Emergency braking for autonomous vehicles

Aspects of the disclosure provide for generation of trajectories for a vehicle driving in an autonomous driving mode. For instance, information identifying a plurality of objects in the vehicle's environment and a confidence value for each of the objects is received. A set of constraints may be generated. That one or more processors are unable to solve for a trajectory given the set of constraints and an acceptable braking limit may be determined. A first constraint is identified as a constraint for which could not be solved and a first confidence value. That the vehicle should apply a maximum braking level is determined based on the identified first confidence value, a threshold, and the determination that the one or more processors are unable to solve for a trajectory. Based on the determination that the vehicle should apply the maximum braking level, the maximum braking level is applied.

BACKGROUND

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 vehicle maneuvers itself to that location by controlling various systems of the vehicles, including acceleration and deceleration (braking) systems of the vehicle.

BRIEF SUMMARY

One aspect of the disclosure provides a method of generating trajectories for a vehicle driving in an autonomous driving mode. The method includes receiving, by one or more processors, from a perception system of the vehicle, information identifying a plurality of objects in an environment of the vehicle and a confidence value for each object of the plurality of objects being a real object; generating, by the one or more processors, a set of constraints, a first constraint of the set of constraints being generated based on a first object of the objects of the plurality of objects; determining, by the one or more processors, that the one or more processors are unable to solve for a trajectory given the set of constraints and an acceptable braking limit; identifying, by the one or more processors, the first constraint as a constraint for which the one or more processors were unable to solve for a trajectory and a first confidence value, the first constraint corresponding to the first object that is associated with the first confidence value; determining, by the one or more processors, that the vehicle should apply a maximum braking level greater than the acceptable braking limit based on the identified first confidence value, a threshold, and the determination that the one or more processors are unable to solve for a trajectory; and based on the determination that the vehicle should apply the maximum braking level, applying, by the one or more processors, the maximum braking level to control the vehicle in the autonomous driving mode and in order to slow the vehicle down.

In one example, the method also includes for each given object of the plurality of objects, determining a priority level based on the confidence value and a type of the given object; comparing the determined priority levels to a threshold value; and based on the comparison, flagging at least the first constraint to indicate that the first object allows for the maximum braking level. In this example, determining that the vehicle should apply a maximum braking level is further based on the flagged first constraint. In another example, the method also includes for each given object of the plurality of objects, determining a priority level based on the confidence value and a type of the given object; comparing the determined priority levels to a threshold value; and based on the comparison, flagging at least the first object to indicate that the first object allows for the maximum braking level. In this example, determining that the vehicle should apply a maximum braking level is further based on the flagged first object. In another example, the first confidence value corresponds to a number of iterations of a LIDAR sensor of the perception system in which the one of the plurality of objects has been observed. In another example, the method also includes receiving for the first object, a type of the first object; and determining a priority level based on the first confidence value and the type of object, and wherein determining that the vehicle should apply a maximum braking level is further based on the priority level. In this example, determining that the vehicle should apply a maximum braking level is further based on a comparison between the priority level and the threshold. In addition or alternatively, the method also includes determining that the priority level meets the threshold, and wherein determining that the vehicle should apply the maximum braking level is further based on the determination that the priority level meets the threshold. In another example, determining that the vehicle should apply the maximum braking level is further based on a current speed of the vehicle. In another example, determining that the vehicle should apply the maximum braking level is further based on a detected speed of one or more of the plurality of objects. In another example, determining that the vehicle should apply the maximum braking level is further based on a type of road on which the vehicle is currently driving. In another example, determining that the vehicle should apply the maximum braking level is further based on whether the vehicle is currently driving on a highway. In another example, the maximum braking level corresponds to a maximum braking limit for brakes of the vehicle. In another example, determining that the vehicle should apply the maximum braking level is further based on a speed limit of a road on which the vehicle is traveling. In another example, determining that the vehicle should apply the maximum braking level is further based on a speed of a second vehicle in the environment of the vehicle. In another example, applying the maximum braking level is further based on whether a second vehicle is behind the vehicle. In this example, applying the maximum braking level is further based on whether the second vehicle meets a predetermined threshold with respect to a current location of the vehicle. In another example, determining that the vehicle should apply the maximum braking level is further based on a current driving situation for the vehicle, wherein the current driving situation relates to an environment in which the vehicle is currently driving.

Another aspect of the disclosure provides a system for generating trajectories for a vehicle driving in an autonomous driving mode. The system includes one or more processors configured to receive from a perception system of the vehicle, information identifying a plurality of objects in an environment of the vehicle and a confidence value for each object of the plurality of objects being a real object; generate a set of constraints, a first constraint of the set of constraints being generated based on a first object of the objects of the plurality of objects; determine that the one or more processors are unable to solve for a trajectory given the set of constraints and an acceptable braking limit; identify the first constraint as a constraint for which the one or more processors were unable to solve and a first confidence value, the first constraint corresponding to the first object that is associated with the first confidence value; determine that the vehicle should apply a maximum braking level greater than the acceptable braking limit based on the identified first confidence value, a threshold, and the determination that the one or more processors are unable to solve for a trajectory; and based on the determination that the vehicle should apply the maximum braking level, apply the maximum braking level to control the vehicle in the autonomous driving mode and in order to slow the vehicle down. In one example, the system also includes the vehicle.

DETAILED DESCRIPTION

Overview

The technology relates to allowing a vehicle to initiate emergency braking when the confidence an object is in the path of the vehicle is high. In this regard, a vehicle, such as an autonomous vehicle, may follow a projected path at a certain speed and/or acceleration. When an object is detected in the vehicle's projected path, the vehicle may be required to make an emergency maneuver, such as sharply turning or braking hard to avoid colliding with the object. As such, objects which have low confidence values, indicating that there is a low certainty of the objects actually being present, may result in unnecessary hard braking being performed by the autonomous vehicle. However, these hard braking may increase the risk of other vehicles colliding with the autonomous vehicle (for instance, a tailgater may rear-end the vehicle) and decrease rider comfort. To address this, hard braking may only be enabled when the detected objects have high confidence values.

As the vehicle drives around, the vehicle's perception system may detect and identify objects and their characteristics. Each detected object may be associated with a confidence value identifying how confident the perception system is that the object is “real.” The more the object is perceived by the sensors of the perception system, the greater the confidence value that the object is a real, correctly identified object, with more reliable estimates of position, orientation and speed. The confidence values and type for each identified object may be used to determine a priority level for each object.

The vehicle's computing devices may also determine whether the priority level for each object meets a threshold. If so, for instance because the priority is greater than or equal to the threshold, the constraint for that object and/or the object may be flagged or otherwise marked as allowing for an emergency braking or maximum braking level.

The vehicle's computing devices may generate a set of constraints based on the objects. For instance, the objects and their characteristics, such as location, speed, heading, orientation, predicted behaviors, etc. may be used to generate a set of constraints identifying locations at certain times at which the vehicle should not be in order to avoid collision.

The set of constraints may be used to attempt to generate trajectories that allow the vehicle to maintain safe and comfortable traveling conditions. For instance, the set of constraints may be used to generate a trajectory for the vehicle by solving for a trajectory that satisfies all of the constraints while allowing the vehicle to brake at no greater than an acceptable braking level. In certain circumstances, the vehicle's computing devices may not be able to solve for all of the constraints. In such situations, the vehicle's computing devices may apply the brakes of the vehicle at the maximum braking level and, in some cases, also employ a parking brake if available. As such, the vehicle's computing devices may automatically apply the maximum braking level without specifically attempting to solve for the set of constraints.

However, as noted above, higher levels of braking limit may increase risk of other vehicles colliding with the autonomous vehicle and decrease rider comfort. As such, before applying the maximum braking level, the vehicle's computing devices may identify a constraint for which the vehicle's computing devices are not able to solve for a trajectory and determine whether the constraint and/or the corresponding object for that constraint has been flagged to allow for maximum braking level.

The features described herein may allow the vehicle's computing devices to use emergency braking in a safe and reliable way. By tying this braking to the confidence value that an object detected by the perception system exists as well as the type of that object, this reduces the likelihood of unnecessary, and in some cases, dangerous braking in inappropriate circumstances. This, in turn, increases the safety and comfort of passengers within the vehicle.

Example Systems

As shown inFIG. 1, a vehicle100in 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 device110containing one or more processors120, memory130and other components typically present in general purpose computing devices.

The data134may be retrieved, stored or modified by processor120in accordance with the instructions132. As an example, data134of memory130may store predefined scenarios. A given scenario may identify a set of scenario requirements including a type of object, a range of locations of the object relative to the vehicle, as well as other factors such as whether the autonomous vehicle is able to maneuver around the object, whether the object is using a turn signal, the condition of a traffic light relevant to the current location of the object, whether the object is approaching a stop sign, etc. The requirements may include discrete values, such as “right turn signal is on” or “in a right turn only lane”, or ranges of values such as “having an heading that is oriented at an angle that is 30 to 60 degrees offset from a current path of vehicle100.” In some examples, the predetermined scenarios may include similar information for multiple objects.

The one or more processor120may 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. AlthoughFIG. 1functionally illustrates the processor, memory, and other elements of computing device110as 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. As an example, internal electronic display152may be controlled by a dedicated computing device having its own processor or central processing unit (CPU), memory, etc. which may interface with the computing device110via a high-bandwidth or other network connection. In some examples, this computing device may be a user interface computing device which can communicate with a user's client device. Similarly, the memory may be a hard drive or other storage media located in a housing different from that of computing device110. 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 device110may 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 input150(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 display152as well as one or more speakers154to provide information or audio visual experiences. In this regard, internal electronic display152may be located within a cabin of vehicle100and may be used by computing device110to provide information to passengers within the vehicle100. In addition to internal speakers, the one or more speakers154may include external speakers that are arranged at various locations on the vehicle in order to provide audible notifications to objects external to the vehicle100.

In one example, computing devices110may be control computing devices of an autonomous driving computing system or incorporated into vehicle100. The autonomous driving computing system may capable of communicating with various components of the vehicle in order to control the movement of vehicle100according to the autonomous control software of memory130as discussed further below. For example, returning toFIG. 1, computing devices110may be in communication with various systems of vehicle100, such as deceleration system160(for instance, including the features connected or otherwise attached to the brake pedal of the vehicle), acceleration system162(for instance, including the features connected or otherwise attached to the accelerator pedal of the vehicle), steering system164, signaling system166, routing system168, positioning system170, perception system172, and power system174(i.e. the vehicle's engine or motor) in order to control the movement, speed, etc. of vehicle100in accordance with the instructions132of memory130. For instance, the computing devices110may generate trajectories for the vehicle100to follow and control the vehicle according to the trajectory by sending commands to the deceleration system160(to control braking power and accordingly, deceleration) as well as acceleration system162(to control acceleration and changes in acceleration). Again, although these systems are shown as external to computing devices110, in actuality, these systems may also be incorporated into computing devices110, again as an autonomous driving computing system for controlling vehicle100.

The computing device110may control the direction and speed of the vehicle by controlling various components. By way of example, computing device110may navigate the vehicle to a destination location completely autonomously using data from the map information and navigation system168. Computing devices110may use the positioning system170to determine the vehicle's location and perception system172to detect and respond to objects when needed to reach the location safely. In order to do so, computing devices110may cause the vehicle to accelerate (e.g., by increasing fuel or other energy provided to the engine by acceleration system162), decelerate (e.g., by decreasing the fuel supplied to the engine, changing gears, and/or by applying brakes by deceleration system160), change direction (e.g., by turning the front or rear wheels of vehicle100by steering system164), and signal such changes (e.g., by lighting turn signals of signaling system166). Thus, the acceleration system162and deceleration system160may 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 devices110may also control the drivetrain of the vehicle in order to maneuver the vehicle autonomously.

As an example, computing device110may interact with deceleration system160and acceleration system162in order to control the speed of the vehicle. Similarly, steering system164may be used by computing device110in order to control the direction of vehicle100. For example, if vehicle100configured 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. Signaling system166may be used by computing device110in order to signal the vehicle's intent to other drivers or vehicles, for example, by lighting turn signals or brake lights when needed.

Navigation system168may be used by computing device110in order to determine and follow a route to a location. In this regard, the navigation system168and/or data134may store map information, e.g., highly detailed maps that computing devices110can use to navigate or control the vehicle. As an example, these maps may identify the shape and elevation of roadways, lane markers, intersections, crosswalks, speed limits, traffic signal lights, buildings, signs, real time or historical traffic information, vegetation, or other such objects and information. The lane markers may include features such as solid or broken double or single lane lines, solid or broken lane lines, reflectors, etc. A given lane may be associated with left and right lane lines or other lane markers that define the boundary of the lane. Thus, most lanes may be bounded by a left edge of one lane line and a right edge of another lane line. As noted above, the map information may store known traffic or congestion information and/or and transit schedules (train, bus, etc.) from a particular pickup location at similar times in the past. This information may even be updated in real time by information received by the computing devices110.

FIG. 2is an example of map information200for a section of roadway including intersection220. In this example, map information200depicts a portion of the map information that includes information identifying the shape, location, and other characteristics of lane markers or lane lines210,212,214, stop signs240,242,244,246, as well as stop lines250,252,254,256. The lane lines may also define various lanes260,261,264,265, implied by the width of a road, such as lanes262,263,266,267, and/or lanes260-267may also be explicitly identified in the map information200. In this example, map information200also identifies bicycle lanes280,282. 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 devices110to 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.

Although the detailed 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 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.

The perception system172also 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 system172may include one or more LIDAR sensors, sonar devices, radar units, cameras and/or any other detection devices that record data which may be processed by computing devices110. The sensors of the perception system may detect objects and their characteristics such as location, orientation, size, shape, type (for instance, vehicle, pedestrian, bicyclist, etc.), heading, and speed of movement, etc. The raw data from the sensors and/or the aforementioned characteristics can be quantified or arranged into a descriptive function, vector, and or bounding box and sent for further processing to the computing devices110periodically and continuously as it is generated by the perception system172. As discussed in further detail below, computing devices110may use the positioning system170to determine the vehicle's location and perception system172to detect and respond to objects when needed to reach the location safely.

For instance,FIG. 3is an example external view of vehicle100. In this example, roof-top housing310and dome housing312may include a LIDAR sensor as well as various cameras and radar units. In addition, housing320located at the front end of vehicle100and housings330,332on the driver's and passenger's sides of the vehicle may each store a LIDAR sensor. For example, housing330is located in front of driver door350. Vehicle100also includes housings340,342for radar units and/or cameras also located on the roof of vehicle100. Additional radar units and cameras (not shown) may be located at the front and rear ends of vehicle100and/or on other positions along the roof or roof-top housing310. Vehicle100also includes many features of a typical passenger vehicle such as doors350,352, wheels360,362, etc.

Once a nearby object is detected, computing devices110and/or perception system172may determine the object's type, for example, a traffic cone, pedestrian, a vehicle (such as a passenger car, truck, bus, etc.), bicycle, etc. Objects may be identified by various models which may consider various characteristics of the detected objects, such as the size of an object, the speed of the object (bicycles do not tend to go faster than 40 miles per hour or slower than 0.1 miles per hour), the heat coming from the bicycle (bicycles tend to have rider that emit heat from their bodies), etc. In addition, the object may be classified based on specific attributes of the object, such as information contained on a license plate, bumper sticker, or logos that appear on the vehicle.

In one example, computing devices110may be operable to predict another vehicle's future movement based solely on the other vehicle's instant direction, acceleration/deceleration and velocity, e.g., that the other vehicle's current direction and movement will continue. However, memory130may also store behavior models that provides the probability of one or more actions being taken a detected object. To increase the usefulness of these behavior models, each behavior model may be associated with a specific type of object. For instance, one type of behavior model may be used for objects identified as pedestrians, another type of behavior model may be used for objects identified as vehicles, another type of behavior may be used for objects identified as bicycles or bicyclists, etc. The behavior models may be used by the computing devices110in order to predict a detected object future movement by analyzing data relating to the other vehicle's current surroundings and determining how that other object will likely respond to those surroundings. In this regard, the behavior models may function from an object-centric, view of the object's environment, in that the system determines what the other objects are perceiving in order to better predict how those objects will behave. In this regard, in at least some instances, the behavior models may also indicate whether the predicted behavior for an object is responsive to a particular other object including the vehicle100.

Example Methods

As noted above, the computing devices110may use the routing system to determine a route to a destination. The computing devices110may then periodically generate trajectories, for instance every 0.1 times per second or more or less, for the vehicle to follow for some time and distance into the future in order to follow the route to the destination. As noted above, these trajectories are generated as “desired paths” in order to avoid obstacles, obey laws and generally drive safely and effectively. Each trajectory may define various requirements for the vehicle's acceleration, speed, and position at different times along the trajectory. In this regard, the trajectory may include both geometry and speed/acceleration components. The computing devices110may operate to manage steering, powertrain and braking in order to cause the vehicle to follow the trajectory. The computing devices110may receive feedback from the vehicles sensors including sensors of and/or the perception system172and the positioning system170. This feedback may include information such as the current acceleration, speed, and position of the vehicle as well as those characteristics of objects detected in the vehicle's environment.

FIG. 4is an example400bird's eye view of a section of roadway corresponding to map information200. In this example, the shape location and other characteristics of intersection420, lane lines410,412,414, stop signs440,442,444,446, stop lines450,452,454,456, lanes460-467, and bicycle lanes480,482, may generally correspond to the shape location and other characteristics of intersection220, lane lines210,212,214, stop signs240,242,244,246, stop lines250,252,254,256, lanes260-268, and bicycle lanes280,282respectively. In this example, vehicle100is driving in lane460towards intersection420. In addition, another vehicle490is also driving in lane460towards intersection420, and a bicyclist492is riding in bike lane480towards intersection420as well.

As the vehicle drives around, the perception system172and/or computing devices110may detect and identify objects and their characteristics. For instance, as shown in example500ofFIG. 5, the perception system172may detect various objects in the vehicle's environment. In addition to the features of the map information200, the perception system172may also detect and identify objects such as objects represented by bounding boxes590,592, and594. In this example, the bounding boxes represent an estimated size, shape, orientation and location of the detected objects and may “bound” the data points, such as LIDAR sensor data points or camera image data points, relative to the example600.

Each detected object may be associated with a confidence value identifying how confident the perception system is that the object is “real.” These confidence values may be determined by the perception system172and/or computing devices110. For instance, if an object or a point cloud is only perceived by a single sensor, such as a LIDAR sensor, 1 or 2 times (1 or 2 sensor iterations), it is possible that the object is not a real object, but simply noise, reflections, or exhaust vapor from another vehicle. Similarly, some objects, such as small vegetation or debris, may appear to be moving and may be detected as another road user, but may simply be swaying in the wind. Thus, the confidence value would be very low. For instance, bounding box594may be associated with a very low confidence value. As such, turning to example600ofFIG. 6, bounding box594may represent data points for an object that has only been perceived during 1 or 2 sensor iterations. As such, this bounding box is associated with a very low confidence of 0.1 on a scale of 0 to 1, where zero is low confidence and 1 is high confidence. Of course, other scales and values may be used. Moreover, the fewer the number of iterations of the LIDAR sensor that the object is identified, the more difficult it is to determine the object's characteristics such as speed, type, and heading as well as to predict the object's behavior (for instance, using behavior models as discussed above). As such, estimates made by the perception system172and/or computing devices110of the bounding box594's speed, type, heading, and behavior may be very inaccurate. In this regard, bounding box594may be estimated to be a small vehicle, but in reality may simply be exhaust from vehicle490and/or other road debris such as leaves, trash, etc.

The more the object is perceived, for instance, the longer an object is tracked or the more sensor iterations in which the the object is detected by the sensors of the perception system, the greater the confidence value that the object is a real, correctly identified object, with more reliable estimates of position, orientation and speed. In this regard, higher confidence value objects may be perceived over several, for instance at least 3-5 or more iterations of the perception system. As an example, bounding boxes590and592, representing vehicle490and bicyclist492, respectively, are each associated with a relatively high confidence value, here 0.9 on the aforementioned scale of 0 to 1, given the fact that these objects have been observed over the course of many different sensor iterations. In addition, the characteristics of these objects, such as speed, type, heading as well as behavior predictions may also be more accurate. In this regard, bounding box590may be identified as a vehicle by perception system172and/or computing devices110, and bounding box592may be identified as a bicyclist by perception system172and/or computing devices110.

The confidence values and identified type for each identified object may be used by the computing devices110to determine a priority level for those objects. For instance, objects for which the vehicle is more likely to need to slow down, such as vehicles, bicyclists and pedestrians may be assigned a higher priority than objects such as traffic cones, vegetation, or trash. In addition, objects having higher confidence values may be assigned higher priority. For instance, vegetation having a higher confidence value would have a higher priority than vegetation with a lower confidence value. moreover, rather than being a ranked priority level, the priority level may simply be on a fixed scale, such as 0 to 1 where 0 is low priority and 1 is high priority, such that different objects may have the same priority level if such objects would have the same type and confidence value. Again, other scales and values may be used. Turning to example700ofFIG. 7, given their identified types and confidence values, bounding boxes590and592may be relatively high, or 0.8 and 0.9, respectively, on the aforementioned scale of 0 to 1. However, although bounding box594has been identified as a vehicle, because the confidence value is relatively low, 0.1, the priority of the bounding box594, is also relatively low, or 0.2 on a scale of 0 to 1.

The computing devices110may generate a set of constraints. For instance, the objects and their characteristics, such as location, speed, heading, orientation, projected behaviors, etc. may be used to generate a set of constraints identifying locations at certain times at which the vehicle should not be in order to avoid collision. As an example, the constraints may include avoiding or not coming within some predetermined distances of locations on predicted behaviors or trajectories of detected objects. For instance, turning to example900ofFIG. 9, arrows990,992, and994represent predicted trajectories for each of bounding boxes590,592, and594, respectively which may be predicted based on the estimated characteristics of these objects and the map information as discussed above.

The vehicle's computing devices may also determine whether the priority level for each object meets a threshold. If so, for instance because the priority is greater than or equal to the threshold, the constraint for that object and/or the object may be flagged or otherwise marked as allowing for a maximum braking level. In this regard, if the priority is too low, the constraint for that object may not allow for the maximum braking level. As such, only higher priority level objects of certain types with high confidence values would be flagged to allow for emergency braking. For instance, turning to example800ofFIG. 8, if the threshold is 0.6, both bounding boxes590and592, which have priority levels that are higher than 0.6, as shown as flagged to allow for emergency braking. Similarly, lower priority level objects of other types with low confidence values would not be flagged to allow for emergency braking. In this regard, bounding box594is shown as not being flagged to allow for emergency braking. In addition or alternatively, constraints may be flagged as not allowing for maximum braking level.

The set of constraints may be used to attempt to generate trajectories that allow the vehicle to maintain safe and comfortable traveling conditions. For instance, the set of constraints may be used to generate a trajectory for the vehicle by solving for a trajectory that satisfies all of the constraints while allowing the vehicle to brake at no greater than an acceptable braking level. The trajectories may be determined as a single problem (solving for geometry and speed at once) or a two-part problem (solving for geometry and thereafter speed).

The maximum braking level is greater than the acceptable braking level. For instance, the acceptable braking level may define a maximum deceleration and/or a maximum amount of jerk (derivative of deceleration) for the vehicle. These maximums for the acceptable braking level may be hand tuned in order to be comfortable to any passenger in the vehicle and reduce the likelihood of the vehicle being rear-ended by other vehicles. The maximum braking level may thus be a maximum amount of braking, maximum amount of deceleration and/or a maximum amount of jerk of which the vehicle is capable in order to slow the vehicle down as quickly as possible.

In certain circumstances, the vehicle's computing devices may not be able to solve for all of the constraints. For instance, the vehicle's computing devices may not be able to find a speed plan for a specific geometry that satisfies all of the constraints of a set of constraints. As an example, computing devices110may not be able to identify a speed plan which avoids coming too close to the constraints associated with the object represented by bounding box592corresponding to bicyclist492. As another example, computing devices110may not be able to identify a speed plan which avoids coming too close to the constraints associated with the object represented by bounding box594corresponding to exhaust and/or other road debris. In such situations, the vehicle's computing devices may apply the brakes of the vehicle at the maximum braking level and, in some cases, also employ a parking brake if available. As such, the vehicle's computing devices may automatically apply the maximum braking level without specifically attempting to solve for the set of constraints.

However, as noted above, higher levels of braking limit may increase risk of other vehicles colliding with the autonomous vehicle and decrease rider comfort. As such, before applying the maximum braking level, the vehicle's computing devices may identify a constraint for which the vehicle's computing devices are not able to solve for a trajectory and determine whether the constraint and/or the corresponding object for that constraint has been flagged to allow for maximum braking level or really, whether the priority for the object associated with the identified constraint meets a threshold value that would allow for maximum braking level. If so, the vehicle's computing devices may simply automatically apply the maximum braking level without specifically attempting to solve for the set of constraints. For instance, if computing devices110may not be able to identify a speed plan which avoids coming too close to the constraints associated with the object represented by bounding box592, because this bounding box has already been flagged and/or is associated with a priority level which meets the threshold, the computing devices may automatically apply the maximum braking level. As another example, if computing devices110may not be able to identify a speed plan which avoids coming too close to the constraints associated with the object represented by bounding box594while avoiding coming too close to bounding box592, because bounding box594has not been flagged and/or is not associated with a priority level which meets the threshold, the computing devices would not automatically apply the maximum braking level.

At the same time, it may be appropriate to apply the maximum braking level in situations where the vehicle is very unlikely to cause such high risk events. In other words, not all situations may be suitable for emergency braking. For example, it may not be appropriate to increase the acceptable braking limit on a highway where the speed of traffic flow speed is relatively high as this may cause a high risk to the vehicle, passengers as well as other vehicles. In this regard, the maximum braking level may be allowed only when the vehicle's speed and the speed of other nearby vehicles are relatively low such that even if a collision were to happen, the collision would be of very low severity. As another example, before applying the maximum braking level, the computing devices110may first confirm that there are no tailgating vehicles behind vehicle100, such that there are no vehicles within a predetermined threshold, measured in distances (e.g. meters) or time (i.e. seconds) or a combination of both, in a lane of and behind the current location of the vehicle100such that the risk of a rear collision is low.

FIG. 10is a flow diagram1000that may be performed by one or more processors such as one or more processors120of computing device110in order to perform a method of generating trajectories for a vehicle driving in an autonomous driving mode. In this example, at block1010, information identifying a plurality of objects in the vehicle's environment and a confidence value for each object of the plurality of objects being a real object is received from a perception system of the vehicle. At block1020, a set of constraints is generated. A first constraint of the set of constraints is generated based on a first object of the objects of the plurality of objects. At block1030, that the one or more processors are unable to solve for a trajectory given the set of constraints and an acceptable braking limit is determined. At block1040, a first constraint of the set of constraints for which the one or more processors were unable to solve is identified. The first constraint corresponds to a first object of the plurality of objects associated with a first confidence value. At block1050, that the vehicle should apply a maximum braking level greater than the acceptable braking limit based on the first confidence, a threshold, and the determination that the one or more processors are unable to solve for a trajectory is determined. At block1060, based on the determination that the vehicle should apply the maximum braking level, the maximum braking level is applied to control the vehicle in the autonomous driving mode and in order to slow the vehicle down.

The features described herein may allow the vehicle's computing devices to use emergency braking in a safe and reliable way. By tying this braking to the confidence value that an object detected by the perception system exists as well as the type of that object, this reduces the likelihood of unnecessary, and in some cases, dangerous braking in inappropriate circumstances. For instance, if a child were to step in front of the vehicle onto the road, because even where the confidence value for the perception of that child were low, because children may have high priority level, the vehicle would automatically be able to use emergency braking. At the same time, if the perception system identifies an object with a low confidence value that emerges from behind a tree that is later not confirmed or observed again, the priority would be very low and thus, the vehicle would not inappropriately use the emergency braking. This, in turn, increases the safety and comfort of passengers within the vehicle.