Patent Publication Number: US-2022229436-A1

Title: Real-time lane change selection for autonomous vehicles

Description:
CROSS REFERENCE TO RELATED APPLICATIONS 
     The present application is a continuation of U.S. patent application Ser. No. 15/831,678, filed Dec. 5, 2017, the entire disclosure of which is incorporated herein by reference. 
    
    
     BACKGROUND 
     Autonomous vehicles, for instance, vehicles that do not require a human driver, can be used to aid in the transport of passengers or items from one location to another. Such vehicles may operate in a fully autonomous mode where passengers may provide some initial input, such as a pickup or destination location, and the vehicle maneuvers itself to that location, for instance, by determining and following a route which may require one or more lane changes. 
     BRIEF SUMMARY 
     One aspect of the disclosure provide a method of routing an autonomous vehicle. The method includes maneuvering the vehicle along a route in a first lane using map information identifying a first plurality of nodes representing locations within a first lane and a second plurality of nodes representing locations within a second lane different from the first lane; while maneuvering, determining when the vehicle should make a lane change to follow the route by assessing a cost of connecting a first node of the first plurality of nodes with a second node of a second plurality of nodes; and using the assessment to make the lane change from the first lane to the second lane. 
     In one example, assessing a cost includes applying a cost function. In one example, the cost function is based on a duration of the change from the location represented by the first node and the location represented by the second node. In addition or alternatively, the cost function is based on current traffic conditions. In addition or alternatively, the cost function is based on whether the change has been missed previously. the cost function is based on whether the vehicle will cross a solid white line. In addition or alternatively, the cost function is based on whether the vehicle will be making the change in an intersection. In addition or alternatively, the cost function is based on a time of day the change will occur. In another example, the method also includes, when the vehicle is unable to make the change, increasing a cost for changing lanes between the location represented by first node and the location represented by second node. In another example, the method also includes iterating through pairs of nodes of the first and second pluralities of nodes to determine where to make the change. 
     Another aspect of the disclosure provides a system for routing an autonomous vehicle. The system includes one or more processors configured to maneuver the vehicle along a route in a first lane using map information identifying a first plurality of nodes representing locations within a first lane and a second plurality of nodes representing locations within a second lane different from the first lane; while maneuvering, determine when the vehicle should make a lane change to follow the route by assessing a cost of connecting a first node of the first plurality of nodes with a second node of a second plurality of nodes; and use the assessment to make the lane change from the first lane to the second lane. 
     In one example, assessing a cost includes applying a cost function. In one example, the cost function is based on a duration of the change from the location represented by the first node and the location represented by the second node. In addition or alternatively, the cost function is based on current traffic conditions. In addition or alternatively, the cost function is based on whether the change has been missed previously. the cost function is based on whether the vehicle will cross a solid white line. In addition or alternatively, the cost function is based on whether the vehicle will be making the change in an intersection. In addition or alternatively, the cost function is based on a time of day the change will occur. In another example, the one or more processors are also configured to, when the vehicle is unable to make the change, increase a cost for changing lanes between the location represented by first node and the location represented by second node. In another example, the one or more processors are also configured to iterate through pairs of nodes of the first and second pluralities of nodes to determine where to make the change. In another example, the system also includes memory storing the map information. In another example, the system also includes the vehicle. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a functional diagram of an example vehicle in accordance with an exemplary embodiment. 
         FIGS. 2A and 2B  are an example of map information in accordance with aspects of the disclosure. 
         FIG. 3  is an example external view of a vehicle in accordance with aspects of the disclosure. 
         FIG. 4  is a pictorial diagram of an example system in accordance with an exemplary embodiment. 
         FIG. 5  is a functional diagram of the system of  FIG. 4  in accordance with aspects of the disclosure. 
         FIG. 6  is an example bird&#39;s eye view of a geographic area in accordance with aspects of the disclosure. 
         FIGS. 7-9  are examples views of the geographic area of  FIG. 6  with data in accordance with aspects of the disclosure. 
         FIG. 10  is an example bird&#39;s eye view of a traffic circle in accordance with aspects of the disclosure. 
         FIG. 11  is an example flow diagram in accordance with aspects of the disclosure. 
     
    
    
     DETAILED DESCRIPTION 
     Overview 
     The technology relates to determining when to change lanes while a vehicle, such as an autonomous vehicle or a vehicle having an autonomous driving mode, is traveling along a route. In some instances, the vehicle&#39;s computing devices may rely on a plurality of nodes interconnected to one another as a map or roadgraph in order to determine how to route the vehicle through the vehicle&#39;s environment. As such, lane changes effectively cause the vehicle to pass from one set of nodes for one lane to another set of nodes for a different lane. To determine which individual nodes of the different sets to use when transitioning, a cost analysis may be used. 
     This cost analysis may include, for instance, determining a series of individual costs for different factors relating to transitioning between two nodes and using these individual costs to determine an overall cost for a transition between the two nodes. To determine the individual costs, each factor may be converted to an arbitrary value for a particular transition between two nodes. 
     The values for each of the individual costs may then be used to determine an overall cost for the transition between the two nodes. The computing devices may then iterate through pairs of nodes corresponding to different possible transitions or lane changes and identify overall costs for each pair of nodes. After iterating through pairs of nodes corresponding to different possible transitions, the computing devices may select a particular pair of nodes. For instance, a pair of nodes having the lowest overall cost of all of the pairs of nodes may be selected. This selected pair may then be used by the vehicle&#39;s computing devices to determine how to maneuverer the vehicle in order to change lanes. The vehicle may then be controlled in order to complete the lane change. 
     The features described herein allow autonomous vehicles or vehicles operating in an autonomous driving mode to identify the best location at which to make a lane change in real time. In addition, this prevents the vehicle from being stuck in an infinite loop and unable to execute a lane change. 
     Example Systems 
     As shown in  FIG. 1 , a vehicle  100  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 devices  110  containing one or more processors  120 , memory  130  and other components typically present in general purpose computing devices. 
     The memory  130  stores information accessible by the one or more processors  120 , including instructions  134  and data  132  that may be executed or otherwise used by the processor  120 . The memory  130  may be of any type capable of storing information accessible by the processor, including a computing device-readable medium, or other medium that stores data that may be read with the aid of an electronic device, such as a hard-drive, memory card, ROM, RAM, DVD or other optical disks, as well as other write-capable and read-only memories. Systems and methods may include different combinations of the foregoing, whereby different portions of the instructions and data are stored on different types of media. 
     The instructions  134  may be any set of instructions to be executed directly (such as machine code) or indirectly (such as scripts) by the processor. For example, the instructions may be stored as computing device code on the computing device-readable medium. In that regard, the terms “instructions” and “programs” may be used interchangeably herein. The instructions may be stored in object code format for direct processing by the processor, or in any other computing device language including scripts or collections of independent source code modules that are interpreted on demand or compiled in advance. Functions, methods and routines of the instructions are explained in more detail below. 
     The data  132  may be retrieved, stored or modified by processor  120  in accordance with the instructions  134 . For instance, although the claimed subject matter is not limited by any particular data structure, the data may be stored in computing device registers, in a relational database as a table having a plurality of different fields and records, XML documents or flat files. The data may also be formatted in any computing device-readable format. 
     The one or more processor  120  may be any conventional processors, such as commercially available CPUs. Alternatively, the one or more processors may be a dedicated device such as an ASIC or other hardware-based processor. Although  FIG. 1  functionally illustrates the processor, memory, and other elements of computing devices  110  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 devices  110 . 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. 
     Computing devices  110  may all of the components normally used in connection with a computing device such as the processor and memory described above as well as a user input  150  (e.g., a mouse, keyboard, touch screen and/or microphone) and various electronic displays (e.g., a monitor having a screen or any other electrical device that is operable to display information). In this example, the vehicle includes an internal electronic display  152  as well as one or more speakers  154  to provide information or audio visual experiences. In this regard, internal electronic display  152  may be located within a cabin of vehicle  100  and may be used by computing devices  110  to provide information to passengers within the vehicle  100 . 
     Computing devices  110  may also include one or more wireless network connections  156  to facilitate communication with other computing devices, such as the client computing devices and server computing devices described in detail below. The wireless network connections may include short range communication protocols such as Bluetooth, Bluetooth low energy (LE), cellular connections, as well as various configurations and protocols including the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP, and various combinations of the foregoing. 
     In one example, computing devices  110  may be control computing devices of an autonomous driving computing system or incorporated into vehicle  100 . The autonomous driving computing system may be capable of communicating with various components of the vehicle in order to control the movement of vehicle  100  according to primary vehicle control code of memory  130 . For example, returning to  FIG. 1 , computing devices  110  may be in communication with various systems of vehicle  100 , such as deceleration system  160 , acceleration system  162 , steering system  164 , signaling system  166 , routing system  168 , positioning system  170 , perception system  172 , and power system  174  (i.e. the vehicle&#39;s engine or motor) in order to control the movement, speed, etc. of vehicle  100  in accordance with the instructions  134  of memory  130 . Again, although these systems are shown as external to computing devices  110 , in actuality, these systems may also be incorporated into computing devices  110 , again as an autonomous driving computing system for controlling vehicle  100 . 
     As an example, computing devices  110  may interact with one or more actuators of the deceleration system  160  and/or acceleration system  162 , such as brakes, accelerator pedal, and/or the engine or motor of the vehicle, in order to control the speed of the vehicle. Similarly, one or more actuators of the steering system  164 , such as a steering wheel, steering shaft, and/or pinion and rack in a rack and pinion system, may be used by computing devices  110  in order to control the direction of vehicle  100 . For example, if vehicle  100  is configured for use on a road, such as a car or truck, the steering system may include one or more actuators to control the angle of wheels to turn the vehicle. Signaling system  166  may be used by computing devices  110  in order to signal the vehicle&#39;s intent to other drivers or vehicles, for example, by lighting turn signals or brake lights when needed. 
     Routing system  168  may be used by computing devices  110  in order to determine and follow a route to a location. In this regard, the routing system  168  and/or data  132  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, vegetation, or other such objects and information. 
       FIGS. 2A and 2B  is an example of map information for a section of roadway including intersection  220 .  FIG. 2A  depicts a portion of the map information that includes information identifying the shape, location, and other characteristics of lane markers or lane lines  210 ,  212 ,  214 , defining lanes  230 ,  232 , traffic signals  240 ,  242  (not depicted in  FIG. 2A  for clarity and simplicity), as well as stop lines  250 ,  252 ,  254 . In addition to these features, the map information may also include information that identifies the direction of traffic and speed limits for each lane as well as information that allows the computing devices  110  to determine whether the vehicle has the right of way to complete a particular maneuver (i.e. complete a turn or cross a lane of traffic or intersection), as well as other features such as curbs, buildings, waterways, vegetation, signs, etc. 
     In addition to the feature information, the map information may include a series of road or lane segments. These lane segments may be connected to one another to provide rails or smooth curves representing the actual curves of a lane. For instance,  FIG. 2A  includes rails  260 ,  262 ,  264 ,  266 ,  268 . To transition between rails, the computing devices may essentially draw additional curves as discussed further below and control the vehicle in order to follow these curves. In order to determine how to transition between two lanes, the computing devices may identify a plurality of nodes. Each node may essentially be “dropped” along a rail at regular intervals. The intervals may be defined in distance, such as every 5 meters or more or less, or based on duration of travel, for instance using a current or expected speed of the vehicle or a speed limit for the roadway. In this example, instead of dropping a node every 5 meters, a node may be dropped every 2 seconds or more or less. This has the effect of spacing out nodes further apart on higher speed roads, and spacing them closer together in slower areas such as 25 mile per hour zones or parking lots.  FIG. 2B  depicts a plurality of such nodes. Of course, though many nodes are depicted, only a few are referenced in  FIG. 2B  for clarity and simplicity. As can be seen, each of nodes A-D may represent a location along rail  262 . Similarly, each of nodes E-H may represent a location along rail  260 . Although not shown, each of these nodes may be associated with an identifier, for instance, a numeric value corresponding to a relative or actual location of the node or rail. 
     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. 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  170  may be used by computing devices  110  in order to determine the vehicle&#39;s relative or absolute position on a map or on the earth. For example, the position system  170  may include a GPS receiver to determine the device&#39;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  170  may also include other devices in communication with computing devices  110 , 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&#39;s provision of location and orientation data as set forth herein may be provided automatically to the computing devices  110 , other computing devices and combinations of the foregoing. 
     The perception system  172  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  172  may include lasers, sonar, radar, cameras and/or any other detection devices that record data which may be processed by computing device  110 . 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. 3  is an example external view of vehicle  100 . In this example, roof-top housing  310  and dome housing  312  may include a lidar sensor as well as various cameras and radar units. In addition, housing  320  located at the front end of vehicle  100  and housings  330 ,  332  on the driver&#39;s and passenger&#39;s sides of the vehicle may each store a lidar sensor. For example, housing  330  is located in front of driver door  360 . Vehicle  100  also includes housings  340 ,  342  for radar units and/or cameras also located on the roof of vehicle  100 . Additional radar units and cameras (not shown) may be located at the front and rear ends of vehicle  100  and/or on other positions along the roof or rooftop housing  310 . 
     The computing devices  110  may control the direction and speed of the vehicle by controlling various components. By way of example, computing devices  110  may navigate the vehicle to a destination location completely autonomously using data from the detailed map information and routing system  168 . Computing devices  110  may use the positioning system  170  to determine the vehicle&#39;s location and perception system  172  to detect and respond to objects when needed to reach the location safely. In order to do so, computing devices  110  may cause the vehicle to accelerate (e.g., by increasing fuel or other energy provided to the engine by acceleration system  162 ), decelerate (e.g., by decreasing the fuel supplied to the engine, changing gears, and/or by applying brakes by deceleration system  160 ), change direction (e.g., by turning the front or rear wheels of vehicle  100  by steering system  164 ), and signal such changes (e.g., by lighting turn signals of signaling system  166 ). Thus, the acceleration system  162  and deceleration system  160  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  110  may also control the drivetrain of the vehicle in order to maneuver the vehicle autonomously. 
     Computing device  110  of vehicle  100  may also receive or transfer information to and from other computing devices, such as those computing devices that are a part of the transportation service as well as other computing devices.  FIGS. 4 and 5  are pictorial and functional diagrams, respectively, of an example system  400  that includes a plurality of computing devices  410 ,  420 ,  430 ,  440  and a storage system  450  connected via a network  460 . System  400  also includes vehicle  100 , and vehicles  100 A,  100 B which may be configured the same as or similarly to vehicle  100 . Although only a few vehicles and computing devices are depicted for simplicity, a typical system may include significantly more. 
     As shown in  FIG. 4 , each of computing devices  410 ,  420 ,  430 ,  440  may include one or more processors, memory, data and instructions. Such processors, memories, data and instructions may be configured similarly to one or more processors  120 , memory  130 , data  132 , and instructions  134  of computing device  110 . 
     The network  460 , and intervening nodes, may include various configurations and protocols including short range communication protocols such as Bluetooth, Bluetooth LE, the Internet, World Wide Web, intranets, virtual private networks, wide area networks, local networks, private networks using communication protocols proprietary to one or more companies, Ethernet, WiFi and HTTP, and various combinations of the foregoing. Such communication may be facilitated by any device capable of transmitting data to and from other computing devices, such as modems and wireless interfaces. 
     In one example, one or more computing devices  110  may include one or more server computing devices having a plurality of computing devices, e.g., a load balanced server farm, that exchange information with different nodes of a network for the purpose of receiving, processing and transmitting the data to and from other computing devices. For instance, one or more computing devices  410  may include one or more server computing devices that are capable of communicating with computing device  110  of vehicle  100  or a similar computing device of vehicle  100 A as well as computing devices  420 ,  430 ,  440  via the network  460 . For example, vehicles  100 ,  100 A, may be a part of a fleet of vehicles that can be dispatched by server computing devices to various locations. In this regard, the server computing devices  410  may function as a dispatching system. In addition, the vehicles of the fleet may periodically send the server computing devices location information provided by the vehicle&#39;s respective positioning systems as well as other information relating to the status of the vehicles discussed further below, and the one or more server computing devices may track the locations and status of each of the vehicles of the fleet. 
     In addition, server computing devices  410  may use network  460  to transmit and present information to a user, such as user  422 ,  432 ,  442  on a display, such as displays  424 ,  434 ,  444  of computing devices  420 ,  430 ,  440 . In this regard, computing devices  420 ,  430 ,  440  may be considered client computing devices. 
     As shown in  FIG. 4 , each client computing device  420 ,  430 ,  440  may be a personal computing device intended for use by a user  422 ,  432 ,  442 , and have all of the components normally used in connection with a personal computing device including a one or more processors (e.g., a central processing unit (CPU)), memory (e.g., RAM and internal hard drives) storing data and instructions, a display such as displays  424 ,  434 ,  444  (e.g., a monitor having a screen, a touch-screen, a projector, a television, or other device that is operable to display information), and user input devices  426 ,  436 ,  446  (e.g., a mouse, keyboard, touchscreen or microphone). The client computing devices may also include a camera for recording video streams, speakers, a network interface device, and all of the components used for connecting these elements to one another. 
     Although the client computing devices  420 ,  430 , and  440  may each comprise a full-sized personal computing device, they may alternatively comprise mobile computing devices capable of wirelessly exchanging data with a server over a network such as the Internet. By way of example only, client computing device  420  may be a mobile phone or a device such as a wireless-enabled PDA, a tablet PC, a wearable computing device or system, or a netbook that is capable of obtaining information via the Internet or other networks. In another example, client computing device  430  may be a wearable computing system, shown as a wristwatch as shown in  FIG. 4 . As an example the user may input information using a small keyboard, a keypad, microphone, using visual signals with a camera, or a touch screen. 
     In some examples, client computing device  440  may be a concierge work station used by an administrator or operator of a depot to provide depot services for the vehicles of the fleet. Although only a concierge work station  440  is shown in  FIGS. 4 and 5 , any number of such work stations may be included in a typical system. 
     As with memory  130 , storage system  450  can be of any type of computerized storage capable of storing information accessible by the server computing devices  410 , such as a hard-drive, memory card, ROM, RAM, DVD, CD-ROM, write-capable, and read-only memories. In addition, storage system  450  may include a distributed storage system where data is stored on a plurality of different storage devices which may be physically located at the same or different geographic locations. Storage system  450  may be connected to the computing devices via the network  460  as shown in  FIGS. 4 and 5 , and/or may be directly connected to or incorporated into any of the computing devices  110 ,  410 ,  420 ,  430 ,  440 , etc. 
     Storage system  450  may store various types of information as described in more detail below. This information may be retrieved or otherwise accessed by a server computing device, such as one or more server computing devices  410 , in order to perform some or all of the features described herein. In order to provide transportation services to users, the information of storage system  450  may include user account information such as credentials (e.g., a user name and password as in the case of a traditional single-factor authentication as well as other types of credentials typically used in multi-factor authentications such as random identifiers, biometrics, etc.) that can be used to identify a user to the one or more server computing devices. The user account information may also include personal information such as the user&#39;s name, contact information, identifying information of the user&#39;s client computing device (or devices if multiple devices are used with the same user account), one or more unique signals for the user as well as other user preference or settings data. 
     The storage system  450  may also store information which can be provided to client computing devices for display to a user. For instance, the storage system  450  may store predetermined distance information for determining an area at which a vehicle is likely to stop for a given pickup or destination location. The storage system  450  may also store graphics, icons, and other items which may be displayed to a user as discussed below. 
     Example Methods 
     In addition to the operations described above and illustrated in the figures, various operations will now be described. It should be understood that the following operations do not have to be performed in the precise order described below. Rather, various steps can be handled in a different order or simultaneously, and steps may also be added or omitted. 
     The vehicle&#39;s computing devices may control the vehicle in order to follow a route.  FIG. 6  is an example view of vehicle  100  being maneuvered on a section of roadway corresponding to the section of roadway defined in the map information of  FIGS. 2A and 2B . For instance,  FIG. 6  depicts intersection  620  correspond to intersection  220 . In addition the shape, location, and other characteristics of lane lines  610 ,  612 , and  614  correspond to the shape, location and other characteristics of lane lines  210 ,  212 ,  214 . Similarly, traffic signals  630 ,  632  correspond to traffic signals  230 ,  232 , and stop lines  650 ,  652 ,  654  correspond to stop lines  250 ,  252 ,  254 . By following and connecting rails together, the routing system  168  may generate a route between two locations. For instance, as shown in  FIG. 6 , vehicle  100  is currently driving in lane  632  following route  670 . Route  670  requires vehicle  100  to change from lane  632  to lane  630  in order to make a right turn at intersection  624 . 
     In order to determine when and where to make the lane change, the vehicle&#39;s computing devices may assess a cost of transitioning between pairs of nodes of the map information. In order to do so, the computing devices  110  may identify pairs of nodes from the map information. Each pair may include one node from the map information corresponding to a source lane or the current lane of the vehicle and one node from a target lane or rather the lane to which the vehicle needs to transition corresponding to a neighboring lane. In this example, a neighboring lane can mean, for instance, that a lane has a parallel direction of travel immediately adjacent to the left or right. In such cases, the computing devices  110  may pair nodes or rather, allow lane change opportunities between nodes in the source lane to nodes in the target lane so long as the lanes remain neighboring lanes (i.e. there are no forks or merges), and presuming they are not too long. 
     In some instances, once neighboring lanes are identified, the computing devices  110  may identify opportunities, or pairs of nodes, to lane change up to some fixed duration, by using speed limit information from the map in formation or a current or expected future speed of the vehicle. For example, from a node in a lane J, the computing devices  110  may allow lane change transitions into a neighboring lane K, or even to a neighboring lane for lane K, lane L, so long as any of those lane change opportunities are less than the fixed duration. This fixed duration may be as long as 40 seconds long or more of less. In this example, if the speed limit for lanes J, K and L were 30 mph, that would correspond to a maximum distance of approximately 530 meters. Also, to save on memory usage, the computing devices  110  may not identify all possible lane changes or all possible pairs of nodes up to 40 seconds. For example, the computing devices may choose to build a lane change opportunity that is 1 second, 2 seconds, 4 seconds, 10 seconds, 20 seconds, and 40 seconds long. This may cause the computing devices  110  to “skip over” many opportunities, but practically result in similar routing behavior. 
     However, in many cases, a lane&#39;s immediate neighboring lane can be travelling in the opposite direction. In this case, building a transition from a node to a node of an opposing neighboring lane would no longer represent a lane change, but rather a 180 degree change in heading, which could either be accomplished with a U turn or a multi-point turn. Such transitions may only be allowed on low-speed residential roads. In addition, not all transitions between nodes may be feasible, and thus may be ignored. For instance, the computing devices  110  may not pair nodes that would allow the car to go “backwards” during a lane change. 
     In the example of  FIG. 6 , lane  632  is the source lane and lane  630  is the target lane. The computing device  110  may pair nodes between vehicle  100 &#39;s current location and intersection  620  in each of lanes  630  and  632 . Such nodes may includer a plurality of nodes A-D corresponding to source lane  630  and a plurality of nodes E-H corresponding to target lane  632 .  FIG. 7  overlays the nodes A-H of the map information depicted in  FIG. 2B  between the vehicle&#39;s current location and intersection  620  with the example of  FIG. 6  less the route  670 .  FIG. 8  depicts example pairs of nodes  810 ,  820 ,  830  between which vehicle  100  may transition in order to effect a lane change between source lane  632  and target lane  630 . In other words, the computing devices  110  have identified three pairs of nodes which each include one node from the map information corresponding to lane  632  in which vehicle  100  is currently traveling and one node from the map information corresponding to lane  632 . Thus, each of the pairs of nodes  810 ,  820 , and  830  include one node corresponding to lane  632  and one node corresponding to lane  630 . 
     As noted above each of these pairs of nodes, the computing devices  110  may assess a cost for the vehicle to transition between the nodes. For instance, the computing devices  110  may determine a cost value for vehicle  100  moving from node A to node F (pair of nodes  810 ), moving from node B to node G (pair of nodes  820 ), and moving from node C to node H (pair of nodes  820 ). 
     This cost assessment may include, for instance, determining a series of individual costs for different factors relating to transitioning between two nodes of each of the identified pairs of nodes, and using these individual costs to determine an overall cost for a transition between the two nodes. As an example, the factors may include a duration of the change, whether the change has been missed in the past, whether the vehicle will cross solid white lines, whether the vehicle will be initiating or making the change within an intersection, current traffic conditions in the area, the time of day when the change will occur (i.e. whether school is letting out and children will be present), whether the current lane changes to an exit shortly, how others are going to be lane changing or merging in the area, whether the change will put certain vehicles directly in the path of sun glare, etc. As an example of how other vehicles are going to be lane changing, if vehicle  100  is currently on the freeway in a lane that other vehicles are merging into, it would be better (i.e. should be easier and less costly) to have the vehicle change lanes immediately. 
     To determine the individual costs, each factor may be converted to an arbitrary value for a particular transition between two nodes. In this regard, each factor may have its own individual scale or weight which can be used to map that factor to a cost. These weights may include power law, exponential, piecewise, quadratic, linear, etc. 
     For instance, duration may be determined using piecewise linear function. An example range may go from 0 (a significant or maximum of time to make the transition) to 5000 (very little or no time to make the transition). This maximum amount of time may correspond to some value relating to a comfortable lane change experience for a passenger of the vehicle, such as 40 seconds or more or less. In some instances, the cost may never actually go all of the way to zero for One thing you might consider adding is that the cost doesn&#39;t actually go all the way down to 0, but rather may level off at some nonzero value like 100 or more or less. This may have the effect of minimizing a total number of lane changes for a given route. Without this, the computing devices  110  may actually cause the vehicle to make “too many” lane changes, or rather, causing the vehicle to jump back and forth from lane to lane. 
     Whether the vehicle crosses over solid white lines may be a binary (yes or no) value or determined using a piecewise linear function depending on how much of the area between the two nodes includes solid white line. In either of the aforementioned examples, an example range may go from 0 (no or no crossing solid white lines) to “2000” (yes or solid white lines between the entire length of the distance between the two nodes). As another example, whether the vehicle initiates a lane change between the two nodes within an intersection may be a binary (yes or no). An example value for no may be 0 and an example value for yes may be “2000”. 
     The values for each of the individual costs may then be used to determine an overall cost for the transition between the two nodes using a cost function. In one example, the cost function may be a linear function where the values may be summed together to generate the overall cost for that transition or lane change. Alternatively, the cost function may be a weighted linear sum where the values may be weighted and summed together, or normalized and summed together to generate the overall costs. In addition, in some instances, cost function or the sum of the values may be normalized to determine the overall costs. n many instances, the longest lane changes (i.e. those having the longest durations) may have the lowest costs in order to provide the vehicle with as much time as possible to execute the lane change. 
     The computing devices may then iterate through pairs of nodes corresponding to different possible transitions or lane changes and identify overall costs for each pair of nodes using the cost functions as discussed above. In this regard, the computing device  110  may first determine an overall cost for pair of nodes  810 , thereafter determine an overall cost for pair of nodes  820 , and finally determine an overall cost for pair of nodes  830 . For instance, computing devices may determine an overall cost for pair of nodes  810  equal to “2500”, an overall cost for pair of nodes  820  equal to “2000”. 
     After iterating through pairs of nodes corresponding to different possible transitions, the computing devices may select a particular pair of nodes. For instance, a pair of nodes having the lowest overall cost of all of the pairs of nodes may be selected. In this regard, pair of nodes  820 , corresponding to nodes B and G may be selected as having the lowest overall cost. 
     This selected pair may then be used by the vehicle&#39;s computing devices to determine how to maneuverer the vehicle in order to change lanes. In this regard, the computing devices may use these nodes to generate one or more paths or trajectories that define the vehicle&#39;s future locations as passing from one node of the selected pair of nodes to the other node of the pair or between the two nodes of the selected pair of nodes. For instance,  FIG. 9  depicts a trajectory  970  which includes vehicle passing between nodes B and G in order to continue to follow route  670  and make a right turn at intersection  624 . The vehicle may then be controlled to follow the one or more trajectories in order to complete the lane change. 
     In addition, if the vehicle is unable to make the change using the selected pair of nodes, a cost for changing lanes between the selected pair of nodes can be increased. This increase can then be stored for later use, for example, in the map. For instance, increasing the number of times the vehicle has missed a lane change between a selected pair of nodes in the past will increase this individual cost between those same nodes in the future, and thus making the overall cost of transitioning between those nodes greater as well. This allows the vehicle to avoid falling into an infinite loop of not being able to make a particular lane change by decreasing the likelihood that the vehicle will select that lane change (i.e. those two nodes) for a lane change in the area in the future. This can be especially relevant in the case of the vehicle attempting to make a lane change in a circle where the vehicle may get stuck in an infinite loop (i.e. continue going around the circle without being able to change lanes) if the vehicle cannot make a lane change between two nodes having the lowest overall cost when the overall costs for lane changes of the loop are constant. 
     In addition, in the case where the vehicle is unable to make a lane change for the selected pair of nodes, and that lane change is the only way to reach a destination, the computing devices  110  may determine that the destination is not actually reachable. In such cases, the computing devices  110  may pullover as soon as possible, select a new destination, request assistance or further instructions from the server computing devices  410 , the concierge work station  440 , or a client computing device of a passenger, and so on. 
     For instance,  FIG. 10  is an example of a traffic circle  1000 . In this example, vehicle  100  may have entered the traffic circle at point (or node)  1010  and may need to exit the traffic circle at point (or node)  1020  in order to follow a route (not shown) that leads the vehicle around the traffic circle  1000  between point  1010  and point  1010 . Currently vehicle  100  is traveling in an interior lane  1030 , but must change to the exterior lane  1032  in order to exit the traffic circle  1000  at point  1010 . Computing devices  110  may have determined that the pair of nodes having the lowest overall cost to transition between interior lane  1030  and exterior lane  1032  is nodes P and Q. However, if for some reason the vehicle  100  is unable to complete the transition between nodes P and Q, for instance, because there is not enough time or distance to complete the transition or because other vehicles or objects prevents vehicle  100  from making the transition, the vehicle  100  may loop around circle  1000  in interior lane  1030 . In certain circumstances, this looping may even become indefinite. Thus to avoid this, by increasing the overall cost of transitioning between nodes P and Q, the computing devices  110  may identify another pair of nodes for the lane change, and therefore may be able to effect a lane change at another location between a different pair of nodes and be able to leave the traffic circle  1000  either at point  1020  or possibly point (or node)  1040  and reroute to wherever the vehicle&#39;s final destination is location. 
     These cost increases can be limited in time. For instance, a cost increase may be valid only for this one trip, only during business hours, only on weekends, only for the next 20 minutes, etc. As a practical example, a stationary vehicle which is obstructing a particular node in a lane, such as a delivery or garbage truck, may prevent a vehicle from performing a lane change. However, these vehicles would be expected to move shortly, so a brief, such as 20 minutes or more or less, time limitation on the cost increase may be appropriate. Similarly, if the vehicle is unable to make the lane change because of traffic, a slightly longer or more specific time limitation may be used, such as only for the next hour or during the same hour on the same day of the week. In the circle example above, where the difficulty is likely to persist, there may be no time limit. This may allow the vehicle to remove or reduce the costs of some lane changes over time. 
     The vehicle&#39;s computing devices may also share increases or decreases in individual costs for pairs of nodes with other vehicles. For instance, this information may be broadcast to vehicles nearby, such as vehicle  100 A via network or within a predetermined distance (such as 1 mile or more or less), broadcast all vehicles in a fleet of vehicles, or sent to a dispatching server to relay to all or a portion of other vehicles which receive information from the dispatching service. 
     In some instances, the individual costs may be adjusted or the scales changed for certain circumstances. For example, the individual cost for duration of a lane change between two nodes may increase or decrease automatically depending upon traffic or the time of day. Again, this information may be stored in the map information. 
     In addition to considering the circumstances of a single lane change using a locally optimized solution, the computing devices may actually consider the impact of multiple lane changes at once. For example, if vehicle  100  needs to make multiple lane changes in a row, from lane X to lane Y to lane Z, there may be many opportunities to go from X to Y and from Y to Z. In some cases, selecting the best solution for X to Y may limit or prevent lane change opportunities from Y to Z. In this regard, the set of nodes and transitions forms a routing network. The computing devices  110  may therefore choose a globally optimized path through the network. That is, when considering the case of changing from X to Y to Z, the computing devices may select the lane change opportunities which minimizes a total cost to go from X to Y and to go from Y to Z. Neither of these may be the respective local optimal solution for these lane changes, but the overall total cost may be minimized. 
       FIG. 11  includes an example flow diagram  1100  of some of the examples for routing an autonomous vehicle, such as vehicle  100 , which may be performed by one or more processors such as processors  110 . For instance, at block  1110 , the vehicle is maneuvered along a route in a first lane using map information identifying a first plurality of nodes representing locations within the first lane and a second plurality of nodes representing locations within a second lane different from the first lane. At block  1120 , while maneuvering, when the vehicle should make the change is determined by assessing a cost of connecting a first node of the first plurality of nodes with a second node of a second plurality of nodes. At block  1130 , the assessment is used to make the lane change from the first lane to the second lane. 
     Unless otherwise stated, the foregoing alternative examples are not mutually exclusive, but may be implemented in various combinations to achieve unique advantages. As these and other variations and combinations of the features discussed above can be utilized without departing from the subject matter defined by the claims, the foregoing description of the embodiments should be taken by way of illustration rather than by way of limitation of the subject matter defined by the claims. In addition, the provision of the examples described herein, as well as clauses phrased as “such as,” “including” and the like, should not be interpreted as limiting the subject matter of the claims to the specific examples; rather, the examples are intended to illustrate only one of many possible embodiments. Further, the same reference numbers in different drawings can identify the same or similar elements.