Patent Publication Number: US-11027648-B2

Title: Dynamic sound emission for vehicles

Description:
BACKGROUND 
     A majority of vehicles in operation today are equipped with horns that enable an operator of a vehicle to call attention to the vehicle, such as to warn others of a potential hazard in an environment. Conventional vehicle horns are configured to emit a sound at a particular frequency and volume. However, the particular frequency and/or volume of the vehicle horn may often be drowned out by other noises in the environment. As such, the vehicle horn may be ineffective in warning others of the potential hazard. Additionally, the vast majority of vehicle horns are manually operated, and thus are not configured for effective use in autonomous vehicles. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference numbers in different figures indicates similar or identical components or features. 
         FIG. 1  is an illustration of an autonomous vehicle in an environment in which a dynamic sound emission system of the autonomous vehicle may determine a potential conflict between an object in the environment and the vehicle and emit a sound to alert the object of the potential conflict, in accordance with embodiments of the disclosure. 
         FIG. 2  is an illustration of an autonomous vehicle in an environment in which a dynamic sound emission system of the autonomous vehicle may identify a hazard within a safety zone and activate a warning sound and/or control the autonomous vehicle to avoid the hazard. 
         FIGS. 3A and 3B  are illustrations of example environments and in which a vehicle configured with a dynamic sound emission system may operate.  FIG. 3A  is an illustration of an environment in which the dynamic sound emission system may determine to emit a warning signal based at least in part on one or more objects being detected.  FIG. 3B  is an illustration of an environment in which the dynamic sound emission system may determine to not emit a warning signal based at least in part on a lack of objects detected in the environment. 
         FIG. 4  is a block diagram of an example system for implementing the techniques described herein. 
         FIG. 5  depicts an example process for determining at least one of a volume or a frequency of a sound to emit toward an object, in accordance with embodiments of the disclosure. 
         FIG. 6  depicts an example process for determining whether an emission of sound was effective and, based on the effectiveness of the sound, ceasing emission of the sound or causing the vehicle to take an action to avoid a collision with the object, in accordance with embodiments of the disclosure. 
         FIG. 7  depicts an example process for determining a volume of a sound to emit toward an object, in accordance with embodiments of the disclosure. 
         FIG. 8  depicts an example process for avoiding a collision between a vehicle and an object in an environment by emitting a sound and/or causing the vehicle to take an action to avoid the collision, in accordance with embodiments of the disclosure. 
         FIG. 9  depicts another example process for determining at least one of a volume or a frequency of a sound to emit toward an object, in accordance with embodiments of the disclosure 
     
    
    
     DETAILED DESCRIPTION 
     This disclosure is directed to techniques for improving vehicle warning systems. The vehicle warning systems may be configured to emit a sound to warn dynamic objects (e.g., agents) in an environment proximate the vehicle of a potential conflict with the vehicle. The vehicle may include an autonomous or semi-autonomous vehicle. The objects may include pedestrians, bicyclists, other vehicles (e.g., cars, trucks, motorcycles, mopeds, etc.), or the like. A vehicle computing system may be configured to identify an object in the environment and determine that a potential conflict between the vehicle and the object may occur. The vehicle computing system may determine a baseline amount of noise (e.g., noise floor) around the object and one or more volumes and/or one or more frequencies of sound to emit toward the object to alert the object of the potential conflict. 
     The vehicle computing system may be configured to identify objects in the environment. In some examples, the objects may be identified based on sensor data from sensors (e.g., cameras, motion detectors, light detection and ranging (lidar), radio detection and ranging (radar), etc.) of the vehicle. In some examples, the objects may be identified based on sensor data received from remote sensors, such as, for example, sensors associated with another vehicle or sensors mounted in an environment that are configured to share data with a plurality of vehicles. 
     The vehicle computing system may be configured to emit a warning signal toward one or more objects in the environment. In some examples, the vehicle computing system may emit the warning signal based on a determination to alert the object(s) of the presence of the vehicle. For example, the vehicle computing system may detect a bicyclist on the road and may determine that the bicyclist may not hear the vehicle approaching from behind. The vehicle computing system may emit a warning signal toward the bicyclist, such as to warn the bicyclist of the vehicle&#39;s approach so that the bicyclist does not swerve or otherwise maneuver into the road. The warning signal may include one or more particular frequencies and/or volume(s), such as to alert the bicyclist of the vehicle presence, but not cause the bicyclist to become disoriented or startled. 
     In various examples, the vehicle computing system may emit the warning signal based on a determination of a potential conflict between an object and the vehicle. In some examples, the vehicle computing system may determine a trajectory of the object (e.g., position, velocity, acceleration, etc. of the object as it moves through an environment) based on the sensor data. The potential conflict may be based on a comparison of the trajectory of the object and a speed of the vehicle along a path through the environment (e.g., trajectory of the vehicle). For example, the vehicle computing system may identify a pedestrian on a sidewalk that is approaching a curb with a trajectory indicative of an intent to jaywalk across the road. The vehicle computing system may determine that the trajectory of the pedestrian may conflict with the vehicle traveling along a path in the road. Based on the potential conflict, the vehicle computing system may emit a warning signal toward the pedestrian to alert the pedestrian of the potential conflict. In various examples, the trajectory and/or intent of an object may be determined utilizing techniques described in U.S. patent application Ser. No. 15/947,486 filed Apr. 6, 2018 and entitled “Feature-Based Prediction,” the entire contents of which are incorporated herein by reference. 
     In various examples, the vehicle computing system may determine a volume of the warning signal to emit based on one or more noises in the environment. In some examples, the vehicle computing system may be configured to determine a noise floor proximate to (e.g., within a threshold distance of) the potentially conflicting object. The noise floor may be determined based on detecting one or more noise emitting objects (e.g., music playing on sidewalk, construction equipment, etc.) in the environment and determining a noise floor proximate the object to be alerted, given the one or more noise emitting objects and relative distances and speeds to the object to be alerted. In various examples, the vehicle computing system may combine the one or more noises, such as in a logarithmic calculation, to determine the noise floor. The noise emitting objects may include stationary and/or dynamic objects. In various examples, the vehicle computing system may store noise values associated with particular objects. In such examples, the vehicle computing system may access the stored noise values to determine a noise floor proximate to the potentially conflicting object. For example, the vehicle computing system may identify a semi-truck passing 5 meters in front of a pedestrian in the road. The vehicle computing system may access a datastore of noises associated with semi-trucks and determine that a noise floor 5 meters away from a semi-truck is approximately 75 decibels. The vehicle computing system may adjust a volume of the warning signal to be perceived by the pedestrian at 82 decibels, to be louder than the noise floor to get the attention of the pedestrian. 
     In various examples, the vehicle computing system may be configured to extrapolate a volume of a noise emitted from a noise emitting object proximate to the potentially conflicting object based on a perceived noise of the noise emitting object at the vehicle. In such examples, the vehicle computing system may determine a first distance between the noise emitting object and the vehicle and a second distance between the noise emitting object and the potentially conflicting object. The vehicle computing system may then determine a noise of the noise emitting object perceived by the vehicle at the first distance and extrapolate a noise value perceived by the potentially conflicting object at the second distance. 
     In various examples, the vehicle computing system may adjust a volume of the warning signal, as perceived by the potentially conflicting object, based on the noise floor. In such examples, the vehicle computing system may emit the warning signal at a volume to be perceived by the potentially conflicting object at a noise level above the noise floor. In various examples, a volume increase of the warning signal above the noise floor may be based on a probability of conflict with the potentially conflicting object (e.g., low, medium, high, etc.). In some examples, the number of decibel increase in the volume may be based on an urgency of the warning (e.g., low urgency, high urgency, etc.). For example, a first warning signal alerting the object, such as the bicyclist described above, of the presence of the vehicle may be about two to about six decibels above the noise floor, whereas a second warning signal alerting a jaywalker who has entered a roadway of the potential conflict with the vehicle may be about fifteen to about twenty decibels above the noise floor. 
     In some examples, the vehicle computing system may be configured to adjust a volume of the warning signal based on one or more other objects located in the environment between the potentially conflicting object and the vehicle. In such an example, the vehicle computing system may identify the other object(s) and determine that the other object(s) are in an audible path (e.g., path of the directed audio beam, beam formed audio signal) between a speaker of the vehicle emitting the sound and the potentially conflicting object. The vehicle computing system may decrease the volume of the warning signal directed at the potentially conflicting object to ensure the other object(s) do not sustain hearing damage and/or other ill effects from the warning signal. 
     In some examples, the vehicle computing system may be configured to adjust a volume of the warning signal based on a speed associated with the vehicle. In such examples, the vehicle computing system may increase the volume associated with the warning signal as the speed of the vehicle increases, and vice versa. In some examples, the volume increase may be based on ranges of speeds. For example, the warning signal may be emitted at a first volume between 25 and 35 miles per hour, a second volume between 36 and 45 miles per hour, and a third volume between 46 and 55 miles per hour. 
     In various examples, the vehicle computing system may be configured to determine one or more frequencies of the warning signal to emit. The frequencies may be based on an urgency associated with the warning signal, a speed associated with the vehicle and/or the potentially conflicting object, a probability of conflict between the vehicle and the potentially conflicting object, or the like. For example, the vehicle computing system may determine that particular objects, such as pedestrians, are present in the environment and may emit a frequency or range (e.g., set) of frequencies based on the presence of the particular objects, to alert the objects of the vehicle operation in the environment. As a non-limiting example, the vehicle may detect a pedestrian near a large truck and, based on low frequencies emitted from the truck&#39;s engine, determine to emit frequencies toward the pedestrian at relatively higher frequencies in order to notify the pedestrian. For another example, the vehicle computing system may determine that a probability of conflict (e.g., collision) with a jaywalker that has entered a road on which the vehicle is traveling is high (e.g., above a threshold probability of conflict) and may emit a frequency or range of frequencies corresponding to an emergency alert. 
     Additionally or in the alternative, the vehicle computing system may be configured to determine an action for the vehicle to perform to avoid a conflict with a potentially conflicting object in the environment. The action may include yielding to the potentially conflicting object (e.g., slowing down or stopping, using emergency braking, etc.), and/or changing a planned path associated with the vehicle (e.g., lane change right, lane change left, change planned path of vehicle within lane, drive on shoulder, etc.). In various examples, the vehicle computing system may determine the action based on a distance to the potentially conflicting object, a speed of the vehicle, a trajectory of the potentially conflicting object, or the like. For example, the vehicle computing system may identify a person on a scooter that is entering a roadway on which the vehicle is traveling. The vehicle computing system may determine a trajectory of the scooter, a distance between the vehicle and the scooter, a speed of the vehicle, other traffic on the roadway, and/or options for maneuvering the vehicle. The vehicle computing system may determine that, to avoid a collision with the scooter, the vehicle should apply emergency braking. The vehicle computing system may cause the vehicle to apply emergency braking, or any other maneuver. For another example, the vehicle computing system may determine that a lane change to the right would be a more efficient action to avoid a collision with the scooter. The vehicle computing system may determine that the right lane is clear of other objects and may cause the vehicle to change lanes. In some examples, the vehicle computing system may identify an object that may be affected by the sudden lane change of the vehicle and may emit a warning signal directed to the object. In such examples, the vehicle computing system may further prevent damage to other objects in the environment. 
     The techniques described herein may be implemented in a number of ways. Example implementations are provided below with reference to the following figures. Although discussed in the context of an autonomous vehicle, the methods, apparatuses, and systems described herein may be applied to a variety of systems (e.g., a sensor system or a robotic platform), and are not limited to autonomous vehicles. In another example, the techniques may be utilized in an aviation or nautical context, or in any system using machine vision (e.g., in a system using image data). Additionally, the techniques described herein may be used with real data (e.g., captured using sensor(s)), simulated data (e.g., generated by a simulator), or any combination of the two. 
       FIG. 1  is an illustration of an autonomous (or semi-autonomous) vehicle  102  in an environment  100 , in which a dynamic sound emission system of the autonomous vehicle (vehicle  102 ) may determine a potential conflict between an object  104 , such as object  104 ( 1 ), in the environment  100  and the vehicle  102 , such as vehicle  102 ( 1 ) and emit a sound to alert the object  104 ( 1 ) of the potential conflict. A vehicle computing system may perform the dynamic sound emission system of the vehicle  102 ( 1 ). While described as a separate system, in some examples, the sound emission and/or conflict avoidance techniques described herein may be implemented by other vehicle systems, components, and/or computing devices. For example, and as will be described in further detail with regard to  FIG. 4 , the sound emission and/or conflict avoidance techniques described herein may be implemented at least partially by or in associated with a planning component. 
     In various examples, the vehicle computing system may be configured to detect one or more objects  104  in the environment  100 . The vehicle computing system may detect the object(s)  104  based on sensor data received from one or more sensors  106 . In some examples, the sensor(s)  106  may include sensors mounted on the vehicle  102 ( 1 ), such as sensors  106 ( 1 ),  106 ( 2 ),  106 ( 3 ) and  106 ( 4 ). The sensor(s)  106  may include lidar sensors, radar sensors, ultrasonic transducers, sonar sensors, location sensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertial measurement units (IMUs), accelerometers, magnetometers, gyroscopes, etc.), cameras (e.g., RGB, IR, intensity, depth, time of flight, etc.), microphones, wheel encoders, environment sensors (e.g., temperature sensors, humidity sensors, light sensors, pressure sensors, etc.), etc. In various examples, each illustrated sensor  106 , such as sensors  106 ( 1 ) and  106 ( 2 ), may include multiple instances of each of these or other types of sensors. In the illustrative example, the vehicles  102  include four (4) sensors  106 . However, in other examples, the vehicles  102  may include a greater or lesser number of sensors  106 . 
     The sensor(s)  106 , such as sensors  106 ( 1 ) and  106 ( 2 ) may be configured to collect sensor data over an angle θ. Though illustrated as an angle of less than 180 degrees, the angle θ may include angles of greater or lesser degrees (e.g., any angle between 0 and 360 degrees, etc.). In the illustrative example, the sensors  106 ( 1 ) and  106 ( 2 ) collect sensor data over a same angle θ. In other examples, sensors  106 ( 1 ) and  106 ( 2 ) may collect sensor data over different angles. 
     In various examples, the vehicle computing system may be configured to receive sensor data from one or more remote sensors  106 . In some examples, the remote sensor(s)  106  may be mounted on another vehicle  102 , such as vehicle  102 ( 2 ). In some examples, the remote sensor(s)  106  may include sensor(s)  106  mounted in the environment  100 , such as sensor  106 ( 5 ). For example, sensor  106 ( 5 ) may be placed in the environment  100  for traffic monitoring, collision avoidance, or the like. In various examples, vehicle  102 ( 1 ) may be configured to transmit and/or receive data from vehicle  102 ( 2 ) and/or remote sensor  106 ( 5 ). The data may include sensor data, such as data regarding object(s)  104  detected in the environment  100 . 
     In various examples, the vehicle computing system may receive the sensor data and may determine a type of object  104  (e.g., classification of the object), such as, for example, whether the object  104  is a pedestrian, such as objects  104 ( 1 ) and  104 ( 3 ), a semi-trailer truck, such as object  104 ( 2 ), a motorcycle, a moped, a bicyclist, or the like. In various examples, the vehicle computing system may determine a trajectory  108  associated with an object  104  detected in the environment  100 . In some examples, the trajectory  108  may include a direction and/or speed that the object  104  is traveling through the environment  100 . 
     Based on the determined trajectories  108  associated with object(s)  104 , the vehicle computing system may determine that an object  104  may potentially conflict with the vehicle  102 ( 1 ) (e.g., potentially conflicting object  104 ( 1 )). The potentially conflicting object  104 ( 1 ) may include an object  104  that has a trajectory  108 ( 1 ) (e.g., either of a predicted or determined trajectory) that conflicts with a vehicle trajectory  112  (e.g., intersects at a time that, if trajectories  108 ( 1 ) and  112  remain substantially unchanged, could result in a collision between the vehicle  102 ( 1 ) and the potentially conflicting object  104 ( 1 )). The vehicle trajectory  112  may be based on a path, speed and/or accelerations of the vehicle  102 ( 1 ) through the environment  100 . 
     In various examples, the vehicle computing system may determine that an object  104  may potentially conflict with the vehicle  102 ( 1 ) based on a probability of conflict between the object  104  and the vehicle  102 ( 1 ). The probability of conflict may be based on a determined likelihood that the object  104  will continue on the trajectory  108 ( 1 ) and/or alter the trajectory  108 ( 1 ) to one that conflicts with the vehicle  102 ( 1 ). In some examples, the probability of conflict may correspond to a likelihood (e.g., probability) of conflict between the vehicle  102 ( 1 ) and the object  104  being above a threshold level (e.g., threshold probability) of conflict. In some examples, the probability of conflict may be determined based on a classification associated with the object  104 . In such examples, the classification associated with the object  104  may assist in determining the likelihood that the object  104  will maintain or alter a trajectory. For example, a bicyclist riding on the shoulder of a straight road with no intersections nearby is likely to maintain a trajectory  108  that will not conflict with the vehicle  102 ( 1 ), as the vehicle can assume that bicycles are incapable of sudden changes in orientation. The bicyclist may thus be determined to not be a potentially conflicting object  104 . For another example, a deer detected on a side of a roadway may be unpredictable and thus may have a high likelihood of altering a trajectory to conflict with the vehicle  102 ( 1 ). As such, the deer may be determined to be an object  104  that may potentially conflict with the vehicle  102 ( 1 ). 
     In some examples, a detected loss of the sensor(s)  106  may cause the vehicle computing system to tighten parameters with respect to trajectory predictions. For example, a sensor  106 ( 2 ) on the vehicle may cease working, leaving an angle of the vehicle with degraded perception (e.g., less sensor data available). To compensate for the loss, the vehicle computing system may decrease tolerances with respect to objects and predictions of object trajectories, resulting in a more conservative approach toward the object(s)  104 , which may comprise altering (e.g., raising) a volume and/or frequencies of sounds emitted. 
     Based at least in part on determining that a potentially conflicting object  104 ( 1 ) exists in the environment  100 , the vehicle computing system can determine one or more volumes and/or one or more frequencies (e.g., a set/range of frequencies) of a warning signal  110  to emit. The volume(s) and/or frequencies of the warning signal  110  may be based on a baseline noise level (e.g., noise floor) in the environment  100  and/or a noise floor proximate to the potentially conflicting object  104 ( 1 ). The noise floor may include a common decibel value and/or decibel range that represents ambient noise in the environment  100  and/or proximate to the potentially conflicting object  104 ( 1 ). In various examples, the vehicle computing system may determine the volume(s) and/or frequencies of the warning signal  110  to emit in order to give notice to object(s)  104  in the environment, such as potentially conflicting object  104 ( 1 ), that the vehicle computing system is aware of the presence of the object(s)  104  in the environment (e.g., notice that the object(s)  104  are detected). 
     In some examples, the vehicle computing system may determine the noise floor of the environment  100  and/or the noise floor proximate to the potentially conflicting object  104 ( 1 ) based on sensor data, such as from one or more visual and/or auditory perception sensors. In some examples, the vehicle computing system may receive the sensor data corresponding to objects(s)  104  proximate to potentially conflicting object  104 ( 1 ) and determine a level of noise (e.g., noise event) generated by the object  104 , such as based on a classification associated the object(s)  104 . The level of noise may be determined based on data stored on the vehicle computing system and/or a remote computing system and corresponding to the classification associated with the object(s)  104 . In various examples, the vehicle computing system may utilize machine learning techniques to determine the level of noise emitted by the object. In some examples, a data model may be trained to identify (e.g., classify) object(s)  104  and/or determine, based on a classification of an object and/or a distance to the potentially conflicting object  104 ( 1 ), a distinct noise event generated by the object(s)  104 . 
     In some examples, the noise floor(s) may be determined based at least in part on non-distinct noise events that occur (e.g., perceived by the sensor(s)) in the environment. Non-distinct noise events may include noises generated from weather (e.g., wind, rain, hail, etc.), vehicles in the environment (e.g., idling, passing, accelerating, braking, etc.), and/or other noises in the environment  100  (e.g., street music, people talking, etc.). In some examples, the noise floor may be determined based at least in part on distinct noise events, such as horn honks, whistles blowing, brakes hissing, or the like. In various examples, the noise floor proximate to the potentially conflicting object  104 ( 1 ) may be determined based on detected non-distinct and/or distinct noise events emitted from objects within a threshold distance (e.g., 30 meters, 50 feet, one block, etc.) of the potentially conflicting object  104 ( 1 ). In some examples, the noise floor may include a logarithmic summation of the non-distinct and/or distinct noise events that occur in the environment. 
     In various examples, the vehicle computing system may receive the sensor data associated with non-distinct and/or distinct noise events and may apply a smoothing algorithm to the noise events. In some examples, the smoothing algorithm may smooth the overall impact of short-term (e.g., distinct) noise events on the noise floor. For example, the vehicle computing system may receive sensor data corresponding to a car honk that causes a 5-decibel spike in the ambient noise. The smoothing algorithm may be applied to smooth the car honk to 2 decibels, to have less of an impact on a determined noise floor. 
     In various examples, the vehicle computing system may determine the noise floor(s) based on a cumulative distribution of noise events over a period of time (e.g., 2 minutes, 5 minutes, 15 minutes, 60 minutes, etc.). For example, the average noise value in an environment at a time may be 70.5 decibels, the median value may be 69.8 decibels, and the most common value may be 68.3 decibels. Additionally, in the environment at the time, a large majority (80%) of the noise events may be less than 72 decibels. Based on the data, the vehicle computing system may determine that, for at least the example environment at the time, the noise floor in the environment may be between 68-72 decibels. 
     In various examples, the vehicle computing system may determine the noise floor in the environment  100  based on a location associated with the environment  100 , a time of day, a day of the week, a time of year (e.g., season, school in or out of session, etc.), in which the vehicle  102 ( 1 ) is traveling in the environment  100 , or the like. In some examples, the vehicle computing system may access a database of noise floors corresponding to the environment  100  in which the vehicle  102 ( 1 ) is operating. The database of noise floors may be stored locally and/or remotely, such as on a server computing system or other remote computing system. In some examples, the vehicle computing system may receive noise floor data from one or more remote computing systems, such as, for example, from a remote computing system associated with remote sensor  106 ( 5 ). In such examples, the remote computing system may be configured to determine a noise floor in the environment  100  and transmit the noise floor data to vehicles  102 ( 1 ) and  102 ( 2 ) located in proximity to the remote computing device (e.g., within a threshold distance (e.g., 1 block, 3 blocks, ¼ mile, etc.) thereof). 
     In various examples, the vehicle computing system may determine a noise floor proximate to the potentially conflicting object  104 ( 1 ). The noise floor proximate to the potentially conflicting object  104 ( 1 ) may include a baseline noise level as perceived by the potentially conflicting object  104 ( 1 ) (e.g., baseline level of noise the object  104 ( 1 ) hears). The noise floor proximate to the potentially conflicting object  104 ( 1 ) may be substantially similar to the noise floor in the environment  100  or it may be different. 
     In various examples, the noise floor proximate to the potentially conflicting object  104 ( 1 ) may be determined and/or augmented based at least in part on one or more other objects  104  (e.g., dynamic and/or non-dynamic noise emitting objects) proximate to (e.g., within a threshold distance of) the potentially conflicting object  104 ( 1 ). In some examples, the threshold distance may be based on an amount of noise produced by the other object(s)  104 . For example, music emanating from a speaker at a storefront may be released at 70 decibels. The speaker may have an impact on a noise floor as perceived by the potentially conflicting object  104 ( 1 ) 10 feet away from the speaker, but not 20 feet away. Thus, the threshold distance from the speaker may be determined to be at 10 feet. 
     In various examples, the vehicle computing system may be configured to determine the amount of noise produced by the other object(s)  104 , such as other object  104 ( 2 ), proximate to the potentially conflicting object  104 ( 1 ). In some examples, the amount of noise produced by the other object(s)  104 ( 2 ) may be based on a classification associated with the other object(s)  104 ( 2 ). In various examples, the amount of noise produced by a particular class of object  104 ( 2 ) may be based on values previously perceived by one or more sensors  106  and stored in a database of the vehicle computing system and/or one or more remote computing devices. For example, sensor data from the one or more sensors  106  may be used to detect an object  104  and may be further used to classify, by the vehicle computing system, the object  104  as a chopper (e.g., type of motorcycle) operating at a constant speed (e.g., minimal acceleration). The vehicle computing system may determine, based on values stored in the database, that an amount of noise produced by the chopper operating at a constant speed is 80 decibels. The vehicle computing system may further determine a relative distance between the chopper and the potentially conflicting object  104 ( 1 ), as well as the relative distance from the vehicle  102  to the object  104 ( 1 ), so as to determine the relative volume and/or frequencies to emit to the object  104 ( 1 ). 
     In various examples, the vehicle computing system may determine the amount of noise based on environmental considerations (e.g., uphill, downhill, acceleration, slowing and/or stopping at an intersection, etc.). For example, a semi-trailer truck approaching an intersection may utilize engine braking (e.g., jake brake) to slow down. The engine braking may increase an amount of noise emitted from the semi-trailer truck by 10 decibels or more. The vehicle computing system may factor the additional noise into the noise floor calculation based on a determination that the semi-trailer truck is slowing to a stop at the intersection. For another example, traditional motor vehicles traveling uphill have a tendency to downshift and operate an engine at higher revolutions per minute. The increase in revolutions per minute may increase an amount of noise produced by the motor vehicles and/or a relative frequency of noise emitted. As such, the vehicle computing system may factor in the additional amount of noise and/or frequency into the noise floor calculation of the potentially conflicting object  104 ( 1 ). 
     In the illustrative example, the noise floor as perceived by the potentially conflicting object  104 ( 1 ) is based at least in part on an amount of noise emitted by the other object  104 ( 2 ), a semi-trailer truck. In various examples, the vehicle computing system may determine a distance D 1  between the potentially conflicting object  104 ( 1 ) and the other object  104 ( 2 ). In various examples, the vehicle computing system may determine an amount of noise perceived by the potentially conflicting object  104 ( 1 ) based on the distance D 1  (e.g., by extrapolation). For example, the vehicle computing system may determine that the other object  104 ( 2 ) emits a 90-decibel noise that decreases 15 decibels over the distance D 1 , to be perceived by the potentially conflicting object  104 ( 1 ) at 75 decibels. In at least one example, the decibel decrease over the distance D 1  may be determined using the inverse-square law (1/r 2 ). 
     In various examples, the vehicle computing system may determine an amount of noise perceived by the potentially conflicting object  104 ( 1 ) based on a volume of noise perceived by the vehicle  102 ( 1 ) and a difference between the distance D 1  and a second distance (D 2 ) between the vehicle  102 ( 1 ) and the other object  104 ( 2 ). In various examples, vehicle  102 ( 1 ) may receive audio signals (e.g., sounds) in the environment by one or more directional and/or multi-directional microphones. The vehicle computing system may be configured to correlate the audio signals to a sensed object  104 , such as other object  104 ( 2 ), in the environment. For example, the vehicle computing system may perceive a noise emitted by the other object  104 ( 2 ) at 60 decibels. The vehicle computing system may correlate the noise with the other object  104 ( 2 ), classified as a semi-truck. The vehicle computing system may determine that, based on a distance D 2 , the noise volume decreased by 30 decibels, and that the actual noise emitted by the other object  104 ( 2 ) was 90 decibels. The vehicle computing system may determine that the distance D 2  is greater than the distance D 1 . Based on the distance and perceived volume, the vehicle computing system may determine that the potentially conflicting object  104 ( 1 ) may perceive the semi-trailer noise at 75 decibels. 
     In some examples, an impact of a noise produced by the other object(s)  104  on the potentially conflicting object  104 ( 1 ) may be based on the noise floor in the environment  100 . In various examples, the vehicle computing system may factor in noises produced by the other object(s)  104  to the noise floor proximate to the potentially conflicting object  104 ( 1 ) based on the noises being a threshold volume above the noise floor in the environment  100 . For example, the noise floor in the environment  100  may be determined to be 68-72 decibels. The vehicle computing system may determine that first object produces a first noise that is perceived by the potentially conflicting object  104 ( 1 ) at 60 decibels and a second object produces a second noise that is perceived by the potentially conflicting object  104 ( 1 ) at 80 decibels. Based on a determination that the first noise is below the noise floor in the environment  100  and the second noise is above the noise floor in the environment  100 , the vehicle computing system may factor in the second noise produced by the second object into the noise floor proximate to the potentially conflicting object  104 ( 1 ), but not the first noise. 
     In various examples, the vehicle computing system may determine a frequency or range (e.g., set) of frequencies of the warning signal  110  to emit. In various examples, the frequencies of the warning signal  110  may be based on a classification of the potentially conflicting object  104 ( 1 ). For examples, the potentially conflicting object  104 ( 1 ) may be classified as a dog. The vehicle computing system may determine a high pitch frequency that is perceptible to dogs but not humans, thus decreasing an impact on pedestrians, bicyclists, etc. proximate to the potentially conflicting object  104 ( 1 ). 
     In various examples, the vehicle computing system may be configured to determine a baseline frequency of the noise floor in the environment  100  and/or the noise floor proximate to the potentially conflicting object  104 ( 1 ). In some examples, the baseline frequency may include an average frequency of the noise events in the environment  100  and/or proximate to the potentially conflicting object  104 ( 1 ). In some examples, the baseline frequency may include a predominant frequency of the noise events in the environment  100  and/or proximate to the potentially conflicting object  104 ( 1 ). 
     In various examples, the vehicle computing system may determine the volume and/or volume range and/or set of frequencies of the warning signal  110  based in part on the noise floor and/or baseline frequency proximate to the potentially conflicting object  104 ( 1 ). In various examples, the vehicle computing system may determine the set of frequencies of the warning signal  110  based on an average and/or dominant frequency in the environment. In such examples, the set of frequencies of the warning signal  110  may substantially differ from the average and/or dominant frequency in the environment. In some examples, the vehicle computing system may cause the warning signal  110  to be emitted at a particular volume so that the warning signal  110  is perceived by the potentially conflicting object  104 ( 1 ) at the volume and/or volume range. The vehicle computing system may determine the particular volume to emit the warning signal  110  based on a distance D 3  between the vehicle  102 ( 1 ) and the potentially conflicting object  104 ( 1 ). 
     In some examples, the volume and/or volume range of the warning signal  110  may be higher than the noise floor proximate to the potentially conflicting object  104 ( 1 ). The frequencies of the warning signal  110  may be higher or lower than the baseline frequency of the noise floor proximate to the potentially conflicting object  104 ( 1 ). In various examples, the frequencies of the warning signal  110  may include a frequency (or set/range of frequencies) that is perceptible to the potentially conflicting object  104 ( 1 ), despite ambient noise. The volume and/or volume range and/or frequencies may be determined based on an urgency of the warning (e.g., low urgency (e.g., alert), medium urgency ((e.g., caution), high urgency (e.g., warning)), a likelihood of conflict between the vehicle  102 ( 1 ) and the potentially conflicting object  104 ( 1 ), a message to be conveyed to the potentially conflicting object  104 ( 1 ) (e.g., the vehicle  102 ( 1 ) is approaching, please stop, trajectories are rapidly converging). 
     In various examples, the volume and/or volume range and/or frequencies of the warning signal  110  may be determined based on a detected distraction associated with the potentially conflicting object. The detected distraction may include the tactile use of a mobile phone, a determination that the potentially conflicting object is engaged in a conversation (e.g., with another object proximate to the potentially conflicting object, on a mobile phone, or the like), a determination that the potentially conflicting object is wearing headphones, earmuffs, ear plugs, or any other device configured to fit in or around an auditory canal. 
     In some examples, the volume and/or volume range and/or frequencies of the warning signal  110  may be determined based on weather conditions in the environment. The weather conditions may include rain, wind, sleet, hail, snow, temperature, humidity, large pressure changes, or any other weather phenomenon which may affect an auditory perception of an object  104  in an environment. In various examples, the volume and/or volume range and/or frequencies of the warning signal  110  may be determined based on road conditions in the environment. The road conditions may include a smoothness of road surface (e.g., concrete, asphalt, gravel, etc.), a number of potholes, uneven terrain (e.g., rumble strips, washboards, corrugation of road, etc.), or the like. For example, objects  104  and/or vehicles  102  operating on a gravel road may generate a larger amount of noise than when operating on a smooth surface. The increase in noise generated by the objects  104  and/or vehicles  102  (e.g., impact amount of noise from travel) may result in a subsequent increase in the determined volume and/or volume range of the warning signal  110 . 
     In various examples, the volume and/or volume range and/or frequencies of the warning signal  110  may be determined based on a location of the potentially conflicting agent  104 ( 1 ) in the environment. For example, if the potentially conflicting agent  104 ( 1 ) is located in a roadway shared by the vehicle  102 ( 1 ), the volume and/or volume range may be higher than if the potentially conflicting agent  104 ( 1 ) is located on the sidewalk, such as indicating an intent to enter the roadway. For another example, if the potentially conflicting agent  104 ( 1 ) is a pedestrian standing on a median between opposite direction traffic, the volume and/or volume range may be higher than if the potentially conflicting agent  104 ( 1 ) is located in a bike lane, proximate a curb. 
     In some examples, the volume and/or volume range and/or frequencies of the warning signal  110  may be determined based on a detected loss of one or more sensors  106  on the vehicle  102 ( 1 ). For example, the vehicle computing system may determine that a speaker on the vehicle is not functioning at an optimal capacity. Accordingly, the vehicle computing system may increase a volume of the warning signal  110  to compensate for the decreased capacity of the speaker. 
     In various examples, the volume and/or volume range and/or frequencies of the warning signal  110  may be determined based on a detection of a passenger in the vehicle  102 ( 1 ). In some examples, the detection of the passenger may be based on sensor data received from one or more sensor(s)  106  of the vehicle. In some examples, the detection of the passenger may be based on a signal received, such as from a computing device associated with the passenger, indicating the passenger presence in the vehicle. In various examples, the vehicle computing system may decrease the volume and/or volume range and/or frequencies of the warning signal  110  based on the detection of the passenger, such as, for example, to not create a negative experience for the passenger due to the emission of a loud noise. 
     In various examples, the vehicle computing system may identify another object  104 , such as object  104 ( 3 ), that is located substantially between the vehicle  102 ( 1 ) and the potentially conflicting object  104 ( 1 ). In some examples, the vehicle computing system may determine that a trajectory  108  associated with the other object  104 ( 3 ), such as trajectory  108 ( 2 ), does not conflict with the vehicle trajectory  112 . However, based on a location of the other object  104 ( 3 ) being substantially between the vehicle  102 ( 1 ) and the potentially conflicting agent  104 ( 3 ), the other object  104 ( 3 ) may be substantially affected by the warning signal  110 . In various examples, the vehicle computing system may adjust the volume and or volume range based on a consideration associated with the other object  104 ( 3 ). For example, the volume and/or volume range of the warning signal  110  perceived by the other object  104 ( 3 ) may be substantially higher than that perceived by the potentially conflicting object  104 ( 1 ). To mitigate a negative effect on the other object  104 ( 3 ) caused by the warning signal  110 , the vehicle computing system may decrease a determined volume of the warning signal and/or may use a lower volume in a determined volume range as the volume to be perceived by the potentially conflicting object  104 ( 1 ). Additionally or in the alternative, the vehicle computing system may utilize beam steering in a beam formed array to direct the warning signal  110  at the potentially conflicting object  104 ( 1 ). In various examples, the vehicle computing system may utilize beam steering and/or beam formed array techniques discussed in U.S. patent application Ser. No. 14/756,993 entitled “Method for Robotic Vehicle Communication with an External Environment via Acoustic Beam Forming, filed Nov. 4, 2015, and issued as U.S. Pat. No. 9,878,664 on Jan. 30, 2018. 
     In some examples, the vehicle computing system may cause the warning signal  110  to be emitted for a pre-determined period of time (e.g., 5 seconds, 10 seconds, 20 seconds, etc.). The period of time may be based on the urgency of the warning, the likelihood of conflict between the vehicle  102 ( 1 ) and the potentially conflicting object  104 ( 1 ), the message to be conveyed to the potentially conflicting object  104 ( 1 ), a speed associated with the vehicle  102 ( 1 ), or the like. For example, an alert of the presence of the vehicle (e.g., low urgency) may be emitted for 10 seconds, and a warning of highly probable conflict (e.g., high urgency) may be emitted for 20 seconds. 
     In various examples, the vehicle computing system may dynamically determine the period of time associated with warning signal  110  emission. In some examples, the period of time may be based on a determination of a decrease in a likelihood of conflict. In various examples, the vehicle computing system may be configured to determine a change to the trajectory  108 ( 1 ) of the potentially conflicting object in response to the warning signal  110 . In some examples, the vehicle computing system may determine whether the change was sufficient to decrease a likelihood of conflict between the vehicle  102 ( 1 ) and the potentially conflicting object  104 ( 1 ). Based on a determination that the change in trajectory  108 ( 1 ) was sufficient to decrease a likelihood of conflict, such as to a negligible probability of conflict, the vehicle computing system may determine that the warning signal  110  is no longer necessary and may cause the warning signal  110  to stop emitting. 
     In various examples, based on a determination that the change in trajectory  108 ( 1 ) was not sufficient to decrease the likelihood of conflict between the vehicle  102 ( 1 ) and the potentially conflicting object  104 ( 1 ), the vehicle computing system may determine to increase a volume and/or a volume range associated with the warning signal  110 . In some examples, the increase in the volume and/or volume range may be based on a determined escalation of urgency, such as from low urgency to medium or high urgency, an increase in a likelihood and/or probability of conflict, such as from a medium probability to a high probability, or the like. For example, the vehicle computing system may cause a first warning signal  110  to be emitted from the vehicle  102 ( 1 ) at a first volume and/or volume range, to alert the potentially conflicting object  104 ( 1 ) of the vehicle  102 ( 1 ) operating on the road. The vehicle computing system may determine that the trajectory  108 ( 1 ) associated with the potentially conflicting object  104 ( 1 ) did not substantially change as a result of the first warning signal  110 . Based on the determination of an insufficient change to the trajectory  108 ( 1 ), the vehicle computing system may cause a second warning signal  110  to be emitted at a second volume and/or second volume range, the second volume and/or second volume range being greater than the first volume and/or first volume range. 
     In some examples, based on a determination that the change in trajectory  108 ( 1 ) was not sufficient to decrease the likelihood of conflict between the vehicle  102 ( 1 ) and the potentially conflicting object  104 ( 1 ), the vehicle computing system may determine to change a frequency and/or range of frequencies (e.g., higher or lower) of the warning signal  110 . In some examples, the frequency adjustment be based on a determined escalation of urgency, such as from low urgency to medium or high urgency, an increase in a likelihood and/or probability of conflict, such as from a medium probability to a high probability, or the like. For example, the vehicle computing system may cause a first warning signal  110  to be emitted from the vehicle  102 ( 1 ) at a first frequency (or set/range of frequencies), to alert the potentially conflicting object  104 ( 1 ) of the vehicle  102 ( 1 ) operating on the road. The vehicle computing system may determine that the trajectory  108 ( 1 ) associated with the potentially conflicting object  104 ( 1 ) did not substantially change as a result of the first warning signal  110 . Based on the determination of an insufficient change to the trajectory  108 ( 1 ), the vehicle computing system may cause a second warning signal  110  to be emitted at a second frequency (or set/range of frequencies) that is higher than the first frequency. 
     Additionally or in the alternative, the vehicle computing system may determine to emit a sound (e.g., warning signal  110 ) based on a determination that a passenger is entering and/or exiting the vehicle  102 ( 1 ). In some examples, the sound may be emitted when the vehicle is stopped. In such examples, the sound may be used to alert other object(s)  104  in the area that the vehicle is altering a passenger load, to inform the other objects(s)  104  that the vehicle  102 ( 1 ) may be available for use, to stimulate a positive response in the other object(s)  104 , or the like. 
     Additionally or in the alternative, and as will be discussed in further detail below with regard to  FIG. 2 , based on a determination that the change in trajectory  108 ( 1 ) was not sufficient to decrease the likelihood of conflict, the vehicle computing system may determine an action for the vehicle  102 ( 1 ) to take to avoid the conflict. The action may include yielding to the potentially conflicting object  104 ( 1 ) (e.g., slowing down or stopping, using emergency braking, etc.), and/or changing a planned path associated with the vehicle  102 ( 1 ) (e.g., lane change right, lane change left, change planned path of vehicle within lane, drive on shoulder, etc.). 
       FIG. 2  is an illustration of an environment  200 , such as environment  100 , in which a dynamic sound emission system of the autonomous vehicle  202 , such as vehicle  102 , may identify an object  204 ( 1 ) that may conflict with the vehicle  202 , such as potentially conflicting object  104 ( 1 ), and activate a warning signal  206 , such as warning signal  110  and/or control the autonomous vehicle  202  to avoid the object  204 ( 1 ). 
     A vehicle computing system of the vehicle  202  may be configured to detect one or more objects  204  and/or one or more stationary objects  208  in the environment  200 . The vehicle computing system may detect the object(s)  204  and/or the stationary object(s)  208  based on sensor data received from one or more sensors  210 . In the illustrative example, the sensor(s)  210  are coupled to the vehicle  202 . In some examples, the sensor(s)  210  may additionally or alternatively include sensor(s)  210  remotely located in the environment  200 . In various examples, the vehicle computing system may detect the object(s)  204  and may determine a classification associated with each object  204 . For example, the vehicle computing system may identify object  204 ( 2 ) as a car operating in the roadway. For another example, the vehicle computing system may detect the stationary objects  208 ( 1 ) and  208 ( 2 ), and may classify each as a parked car. 
     In various examples, the vehicle computing system may determine trajectories  212  associated with the object(s)  204 . In some examples, the trajectory  212  may include a direction and/or speed that the object  204  is traveling through the environment  200 . In various examples, the vehicle computing system may determine that one of the object(s)  204 , such as object  204 ( 1 ), may potentially conflict with the vehicle  202 . In various examples, a determination of the potentially conflicting object  204 ( 1 ) may be based on the trajectory  212 ( 1 ) associated with the potentially conflicting object  204 ( 1 ) intersecting with a vehicle trajectory  214 . The vehicle trajectory  214  may be determined based on a planned path  216 , speed and/or acceleration of the vehicle  202  operating in the environment. 
     In some examples, the determination may be based on the vehicle  202  and the potentially conflicting object  204 ( 1 ) maintaining a same or substantially similar speed and direction. In some examples, a determination of the potentially conflicting object  204 ( 1 ) may be based on a probability (e.g., likelihood) of conflict between the vehicle  202  and the potentially conflicting object  204 ( 1 ). In some examples, the probability of conflict may be determined based on the classification associated with the object  204 ( 1 ). In such examples, the classification associated with the object  204 ( 1 ) may assist in determining the likelihood that the object  204 ( 1 ) will maintain or alter a trajectory. 
     Based at least in part on determining that a potentially conflicting object  204 ( 1 ) exists in the environment  200 , the vehicle computing system may determine a location  218  associated with a potential conflict (e.g., a collision). The location  218  may be determined based on the trajectory  212 ( 1 ) of the potentially conflicting object  204 ( 1 ) and/or the vehicle trajectory  214  remaining the same. In some examples, the location  218  may include an area of the vehicle path  216  that the potentially conflicting object  204 ( 1 ) travel through on the trajectory  212 ( 1 ). 
     In various examples, the vehicle computing system may determine that the location  218  is outside of a safety zone  220  associated with the vehicle  202 . The safety zone  220  may include an area in which the vehicle  202 , traveling through the environment  200  on the trajectory  214 , may apply maximum braking and stop. As illustrated in  FIG. 2 , the safety zone  220  may include a reaction distance (D R ) and a braking distance (D B ). Both distances (D R ) and (D B ) may be based on a current speed, and/or acceleration of the vehicle  202 , wear on tires and/or brakes of the vehicle  202 , road materials (e.g., asphalt, concrete, etc.), road conditions (e.g., potholes, grading, paint strips, etc.), environmental considerations (e.g., rain, snow, hail, etc.), or the like. In various examples, the vehicle computing system may receive sensor data from the sensors  210  to determine the safety zone  220 . In some examples, the safety zone  220  associated with a particular area in an environment  200  and/or speed may be pre-determined and stored in a database accessible by the vehicle computing system. In such examples, the vehicle computing system may access the stored values periodically (e.g., every minute, every 5 minutes, etc.), at a time of a detected change in condition and/or speed/acceleration, and/or randomly while operating in an environment  200 . In at least some examples, such a safety zone  220  may be determined based on a number of occlusions detected. In some examples, object  204 ( 1 ) may be occluded from sensors on vehicle  202  by, for example, object  208 ( 2 ). In such an example, vehicle  202  may determine to emit a sound at a different volume and/or frequency as compared to other areas due to the risk of objects, such as object  204 ( 1 ) which may be occluded, yet present a high safety risk. In some examples, the vehicle computing system may implement occlusion determination techniques discussed in U.S. patent application Ser. No. 16/011,436 entitled “Occlusion Aware Planning” and filed Jun. 18, 2018 and U.S. patent application Ser. No. 16/011,468 entitled “Occlusion Aware Planning and Control” and filed Jun. 18, 2018, the entire contents of which are incorporated herein by reference. 
     In various examples, the vehicle computing system may determine one or more volumes and/or one or more frequencies of the warning signal  206  to emit. As discussed above, the volume(s) and/or frequencies of the warning signal  206  may be determined based on a noise floor in the environment  200  and/or a noise floor proximate to the potentially conflicting object  204 ( 1 ). In some examples, the volume(s) and/or frequencies of the warning signal  206  may be based an urgency of the warning, a likelihood of conflict between the vehicle  202  and the potentially conflicting object  204 ( 1 ), and/or a message to be conveyed to the potentially conflicting object  204 ( 1 ). In various examples, the urgency of the warning, the likelihood of conflict, and/or the message to be conveyed may be based at least in part on the location  218  with respect to the safety zone  220 . For example, based on a determination that the location  218  is located greater than a threshold distance from the safety zone  220 , the warning signal  206  may include a cautionary signal (e.g., moderate urgency and/or danger). For another example, based on a determination that the location  218  is located substantially proximate to (e.g., within a threshold distance of) the safety zone  220 , the warning signal  206  may include an urgent warning (e.g., high likelihood of conflict, extreme danger, etc.). 
     Based at least on determining the volume(s) and/or frequencies of the warning signal  206 , the vehicle computing system may cause a first warning signal  206 ( 1 ) to be emitted from the vehicle  202 , such as via one or more speakers  222  coupled to the vehicle  202 . In some examples, the first warning signal  206 ( 1 ) may include an audio beam emitted in a direction relative to the potentially conflicting object  204 ( 1 ). In some examples, the first warning signal  206 ( 1 ) may include a beam formed audio signal directed at the potentially conflicting object  204 ( 1 ). In some examples, the direction relative to the potentially conflicting object  204 ( 1 ) may include a position in which the potentially conflicting object  204 ( 1 ) is first detected and/or an anticipated position based on the trajectory  212 ( 2 ). In some examples, the first warning signal  206 ( 1 ) may include an audio beam emitted in a general direction associated with the potentially conflicting object  204 ( 1 ). The general direction may include an angle (e.g., 45 degrees, 90 degrees, 180 degrees, etc.) at which the potentially conflicting object is traveling relative to the vehicle  202 . In some examples, the first warning signal  206 ( 1 ) may include an audio signal emitted around the vehicle  202 , such as up to and including 360 degrees around the vehicle. 
     Additionally or in the alternative, the vehicle computing system may determine an action for the vehicle  202  to take to avoid the conflict with the potentially conflicting object  204 ( 1 ). The action may include yielding to the potentially conflicting object  204 ( 1 ) (e.g., slowing down or stopping, using emergency braking, etc.), and/or changing a planned path associated with the vehicle  202  (e.g., lane change right, lane change left, change planned path of vehicle within lane, drive on shoulder, etc.). In various examples, the action may be determined, based at least in part on the location  218  and the safety zone  220 . For example, if the location  218  is located at least a threshold distance from the safety zone  220 , the vehicle computing system may determine that the most efficient action to avoid conflict is to stop the vehicle  202  to avoid the conflict. For another example, if the location  218  is located within the safety zone  220 , the vehicle  202  may determine that slowing to a stop will not avoid the conflict. Accordingly, the vehicle computing system may cause the vehicle  202  to change lanes to avoid the conflict. 
     In some examples, the vehicle computing system may determine that the conflict can be avoided by taking two or more actions. In such examples, the action may be determined based on an efficiency associated with each action of the two or more actions. In some examples, the efficiency may be based on a calculated efficiency score corresponding to each action. In such examples, the efficiency score may be based on an amount of time associated with each action (e.g., time to stop, avoid the conflict, and accelerate back up to speed), a probability of conflict with another object  204 , such as object  204 ( 2 ), traffic laws associated with actions, or the like. In various examples, the vehicle computing system may determine a particular action based on the particular action being associated with a highest efficiency score. 
     In various example, the vehicle computing system may determine to perform two or more actions, such as to increase a safety margin associated with the conflict. The safety margin may be based on a probability (e.g., likelihood) of avoiding the conflict, (e.g., distance between the vehicle  202  and the potentially conflicting object  204 ( 1 ) on the trajectory  212 ( 1 ) at a closest point of approach, etc.). For example, the vehicle computing system may determine that a lane change to the right will avoid the conflict and that additionally slowing the vehicle  202  will increase a safety margin associated with the conflict. 
     Based on the determined action, the vehicle computing system may cause the vehicle  202  to perform the action. In some examples, the action may include slowing or stopping in the vehicle path  216 . In some examples, the action may include changing the vehicle path  216 , such as a lane change right or left, altering a position in a current lane, swerving left or right, such as into a shoulder of the roadway, or the like. In the illustrative example, the vehicle computing system causes the vehicle  202  to perform a lane change to the right. 
     In various examples, based on the determined action, the vehicle computing system may identify one or more objects  204  that could potentially be affected by the action (e.g., perform subsequent action, as desired). In the illustrative example, the vehicle computing system may determine that the object  204 ( 2 ), located in the right-hand lane, may be affected by the vehicle  202  moving into the right lane. For example, the object  204 ( 2 ) may brake to increase a following distance behind the vehicle  202 . 
     In various examples, based on an identification of the object  204 ( 2 ), the vehicle computing system may emit a second warning signal  206 ( 2 ) directed at the object  204 ( 2 ) (e.g., in a beam formed audio signal), such as to warn the object  204 ( 2 ) of the lane change and/or of the potentially conflicting object  204 ( 1 ) entering the roadway. A frequency (or set/range of frequencies) and/or volume(s) of the second warning signal  206 ( 2 ), similar to the first warning signal  206 ( 1 ), may be based on a noise floor (including frequency ranges) in the environment, noise floor (including frequency ranges) proximate to the object  204 ( 2 ), an urgency of the warning, a likelihood of conflict, and/or a message to be conveyed. Additionally, the frequency (or set/range of frequencies) and/or volume(s) of the second warning signal  206 ( 2 ) may be based on a classification of the object  204 ( 2 ), an enclosure in which an operator of the object  204 ( 2 ) is located, and/or an amount of noise (including frequency ranges) experienced by the operator. For example, the vehicle computing system may determine that the object  204 ( 2 ) is classified a non-autonomous hard-top car with the windows rolled up. The vehicle computing system may determine that the warning signal  206 ( 2 ) may be emitted at 80 decibels, for an operator of the car to hear. For another example, the vehicle computing system may determine that the object  204 ( 2 ) is a convertible non-autonomous car operating with the top down. The vehicle computing system may determine that the warning signal  206 ( 2 ) may be emitted at 60 decibels for the operator to hear. 
     In various examples, the vehicle computing system may determine that an additional warning signal  206 ( 3 ) directed toward the potentially conflicting object  204 ( 1 ) may assist in avoiding conflict between the vehicle  202  and the potentially conflicting object  204 ( 1 ). In such examples, the vehicle computing system may determine a volume (or set/range of volumes) and/or a frequency (or set/range of frequencies) for the additional warning signal  206 ( 3 ). The volume(s) and/or frequencies may be the same or different from the first warning signal  206 ( 1 ). 
       FIGS. 3A and 3B  are illustrations of example environments  300  and  302  in which a vehicle  304 , such as vehicle  102 , configured with a dynamic sound emission system may operate.  FIG. 3A  is an illustration of an environment  300  in which one or more objects  306 , such as objects  104  are detected. As discussed above, a vehicle computing system of the vehicle  304  may be configured to detect the object(s)  306  based at least in part on sensor data received from one or more sensors. In some examples, the vehicle computing device may be configured to identify a type of object (e.g., classification) associated with each of the detected object(s). 
     As illustrated in  FIG. 3A , based in part on the detection of the object(s)  306 , the vehicle computing system may determine to emit a warning signal  308 , such as warning signal  110 , to alert the object(s)  306  of the vehicle presence. In some examples, a determination to emit the warning signal  308  may be based on a detection a particular type of object  306  detected. For example, the vehicle computing system may identify objects  306  classified as cars and trucks operating on a roadway of an environment. Based on a determination that only cars and trucks are in the environment, the vehicle computing device may determine to not emit the warning signal  308 . For another example, the vehicle computing system may identify objects  306  classified as pedestrians and bicyclists in the environment. Based on the determination that pedestrians and bicyclists are in the environment, the vehicle computing system may determine to emit the warning signal  308 . 
     In various examples, a determination to emit the warning signal  308  may be based on a proximity of at least one object  306  of the object(s)  306  to the vehicle  304 ( 1 ) and/or a path of the vehicle  304 ( 1 ). In some examples, the at least one object  306  may include an object that is located closest (e.g., a shortest distance) to the vehicle  304 ( 1 ) and/or a path of the vehicle  304 ( 1 ). In some examples, the determination to emit the warning signal may be based on the at least one object  306  being within a threshold distance (e.g., 1 block, 100 yards, 200 meters, etc.) of the vehicle and/or the path of the vehicle  304 ( 1 ). In various examples, the determination to emit the warning signal may be based on the object(s)  306  being located on a side of a roadway in which the vehicle  304 ( 1 ) is operating. For example, as illustrated in  FIG. 3A , the vehicle is operating in the right lane on the right side of a roadway. The vehicle computing system may determine to emit the warning signal  308  based on the detection of objects  306  on the right side of the road and/or the classification associated with the objects  306  located on the right side of the road. 
     In various examples, the warning signal  308  may include one or more directed audio signals emitted toward the object(s)  306  (e.g., direct beam sent in the direction of the object). In some examples, the warning signal  308  may be emitted directly at one or more of the object(s)  306 , such as by utilizing beam forming techniques described above. In the illustrative example, the warning signal  308  is emitted in directions corresponding to the four corners of the car. In some examples, the warning signal may be emitted in specific quadrants (e.g., front right, back left, etc.) corresponding to the location of the object(s)  306 . In some examples, the warning signal  308  may be emitted on a side of the vehicle  304 ( 1 ) (e.g., right side or left side) corresponding to the location of the object(s)  306 . In various examples, the warning signal  308  may be emitted at any angle up to an including 360 degrees around the vehicle, such as 30, 45, 60, 90, 360 degrees, etc. 
     In various examples, the warning signal  308  may be emitted to alert the object(s)  306  of the presence of the vehicle  304 ( 1 ). For example, an electrically operated vehicle may produce a negligible amount of operating noise and may be substantially aurally imperceptible to objects  306  in the environment  300 . Accordingly, the warning signal  308  may provide a means by which the object(s)  306  may determine the presence of the vehicle  304 ( 1 ). A frequency or set of frequencies (e.g., waveform) of the warning signal  308  may be determined according to techniques described above with respect to  FIGS. 1 and 2 . In various examples, the frequencies of the warning signal  308  may be determined based on the purpose of alerting the object(s)  306  of the presence of the vehicle  304 ( 1 ). In some examples, the frequencies may be determined based on an impression the warning signal  308  may have on the object(s)  306 . For example, the frequencies may be associated with a soft, melodic sound that is intended to leave the object(s)  306  with a positive impression of the vehicle  304 ( 1 ). For another example, the frequencies in a set of frequencies may be selected based on a determination to emulate the sound of a gas-powered vehicle engine. In such an example, the frequencies of the warning signal  308  may adjust higher and lower to simulate an engine adjusting revolutions per minute while operating in the environment. 
     A volume (or set/range of volumes) of the warning signal  308  may be determined in accordance with volume determinations described above with respect to  FIGS. 1 and 2 . In various examples, the volume(s) of the warning signal  308  may be determined based at least in part on a speed associated with the vehicle. In some examples, the volume(s) of the warning signal  308  may increase as the speed of the vehicle  304 ( 1 ) increases, or vice versa. For example, the vehicle computing system may determine to emit a warning signal  308  at 50 decibels based on a determined vehicle  304 ( 1 ) speed of 15 miles per hour. After increasing the speed to 30 miles per hour, the vehicle computing system may determine to emit the warning signal  308  at 70 decibels. 
     In various examples, the frequencies and/or volume(s) of the warning signal  308  may be determined based on an area in which the vehicle  304 ( 1 ) is operating and/or a time (e.g., time of the day, of the week, of the month, of the year, etc.) associated with the operation. For example, the vehicle computing system may determine that the vehicle  304 ( 1 ) is operating in a school zone during a school day (e.g., during school hours). Based on an operation in the school zone during school hours, the vehicle computing system may select a frequency (or set/range of frequencies) of warning signal  308  that may be audibly appealing to (e.g., catch the attention of) school children. 
     In various examples, based on a determination that objects  306  and/or objects  306  of a particular type are no longer detected proximate to the vehicle  304 ( 1 ), the vehicle computing system may determine to not emit the warning signal  308 . 
       FIG. 3B  is an illustration of an environment  302  in which the dynamic sound emission system may determine to not emit a warning signal, such as warning signal  308 , based at least in part on a lack of objects, such as objects  306 , and/or a lack of a particular type of objects detected in the environment  302 . 
     In the illustrative example of  FIG. 3B , the environment  302  includes a highway-type environment in which the vehicle  304 ( 1 ) and another vehicle  304 ( 2 ) may operate. In some examples, based on a determination that no objects exist in the environment  302 , the vehicle computing system of the vehicle  304 ( 1 ) may determine to not emit the warning signal. In some examples, the environment  302  may include objects classified as cars, motorcycles, trucks, and/or other motorized or electric vehicles configured for travel on a roadway. In such examples, based on a determination that no objects classified as pedestrians, bicyclists, scooters, or other objects that are self-powered are present in the environment  302 , the vehicle computing device may determine to not emit the warning signal. 
     In some examples, a determination to not emit the warning signal may be based at least in part on a speed associated with the vehicle  304 ( 1 ). In such examples, the vehicle computing device may determine that the speed of the vehicle has increased above an upper threshold value (e.g., 50 miles per hour, 60 miles per hour, 65 miles per hour, etc.) or decreased below a lower threshold value (e.g., 15 miles per hour, 10 miles per hour, 7 miles per hour, etc.), the vehicle computing device may determine to not emit the warning signal. In some examples, the upper threshold value may be determined based on a probability (e.g., likelihood) of conflict with an object and/or presence of a potentially conflicting object. In some examples, the lower threshold value may be determined based on a distance required to stop the vehicle  304 ( 1 ) at maximum (e.g., emergency) braking. For example, the vehicle computing device may determine that on a road in which the vehicle  304 ( 1 ) may travel at 55 miles per hour, a likelihood of detecting objects classified as pedestrians is below a threshold likelihood. Accordingly, the vehicle computing device may determine to not emit the warning signal. For another example, the vehicle computing device may determine that the speed of the vehicle  304 ( 1 ) has slowed below 10 miles per hour (e.g., a lower threshold speed). Based on a determination that the vehicle is below the lower threshold speed, the vehicle computing device may determine to not emit the warning signal. 
       FIG. 4  is a block diagram of an example system  400  for implementing the techniques described herein. In at least one example, the system  400  may include a vehicle  402 , such as vehicle  102 . 
     The vehicle  402  may include one or more vehicle computing devices  404  (e.g., vehicle computing system), one or more sensor systems  406 , one or more emitters  408 , one or more communication connections  410 , at least one direct connection  412 , and one or more drive modules  414 . 
     The vehicle computing device(s)  404  may include one or more processors  416  and memory  418  communicatively coupled with the one or more processors  416 . In the illustrated example, the vehicle  402  is an autonomous vehicle; however, the vehicle  402  could be any other type of vehicle, such as a semi-autonomous vehicle, or any other system having at least an image capture device (e.g., a camera enabled smartphone). In the illustrated example, the memory  418  of the vehicle computing device(s)  404  stores a localization component  420 , a perception component  422 , a planning component  424 , one or more system controllers  426 , and a warning signal component  428  including an object trajectory component  430 , a risk component  432 , a frequency component  434 , a volume component  436 , and an action component  438 . Though depicted in  FIG. 4  as residing in the memory  418  for illustrative purposes, it is contemplated that the localization component  420 , a perception component  422 , a planning component  424 , one or more system controllers  426 , and a warning signal component  428  including an object trajectory component  430 , a risk component  432 , a frequency component  434 , a volume component  436 , and an action component  438  may additionally, or alternatively, be accessible to the vehicle  402  (e.g., stored on, or otherwise accessible by, memory remote from the vehicle  402 , such as, for example, on memory  440  of a remote computing device  442 ). 
     In at least one example, the localization component  420  may include functionality to receive data from the sensor system(s)  406  to determine a position and/or orientation of the vehicle  402  (e.g., one or more of an x-, y-, z-position, roll, pitch, or yaw). For example, the localization component  420  may include and/or request/receive one or more map(s) of an environment and may continuously determine a location and/or orientation of the autonomous vehicle within the map(s). For the purpose of this discussion, a map may be any number of data structures modeled in two dimensions, three dimensions, or N-dimensions that are capable of providing information about an environment, such as, but not limited to, topologies (such as intersections), streets, mountain ranges, roads, terrain, and the environment in general. In some instances, a map may include, but is not limited to: texture information (e.g., color information (e.g., RGB color information, Lab color information, HSV/HSL color information), and the like), intensity information (e.g., lidar information, radar information, and the like); spatial information (e.g., image data projected onto a mesh, individual “surfels” (e.g., polygons associated with individual color and/or intensity)), reflectivity information (e.g., specularity information, retroreflectivity information, BRDF information, BSSRDF information, and the like). In at least one example, a map may include a three-dimensional mesh of the environment. In some examples, the vehicle  402  may be controlled based at least in part on the map(s). That is, the map(s) may be additionally used in connection with the perception component  422  and/or the planning component  424  to determine a location of the vehicle  402 , detect objects in an environment, and/or generate routes and/or trajectories to navigate within an environment. 
     In some examples, the one or more maps may be stored on a remote computing device(s) (such as the computing device(s)  442 ) accessible via network(s)  444 . In some examples, multiple maps may be stored based on, for example, a characteristic (e.g., type of entity, time of day, day of week, season of the year, etc.). Storing multiple maps may have similar memory requirements but increase the speed at which data in a map may be accessed. 
     In various examples, the localization component  420  may be configured to utilize SLAM (simultaneous localization and mapping), CLAMS (calibration, localization and mapping, simultaneously), relative SLAM, bundle adjustment, non-linear least squares optimization, or the like to receive image data, lidar data, radar data, IMU data, GPS data, wheel encoder data, and the like to accurately determine a location of the vehicle  402 . In some instances, the localization component  420  may provide data to various components of the vehicle  402  to determine an initial position of an autonomous vehicle for determining a likelihood (e.g., probability) of conflict with an object, as discussed herein. 
     In some examples, the perception component  422  may include functionality to perform object detection, segmentation, and/or classification. In some examples, the perception component  422  may provide processed sensor data that indicates a presence of an object (e.g., entity, dynamic object) that is proximate to the vehicle  402  and/or a classification of the object as an object type (e.g., car, pedestrian, cyclist, dog, cat, deer, unknown, etc.). In some examples, the perception component  422  may provide processed sensor data that indicates a presence of a stationary entity that is proximate to the vehicle  402  and/or a classification of the stationary entity as a type (e.g., building, tree, road surface, curb, sidewalk, unknown, etc.). In additional or alternative examples, the perception component  422  may provide processed sensor data that indicates one or more characteristics associated with a detected object (e.g., a tracked object) and/or the environment in which the object is positioned. In some examples, characteristics associated with an object may include, but are not limited to, an x-position (global and/or local position), a y-position (global and/or local position), a z-position (global and/or local position), an orientation (e.g., a roll, pitch, yaw), an object type (e.g., a classification), a velocity of the object, an acceleration of the object, an extent of the object (size), etc. Characteristics associated with the environment may include, but are not limited to, a presence of another object in the environment, a state of another object in the environment, a time of day, a day of a week, a season, a weather condition (e.g., rain, sleet, hail, snow, temperature, humidity, etc.), an indication of darkness/light, etc. 
     In general, the planning component  424  may determine a path for the vehicle  402  to follow to traverse through an environment. For example, the planning component  424  may determine various routes and trajectories and various levels of detail. For example, the planning component  424  may determine a route to travel from a first location (e.g., a current location) to a second location (e.g., a target location). For the purpose of this discussion, a route may include a sequence of waypoints for travelling between two locations. As non-limiting examples, waypoints include streets, intersections, global positioning system (GPS) coordinates, etc. Further, the planning component  424  may generate an instruction for guiding the vehicle  402  along at least a portion of the route from the first location to the second location. In at least one example, the planning component  424  may determine how to guide the vehicle  402  from a first waypoint in the sequence of waypoints to a second waypoint in the sequence of waypoints. In some examples, the instruction may be a trajectory, or a portion of a trajectory. In some examples, multiple trajectories may be substantially simultaneously generated (e.g., within technical tolerances) in accordance with a receding horizon technique, wherein one of the multiple trajectories is selected for the vehicle  402  to navigate. 
     In some examples, the planning component  424  may include a prediction component to generate predicted trajectories of objects in an environment. For example, a prediction component may generate one or more predicted trajectories for objects within a threshold distance from the vehicle  402 . In some examples, a prediction component may measure a trace of an object and generate a trajectory for the object based on observed and predicted behavior. 
     In at least one example, the vehicle computing device(s)  404  may include one or more system controllers  426 , which may be configured to control steering, propulsion, braking, safety, emitters, communication, and other systems of the vehicle  402 . The system controller(s)  426  may communicate with and/or control corresponding systems of the drive module(s)  414  and/or other components of the vehicle  402 . 
     As illustrated in  FIG. 4 , the vehicle computing device(s)  404  may include a warning signal component  428 . The warning signal component  428  may include an object trajectory component  430  configured to determine trajectories of objects in the environment. An object trajectory may include a direction and/or speed the object may travel from a current position (e.g., at the time of perception) and/or based on a direction of travel. In some examples, the object trajectory component  430  may determine that an object is within a threshold distance (e.g., one block, 200 meters, 300 feet, etc.) of the vehicle  402 . Based on the determination that the object is within the threshold distance to the vehicle, the object trajectory component  430  may determine the trajectories associated with the object. 
     In various examples, the object trajectory component  430  may receive trajectory data from a prediction component of the planning component  424 . In some examples, the object trajectory component  430  may be configured to receive sensor data from the sensor system(s)  406  and/or object data corresponding to detected objects and/or particular types of objects (e.g., pedestrians, bicyclists, etc.) from the perception component  422 . In such examples, the object trajectory component  430  may process the received data and measure a trace of an object in order to generate a trajectory for the object based on observed and predicted behavior. 
     The warning signal component  428  may include a risk component  432  configured to identify one or more objects in an environment that may potentially conflict with the vehicle  402  (e.g., potentially conflicting object(s)). As discussed above, a risk (e.g., probability, likelihood, etc.) of conflict may be based on a relationship between a determined vehicle trajectory and a determined object trajectory. In some examples, the risk may be determined based on a likelihood of the vehicle  402  and the potentially conflicting object occupying the same or a similar space in the environment at the same or a substantially similar time. In various examples, the risk of conflict may be based on a safety zone associated with the vehicle  402  as discussed above with regard to  FIG. 2 . 
     In various examples, the risk component  432  may receive object trajectory data from the object trajectory component  432  and/or the planning component. In some examples, the risk component  432  may receive location data and/or vehicle trajectory data from the localization component  420 , the perception component  422 , and/or the planning component  424 . The risk component  432  may process the vehicle and object trajectory data to determine the risk of conflict between the vehicle  402  and an object. 
     In various examples, the frequency component  434  may be configured to determine one or more frequencies of a warning signal to emit. As discussed above, the frequencies may be determined based on a detection of objects in the environment, a detection of a particular type of object, a determination of a potentially conflicting object, a speed of the vehicle  402 , an impression the warning signal may have on objects in the environment, locations and/or speeds of the detected objects in the environment, or the like. In various examples, the frequency component  434  may access a database of warning signal frequencies to determine the frequency and/or set of frequencies associated with the warning signal. In some examples, the database of warning signal frequencies may be stored on the memory  418 . In some examples, the database of warning signal frequencies may be stored on a memory  440  of the remote computing device(s)  442  and accessible via the network(s)  444 . 
     In various examples, the volume component  436  of the warning signal component  428  may determine one or more volumes of the warning signal. In some examples, the volume(s) may be based on a noise floor of the environment as perceived by the vehicle. In some examples, the volume(s) may be based on a noise floor as perceived by the potentially conflicting object to which the warning signal is directed. In some examples, the volume(s) may be additionally, or alternatively, based on a distance between the vehicle  402  and the potentially conflicting object. Furthermore, and as discussed above, the volume(s) may be based on one or more objects being detected in the environment, such as those located between the vehicle  402  and the potentially conflicting object, a speed of the vehicle, a type of object to which the warning signal is directed (e.g., car, pedestrian, etc.), a speed and/or location of the object relative to the potentially conflicting object, a condition associated with the object (e.g., pedestrian wearing headphones or earmuffs, a pedestrian having a conversation in person and/or on the phone, an operator of a convertible car, a hard-top car, a vehicle that generates a substantial amount of noise (e.g., semi-trailer truck, etc.), etc.). 
     In various examples, the action component  438  may, based on a determination of risk (e.g., high, medium, or low risk), determine to emit the warning signal. The warning signal may be emitted at the frequency (or set/range of frequencies) determined by the frequency component  434  and/or the volume(s) determined by the volume component  436 . Based on the determination to emit a warning signal, the vehicle computing device(s)  404 , such as through the sensor systems  406 , may emit the warning signal. The warning signal may be emitted in a direction determined by the action component  438 , such as directed in any angle up to and including 360 degrees around the vehicle  402 . In various examples, the action component  438  may be configured to cause multiple warning signals to be emitted in multiple directions. For example, the warning signal component  428  may identify two potentially conflicting objects. The warning signal component  428  may determine a risk associated with each of the potentially conflicting objects and may determine a frequency (or set/range of frequencies) and/or volume (or set/range of volumes) of each warning signal to emit based at least in part on the risk associated therewith. The action component  438  may cause the respective warning signals to be emitted in respective directions of the potentially conflicting objects. 
     In various examples, the action component  438  may, based on the determination of risk (e.g., high, medium, or low risk), determine an action for the vehicle to take. The action may include slowing the vehicle to yield to the object, stopping the vehicle to yield to the object, changing lanes left, or changing lanes right. Based on the determined action, the vehicle computing device(s)  404 , such as through the system controller(s)  426 , may cause the vehicle to perform the action. In at least some examples, such an action may be based on the probability of collision, as described in detail above. 
     As can be understood, the components discussed herein (e.g., the localization component  420 , the perception component  422 , the planning component  424 , the one or more system controllers  426 , the warning signal component  428  including the object trajectory component  430 , the risk component  432 , the frequency component  434 , the volume component  436 , and the action component  438  are described as divided for illustrative purposes. However, the operations performed by the various components may be combined or performed in any other component. 
     In some instances, aspects of some or all of the components discussed herein may include any models, techniques, and/or machine learning techniques. For example, in some instances, the components in the memory  418  (and the memory  440 , discussed below) may be implemented as a neural network. As described herein, an exemplary neural network is a biologically inspired technique which passes input data through a series of connected layers to produce an output. Each layer in a neural network may also comprise another neural network, or may comprise any number of layers (whether convolutional or not). As can be understood in the context of this disclosure, a neural network may utilize machine learning, which may refer to a broad class of such techniques in which an output is generated based on learned parameters. 
     In some examples, the vehicle computing device(s)  404  may utilize machine learning techniques to determine one or more volumes and/or one or more frequencies of noises emitted by identified (e.g., perceived, classified, etc.) objects. In some examples, one or more data models may be trained to determine a noise emitted by an identified object based on one or more conditions in the environment. The condition(s) may include terrain features, a road condition (e.g., gravel, smooth pavement, etc.), speed and/or acceleration of the identified object, weather conditions, or the like. In various examples, the data model(s) may be trained to output a noise (e.g., noise level, an number of decibels, etc.) emitted by an object based at least in part on the condition(s) present in the environment. 
     Although discussed in the context of neural networks, any type of machine learning may be used consistent with this disclosure. For example, machine learning techniques may include, but are not limited to, regression techniques (e.g., ordinary least squares regression (OLSR), linear regression, logistic regression, stepwise regression, multivariate adaptive regression splines (MARS), locally estimated scatterplot smoothing (LOESS)), instance-based techniques (e.g., ridge regression, least absolute shrinkage and selection operator (LASSO), elastic net, least-angle regression (LARS)), decisions tree techniques (e.g., classification and regression tree (CART), iterative dichotomiser 3 (ID3), Chi-squared automatic interaction detection (CHAID), decision stump, conditional decision trees), Bayesian techniques naïve Bayes, Gaussian naïve Bayes, multinomial naïve Bayes, average one-dependence estimators (AODE), Bayesian belief network (BNN), Bayesian networks), clustering techniques (e.g., k-means, k-medians, expectation maximization (EM), hierarchical clustering), association rule learning techniques (e.g., perceptron, back-propagation, hopfield network, Radial Basis Function Network (RBFN)), deep learning techniques (e.g., Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Network (CNN), Stacked Auto-Encoders), Dimensionality Reduction Techniques (e.g., Principal Component Analysis (PCA), Principal Component Regression (PCR), Partial Least Squares Regression (PLSR), Sammon Mapping, Multidimensional Scaling (MDS), Projection Pursuit, Linear Discriminant Analysis (LDA), Mixture Discriminant Analysis (MDA), Quadratic Discriminant Analysis (QDA), Flexible Discriminant Analysis (FDA)), Ensemble Techniques (e.g., Boosting, Bootstrapped Aggregation (Bagging), AdaBoost, Stacked Generalization (blending), Gradient Boosting Machines (GBM), Gradient Boosted Regression Trees (GBRT), Random Forest), SVM (support vector machine), supervised learning, unsupervised learning, semi-supervised learning, etc. Additional examples of architectures include neural networks such as ResNet70, ResNet101, VGG, DenseNet, PointNet, and the like. 
     In at least one example, the sensor system(s)  406  may include lidar sensors, radar sensors, ultrasonic transducers, sonar sensors, location sensors (e.g., GPS, compass, etc.), inertial sensors (e.g., inertial measurement units (IMUs), accelerometers, magnetometers, gyroscopes, etc.), cameras (e.g., RGB, IR, intensity, depth, time of flight, etc.), microphones, wheel encoders, environment sensors (e.g., temperature sensors, humidity sensors, light sensors, pressure sensors, etc.), etc. The sensor system(s)  406  may include multiple instances of each of these or other types of sensors. For instance, the lidar sensors may include individual lidar sensors located at the corners, front, back, sides, and/or top of the vehicle  402 . As another example, the camera sensors may include multiple cameras disposed at various locations about the exterior and/or interior of the vehicle  402 . The sensor system(s)  406  may provide input to the vehicle computing device(s)  404 . Additionally or alternatively, the sensor system(s)  406  may send sensor data, via the one or more networks  444 , to the one or more computing device(s)  442  at a particular frequency, after a lapse of a predetermined period of time, in near real-time, etc. 
     The vehicle  402  may also include one or more emitters  408  for emitting light and/or sound, as described above. The emitters  408  in this example include interior audio and visual emitters to communicate with passengers of the vehicle  402 . By way of example and not limitation, interior emitters may include speakers, lights, signs, display screens, touch screens, haptic emitters (e.g., vibration and/or force feedback), mechanical actuators (e.g., seatbelt tensioners, seat positioners, headrest positioners, etc.), and the like. The emitters  408  in this example also include exterior emitters. By way of example and not limitation, the exterior emitters in this example include lights to signal a direction of travel or other indicator of vehicle action (e.g., indicator lights, signs, light arrays, etc.), and one or more audio emitters (e.g., speakers, speaker arrays, horns, etc.) to audibly communicate with pedestrians or other nearby vehicles, one or more of which comprising acoustic beam steering technology. 
     The vehicle  402  may also include communication connection(s)  410  that enable communication between the vehicle  402  and one or more other local or remote computing device(s)  442 . For instance, the communication connection(s)  410  may facilitate communication with other local computing device(s) on the vehicle  402  and/or the drive module(s)  414 . Also, the communication connection(s)  410  may allow the vehicle to communicate with other nearby computing device(s) (e.g., computing device(s)  442 , other nearby vehicles, etc.) and/or one or more remote sensor system(s)  446  for receiving sensor data. 
     The communications connection(s)  410  may include physical and/or logical interfaces for connecting the vehicle computing device(s)  404  to another computing device or a network, such as network(s)  444 . For example, the communications connection(s)  410  can enable Wi-Fi-based communication such as via frequencies defined by the IEEE 802.11 standards, short range wireless frequencies such as Bluetooth, cellular communication (e.g., 2G, 3G, 4G, 4G LTE, 5G, etc.) or any suitable wired or wireless communications protocol that enables the respective computing device to interface with the other computing device(s). 
     In at least one example, the vehicle  402  may include one or more drive modules  414 . In some examples, the vehicle  402  may have a single drive module  414 . In at least one example, if the vehicle  402  has multiple drive modules  414 , individual drive modules  414  may be positioned on opposite ends of the vehicle  402  (e.g., the front and the rear, etc.). In at least one example, the drive module(s)  414  may include one or more sensor systems to detect conditions of the drive module(s)  414  and/or the surroundings of the vehicle  402 . By way of example and not limitation, the sensor system(s) may include one or more wheel encoders (e.g., rotary encoders) to sense rotation of the wheels of the drive modules, inertial sensors (e.g., inertial measurement units, accelerometers, gyroscopes, magnetometers, etc.) to measure orientation and acceleration of the drive module, cameras or other image sensors, ultrasonic sensors to acoustically detect objects in the surroundings of the drive module, lidar sensors, radar sensors, etc. Some sensors, such as the wheel encoders may be unique to the drive module(s)  414 . In some cases, the sensor system(s) on the drive module(s)  414  may overlap or supplement corresponding systems of the vehicle  402  (e.g., sensor system(s)  406 ). 
     The drive module(s)  414  may include many of the vehicle systems, including a high voltage battery, a motor to propel the vehicle, an inverter to convert direct current from the battery into alternating current for use by other vehicle systems, a steering system including a steering motor and steering rack (which can be electric), a braking system including hydraulic or electric actuators, a suspension system including hydraulic and/or pneumatic components, a stability control system for distributing brake forces to mitigate loss of traction and maintain control, an HVAC system, lighting (e.g., lighting such as head/tail lights to illuminate an exterior surrounding of the vehicle), and one or more other systems (e.g., cooling system, safety systems, onboard charging system, other electrical components such as a DC/DC converter, a high voltage junction, a high voltage cable, charging system, charge port, etc.). Additionally, the drive module(s)  414  may include a drive module controller which may receive and preprocess data from the sensor system(s)  406  and to control operation of the various vehicle systems. In some examples, the drive module controller may include one or more processors and memory communicatively coupled with the one or more processors. The memory  418  may store one or more modules to perform various functionalities of the drive module(s)  414 . Furthermore, the drive module(s)  414  may also include one or more communication connection(s) that enable communication by the respective drive module with one or more other local or remote computing device(s)  442 . 
     In at least one example, the direct connection  412  may provide a physical interface to couple the one or more drive module(s)  414  with the body of the vehicle  402 . For example, the direct connection  412  may allow the transfer of energy, fluids, air, data, etc. between the drive module(s)  414  and the vehicle. In some instances, the direct connection  412  may further releasably secure the drive module(s)  414  to the body of the vehicle  402 . 
     In at least one example, the localization component  420 , the perception component  422 , the planning component  424 , the one or more system controllers  426 , and the warning signal component  428  and various components thereof, may process sensor data, as described above, and may send their respective outputs, over the one or more network(s)  444 , to the computing device(s)  442 . In at least one example, the localization component  420 , the perception component  422 , the planning component  424 , the one or more system controllers  426 , and the warning signal component  428  may send their respective outputs to the computing device(s)  442  at a particular frequency, after a lapse of a predetermined period of time, in near real-time, etc. 
     In some examples, the vehicle  402  may send sensor data to the computing device(s)  442  via the network(s)  444 . In some examples, the vehicle  402  may receive sensor data from the computing device(s)  442  via the network(s)  444 . The sensor data may include raw sensor data and/or processed sensor data and/or representations of sensor data. In some examples, the sensor data (raw or processed) may be sent and/or received as one or more log files. 
     The computing device(s)  442  may include processor(s)  448  and a memory  440  storing a map component  450  and a sensor data processing component  452 . In some examples, the map component  450  may include functionality to generate maps of various resolutions. In such examples, the map component  450  may send one or more maps to the vehicle computing device(s)  404  for navigational purposes. In various examples, the sensor data processing component  452  may be configured to receive data from one or more remote sensors, such as sensor systems  406  and/or remote sensor system(s)  446 . In some examples, the sensor data processing component  452  may be configured to process the data and send processed sensor data to the vehicle computing device(s)  404 , such as for use by the warning signal component  428 . In some examples, the sensor data processing component  452  may be configured to send raw sensor data to the vehicle computing device(s)  404 . 
     The processor(s)  416  of the vehicle  402  and the processor(s)  448  of the computing device(s)  442  may be any suitable processor capable of executing instructions to process data and perform operations as described herein. By way of example and not limitation, the processor(s)  416  and  448  may comprise one or more Central Processing Units (CPUs), Graphics Processing Units (GPUs), or any other device or portion of a device that processes electronic data to transform that electronic data into other electronic data that may be stored in registers and/or memory. In some examples, integrated circuits (e.g., ASICs, etc.), gate arrays (e.g., FPGAs, etc.), and other hardware devices may also be considered processors in so far as they are configured to implement encoded instructions. 
     Memory  418  and  440  are examples of non-transitory computer-readable media. The memory  418  and  440  may store an operating system and one or more software applications, instructions, programs, and/or data to implement the methods described herein and the functions attributed to the various systems. In various implementations, the memory may be implemented using any suitable memory technology, such as static random access memory (SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type memory, or any other type of memory capable of storing information. The architectures, systems, and individual elements described herein may include many other logical, programmatic, and physical components, of which those shown in the accompanying figures are merely examples that are related to the discussion herein. 
     In some instances, the memory  418  and  440  may include at least a working memory and a storage memory. For example, the working memory may be a high-speed memory of limited capacity (e.g., cache memory) that is used for storing data to be operated on by the processor(s)  416  and  440 . In some instances, the memory  418  and  440  may include a storage memory that may be a lower-speed memory of relatively large capacity that is used for long-term storage of data. In some cases, the processor(s)  416  and  440  cannot operate directly on data that is stored in the storage memory, and data may need to be loaded into a working memory for performing operations based on the data, as discussed herein. 
     It should be noted that while  FIG. 4  is illustrated as a distributed system, in alternative examples, components of the vehicle  402  may be associated with the computing device(s)  442  and/or components of the computing device(s)  442  may be associated with the vehicle  402 . That is, the vehicle  402  may perform one or more of the functions associated with the computing device(s)  442 , and vice versa. 
       FIGS. 5-9  illustrate example processes in accordance with embodiments of the disclosure. These processes are illustrated as logical flow graphs, each operation of which represents a sequence of operations that may be implemented in hardware, software, or a combination thereof. In the context of software, the operations represent computer-executable instructions stored on one or more computer-readable storage media that, when executed by one or more processors, perform the recited operations. Generally, computer-executable instructions include routines, programs, objects, components, data structures, and the like that perform particular functions or implement particular abstract data types. The order in which the operations are described is not intended to be construed as a limitation, and any number of the described operations may be combined in any order and/or in parallel to implement the processes. 
       FIG. 5  depicts an example process  500  for determining at least one of a volume or a frequency of a warning sound to emit toward an object, in accordance with embodiments of the disclosure. For example, some or all of the process  500  may be performed by one or more components in  FIG. 4 , as described herein. For example, some or all of the process  500  may be performed by the vehicle computing device(s)  404 . 
     At operation  502 , the process may include identifying an object within a first threshold distance of a path of the vehicle. In various examples, an identification may include a vehicle computing system detecting the object, such as by processing sensor data received from one or more sensors to determine that the object is present in the environment. In some examples, the identification of the object may include determining a classification (e.g., type) of the object. 
     In various examples, the first threshold distance (e.g., 1 block, 4 blocks, 400 meters, ½ mile, etc.) may be a pre-determined distance from the vehicle. In some examples, the first threshold distance may be determined based on a speed in which the vehicle is operating, a number of objects present in the environment, one or more types (e.g., classes) of objects present in the environment, a zone in which the vehicle is operating (e.g., school zone, construction zone, etc.), and/or other factors that could affect the warning signal determination. 
     At operation  504 , the process may include determining a trajectory associated with the object. As discussed above, the vehicle computing system may process sensor data received from the sensor(s). The vehicle computing system may be configured to determine the trajectory of the object based on observed and/or predicted behaviors. The trajectory of the object may include a direction, velocity and/or acceleration of the object. In some examples, trajectory may be based, at least in part on a classification of the object. 
     At operation  506 , the process may include determining whether the object will be within a second threshold distance of the vehicle at a time in the future. In some examples, the vehicle computing device may determine a distance between the vehicle and the object at the time in the future based on a comparison of trajectory of the vehicle and the trajectory of the object. In various examples, the second threshold distance may be a pre-defined distance (e.g., 3 meters, 5 meters, 15 feet, 25 feet, etc.). In some examples, the second threshold distance may include a distance related to an intersection (i.e., 0 feet) between the trajectory of the vehicle and the trajectory of the object at a same or similar time in the future. In some examples, the second threshold distance may be determined based on a speed at which the vehicle is operating, a speed of the object, a number of objects present in the environment, one or more types of objects present in the environment, a zone in which the vehicle is operating, or the like. In various examples, the second threshold distance may be determined based on a likelihood (e.g., probability) of conflict between the vehicle and the object. 
     Based on a determination that the object will not be within the second threshold distance of the vehicle at the time in the future (e.g., “No” at 506), the vehicle computing system may return to operation  502  and identify another object within the first threshold distance of a path of the vehicle. 
     Based on a determination that the object will be within the second threshold distance of the vehicle at the time in the future (e.g., “Yes” at  506 ), the process may continue to operation  508 , which may include determining a noise level proximate to the object. As discussed above, the noise level proximate to the object may be determined based on one or more other noise generating objects (e.g., dynamic and/or non-dynamic) located proximate to the object. In some examples, the noise level may be determined based at least in part on a baseline noise level detected by the vehicle computing system in the environment. In various examples, the vehicle computing device may determine the noise level by extrapolating distinct and non-distinct noise events detected by sensors of the vehicle based on a first distance between the noise generating object(s) and the vehicle and a second distance between the noise generating object(s) and the object. 
     At operation  510 , the process may include determining at least one of a volume or a frequency of a sound to emit. In some examples, the vehicle computing system may acquire sounds in the form of a Fourier analysis to determine the at least one of the volume or the frequency of the sound to emit. In various examples, the vehicle computing system may determine one or more volumes and/or one or more frequencies of a sound (e.g., warning signal) to emit. In such examples, the warning signal may include a variable frequency and/or variable amplitude audio signal. 
     As discussed above, the volume(s) and/or frequencies may be determined based on various factors in the environment, such as a noise floor proximate to the object, a noise floor in the environment, a speed the vehicle is operating, type(s) of object(s) present in the environment, a number of objects present in the environment, presence of a second object being located between the object and the vehicle in the environment, or the like. In some examples, the at least one of the volume(s) and/or frequencies may be determined based on an urgency of the warning signal to be emitted, a likelihood of conflict, or the like. 
     At operation  512 , the process may include emitting the sound toward the object at the at least one of the volume (or set/range of volumes) or the frequency (or set/range of frequencies). In various examples, the vehicle computing system may cause the sound to be emitted via one or more speakers coupled to the vehicle. In some examples, the sound may be emitted in a directed toward the object, such as in a directed audio beam. In some examples, the sound may be emitted at an angle up to and including 360 degrees around the vehicle. 
       FIG. 6  depicts an example process  600  for determining whether an emission of sound, such as that described in  FIG. 5 , was effective and, based on the effectiveness of the sound, ceasing emission of the sound or causing the vehicle to take an action to avoid a collision with the object, in accordance with embodiments of the disclosure. For example, some or all of the process  600  may be performed by one or more components in  FIG. 4 , as described herein. For example, some or all of the process  600  may be performed by the vehicle computing device(s)  404 . 
     At operation  602 , the process may include determining a second trajectory associated with the object. The second trajectory, similar to the trajectory described at operation  504  of  FIG. 5 , may be determined by processing sensor data received from the sensor(s). The second trajectory of the object may include a direction, velocity, and/or acceleration of the object. 
     At operation  604 , the process may include determining whether the second trajectory differs from the first trajectory by a threshold amount (e.g., value). In various examples, the trajectories may differ in speed and/or direction. In such examples, the threshold amount may include an amount of speed change (e.g., 3 miles per hour to 1 mile per hour, 5 miles per hour to 0 miles per hour, etc.) and/or a change in direction (e.g., adjusted angle above a threshold angle (e.g., 15 degrees, 25 degrees, etc.), etc.). In various examples, the threshold amount may correspond to a determination that the object will not be within the second threshold distance of the path of the vehicle at a time in the future, as described above with regard to operation  506  of  FIG. 5 . In some examples, the threshold amount may correspond to a likelihood (e.g., probability) of conflict between the vehicle and the object being below a threshold level (e.g., threshold probability) of conflict. 
     If the second trajectory differs from the first trajectory by the threshold amount (e.g., “yes” in the operation  604 ), the process continues to operation  606 . At operation  606 , the process may include ceasing emission of the sound. 
     If the second trajectory does not differ from the first trajectory by the threshold amount (e.g., “no” in the operation  604 ), the process continues to operation  608 . At operation  608 , the process may include determining an action for the vehicle to take to avoid a collision with the object. The action may include determining to emit a second warning signal and/or causing the vehicle to alter the vehicle trajectory to avoid the collision. The second warning signal may include a volume (or set/range of volumes) and/or frequency (or set/range of frequencies) associated with a warning signal of increased urgency (e.g., low urgency alert elevated to high urgency warning, low probability of conflict elevated to medium and/or high probability of conflict). An change (e.g., adjustment, alteration, etc.) to the vehicle trajectory may include slowing or stopping the vehicle and/or changing the vehicle path, such as a lane change right or left, altering a position in a current lane, swerving left or right, such as into a shoulder of the roadway, or the like. 
     At operation  610 , the process may include causing the vehicle to take the action. In various examples, the vehicle computing system may cause the second warning signal to be emitted from the speaker(s) coupled to the vehicle. In some examples, the vehicle computing device may cause the vehicle, such as via the drive module  414  of  FIG. 4 , to take the action. 
       FIG. 7  depicts an example process  700  for determining a volume of a sound to emit toward an object, in accordance with embodiments of the disclosure. For example, some or all of the process  700  may be performed by one or more components in  FIG. 4 , as described herein. For example, some or all of the process  700  may be performed by the vehicle computing device(s)  404 . 
     At operation  702 , the process may include receiving sensor data from one or more sensors. The sensor(s) may include sensor(s) coupled to a vehicle and/or remote sensor(s) in an environment. Based on the sensor data, a vehicle computing system may detect and/or identify a potentially conflicting object in the environment. 
     At operation  704 , the process may include determining a distance to the potentially conflicting object from the vehicle. The distance to the potentially conflicting object may be determined based on at least some of the sensor data received at operation  702 . 
     At operation  706 , the process may include determining a predicted noise floor proximate to the object. As discussed above, the noise floor proximate to the potentially conflicting object may be determined based on a detection of one or more noise-producing objects proximate to the potentially conflicting object. In some examples, the noise floor proximate to the potentially conflicting object may be based on distinct and non-distinct noise events occurring in the environment, classifications of proximate objects, relative distances and/or speeds to the proximate objects from the potentially conflicting object, a time of day, a location of the potentially conflicting object, and the like. 
     At operation  708 , the process may include applying a smoothing algorithm to the predicted noise floor based on the distance to the object. In various examples, an application of the smoothing algorithm may include determining a gain associated with the environment. In various examples, the smoothing algorithm may adjust an effect of distinct noise events to the noise floor proximate to the object. 
     At operation  710 , the process may include determining a behavior volume code associated with a warning signal. The behavior volume code may be based on an urgency of the warning, a likelihood of conflict, or the like. In some examples, each behavior volume code may be associated with a range of volumes. For example, a behavior volume code may include an emergency volume range of 39-44 decibels above the noise floor, a friendly but clear and intentional volume range of 16-25 decibels above the noise floor, and an audible but subtle (e.g., subliminal) alert volume range of 2-9 decibels above the noise floor. 
     At operation  712 , the process may include determining a volume (or set and/or range of volumes) of warning signal to emit. In various examples, the volume(s) may be determined based on the input of the smoothed noise floor proximate to the object (post smoothing algorithm) based on the distance to the object and the determined behavior volume code. 
       FIG. 8  depicts an example process  800  for avoiding a collision between a vehicle and an object in an environment by emitting a sound and/or causing the vehicle to take an action to avoid the collision, in accordance with embodiments of the disclosure. For example, some or all of the process  800  may be performed by one or more components in  FIG. 4 , as described herein. For example, some or all of the process  800  may be performed by the vehicle computing device(s)  404 . 
     At operation  802 , the process may include determining a first trajectory associated with the vehicle in the environment. The first trajectory associated with the vehicle may include a velocity and/or acceleration of the vehicle along a vehicle path (e.g., direction of the vehicle). The first trajectory may be determined based on sensor data received from the sensor(s) and/or a planned path, velocity, and/or acceleration of the vehicle, such as that stored in a database of the vehicle computing system and sent to a drive module of the vehicle for execution. 
     At operation  804 , the process may include identifying an object in the environment. As discussed above, a vehicle computing system may detect and identify (e.g., classify) the object based on sensor data received from one or more sensors. 
     At operation  806 , the process may include determining a second trajectory associated with the object. The vehicle computing system may determine the second trajectory based on the sensor data received from the sensor(s). The second trajectory may include a direction, velocity, and/or acceleration of the object. 
     At operation  808 , the process may include determining, based on the first trajectory and the second trajectory, a possible collision between the vehicle and the object. The possible collision may include a probability (e.g., likelihood) of conflict between the vehicle and the object. In various examples, the vehicle computing device may determine that a possible collision may exist based on a determination that the vehicle on the first trajectory and the object on the second trajectory will be within a threshold distance of one another at a time in the future. 
     At operation  810 , the process may include causing the vehicle to perform an action comprising at least one of emitting a sound (e.g., warning signal) or yielding to the object. In various examples, the vehicle computing system may determine one or more volumes and/or one or more frequencies of the sound to emit. In such examples, the volumes and/or frequencies of the sound may be determined based on an urgency of the warning signal, a probability of collision, a velocity and/or acceleration associated with the vehicle and/or the object, or the like. 
     As discussed above, the action may include yielding to the object, such as by slowing or stopping the vehicle, changing lanes left or right, swerving away from the object, or the like. As discussed above with regard to  FIG. 2 , a determination to perform the action may be based on a future location of the potentially conflicting object being within a safety zone associated with the vehicle. The vehicle computing device may determine the action based on a determination that a particular action may decrease the probability of collision below a threshold level (e.g., low probability). 
       FIG. 9  depicts an example process  900  for determining at least one of a volume or a frequency of a warning sound to emit toward an object, in accordance with embodiments of the disclosure. For example, some or all of the process  900  may be performed by one or more components in  FIG. 4 , as described herein. For example, some or all of the process  900  may be performed by the vehicle computing device(s)  404 . 
     At operation  902 , the process may include identifying an object in an environment associated with a vehicle. In various examples, an identification may include a vehicle computing system detecting the object, such as by processing sensor data received from one or more sensors to determine that the object is present in the environment. In some examples, the identification of the object may include determining a classification (e.g., type) of the object. 
     At operation  904 , the process may include determining a noise level proximate to the object. As discussed above, the noise level proximate to the object may be determined based on one or more other noise generating objects located proximate to the object. In some examples, the noise level may be determined based on a baseline noise level detected by the vehicle computing system in the environment. In various examples, the vehicle computing device may determine the noise level by extrapolating distinct and non-distinct noise events detected by sensors of the vehicle based on a first distance between the noise generating object(s) and the vehicle and a second distance between the noise generating object(s) and the object. 
     At operation  906 , the process may include determining at least one of a volume (or set/range of volumes) or a frequency (or set/range of frequencies) of a sound to emit. In various examples, the vehicle computing system may determine one or more volumes and/or one or more frequencies of a sound (e.g., warning signal) to emit. In such examples, the warning signal may include a variable frequency and/or variable amplitude audio signal. 
     As discussed above, the volume(s) and/or frequencies may be determined based on various factors in the environment, such as a noise floor proximate to the object, a noise floor in the environment, a speed the vehicle is operating, type(s) of object(s) present in the environment, a number of objects present in the environment, presence of a second object being located between the object and the vehicle in the environment, or the like. In some examples, the at least one of the volume(s) and/or frequencies may be determined based on an urgency of the warning signal to be emitted, a likelihood of conflict, or the like. 
     At operation  908 , the process may include causing the sound to be emitted via a speaker coupled to the vehicle at the at least one of the volume or the frequency. In various examples, the vehicle computing system may cause the sound to be emitted via one or more speakers coupled to the vehicle. In some examples, the sound may be emitted in a directed toward the object, such as in a directed audio beam. In some examples, the sound may be emitted at an angle up to and including 360 degrees around the vehicle. 
     EXAMPLE CLAUSES 
     A: A system comprising: a sensor coupled to a vehicle; a speaker coupled to the vehicle; one or more processors; and one or more computer-readable media storing instructions that, when executed, configure the system to: identify, based on sensor data from the sensor, an object in an environment associated with the vehicle; determine a noise level proximate to the object, wherein the noise level is based at least in part on noise events generated by one or more other objects located within a threshold distance of the object; determine, based at least in part on the noise level, at least one of a volume or a frequency of a sound to emit; and emit, via the speaker, the sound at the at least one of the volume or the frequency. 
     B: The system as paragraph A describes, wherein the instructions further cause the system to: determine a first range of frequencies corresponding to the noise events generated by the other objects, wherein the frequency is based at least in part on the first range of frequencies. 
     C: The system as either of paragraphs A or B describe, wherein the at least one of the volume or the frequency are determined based on machine learned outputs of noises emitted by identified objects proximate to the object. 
     D: The system as any of paragraphs A-C describe, wherein the sound is emitted in a beam formed audio signal directed toward the object. 
     E: The system as any of paragraphs A-C describe, wherein the instructions further cause the system to: determine, based on the sensor data, a first trajectory associated with the vehicle; determine, based on the sensor data, a second trajectory associated with the object; determine a potential conflict between the vehicle and the object based on the first trajectory and the second trajectory; determine an action to take to avoid the potential conflict; and cause the vehicle to take the action. 
     F: A computer-readable medium having thereon computer-executable instructions that, responsive to execution, configure a computer to perform a system as any of paragraphs A-E describe. 
     G: A method comprising: identifying, based on sensor data from a sensor coupled to a vehicle, an object in an environment associated with the vehicle; determining a noise level proximate to the object; determining, based at least in part on the noise level proximate to the object, at least one of a volume or a frequency of a sound to emit; and causing the sound to be emitted via a speaker at the at least one of the volume or the frequency. 
     H: The method as paragraph G describes, wherein the noise level is based on noise events generated within a threshold distance of the object, the method further comprising: determining a first range of frequencies corresponding to the noise level proximate to the object, wherein the frequency is based at least in part on the first range of frequencies. 
     I: The method as either of paragraphs G or H describe, wherein the object is a first object, the method further comprising: identifying a second object wherein determining the volume of the sound to emit is based at least in part on the second object. 
     J: The method as any of paragraphs G-I describe, further comprising: determining a speed of the vehicle traveling through the environment, wherein determining the at least one of the volume or the frequency is based at least in part on the speed of the vehicle. 
     K: The method as any of paragraphs G-J describe, further comprising: determining a trajectory associated with the object; and determining, based at least in part on the trajectory, a probability that the object will be within a threshold distance of the vehicle at a future time, wherein determining the at least one of the volume or the frequency of the sound to emit is based at least in part on the probability. 
     L: The method as any of paragraphs G-K describe, further comprising: determining a change in the trajectory associated with the object; determining, based at least in part on the change in the trajectory, to stop emitting the sound via the speaker. 
     M: The method as any of paragraphs G-L describe, wherein determining the at least one of the volume or the frequency is based in part on at least one of: an occupancy of the vehicle; a road condition; a location of the vehicle in the environment; a speed of the vehicle in the environment; a time of day in which the vehicle is operating; a day in a week in which the vehicle is operating; or a weather condition in the environment. 
     N: The method as any of paragraphs G-M describe, further comprising: determining a first trajectory associated with the object at a first time, wherein the first trajectory corresponds to a potential conflict between the vehicle and the object; determining a second trajectory associated with the object at a second time; determining that a difference between the first trajectory and the second trajectory is less than a threshold value; determining at least one of a second volume or a second frequency of a second sound to emit based at least in part on the difference between the first trajectory and the second trajectory being less than the threshold value; and causing the second sound to be emitted via the speaker at the at least one of the second volume or the second frequency. 
     O: The method as any of paragraphs G-N describe, further comprising: identify a classification associated with the object, wherein determining the at least one of the volume or the frequency of the sound to emit is based at least in part on the classification associated with the object. 
     P: A system or device comprising: a processor, and a computer-readable medium coupled to the processor, the computer-readable medium including instructions to configure the processor to perform a computer-implemented method as any of paragraphs G-O describe. 
     Q: A system or device comprising: a means for processing; and a means for storing coupled to the means for processing, the means for storing including instructions to configure one or more devices to perform a computer-implemented method as any of paragraphs G-O describe. 
     R: A computer-readable medium having thereon computer-executable instructions that, responsive to execution, configure a computer to perform a method as any one of paragraphs G-O describe. 
     S: One or more non-transitory computer-readable media storing instructions that, when executed, cause a vehicle to perform operations comprising: identifying, based on sensor data from a sensor coupled to the vehicle, an object in an environment associated with the vehicle; determining a noise level proximate to the object; determining, based at least in part on the noise level proximate to the object, at least one of a volume or a frequency of a sound to emit; and causing the sound to be emitted via a speaker coupled to the vehicle at the at least one of the volume or the frequency. 
     T: One or more non-transitory computer-readable media as paragraph S describes, wherein the noise level is determined based on: determining a first distance between the object and a first noise generating object proximate to the object; accessing a database of noises to determine a first noise associated with the first noise generating object; and determining a perceived noise level of the first noise by the object based at least in part on the first distance. 
     U: One or more non-transitory computer-readable media as paragraph T describes, wherein the noise level is further determined based on: determining a second distance between the object and a second noise generating object proximate to the object; accessing the database of noises to determine a second noise associated with the second noise generating object; determining a second perceived noise level of the second noise by the object based at least in part on the second distance; and combining the first noise and the second noise to determine the noise level proximate to the object. 
     V: One or more non-transitory computer-readable media as any of paragraphs S-U describe, the operations further comprising: determining a speed of the vehicle traveling through the environment, wherein determining the at least one of the volume or the frequency is based at least in part on the speed of the vehicle. 
     W: One or more non-transitory computer-readable media as any of paragraphs S-V describe, the operations further comprising: determining a trajectory associated with the object; and determining, based at least in part on the trajectory, a probability that the object will be within a threshold distance of the vehicle at a future time, wherein determining the at least one of the volume or the frequency of the sound to emit is based at least in part on the probability. 
     X: One or more non-transitory computer-readable media as paragraph W describes, wherein the sound is a first sound and the trajectory is a first trajectory associated with the object at a first time, the operations further comprising: determining a second trajectory associated with the object at a second time; determining, based at least in part on the second trajectory, a second probability that the object will be within a second threshold distance of the vehicle at a second future time; determining at least one of a second volume or a second frequency of a second sound to emit based at least in part on the second probability; and causing the second sound to be emitted via the speaker at the at least one of the second volume or the second frequency. 
     Y: A system or device comprising: a processor; and a one or more non-transitory computer-readable media as any of paragraphs S-W describe. 
     While the example clauses described above are described with respect to one particular implementation, it should be understood that, in the context of this document, the content of the example clauses may also be implemented via a method, device, system, a computer-readable medium, and/or another implementation. 
     CONCLUSION 
     While one or more examples of the techniques described herein have been described, various alterations, additions, permutations and equivalents thereof are included within the scope of the techniques described herein. 
     In the description of examples, reference is made to the accompanying drawings that form a part hereof, which show by way of illustration specific examples of the claimed subject matter. It is to be understood that other examples can be used and that changes or alterations, such as structural changes, can be made. Such examples, changes or alterations are not necessarily departures from the scope with respect to the intended claimed subject matter. While the steps herein may be presented in a certain order, in some cases the ordering may be changed so that certain inputs are provided at different times or in a different order without changing the function of the systems and methods described. The disclosed procedures could also be executed in different orders. Additionally, various computations that are herein need not be performed in the order disclosed, and other examples using alternative orderings of the computations could be readily implemented. In addition to being reordered, the computations could also be decomposed into sub-computations with the same results.