG>, at block <NUM>, whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle based on context for the pull over with respect to the passenger is determined. At the point where the vehicle <NUM> is maneuvered in order to pull over by pulling forward or shortly before when the vehicle is approaching the identified pullover location, the computing devices <NUM> may determine whether the vehicle should immediately reverse in order to create a gap between the vehicle and another object (such as another parked vehicle). In some cases, reversing may not be required, such as where there is no pullover location in front of the vehicle <NUM> (i.e. no parking in front of the vehicle); however, in many cases, it may be useful to reverse in order to prepare the vehicle <NUM> to pull out of the identified pullover location. Turning to <FIG>, the planning system <NUM> may generate a trajectory <NUM> for the vehicle <NUM> to follow in order to reverse in the pullover location (here parking spot <NUM>) to make a gap between vehicle <NUM> and vehicle <NUM>. Of course, as noted above, the computing devices <NUM> must determine when the vehicle <NUM> should reverse along trajectory <NUM> before or after the passenger has entered the vehicle <NUM>.

In order to determine whether the vehicle <NUM> should reverse before or after the passenger has entered the vehicle, the computing devices <NUM> may determine context for the pull over with respect to the passenger for the vehicle. For instance, if the context indicates that the actual passenger or a potential passenger (i.e. a pedestrian who may be the passenger) is nearby, the computing devices <NUM> should wait for the passenger to enter the vehicle <NUM> (and also close the door and/or buckle a seatbelt or other restraint device) before maneuvering the vehicle in reverse. If the context indicates that the actual passenger or a potential passenger is not nearby the vehicle, the computing devices <NUM> may maneuver the vehicle <NUM> in reverse before the passenger enters the vehicle. This may include reversing away from an object, such as vehicle <NUM>, in front of the vehicle <NUM>.

In some instances, despite the context indicating that the passenger is likely to be nearby, the computing devices <NUM> may still determine that reversing before the passenger enters the vehicle, is in fact prudent in order to prevent the vehicle <NUM> from being "boxed in" by one or more other vehicles. This may be the case, for instance because another vehicle may be approaching the vehicle <NUM> from behind and appears to be pulling behind the vehicle <NUM> or because another vehicle behind the vehicle <NUM> is pulling out of a pullover location behind the vehicle <NUM> (such as, for example, vehicle <NUM> in parking spot <NUM>). This may also be the case in high traffic areas where historically vehicles are attempting to locate and pull into parking spots and other pullover locations at the same or similar times (i.e. same date and time, same day of the week, etc.), even if the perception system is not actually observing a vehicle pulling in or out. For example, the computing devices <NUM> may have access to historical information about the volume of traffic in certain areas and/or the number of vehicles that were parked in a pullover location in the past. In those cases, it may be prudent to reverse in order to preserve space for the vehicle <NUM> to be able to pull out.

In addition, the computing devices <NUM> may estimate a time to complete a maneuver, such as pulling into a pullover location, reversing and/or pulling the vehicle forward. For example, the estimated time to pull into a pullover location and reverse exceeds an estimate of how much time before the passenger is likely to arrive at the vehicle <NUM> (discussed further below), the computing devices <NUM> may determine that the vehicle should abandon the maneuver altogether (e.g. double park or simply stop and wait). As another example, if the vehicle is already located within the pullover location and the estimated time to pull forward and then reverse exceeds an estimate of how much time before the passenger is likely to arrive at the vehicle <NUM>, the computing devices <NUM> may control the vehicle to pull into the pullover location forward (without reversing), wait for the passenger to board the vehicle, and thereafter reverse. As another example, if the estimated time to pull forward and then backup exceeds an estimate of how soon another vehicle is likely to arrive at the vehicle <NUM> and "box" the vehicle into the pullover location from in front of the vehicle, the computing devices <NUM> may determine to maneuver the vehicle <NUM> forward in the pullover location and wait for the passenger to board, and thereafter reverse. This estimate of how soon another vehicle is likely to arrive at the vehicle <NUM> may be based on historical data for the pullover location or area around the pullover location and/or observations of the number of vehicles pulling into nearby pullover locations, the current volume of traffic, time of day, day of the week, day of the year, etc..

As another example, if a vehicle in front of the vehicle <NUM> (such as, for example, vehicle <NUM> in parking spot <NUM>) is likely to be leaving shortly, the computing devices <NUM> may actually pull the vehicle forward, rather than reversing despite the context indicating that the passenger is likely to be nearby, the computing devices may actually pull the vehicle forward in order to preserve space. For example, if certain signals are observed by the perception system such as a person entering the other vehicle, a turn signal being activated, exhaust appearing from the other vehicle, etc., the computing devices <NUM> may determine that the other vehicle is likely to be pulling out of a pullover location. Again, in such cases, the computing devices may actually pull the vehicle forward in order to preserve space.

When the computing devices <NUM> determine that the vehicle should reverse or pull forward despite the context indicating that the passenger is likely to be nearby, the computing devices <NUM> may communicate a message to the passenger via external tertiary communications systems (for example audio via a speaker, displaying on a display screen, etc.) or by sending a message (for example, via network <NUM>), to the passengers client computing device, to indicate to the passenger that the passenger should wait for the vehicle to complete a reversing maneuver before attempting entering the vehicle.

In some instances, determining the context may include determining how much time before the passenger is likely to arrive at the vehicle <NUM>. This determination may be made for instance, based on location information for a client computing device associated with the passenger. For example, the computing devices 110may periodically receive location information (such as GPS coordinates) generated by the passenger's client computing device (e.g. mobile phone). This location information may be received from a server computing device of a dispatching system for the vehicle <NUM> and/or directly from the client computing device. The computing devices 110may determine if this location information indicates that the passenger is likely to reach the vehicle <NUM>'s current location within a predetermined threshold period of time assuming a normal walking speed, such as <NUM> meters per second. If so, this may indicate that the actual passenger is nearby the vehicle <NUM>, and in such circumstances, the computing devices <NUM> should wait for the passenger to enter the vehicle <NUM> before maneuvering the vehicle in reverse. If not, the computing devices <NUM> may maneuver the vehicle <NUM> in reverse before the passenger enters the vehicle.

Alternatively, the computing devices <NUM> may determine how much time before the passenger is likely to arrive at the vehicle <NUM> based on historical data for the passenger and/or other passengers. For instance, the historical data may include aggregated statistics about how long on average this passenger and/or other passengers take to reach a vehicle once a vehicle is stopped to pick up such passengers at the identified pullover locations, nearby pullover locations (e.g. no more than <NUM> meters in walking distance or straight line distance away or more or less), or other similar locations (such as similarly sized or situated parking lots or similar types of stores). The historical data may also be "sliced" that is segmented for different predetermined times of day and/or days of the week. This historical data may be embedded in map information and associated with geographic regions including a plurality of pullover locations and/or with specific pullover locations. From this historical data, the computing devices <NUM> may determine an expected amount of time for the passenger to reach the vehicle <NUM>.

The computing devices <NUM> may then determine if this expected amount of time indicates that the passenger is likely to reach the vehicle <NUM>'s current location within a predetermined threshold period of time assuming a normal walking speed (which may be <NUM> meters per second or more or less). If so, this may indicate that the actual passenger is nearby the vehicle <NUM>, and in such circumstances, the computing devices <NUM> should wait for the passenger to enter the vehicle before maneuvering the vehicle in reverse. If not, the computing devices <NUM> may maneuver the vehicle <NUM> in reverse before the passenger enters the vehicle.

In addition or alternatively, determining the context may include determining whether there are any pedestrians nearby of the vehicle <NUM>. For example, the computing devices <NUM> may use sensor data from the vehicle <NUM>'s perception system to identify or detect pedestrians within a predetermined threshold distance of the vehicle <NUM>, for example within <NUM> meters or more or less. This may indicate that a potential passenger is nearby the vehicle <NUM> and possibly ready to approach the vehicle, and in such circumstances, the computing devices <NUM> should wait for the passenger to enter the vehicle before maneuvering the vehicle in reverse. If not, the computing devices <NUM> may maneuver the vehicle <NUM> in reverse before the passenger enters the vehicle.

In addition or alternatively, determining the context may include determining whether there are any pedestrians who are actively making progress towards the vehicle <NUM>. For example, the computing devices <NUM> may predict a future trajectory for the object and if it overlaps with the location of the vehicle <NUM>, this may indicate that the pedestrian is actively making progress towards the vehicle. Again, this may indicate that a potential passenger is nearby the vehicle <NUM> and possibly approaching the vehicle, and in such circumstances, the computing devices <NUM> should wait for the passenger to enter the vehicle before maneuvering the vehicle in reverse. If not, the computing devices <NUM> may maneuver the vehicle <NUM> in reverse before the passenger enters the vehicle.

In addition or alternatively, determining the context may include determining whether the computing devices <NUM> have established a communications link with and/or authenticated a client computing device associated with the passenger (whether or not a pedestrian is detected nearby the vehicle <NUM>). For example, once within a certain distance or time, such as <NUM> meters or more or less or <NUM> seconds or more or less, of a pickup location for the passenger, the computing devices <NUM> may attempt to establish a Bluetooth (R) communication link between the passenger's client computing device (e.g. mobile phone) and the computing devices <NUM>. Once this has occurred, because Bluetooth (R) connections are generally made over fairly short distances, this may indicate that the actual passenger is nearby the vehicle <NUM>, and in such circumstances, the computing devices <NUM> should wait for the passenger to enter the vehicle <NUM> before maneuvering the vehicle in reverse. If not, the computing devices <NUM> may maneuver the vehicle <NUM> in reverse before the passenger enters the vehicle.

In addition or alternatively, determining the context may include determining whether location information for a client computing device of the passenger indicates that the passenger is nearby the vehicle <NUM>. For example, the computing devices <NUM> may periodically receive location information (such as GPS coordinates) generated by the passenger's client computing device (e.g. mobile phone). This location information may be received from a server computing device of a dispatching system for the vehicle <NUM> and/or directly from the client computing device. The computing devices <NUM> may determine if this location information indicates that the passenger is within a predetermined threshold distance, such as <NUM> meters or more or less, of the current location of the vehicle <NUM>. If so, this may indicate that the actual passenger is nearby the vehicle <NUM>, and in such circumstances, the computing devices <NUM> should wait for the passenger to enter the vehicle before maneuvering the vehicle in reverse. If not, the computing devices <NUM> may maneuver the vehicle <NUM> in reverse before the passenger enters the vehicle.

Returning to <FIG>, at block <NUM>, the vehicle is maneuvered in the autonomous driving mode in reverse based on the determination of whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle. For instance, based on the examples above, if any of the context indicates that the vehicle <NUM> should wait for the passenger to enter the vehicle <NUM>, then the computing devices <NUM> may wait for a passenger to enter a vehicle, for instance in the location depicted in <FIG>, before following trajectory <NUM> in order to reverse. Again, the vehicle <NUM> may be maneuvered in the autonomous driving mode towards vehicle <NUM>, and the distance between vehicle <NUM> and vehicle <NUM>, here D2 in <FIG> may be on the order of a few inches or more or less. The computing devices <NUM> may then wait for the passenger to enter the vehicle. This may be confirmed, for instance by the door of the vehicle being opened and closed and/or a passenger initiating a ride, for example, by pressing a button within the vehicle or by speaking a command for the ride to start which may also cause the computing devices <NUM> to automatically close the doors of the vehicle. At some point, the planning system <NUM> may also generate trajectory <NUM> in order to enable the vehicle <NUM> to pull out of the pullover location (here parking spot <NUM>). In this regard, after the passenger has entered the vehicle <NUM>, the computing devices <NUM> may also control the vehicle in the autonomous driving mode in order to follow trajectory <NUM>.

If none of the context indicates that the vehicle <NUM> should wait for the passenger to enter the vehicle, then the computing devices <NUM> may control the vehicle to follow trajectory <NUM> as depicted in <FIG> before waiting for the passenger to enter the vehicle. Again, the vehicle <NUM> may be maneuvered in the autonomous driving mode towards vehicle <NUM>, and the distance between vehicle <NUM> and vehicle <NUM>, here D2 in <FIG> may be on the order of a few inches or more or less. At some point, the planning system <NUM> may generate trajectory <NUM> in order to enable the vehicle <NUM> to pull out of the pullover location (here parking spot <NUM>). In this regard, once the passenger has entered the vehicle, the computing devices <NUM> may also control the vehicle in the autonomous driving mode in order to follow trajectory <NUM>.

Claim 1:
A method of maneuvering a vehicle (<NUM>) having an autonomous driving mode, the method comprising:
identifying (<NUM>), by one or more processors, a pullover location (<NUM>) for the vehicle to stop and wait for a passenger;
maneuvering (<NUM>), by the one or more processors, the vehicle in the autonomous driving mode in order to pull over by pulling forward into the pullover location;
determining (<NUM>), by the one or more processors, whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle based on context for the pull over with respect to the passenger; and
maneuvering (<NUM>), by the one or more processors, the vehicle in the autonomous driving mode in reverse based on the determination of whether to maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicle, wherein the determination of whether of maneuver the vehicle in reverse in the pullover location before or after the passenger enters the vehicles is based on:
determining the context by determining an expected amount of time for the passenger to reach the vehicle once the vehicle is ready to be maneuvered in reverse in the pullover location; and
comparing the expected amount of time to a threshold value.