Patent Description:
Autonomous vehicles, such as 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 autonomous vehicle maneuvers itself to that location. In order to do so safely, these vehicles must be able to detect and identify objects in the environment as well as respond to them quickly. Typically, these objects are identified from information that can be perceived by sensors such as LIDAR, radar, or cameras.

In some instances, detection and identification of other vehicles in the vicinity of the autonomous vehicle is paramount to safely maneuvering the autonomous vehicle to its destination. For instance, an autonomous vehicle's trajectory may be impacted by the actions of other vehicle's traveling around the autonomous vehicle. Therefore being able to detect and respond to such action can be especially important to ensuring a safe and effective autonomous driving experience. United States patent no. <CIT> presents a drive control apparatus and method for an autonomous vehicle.

The invention is defined in the claims. This technology generally relates to a method for controlling an autonomous vehicle through a multi-lane turn. According to an aspect of the invention, a method comprising the features of claim <NUM> is provided. In some examples, the historical data may correspond to previous trajectories of the autonomous vehicle. In some instances, adjusting the trajectory based on the historical data may comprise determining an average, lateral displacement of the previous trajectories relative to the trajectory of the autonomous vehicle; and adjusting the trajectory of the autonomous vehicle by the average, lateral displacement. In some embodiments, the average, lateral displacement may be limited to a predefined distance to a left and right direction of the trajectory.

In some embodiments, upon determining the autonomous vehicle is positioned behind the another vehicle, the trajectory may be adjusted based on a trajectory of the another vehicle. In some examples, the trajectory of the another vehicle may be tracked by imaging sensors on the autonomous vehicle. In some instances, the adjusted trajectory may be bound by a predefined distance to the left and right of the trajectory.

In some embodiments adjusting the trajectory of the autonomous vehicle occurs continuously through the multi-lane turn.

In some embodiments upon determining the autonomous vehicle is positioned behind multiple vehicles, adjusting the trajectory based on trajectories of the multiple vehicles through the multi-lane turn.

According to another aspect of the invention, a system comprising the features of claim <NUM> is provided. In some examples, the historical data corresponds to previous trajectories of the autonomous vehicle.

In some instances, adjusting the trajectory based on the historical data comprises: determining an average, lateral displacement of the previous trajectories relative to the trajectory of the autonomous vehicle; and adjusting the trajectory of the autonomous vehicle by the average, lateral displacement. In some examples, the average, lateral displacement may be limited to a predefined distance to a left and right of the trajectory.

In some embodiments, the one or more processors may be further configured to, upon determining the autonomous vehicle is positioned behind the another vehicle, adjust the trajectory based on a trajectory of the another vehicle. In some examples, the trajectory of the another vehicle may be tracked by imaging sensors on the autonomous vehicle. In some instances, the adjusted trajectory may be bound by a predefined distance to the left and right of the trajectory.

In some embodiments, adjusting the trajectory of the autonomous vehicle may occur continuously through the multi-lane turn.

In some embodiments the one or more processors may be further configured to, upon determining the autonomous vehicle is positioned behind multiple vehicles, adjust the trajectory based on trajectories of the multiple vehicles through the multi-lane turn.

This technology relates to controlling an autonomous vehicle through a multi-lane turn. In this regard, when traversing a multi-lane turn, such as a double or triple lane, left or right hand turn, drivers frequently cut corners and cross lane boundaries. For instance, <FIG> shows a portion of roadway <NUM> on which vehicles <NUM> and <NUM> are traversing a double lane left turn <NUM> in the inside lane <NUM> and outside lane <NUM>, respectively. The vehicle <NUM> traversing the outside lane <NUM> is crossing into the inside lane <NUM> and into the path of vehicle <NUM> which is traversing the inside lane <NUM>. To avoid hitting vehicle <NUM>, vehicle <NUM> may be forced to adjust its trajectory. Vehicles, such as vehicle <NUM>, are commonly cut-off or pushed out of their current trajectory by vehicles traveling in an adjacent lane. These issues are magnified with regard to autonomous vehicles, as autonomous vehicles may be programmed to follow a trajectory within a lane in which the vehicle is positioned. As such, when an autonomous vehicle is "cut-off" or "pinched" by another vehicle in the midst of the autonomous vehicle performing a turn through a multi-lane turn, an evasive action may be required. Such evasive actions may lead to uncomfortable or unsafe conditions for the passengers of the autonomous vehicle.

To address these issues, the actions and trajectory of the autonomous vehicle may be adjusted as it traverses through the multi-lane turn. The trajectory may be adjusted based on the vehicle's position relative to other vehicles or based on historical data of vehicle's traversing the multi-lane turn. In this regard, when traversing a multi-lane turn, the autonomous vehicle may be in a number of positions relative to the other vehicles, such as, for instance the first vehicle in a line of vehicles, the last vehicle in a line of vehicles, or in between vehicles. For instance, when the autonomous vehicle is positioned first in a line of vehicles, the trajectory of the autonomous vehicle may be adjusted such that it may follow an alternate trajectory based on historical data corresponding to previous paths vehicles took through the multi-lane turn. In instances where the autonomous vehicle is positioned in the middle or behind other vehicles, the trajectory of the autonomous vehicle may be adjusted such that it follows the trajectory of vehicles positioned ahead.

The historical data may be comprised of previous paths the autonomous vehicle or other vehicles have traversed around multi-lane turns may be monitored and used to alter the autonomous vehicle's nominal trajectory. Based on this historical data, the alternate trajectory may be followed in lieu of the autonomous vehicle's nominal trajectory to more closely resemble the previous trajectories of vehicles traversing the turn. In some instances, the alternate trajectory may be limited such to prevent the autonomous vehicle from deviating too far outside of a safe operating trajectory.

In instances where vehicles are traversing a lane adjacent to the autonomous vehicle, the autonomous vehicle may adjust its trajectory such that it staggers itself relative to adjacent vehicles to increase its visibility to drivers of the adjacent vehicles. In other words, the autonomous vehicle may continually position itself such that it is between the vehicles of the adjacent lane, such that the vehicles of the adjacent lane can see the autonomous vehicle.

In instances where the autonomous vehicle is unable to maintain a staggered position relative to the vehicles of the adjacent lane, the autonomous vehicle may either pass or yield to one of the surrounding vehicles. The determination whether to pass or yield may be based on passenger comfort levels, such that any maneuvers to pass or yield should not result in undue passenger discomfort.

The features described herein allow for improved and safer travel of an autonomous vehicle through a multi-lane turn. In this regard, the features described herein provide for more comfortable turning conditions for passengers of the autonomous vehicle, as evasive maneuvers, such as hard braking or quick turns may be avoided. Moreover, the autonomous vehicle may be positioned such that it is more visible to surrounding vehicles to reduce the risk of a driver not seeing the autonomous vehicle while traversing the multi-lane turn. In addition, the movements of the autonomous vehicle through a multi-lane turn may be more typical of human drivers, allowing drivers of surrounding vehicles to more easily predict the movements of the autonomous vehicle.

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, busses, recreational vehicles, etc. The vehicle may have one or more computing devices, such as computing devices <NUM> containing one or more processors <NUM>, memory <NUM> and other components typically present in general purpose computing devices.

The processor <NUM> may be any one or more 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> functionally illustrates the processor, memory, and other elements of computing devices <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 <NUM> may be a hard drive and/or other storage media located in 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.

Computing device <NUM> may include all of the components normally used in connection with a computing device such as the processor and memory described above as well as one or more user inputs <NUM> (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 one or more internal displays <NUM> as well as one or more speakers <NUM> to provide information or audio visual experiences. In this regard, display <NUM> may be located within a cabin of vehicle <NUM> and may be used by computing device <NUM> to provide information to passengers or maintenance personnel within or otherwise in the vicinity of, the vehicle <NUM>.

Computing device <NUM> may also include one or more wireless network connections <NUM> 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, Wi-Fi and HTTP, and various combinations of the foregoing. Computing device <NUM> of vehicle <NUM> may also receive or transfer information to and from other computing devices (not shown), such as computing devices which contain or otherwise store further map or perception data.

In one example, computing device <NUM> may control the computing devices of an autonomous driving computing system incorporated into vehicle <NUM>. The autonomous driving computing system may capable of communicating with various components of the vehicle in order to control the movement of vehicle <NUM> according to primary vehicle control code stored in memory <NUM>. For example, computing device <NUM> may be in communication with various systems of vehicle <NUM>, such as deceleration system <NUM>, acceleration system <NUM>, steering system <NUM>, signaling system <NUM>, navigation 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>. Again, although these systems are shown as external to computing device <NUM>, in actuality, these systems may also be incorporated into computing device <NUM>, again as an autonomous driving computing system for controlling vehicle <NUM>.

As an example, computing device <NUM> may interact with one or more actuators or other such components of the deceleration system <NUM> and/or acceleration system <NUM>, 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 or other such components of the steering system <NUM>, such as a steering wheel, steering shaft, and/or pinion and rack in a rack and pinion system, may be used by computing device <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 one or more actuators or other such devices to control the angle of wheels to turn the vehicle. Signaling system <NUM> may be used by computing device <NUM> in order to signal the vehicle's intent to other drivers or vehicles, for example, by lighting turn signals or brake lights when needed.

Navigation system <NUM> may be used by computing device <NUM> in order to determine and follow a route to a location. In this regard, the navigation system <NUM> and/or data <NUM> may store detailed map/roadmap 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. For instance, <FIG> shows a portion of roadway <NUM> illustrating example map information identifying the shape, location, and other characteristics of various road features proximate to intersection <NUM>. In this example, the portion of roadway <NUM> corresponding to the map information includes information defining the shape and location of lane markers <NUM>-<NUM>, stop lines <NUM>, crosswalks <NUM>, <NUM>, sidewalk <NUM>, traffic lights <NUM>, <NUM>, lane markings <NUM>, <NUM>, <NUM> as well as the shape and direction of traffic for lanes <NUM>, <NUM>, <NUM>, <NUM>, etc. The portion of roadway <NUM> illustrates a few road features contained in the map information. The map information may also include additional features of the roadway, such as, for instance, lane lines, shoulder areas, an intersection, and lanes and orientations. Map information may also identify various other road features such as stop signs, yield signs, railroad tracks, railroad crossings, speed limit signs, road signs, speed bumps, etc. Although not shown in the portion of roadway <NUM>, the map information may also include information identifying speed limits and other legal traffic requirements, such as which vehicle has the right of way given the location of stop signs or state of traffic signals, etc..

Although the detailed map information corresponding to portion of roadway <NUM> is depicted herein as an image-based map, the map information need not be entirely image based (for example, raster). For example, the detailed 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 <NUM> may be used by computing device <NUM> in order to determine the vehicle's relative or absolute position on a map or on the earth. For example, the positioning system <NUM> may include a GPS receiver to determine the positioning system'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 computing device <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> may also include 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 computing device <NUM>. 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 device. In some instances, the perception system may include a laser or other sensors mounted on the roof or other convenient location of a vehicle. For instance, the perception system <NUM> may use various sensors, such as LIDAR, sonar, radar, cameras, etc. to detect objects and their characteristics such as location, orientation, size, shape, type, direction and speed of movement, etc. 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, a roof-top sensor housing <NUM> and a dome sensor housing <NUM> may include one or more lidar sensors, cameras, and/or 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 sensor housing <NUM>. In this regard, each of housings <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM> may be considered sensor housings any or all of the aforementioned sensors may be considered a part of the vehicle's perception system <NUM>.

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

It should be further understood that the term autonomous may include semi-autonomous vehicles, including vehicles where a human driver may take over control of the vehicle.

When traversing a multi-lane turn, the autonomous vehicle may be in a number of positions relative to the other vehicles. For instance, the autonomous vehicle <NUM>, which may be compared to vehicle <NUM>, may be the first vehicle in a line of vehicles, as shown in <FIG>, or the autonomous vehicle <NUM> may be in the middle or the back of a line of vehicles, as shown in <FIG> and <FIG>, respectively. Although the examples described herein show an autonomous vehicle <NUM> traversing a double lane left turn <NUM>, the features described herein may be applied to any multi-lane turn. For instance, the features may be used in a triple lane left turn, double lane right turn, triple lane right turn, a straight section of a multi-lane road where one or more offsets and/or displacements are present, such as an intersection entrance, exit location, on-ramp, off-ramp, or other such road sections having multiple lanes.

Depending upon the position of the autonomous vehicle relative to other vehicles traversing the multi-lane turn, the trajectory of the autonomous vehicle may be adjusted. For instance, and as illustrated on the portion of roadway <NUM> in <FIG> and <FIG>, an autonomous vehicle <NUM> is at the front of the line of vehicles, including vehicle <NUM> in double left turn lane <NUM>. When positioned at the front of the line of vehicles (e.g., the first vehicle), the autonomous vehicle may travel a nominal trajectory <NUM>, such as if it was making a single lane turn, as shown in <FIG>. In other words, the autonomous vehicle's computing device, such as computing device <NUM>, may instruct the autonomous vehicle <NUM> to travel within its normal operating parameters or rather, to follow a nominal trajectory, such that the autonomous vehicle is centered or nearly centered in the turning lane, in accordance with typical driving practices.

Historical data corresponding to previous paths the autonomous vehicle or other vehicles have traversed around multi-lane turns may be monitored and used to alter the autonomous vehicle's nominal trajectory. In this regard, the past trajectories, such as the actual paths traveled by the vehicles or actual paths traveled by the vehicles relative to a nominal driving corridor defined in the map information (i.e., a portion of the road through which vehicles typically travel,) may be tracked. Behaviors, such as acceleration and deceleration, of other vehicles or the autonomous vehicle itself may also be tracked. The historical data may be tracked by the autonomous vehicle's sensors, such as the sensors of perception system <NUM>, the sensors of other vehicles, and/or sensors positioned at, or near, the multi-lane turn.

The historical data collected by the sensors may include these previous trajectories of vehicles traversing the inner and outer lanes of a double lane left turn. For instance, and as shown in <FIG>, vehicles <NUM> and <NUM> in the outside lane <NUM> of the double lane left turn (i.e., the lane having a wider radius turn) may tend to move towards and/or cut into the inner lane <NUM> (i.e., the lane having a shorter radius turn). To avoid colliding with the vehicles of the outside lane <NUM>, vehicles traversing the inner lane <NUM>, such as autonomous vehicle <NUM> and vehicle <NUM> may typically follow a trajectory which results in a sharper turn than would typically be followed if there were no vehicles in the outside lane <NUM>.

Based on this historical data, the vehicle <NUM>'s computing device such as computing device <NUM>, or other such computer, may determine an alternate trajectory should be followed in lieu of the nominal trajectory. In this regard, the trajectory of the autonomous vehicle <NUM> may be adjusted from its nominal trajectory to an alternate trajectory which more closely resembles the previous trajectories of vehicles traversing the turn. For example, and as previously described, the historical data may indicate that vehicle's travelling in an outside lane <NUM> tend to move into the inner lane <NUM> during a turn. Based on the historical data, the computing device <NUM> may determine an average, lateral displacement of the vehicles traversing the turn relative to the nominal trajectory <NUM>. The autonomous vehicle's nominal trajectory <NUM> may be adjusted to more closely follow by the average, lateral displacement such that the autonomous vehicle's adjusted trajectory <NUM> is similar to that of other vehicles, as further shown in <FIG>. The trajectory of the autonomous vehicle may be adjusted continuously as the autonomous vehicle traverses the multi-lane turn. The autonomous vehicle's computing devices, such as computing device <NUM>, may then control the autonomous vehicle <NUM> according to the adjusted trajectory <NUM>. In some instances, the historical data may be filtered prior to determining the average, lateral displacement to remove vehicle trajectories which are more than a predefined distance from the nominal trajectory.

The alternate trajectory may be limited such that the autonomous vehicle does not deviate too far outside of a safe operating trajectory. In this regard, the autonomous vehicle <NUM> may be bounded by a range in a lateral direction around the trajectory of a turn (i.e., limited to a distance to the left and/or right of the trajectory). For instance, as shown in the map information <NUM> an unbounded, initial alternate trajectory <NUM> may be such that it deviates by a certain amount, for instance, two or three feet, or more or less, to the right or left of a nominal trajectory and outside of a range illustrated as "X", which may result in unsafe driving conditions for the autonomous vehicle <NUM> and/or other surrounding vehicles.

To address this, the initial alternate trajectory <NUM> may be modified such that it is within the predefined range "X". For example, the unbounded, initial alternate trajectory <NUM>, as shown in <FIG>, may result in the autonomous vehicle <NUM> crossing over the stop line <NUM> of traffic traveling on a lane going in the opposite direction as the autonomous vehicle <NUM>. As vehicles may be positioned on, or past the stop line <NUM>, the initial alternate trajectory <NUM> may be adjusted such that it is within predefined range "X", as shown by adjusted trajectory <NUM>. The adjusted trajectory <NUM> may provide sufficient space between the autonomous vehicle <NUM> and the stop line <NUM>, such that the autonomous vehicle does not travel over the stop line. Other boundaries may be based on the positioning of lane dividers and other such obstacles which may be in the trajectory of the autonomous vehicle.

A confidence interval may be determined for each portion of a turn to determine whether the alternate trajectory is within a certain distance from the nominal trajectory for each portion of the turn. In other words, the confidence interval may be a parameter that can be tuned to provide a trade-off between the autonomous vehicle <NUM> following the lead vehicle's path, as long as the lead vehicle's path is within an arbitrarily determined range of the nominal path, otherwise the autonomous vehicle may follow its nominal path. In this regard, the turn may be subdivided into a series of fixed, or non-fixed, length intervals. Within every interval, a distribution of potential lateral displacements relative to a nominal trajectory may be observed via the historical data or generated using models. Based on the distributions of displacements, a sample may be generated and assigned an arbitrary confidence interval, such as <NUM>% or more or less. The alternate trajectory may be compared to the sample of displacements to assure the alternate trajectory is within the range of distances, such as <NUM> meters, or more or less, from the nominal trajectory defined by the sample of displacements having the assigned confidence interval.

In instances where the autonomous vehicle is in the middle, or at the end of a line of vehicles, the autonomous vehicle may follow the trajectory of another vehicle or vehicles in front of it. In this regard, the perception system of the autonomous vehicle <NUM>, such as the perception system <NUM>, may track, in real time, the path of the vehicles traversing the same lane of the multi-lane turn as the autonomous vehicle. Based on the path tracked by the perception system, the autonomous vehicle may follow the same, or a similar trajectory. For instance, and as shown in <FIG>, autonomous vehicle <NUM> is positioned in the inner lane <NUM> of double lane left turn <NUM> between vehicles <NUM> and <NUM>. The perception system <NUM> of autonomous vehicle <NUM> may track the trajectory <NUM> of vehicle <NUM> as vehicle <NUM> traverses lane <NUM>. The autonomous vehicle <NUM> may then follow the same trajectory <NUM> as the autonomous vehicle traverses the double lane left turn. In some instances, the trajectory of the autonomous vehicle may be bounded as described herein, such that should the other vehicle's trajectory fall outside of the bounded range, the autonomous vehicle may deviate from the other vehicle's trajectory.

In some instances, vehicles in side-by-side lanes of a multi-lane turn may be positioned too closely together, thereby reducing visibility of other vehicles to drivers of the other vehicles. In common parlance, adjacent vehicles are considered to be in the "blind spot" of the drivers of surrounding vehicles. In such conditions, there is an increased risk that the driver of a vehicle may cross into an adjacent lane, not realizing another vehicle is in his or her blind spot. For instance, and as shown in <FIG>, vehicle <NUM> is traversing the inside lane <NUM> of double lane left turn <NUM>. The driver of vehicle <NUM>, which is positioned adjacent to vehicle <NUM> (and within a blind spot of vehicle <NUM>) and traversing outer the outer lane <NUM>, may not see vehicle <NUM> and attempt to cross into the inner lane <NUM>, thereby cutting off vehicle <NUM>.

To avoid these issues when traversing a multi-lane turn, the autonomous vehicle's computing device may stagger the autonomous vehicle relative to surrounding vehicles. By doing such, the autonomous vehicle may increase visibility to other drivers of the vehicles ahead and behind it. For instance, as shown in <FIG>, autonomous vehicle <NUM> is travelling inside lane <NUM> of the double lane left turn <NUM> may position itself such that it is between the vehicles <NUM> and <NUM> which are traversing the adjacent, outside lane <NUM>. By doing such, the vehicles of the adjacent, outside lane <NUM> can see the autonomous vehicle <NUM>. Moreover, by staggering the autonomous vehicle <NUM> relative to the vehicles of the adjacent, outside lane <NUM>, less actuation, such as through braking and/or accelerating, may be required to avoid a collision.

The autonomous vehicle's computing device may continually adjust the trajectory and positioning of the autonomous vehicle relative to the positions of the surrounding vehicles in an adjacent lane. In this regard, the autonomous vehicle's perception system, such as perception system <NUM> may track the positions of the surrounding vehicles in front of (forward surrounding) and behind (rear surrounding) the autonomous vehicle <NUM> to determine their position relative to the autonomous vehicle <NUM>. The distance the autonomous vehicle <NUM> may maintain between a rear surrounding vehicle and a forward surrounding vehicle may be based on fixed stop ranges. For instance, and referring to <FIG>, the front bumper of the autonomous vehicle <NUM> may be one meter, or more or less, from the rear bumper of a vehicle <NUM>, which is positioned in front of the autonomous vehicle. For surrounding vehicles behind the autonomous vehicle, such as vehicle <NUM>, the rear bumper of the autonomous vehicle <NUM> may be one meter from the front bumper of the surrounding vehicle <NUM>.

Based on the distance between the rear surrounding vehicle and the forward surrounding vehicle, the autonomous vehicle's computing device may adjust the autonomous vehicle's velocity and/or acceleration to maintain a staggered position. For instance, when the autonomous vehicle <NUM>, traversing the inner lane of double left turn lane <NUM>, is too close to the rear, surrounding vehicle <NUM> traversing the adjacent, outer lane <NUM>, as shown in <FIG>, the autonomous vehicle's computing device, such as computing device <NUM>, may cause the autonomous vehicle <NUM> to accelerate and increase its velocity until appropriate distance between the rear surrounding vehicle is reached.

In instances where the autonomous vehicle is positioned too close to the forward surrounding vehicle the computing device of the autonomous vehicle may decrease the autonomous vehicle's velocity and/or reduce acceleration to allow the forward surrounding vehicle time to pull further ahead of the autonomous vehicle. For instance, when the autonomous vehicle <NUM>, traversing the inner lane of double left turn lane <NUM>, is too close to the forward, surrounding vehicle <NUM>, as shown in <FIG>, the autonomous vehicle's computing device, such as computing device <NUM>, may cause the autonomous vehicle <NUM> to decelerate and decrease its velocity until appropriate distance between the forward surrounding vehicle <NUM> is reached.

In the event there is appropriate spacing between the forward and rear surrounding vehicles is reached, the computing devices of the autonomous vehicle may maintain the autonomous vehicle's current velocity and/or acceleration.

In some instances, forward and/or rear surrounding vehicles may prevent the autonomous vehicle from staggering. In this regard, the forward and/or rear surrounding vehicles may be positioned too closely together to allow the autonomous vehicle to stagger. In such a situation, the autonomous vehicle may either pass or yield to one of the surrounding vehicles. For instance, and as shown in <FIG>, the rear surrounding vehicle <NUM> traveling in the outside lane <NUM> of double left turn lane <NUM> is too close to the front surrounding vehicle <NUM> to allow the autonomous vehicle to stagger. In response, the autonomous vehicle <NUM>, which is traversing the inside lane <NUM>, may either yield to the rear surrounding vehicle <NUM> by decreasing the vehicle's velocity and/or acceleration in order to allow the rear surrounding vehicle to pass (e.g., the rear surrounding vehicle maneuvers into a location ahead of the autonomous vehicle,) or pass the forward surrounding vehicle <NUM> (e.g., the autonomous vehicle moves into a location ahead of the forward surrounding vehicle,) by increasing the vehicle's velocity and/or acceleration.

The determination whether to pass or yield may be based on passenger comfort levels. In this regard, the computing device, such as computing device <NUM>, may monitor the angular trajectory of the autonomous vehicle, its current velocity, its current acceleration, and its position relative to surrounding vehicles. Based on these factors, the computing device may determine whether the autonomous vehicle would be more comfortable to a passenger if the vehicle were to pass a forward surrounding vehicle or if the vehicle were to yield to the rear surrounding vehicle, as it can be disconcerting to a passenger if the autonomous vehicle comes too close to another vehicle laterally and/or if the autonomous vehicle takes a turn too fast or too slow. For instance, an autonomous vehicle may monitor factors such as headway between vehicles ahead and/or behind it, lateral separation distance between surrounding vehicles, braking actions of surrounding vehicles, etc. Based on these factors, and their potential or actual effects on the operation of the vehicle, the autonomous vehicle's computing device may determine whether a passenger would be more comfortable to pass a surrounding vehicle, yield to a surrounding vehicle, or maintain the autonomous vehicle's current position.

<FIG> includes an example flow diagram <NUM> of some of the examples for controlling a vehicle as described above. In this example, the steps of flow diagram may be performed by one or more processors of one or more computing devices, such as processors <NUM> of computing devices <NUM> of vehicle <NUM>. For instance at block <NUM>, one or more processors receive data corresponding to a position of the autonomous vehicle in a lane of the multi-lane turn and a trajectory of the autonomous vehicle. At block <NUM>, one or more processors receive data corresponding to positions of objects in a vicinity of the autonomous vehicle. At block <NUM>, a determination is made based on a position of the autonomous vehicle in the lane relative to the positions of the objects, whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane. The trajectory of the autonomous vehicle through the lane may be adjusted by one or more processors based on whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane, as shown in block <NUM>. The autonomous vehicle may be controlled through the multi-lane turn based on the adjusted trajectory, as shown in block <NUM>.

Claim 1:
A method for controlling an autonomous vehicle (<NUM>) through a multi-lane turn, the method comprising:
receiving, by one or more processors (<NUM>), data corresponding to a position of the autonomous vehicle in a lane of the multi-lane turn and a trajectory of the autonomous vehicle;
receiving, by the one or more processors, data corresponding to positions of objects in a vicinity of the autonomous vehicle;
determining based on a position of the autonomous vehicle in the lane relative to the positions of the objects, whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane;
adjusting, based on whether the autonomous vehicle is positioned as a first vehicle in the lane or positioned behind another vehicle in the lane, by one or more processors, the trajectory of the autonomous vehicle through the lane, wherein upon determining the autonomous vehicle is positioned as the first vehicle in the lane, adjusting the trajectory of the autonomous vehicle through the lane is based on historical data corresponding to previous trajectories of one or more vehicles which traversed the lane; and
controlling the autonomous vehicle through the multi-lane turn based on the adjusted trajectory.