Adjusting vehicle sensor field of view volume in real time

Abstract

To provide a system and a method available for adjusting field of view volumes of one or more sensors of an autonomous vehicle.SOLUTION: In a system and a method, each sensor in one or more sensors is configured to operate in accordance with a field of view volume equal to or less than a maximum field of view volume. The system and the method include determining an operation environment of an autonomous vehicle. The system and the method also include based on the determined operation environment of the autonomous vehicle, adjusting a field of view volume of at least one sensor in the one or more sensors from a first field of view volume to an adjusted field of view volume which is different from the first field of view volume. In addition, the system and the method include controlling the autonomous vehicle so as to operate using at least one sensor having the adjusted field of view volume.SELECTED DRAWING: Figure 9

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This disclosure claims priority to U.S. Nonprovisional Application No. 17 / 002,092, filed August 25, 2020, which claims priority to U.S. Provisional Application No. 62 / 952,879, filed December 23, 2019, the contents of which are incorporated herein by reference in their entirety. [Background technology]

[0002] A vehicle may be configured to operate in an autonomous mode in which the vehicle navigates through an environment with little or no input from a driver. Such an autonomous vehicle may include one or more systems (e.g., sensors and associated computing devices) configured to detect information about the environment in which the vehicle operates. The vehicle and its associated computer-implemented controller use the detected information to navigate through the environment. For example, if the system detects that the vehicle is approaching an obstacle, as determined by the computer-implemented controller, the controller adjusts directional control of the vehicle to cause the vehicle to navigate around the obstacle.

[0003] For example, an autonomous vehicle may include lasers, sonar, radar, cameras, thermal imagers, and other sensors that scan and / or record data about the vehicle's surroundings. Sensor data from one or more of these devices may be used to detect objects and their respective characteristics (position, shape, heading, speed, etc.). This detection and identification aids in the operation of the autonomous vehicle. Summary of the Invention

[0004] In one example, the disclosure provides a system. The system includes one or more sensors, each sensor of the one or more sensors configured to operate according to a field of view volume up, the field of view volume representing a space around the autonomous vehicle in which the sensor is expected to detect an object with a confidence level higher than a predefined confidence threshold. The system also includes one or more processors coupled to the one or more sensors. The system also includes a memory coupled to the one or more processors, the memory having instructions stored therein that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include identifying a plurality of operational design domains (ODDs) for the autonomous vehicle, each ODD including at least one of an environmental condition, a geographic condition, a time of day condition, a traffic condition, or a road condition, and each ODD being associated with a predetermined field of view volume of at least one sensor of the one or more sensors. The operations also include associating the autonomous vehicle with a first ODD of the plurality of ODDs. The operations also include detecting a change in an operating environment of the autonomous vehicle. The operations also include associating the autonomous vehicle with a second ODD of the plurality of ODDs in response to the detecting. The operations also include, in response to the autonomous vehicle being associated with the second ODD, operating the at least one sensor using a predetermined field of view volume associated with the second ODD.

[0005] Some examples of the present disclosure provide a method executed by a computing device configured to control operation of an autonomous vehicle. The method includes identifying a plurality of operating design domains (ODDs) of the autonomous vehicle, each ODD including at least one of an environmental condition, a geographic condition, a time condition, a traffic condition, or a road condition, and each ODD is associated with a predetermined field of view volume of at least one sensor of one or more sensors, each sensor of the one or more sensors being configured to operate according to the field of view volume, the field of view volume representing a space around the autonomous vehicle in which the sensor is expected to detect an object with a confidence level higher than a predefined confidence threshold. The method also includes associating the autonomous vehicle with a first ODD of the plurality of ODDs. The method also includes detecting a change in an operating environment of the autonomous vehicle. The method also includes associating the autonomous vehicle with a second ODD of the plurality of ODDs in response to the detecting. The method also includes operating the at least one sensor using the predetermined field of view volume associated with the second ODD in response to the autonomous vehicle being associated with the second ODD.

[0006] Some examples of the present disclosure provide a non-transitory computer-readable storage medium having program instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations. The operations include identifying a plurality of operating design domains (ODDs) for an autonomous vehicle, each ODD including at least one of an environmental condition, a geographic condition, a time condition, a traffic condition, or a road condition, and each ODD being associated with a predefined field of view volume of at least one sensor of the one or more sensors, each sensor of the one or more sensors being configured to operate according to the field of view volume, the field of view volume representing a space around the autonomous vehicle in which the sensor is expected to detect an object with a confidence level higher than a predefined confidence threshold. The operations also include associating the autonomous vehicle with a first ODD of the plurality of ODDs. The operations also include detecting a change in an operating environment of the autonomous vehicle. The operations also include associating the autonomous vehicle with a second ODD of the plurality of ODDs in response to the detecting. The operations also include operating the at least one sensor using the predefined field of view volume associated with the second ODD in response to the autonomous vehicle being associated with the second ODD.

[0007] Some examples of the disclosure provide a system. The system includes one or more sensors, each sensor of the one or more sensors configured to operate according to a field of view volume. The system also includes one or more processors coupled to the one or more sensors. The system also includes a memory coupled to the one or more processors, the memory having instructions stored therein that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include determining an operating environment of the autonomous vehicle. The operations also include adjusting a field of view volume of at least one sensor of the one or more sensors from a first field of view volume to an adjusted field of view volume different from the first field of view volume based on the determined operating environment of the autonomous vehicle.

[0008] Some examples of the present disclosure provide a method performed by a computing device configured to control operation of an autonomous vehicle. The method includes determining an operating environment of the autonomous vehicle, the autonomous vehicle comprising one or more sensors, each sensor of the one or more sensors configured to operate according to a field of view volume. The method also includes adjusting a field of view volume of at least one sensor of the one or more sensors from a first field of view volume to an adjusted field of view volume different from the first field of view volume based on the determined operating environment of the autonomous vehicle. The method also includes controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume.

[0009] Some examples of the present disclosure provide a non-transitory computer-readable storage medium having program instructions stored thereon that, when executed by one or more processors, cause the one or more processors to perform operations. The operations include determining an operating environment of an autonomous vehicle, where the autonomous vehicle comprises one or more sensors, each sensor of the one or more sensors configured to operate according to a field of view volume. The operations also include adjusting a field of view volume of at least one sensor of the one or more sensors from a first field of view volume to an adjusted field of view volume different from the first field of view volume based on the determined operating environment of the autonomous vehicle. The operations also include controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume.

[0010] Some examples of the present disclosure provide a method for operating a sensor attached to an autonomous vehicle. The method includes operating a sensor of the autonomous vehicle according to a first field of view volume, the first field of view volume being associated with a first operating environment of the autonomous vehicle. The method also includes receiving data indicative of a second operating environment of the autonomous vehicle, the second operating environment being associated with environmental conditions within the environment of the autonomous vehicle. The method also includes automatically adjusting operation of the sensor to operate according to the second field of view volume, the second field of view volume being associated with the second operating environment.

[0011] Some examples of the disclosure provide a system for operating a sensor attached to an autonomous vehicle. The system includes a sensor and one or more processors coupled to the sensor. The system also includes a memory coupled to the one or more processors, the memory having instructions stored therein that, when executed by the one or more processors, cause the one or more processors to perform operations. The operations include operating a sensor of the autonomous vehicle according to a first field of view volume, the first field of view volume associated with a first operating environment of the autonomous vehicle. The operations also include receiving data indicative of a second operating environment of the autonomous vehicle, the second operating environment associated with environmental conditions within the environment of the autonomous vehicle. The operations also include automatically adjusting operation of the sensor to operate according to the second field of view volume, the second field of view volume associated with the second operating environment.

[0012] Some examples of the present disclosure provide a non-transitory computer-readable storage medium having stored thereon program instructions that, when executed by one or more processors, cause the one or more processors to perform operations. The operations include operating a sensor attached to the autonomous vehicle according to a first field of view volume, the first field of view volume being associated with a first operating environment of the autonomous vehicle. The operations also include receiving data indicative of a second operating environment of the autonomous vehicle, the second operating environment being associated with environmental conditions within the environment of the autonomous vehicle. The operations also include automatically adjusting operation of the sensor to operate according to the second field of view volume, the second field of view volume being associated with the second operating environment.

[0013] These and other aspects, advantages, and alternatives will become apparent to those skilled in the art from a reading of the following detailed description, where appropriate with reference to the accompanying drawings. [Brief description of the drawings]

[0014] [Figure 1] FIG. 1 is a functional block diagram illustrating aspects of an exemplary autonomous vehicle. [Diagram 2] FIG. 1 illustrates an exterior view of an exemplary autonomous vehicle. [Diagram 3] FIG. 1 is a conceptual diagram of wireless communication between various computing systems associated with an autonomous vehicle. [Figure 4] 1 shows an example of a set of sensor fields of view. [Diagram 5] An exemplary method is shown. [Figure 6] 1 shows an example of a mapping of sensor field of view parameters (range) to an operating environment. [Figure 7] 1 illustrates an example communication system in which a vehicle communicates with a local weather station server and a global weather station server. [Figure 8] Another exemplary method is shown. [Figure 9] Another exemplary method is shown. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0015] Exemplary methods, devices, and systems are described herein. It should be understood that the words "example" and "exemplary" are used herein to mean "serving as an example, instance, or illustration." Any embodiment or feature described herein as "example" or "exemplary" should not necessarily be construed as preferred or advantageous over other embodiments or features. Other embodiments may be utilized, and other changes may be made, without departing from the scope of the subject matter presented herein.

[0016] Accordingly, the exemplary embodiments described herein are not meant to be limiting, and the aspects of the present disclosure, as generally described herein and illustrated in the Figures, can be arranged, substituted, combined, separated, and designed in a wide variety of different configurations, all of which are contemplated herein.

[0017] Moreover, unless the context indicates otherwise, the features illustrated in each figure may be used in combination with one another, and the figures should therefore generally be viewed as partial aspects of one or more overall embodiments, with the understanding that not all of the illustrated features are necessarily required for each embodiment.

[0018] I. Overview Many vehicles include various sensing systems that aid in the navigation and control of the vehicle. Some vehicles may operate in a fully autonomous mode where no human interaction is used for operation, a semi-autonomous mode where little human interaction is used for operation, or a human-operated mode where a human operates the vehicle and sensors may assist the human. Sensors may be used to provide information about the area around the vehicle. This information may be used to identify characteristics of the roadway and other objects (other vehicles, pedestrians, etc.) near the vehicle.

[0019] A vehicle's sensor system may include, for example, light detection and ranging (LIDAR) systems and radar systems. LIDAR uses laser pulses to measure the distance and speed of objects that reflect the laser light. Radar uses radio waves to measure the distance and speed of objects that reflect the radio waves. Data from LIDAR and radar systems, possibly along with data from other sensors in the vehicle's sensor system, such as cameras, may be used to determine where objects are located in the vehicle's surrounding environment. A particular LIDAR sensor, radar sensor, and / or camera may each have a field of view. A sensor's field of view may include one or more angular (or other shaped) regions in which the sensor can detect objects, and an associated range that corresponds to a maximum distance from the sensor at which the sensor can reliably detect an object within its field of view. In some cases, the associated range may vary with various azimuth / elevation angles within the field of view. The values ​​of the parameters that define this field of view, e.g., range, azimuth, and elevation values, together form a volume that may be referred to as a field of view volume.

[0020] The field of view volume of a particular sensor can be thought of as an accurate representation of the space in which the particular sensor can be considered to detect objects with a high degree of confidence. In other words, one or more processors of the vehicle system (e.g., a chip controlling the operation of the sensor, or a processor of the vehicle's control system) can be configured to confidently rely on sensor data acquired within the space defined by the field of view volume of the sensor. For example, a processor associated with a particular sensor may be configured to associate a higher confidence level (e.g., higher than a predefined confidence threshold level) with objects or other information detected at ranges, azimuth angles, and / or elevation angles within that sensor's field of view volume, and a lower confidence level (e.g., below a predefined confidence threshold level) with objects or other information detected at ranges, azimuth angles, and / or elevation angles outside that field of view volume.

[0021] A vehicle may be exposed to a variety of conditions during operation, such as changes in weather (fog, rain, snow, etc.), changes in time of day, changes in speed limits, changes in terrain or other geographic conditions, changes in residential location (e.g., urban, suburban, rural), changes in the number of other vehicles or objects in proximity to the vehicle, other changes outside the vehicle, and / or changes within the vehicle's systems (e.g., sensor errors, sensor surface cleanliness, vehicle subsystem failures, etc.). At any given time, one or more of these or other conditions may be present in the vehicle's operating environment. In the context of this disclosure, the "operating environment" of a vehicle may be or include one or more conditions inside and / or outside the vehicle that may change over time, including, but not limited to, the conditions listed above and other conditions described elsewhere in this disclosure. Thus, a change in one or more of such conditions may cause the vehicle's operating environment to change.

[0022] In some cases, the operating environment may be associated with a particular geographic location and / or bound by some geographical restriction. For example, a first operating environment may be associated with a first route between two locations, and a second, different operating environment may be associated with a second route between the same two locations. Thus, when traveling the first route, the vehicle may operate according to the first operating environment, and when traveling the second route, the vehicle may operate according to the second operating environment. As another example, the first operating environment may be associated with a first portion of the route between a pick-up or loading location and a drop-off or unloading location, and the second operating environment may be associated with a second portion of the route between the pick-up or loading location and a drop-off or unloading location. As another example, the operating environment may be limited to a geographically defined area, such as an airport, a university, or the property lines of a private residential community. Other examples are possible as well.

[0023] The present disclosure is directed to systems and methods for adjusting the field of view volume of one or more of a vehicle's sensors based on the vehicle's operating environment (e.g., based on the vehicle system detecting the operating environment or detecting a change from one operating environment to another), which may occur in real-time or near real-time as the vehicle system detects the operating environment or detects a change in the operating environment.

[0024] As described herein, the act of adjusting the field of view volume of a particular sensor can be performed in a variety of ways. As one example, the act of adjusting the field of view volume of a particular sensor can be performed in response to detecting a change in the vehicle's operating environment from a first operating environment to a second operating environment, and thus may include the vehicle system adjusting the field of view volume of the sensor from a first field of view volume corresponding to the first operating environment to a second adjusted field of view volume corresponding to the second operating environment. As another example, the vehicle system may decide to actively switch itself from an operating mode associated with one operating environment to an operating mode associated with another operating environment, and the switch may involve or cause a switch from a state in which each of one or more vehicle sensors operates according to a respective field of view volume to a state in which each operates with a different respective field of view volume. As another example, the act of adjusting the field of view volume of a particular sensor can include the vehicle system initializing the field of view volume of that sensor, for example, by selecting a predetermined field of view volume based on the detected operating environment of the vehicle, or by determining the field of view volume using other techniques. In some examples, there may be a finite / predetermined number of operating environments in which a vehicle may be designed to operate or with which the vehicle may otherwise be associated, and such operating environments, when detected, may be used to determine the field of view volume of one or more of the vehicle's sensors.

[0025] According to the present disclosure, the sensor may be configured to operate according to a field of view volume equal to or less than the maximum field of view volume. For example, the LIDAR sensor may be configured to operate with a maximum field of view range of 200 meters, a maximum azimuth angle of 210 degrees, and a maximum elevation angle of 20 degrees above the horizon and 75 degrees below the horizon, and the maximum distance from the LIDAR sensor at which the LIDAR sensor data is treated as having detected an object with confidence is 200 meters within these azimuth and elevation ranges. In one example, a vehicle may be traveling along a road while operating using a maximum field of view range, such as 200 meters, for one of the vehicle's LIDAR sensors. However, the vehicle may encounter (or may be expected to encounter soon) heavy fog, which may degrade the sensor capabilities of the LIDAR. Thus, the vehicle system (e.g., one or more processors, computing devices, etc.) may determine that the vehicle is operating in a foggy weather condition (or will soon switch to operating in a foggy weather condition) and may accordingly adjust the field of view volume of at least one LIDAR sensor (e.g., all LIDAR sensors) to a lower field of view range, such as 70 meters, within the same azimuth and elevation angle, or a different azimuth and elevation angle range instead. Thus, the vehicle then operates such that the maximum distance from the LIDAR sensor at which the LIDAR sensor's data is treated as reliably detecting an object is 70 meters. Once the fog clears or the vehicle leaves the foggy area, the vehicle system may readjust (e.g., raise) the field of view volume of the LIDAR sensor. Other examples are possible as well.

[0026] In some embodiments, to determine which field of view volume may be most appropriate for a particular sensor to match the vehicle's current operating environment, the vehicle system may store in memory (e.g., in the form of a look-up table) a mapping between a number of different operating environments of the vehicle and corresponding field of view volumes (or particular volume parameters, e.g., ranges) of one or more sensors or sensor types of the vehicle. These corresponding field of view volumes may be pre-determined using various techniques (e.g., sensor-database machine learning, physics-based calculations, etc.). Additionally or alternatively, the field of view volume may be determined in real-time in response to a determination of the vehicle's operating environment and / or the pre-defined field of view volume may be compared to newly acquired sensor data to determine whether the pre-defined field of view volume still accurately represents the extent to which the sensor should be trusted for the determined operating environment.

[0027] In some embodiments, the field of view volume of at least one of the sensors of the vehicle may be adjusted based on the operational design domain (ODD) of the vehicle. The ODD is defined by or includes conditions under which a given vehicle or other driving automation system or function thereof is specifically designed to operate. Such conditions include, but are not limited to, environmental, geographic, and time-of-day constraints, and / or the presence or absence of specific traffic or road features required. A vehicle may have multiple ODDs, each of which may include at least one of geographic, time-of-day, traffic, or road conditions, and each of which may be associated with a respective predefined field of view volume of at least one of the sensors of the vehicle. Thus, a vehicle system may associate a vehicle with a first ODD (e.g., clear weather conditions), but in response to detecting a change in the vehicle's operating environment, may instead associate the vehicle with a second, different ODD (e.g., foggy, rainy, or snowy weather conditions), thus causing the vehicle system to activate at least one of the sensors of the vehicle using the predefined field of view volume of the sensor associated with the second ODD.

[0028] The present disclosure also provides systems and methods that aid in determining a vehicle's operating environment, particularly a weather-based operating environment. In some examples, a vehicle system may receive weather data from one or more servers or other computing devices associated with one or more weather stations. The weather data may identify weather conditions associated with (e.g., present in) a particular region, such as fog, rain, snow, among other possibilities. Based on this weather data, the vehicle system may determine the vehicle's operating environment and therefore how to adjust the sensor field of view volume depending on the region with which the weather data is associated. In one example, a global or local computing system may collect weather data and distribute the weather data to various vehicle systems, possibly along with other information, such as recommended sensor adjustments for a particular region in light of the weather conditions present in that region. Other examples are possible as well.

[0029] Implementations of the disclosed systems and methods advantageously enable a vehicle system to adjust received sensor data in real time to dynamically adapt to changing conditions while traveling, and enable the vehicle to continuously detect objects in its environment accurately and reliably. Similarly, the disclosed systems and methods advantageously enable a vehicle system to prioritize sensor systems that may be more likely to operate reliably than other sensor systems and / or sensor data that may be more likely to be reliable than other sensor data, thus enabling the vehicle system to control the vehicle using the most reliable sensor data. For example, a vehicle system may prioritize or allow more power to a particular sensor system or sensor function over other systems, or prioritize or allow computational resources to process particular sensor data over other sensors.

[0030] II. Exemplary Systems and Devices Exemplary systems and devices will now be described in more detail. In general, the embodiments disclosed herein can be used in any system that includes one or more sensors that scan the system's environment. Illustrative embodiments described herein include vehicles employing sensors such as LIDAR, radar, sonar, cameras, etc. However, example systems may also be implemented as or take the form of other devices, such as robotic devices, industrial systems (e.g., assembly lines, etc.), or mobile communication systems or devices, among other possibilities.

[0031] The term "vehicle" is construed broadly herein to encompass any moving object including, for example, an aircraft, watercraft, spacecraft, automobile, truck, van, semi-trailer truck, motorcycle, golf cart, off-road vehicle, warehouse transporter, tractor, or farm vehicle, as well as rail-riding vehicles such as roller coasters, trolleys, trams, or rail cars. Some vehicles may operate in a fully autonomous mode where no human interaction is used for operation, a semi-autonomous mode where no human interaction is used for operation, or a human operated mode where a human operates the vehicle and sensors may assist the human.

[0032] In an exemplary embodiment, an example vehicle system may include one or more processors, one or more forms of memory, one or more input devices / interfaces, one or more output devices / interfaces, and machine-readable instructions that, when executed by the one or more processors, cause the system to perform various functions, tasks, capabilities, etc. described above. Example systems within the scope of the present disclosure are described in more detail below.

[0033] FIG. 1 is a functional block diagram illustrating a vehicle 100 according to an exemplary embodiment. The vehicle 100 may be configured to operate in a fully or partially autonomous mode and thus may be referred to as an "autonomous vehicle." The vehicle may also be configured to be operated by a human but provide information to the human via the vehicle's sensing systems. For example, the computing system 111 may control the vehicle 100 during the autonomous mode via control instructions to the control system 106 of the vehicle 100. The computing system 111 may receive information from one or more sensor systems 104 and automatically base one or more control processes on the received information (e.g., setting a direction to avoid a detected obstacle, etc.).

[0034] Autonomous vehicle 100 may be fully autonomous or partially autonomous. In a partially autonomous vehicle, some functions may be optionally controlled manually (e.g., by a driver), either temporarily or permanently. Additionally, a partially autonomous vehicle may be configured to switch between a fully manual mode of operation and a partially autonomous and / or fully autonomous mode of operation.

[0035] Vehicle 100 includes a propulsion system 102, a sensor system 104, a control system 106, one or more peripherals 108, a power source 110, a computing system 111, and a user interface 112. Vehicle 100 may include more or fewer subsystems, and each subsystem may optionally include multiple components. Additionally, each of the subsystems and components of vehicle 100 may be interconnected and / or in communication with each other. Thus, one or more functions of vehicle 100 described herein may, optionally, be divided among additional functional or physical components or combined into fewer functional or physical components. In some further examples, additional functional and / or physical components may be added to the example illustrated by FIG. 1.

[0036] The propulsion system 102 may include components operable to provide powered motion to the vehicle 100. In some embodiments, the propulsion system 102 includes an engine / motor 118, an energy source 120, a transmission 122, and wheels / tires 124. The engine / motor 118 converts the energy source 120 into mechanical energy. In some embodiments, the propulsion system 102 may optionally include one or both of an engine and / or a motor. For example, a gas-electric hybrid vehicle may include both a gasoline / diesel engine and an electric motor.

[0037] Energy source 120 represents a source of energy, such as electrical and / or chemical energy, which may, in whole or in part, power engine / motor 118. That is, engine / motor 118 may be configured to convert energy source 120 into mechanical energy to operate the transmission. In some embodiments, energy source 120 may include gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, capacitors, flywheels, regenerative braking systems, and / or other power sources, etc. Energy source 120 may also provide energy to other systems of vehicle 100.

[0038] The transmission 122 includes appropriate gears and / or mechanical elements suitable for transmitting mechanical power from the engine / motor 118 to the wheels / tires 124. In some embodiments, the transmission 122 includes a gearbox, a clutch, a differential, a drive shaft, and / or an axle, etc.

[0039] The wheels / tires 124 are arranged to stably support the vehicle 100 while providing frictional traction to a surface, such as a road, on which the vehicle 100 travels. Thus, the wheels / tires 124 are configured and arranged according to the nature of the vehicle 100. For example, the wheels / tires may be arranged as a unicycle, bicycle, motorcycle, tricycle, or car / truck four-wheel configuration. Other wheel / tire configurations are possible, such as those including six or more wheels. Any combination of wheels / tires 124 of the vehicle 100 may be operable to rotate differentially relative to other wheels / tires 124. The wheels / tires 124 may optionally include at least one wheel rigidly attached to the transmission 122 and at least one tire coupled to a corresponding wheel rim that contacts the riding surface. The wheels / tires 124 may include any combination of metal and rubber, and / or other materials or combinations of materials.

[0040] The sensor system 104 generally includes one or more sensors configured to detect information about the surrounding environment of the vehicle 100. For example, the sensor system 104 may include a global positioning system (GPS) 126, an inertial measurement unit (IMU) 128, a radar unit 130, a laser rangefinder / LIDAR unit 132, a camera 134, and / or a microphone 136. The sensor system 104 may also include sensors configured to monitor internal systems of the vehicle 100 (e.g., an O2 monitor, a fuel gauge, engine oil temperature, wheel speed sensors, etc.). One or more sensors included in the sensor system 104 may be configured to be actuated separately and / or collectively to modify the position and / or orientation of the one or more sensors.

[0041] The GPS 126 is a sensor configured to estimate the geographic position of the vehicle 100. To this end, the GPS 126 may include a transceiver operable to provide information regarding the position of the vehicle 100 relative to the Earth.

[0042] The IMU 128 may include any combination of sensors (e.g., accelerometers and gyroscopes) configured to sense changes in position and orientation of the vehicle 100 based on inertial acceleration.

[0043] Radar unit 130 may represent a system that utilizes radio signals to sense objects in the local environment of vehicle 100. In some embodiments, in addition to sensing objects, radar unit 130 and / or computer system 111 may be further configured to sense the speed and / or orientation of the objects. Radar unit 130 may include any antennas, waveguide networks, communication chips, and / or other components that facilitate radar operation.

[0044] Similarly, the laser rangefinder or LIDAR unit 132 may be any sensor configured to sense objects in an environment of the vehicle 100 using lasers. The laser rangefinder / LIDAR unit 132 may include one or more laser sources, a laser scanner, and one or more detectors, among other system components. The laser rangefinder / LIDAR unit 132 may be configured to operate in a coherent or incoherent detection mode (e.g., using heterodyne detection).

[0045] Camera 134 may include one or more devices configured to capture multiple images of the environment surrounding vehicle 100. Camera 134 may be a still camera or a video camera. In some embodiments, camera 134 may be mechanically movable, such as by rotating and / or tilting a platform on which the camera is mounted. In this manner, a control process of vehicle 100 may be implemented to control the movement of camera 134.

[0046] The sensor system 104 may also include a microphone 136. The microphone 136 may be configured to capture sounds from the environment surrounding the vehicle 100. In some cases, multiple microphones may be arranged as a microphone array, or possibly as a multiple microphone array.

[0047] The control system 106 is configured to control operations that regulate the acceleration of the vehicle 100 and its components. To effect the acceleration, the control system 106 may include a steering unit 138, a throttle 140, a braking unit 142, a sensor fusion algorithm 144, a computer vision system 146, a navigation / routing system 148, and / or an obstacle avoidance system 150, etc.

[0048] The steering unit 138 is operable to adjust the heading of the vehicle 100. For example, the steering unit may adjust an axis (or axes) of one or more wheels / tires 124 to effect a turn of the vehicle. The throttle 140 is configured, for example, to control the operating speed of the engine / motor 118, which in turn adjusts the forward acceleration of the vehicle 100 via the transmission 122 and the wheels / tires 124. The brake unit 142 slows the vehicle 100. The brake unit 142 may use friction to slow the wheels / tires 124. In some embodiments, the brake unit 142 inductively slows the wheels / tires 124 through a regenerative braking process, converting the kinetic energy of the wheels / tires 124 into electrical current.

[0049] The sensor fusion algorithm 144 is an algorithm (or a computer program product storing an algorithm) configured to accept data from the sensor system 104 as input. The data may include, for example, data representing information sensed by sensors of the sensor system 104. The sensor fusion algorithm 144 may include, for example, a Kalman filter, a Bayesian network, etc. The sensor fusion algorithm 144 provides an assessment regarding the vehicle's surroundings based on the data from the sensor system 104. In some embodiments, the assessment may include an assessment of individual objects and / or features in the vehicle's 100 surroundings, an assessment of a particular situation, and / or an assessment of potential interference between the vehicle 100 and features in the environment based on the particular situation (e.g., predicting a collision and / or impact, etc.).

[0050] Computer vision system 146 may process and analyze images captured by camera 134 to identify objects and / or features in the vehicle 100's surrounding environment. Detected features / objects may include traffic signals, road boundaries, other vehicles, pedestrians, and / or obstacles, etc. Computer vision system 146 may optionally employ object recognition algorithms, multi-view 3D reconstruction (SFM: Structure From Motion) algorithms, video tracking, and / or available computer vision techniques to classify and / or identify detected features / objects. In some embodiments, computer vision system 146 may be additionally configured to map the environment, track perceived objects, estimate object speeds, etc.

[0051] The navigation and routing system 148 is configured to determine a driving path for the vehicle 100. For example, the navigation and routing system 148 can be set according to user input, e.g., via the user interface 112, to determine a series of speeds and indicated headings for moving the vehicle along a path that substantially avoids perceived obstacles while generally moving the vehicle forward along a roadway-based path to a final destination. The navigation and routing system 148 can additionally be configured to dynamically update the driving path while the vehicle 100 is operating based on perceived obstacles, traffic patterns, weather / road conditions, etc. In some embodiments, the navigation and routing system 148 can be configured to incorporate data from the sensor fusion algorithm 144, the GPS 126, and one or more predefined maps to determine a driving path for the vehicle 100.

[0052] Obstacle avoidance system 150 may represent a control system configured to identify, evaluate, and avoid or otherwise navigate around potential obstacles in the vehicle's 100 environment. For example, obstacle avoidance system 150 may effect changes to the vehicle's navigation by operating one or more subsystems in control system 106 to perform a change of course maneuver, a turning maneuver, a braking maneuver, etc. In some embodiments, obstacle avoidance system 150 is configured to automatically determine feasible ("available") obstacle avoidance maneuvers based on surrounding traffic patterns, road conditions, etc. For example, obstacle avoidance system 150 may be configured to prevent a change of course maneuver from being performed if other sensor systems detect vehicles, construction barricades, other obstacles, etc., in the immediate area of ​​the vehicle to be turned into. In some embodiments, obstacle avoidance system 150 may automatically select an available maneuver that maximizes the safety of occupants within the vehicle. For example, obstacle avoidance system 150 may select an avoidance maneuver that is predicted to minimize the acceleration inside the vehicle 100.

[0053] Vehicle 100 also includes peripherals 108 configured to enable interaction between vehicle 100 and external sensors, other vehicles, other computer systems, and / or users, such as occupants of vehicle 100. For example, peripherals 108 for receiving information from occupants, external systems, etc. may include a wireless communication system 152, a touch screen 154, a microphone 156, and / or a speaker 158.

[0054] In some embodiments, the peripherals 108 function to receive inputs for a user of the vehicle 100 to interact with the user interface 112. To this end, the touchscreen 154 can both provide information to the user of the vehicle 100 and convey information from the user indicated via the touchscreen 154 to the user interface 112. The touchscreen 154 can be configured to sense both touch location and touch gestures from a user's finger (or stylus, etc.) via capacitive sensing, resistive sensing, optical sensing, surface acoustic processes, etc. The touchscreen 154 can sense finger movement in a direction parallel or planar to the touchscreen surface, normal to the touchscreen surface, or both, and can also sense the level of pressure applied to the touchscreen surface. Occupants of the vehicle 100 can also utilize a voice command interface. For example, the microphone 156 can be configured to receive audio (e.g., voice commands or other audio input) from the user of the vehicle 100. Similarly, the speaker 158 can be configured to output audio to the user of the vehicle 100.

[0055] In some embodiments, the peripherals 108 function to enable communication between the vehicle 100 and external systems, such as devices, sensors, other vehicles in the vehicle's environment, and / or controllers, servers, etc., located physically remote from the vehicle that provide useful information about the vehicle's surroundings, such as traffic information, weather information, etc. For example, the wireless communication system 152 can wirelessly communicate with one or more devices directly or through a communication network. The wireless communication system 152 may optionally use 3G cellular communications, such as CDMA, EVDO, GSM / GPRS, or 4G cellular communications, such as WiMAX or LTE. Additionally or alternatively, the wireless communication system 152 may communicate with a wireless local area network (WLAN), for example, using WiFi. In some embodiments, the wireless communication system 152 may communicate directly with the devices, for example, using an infrared link, Bluetooth, and / or ZigBee. The wireless communication system 152 may include one or more dedicated short-range communication (DSRC) devices, which may include public and / or private data communications between the vehicles and / or roadside gas stations. Other wireless protocols for transmitting and receiving information embedded in signals, such as various vehicular communication systems, may also be employed by wireless communication system 152 within the context of this disclosure.

[0056] As described above, the power source 110 may provide power to components of the vehicle 100, such as electronics included in the peripherals 108, the computing system 111, the sensor system 104, etc. The power source 110 may include, for example, a lithium-ion battery or a lead-acid battery that is rechargeable to store and discharge electrical energy to the various powered components. In some embodiments, one or more banks of batteries may be configured to provide the power. In some embodiments, the power source 110 and the energy source 120 may be implemented together, such as in some all-electric vehicles.

[0057] Many or all of the functions of vehicle 100 may be controlled via a computing system 111 that receives inputs from sensor system 104, peripherals 108, etc., and communicates appropriate control signals to propulsion system 102, control system 106, peripherals, etc., resulting in automated operation of vehicle 100 based on the vehicle's 100 surroundings. Computing system 111 includes at least one processor 113 (which may include at least one microprocessor) that executes instructions 115 stored on a non-transitory computer-readable medium, such as data storage 114. Computing system 111 may also represent multiple computing devices that function to provide distributed control of individual components or subsystems of vehicle 100.

[0058] In some embodiments, data storage 114 includes instructions 115 (e.g., program logic) executable by processor 113 to perform various functions of vehicle 100, including those described above in connection with Figure 1. Data storage 114 may also include additional instructions, including instructions for transmitting data to, receiving data from, interacting with, and / or controlling one or more of propulsion system 102, sensor system 104, control system 106, and peripherals 108.

[0059] In addition to instructions 115, data storage 114 may store data such as road maps, route information, etc., as map data 116. Such information may be used by vehicle 100 and computer system 111 to select available roads to a final destination, interpret information from sensor system 104, etc., during operation of vehicle 100 in autonomous, semi-autonomous, and / or manual modes.

[0060] The vehicle 100 and associated computer system 111 provide information to and / or receive input from a user of the vehicle 100, such as an occupant inside the vehicle 100. The user interface 112 may accordingly include one or more input / output devices within the set of peripherals 108, such as a wireless communication system 152, a touch screen 154, a microphone 156, and / or a speaker 158, to enable communication between the computer system 111 and the occupant of the vehicle.

[0061] The computing system 111 controls the operation of the vehicle 100 based on inputs received from various subsystems (e.g., the propulsion system 102, the sensor system 104, and / or the control system 106) indicative of vehicle and / or environmental conditions, as well as inputs received from the user interface 112 indicative of user preferences. For example, the computing system 111 may utilize inputs from the control system 106 to control the steering unit 138 to avoid obstacles detected by the sensor system 104 and the obstacle avoidance system 150. The computing system 111 may be configured to control many aspects of the vehicle 100 and its subsystems. However, generally, provisions are made for manually overriding the automated controller-driven operation, such as in an emergency or simply in response to a user-activated override.

[0062] The components of vehicle 100 described herein may be configured to operate in interconnection with other components within or outside of their respective systems. For example, camera 134 may capture multiple images representing information about the environment of vehicle 100 while operating in an autonomous mode. The environment may include other vehicles, traffic lights, road signs, pavement markings, pedestrians, etc. Computer vision system 146 may classify and / or recognize various aspects within the environment based on object recognition models pre-stored in data storage 114 and / or by other techniques in cooperation with sensor fusion algorithms 144, computer system 111, etc.

[0063] Although vehicle 100 is described and illustrated in FIG. 1 as having various components of vehicle 100 integrated therein, such as wireless communication system 152, computing system 111, data storage 114, and user interface 112, one or more of these components may optionally be separately mounted or associated with vehicle 100. For example, data storage 114 may exist partially or completely separate from vehicle 100, such as, for example, in a cloud-based server. Thus, one or more of the functional elements of vehicle 100 may be implemented in the form of device elements located separately or together. The functional device elements that make up vehicle 100 may generally be communicatively coupled together in a wired and / or wireless manner.

[0064] Figure 2 illustrates an example vehicle 200 that may include some or all of the features described in connection with vehicle 100 with reference to Figure 1. Vehicle 200 is illustrated in Figure 2 as a four-wheeled vehicle for illustrative purposes, although the disclosure is not so limited. For example, vehicle 200 may represent a truck, a van, a semi-trailer truck, a motorcycle, a golf cart, an off-road vehicle, an agricultural vehicle, or the like.

[0065] The example vehicle 200 includes a sensor unit 202, a first LIDAR unit 204, a second LIDAR unit 206, a first radar unit 208, a second radar unit 210, a first LIDAR / radar unit 212, a second LIDAR / radar unit 214, and two additional locations 216, 218 where one or more sensors, such as radar units, LIDAR units, laser range finder units, and / or other types, may be located on the vehicle 200. Each of the first LIDAR / radar unit 212 and the second LIDAR / radar unit 214 may take the form of a LIDAR unit, a radar unit, or both.

[0066] Further, the example vehicle 200 may include any of the components described in connection with the vehicle 100 of FIG 1. The first and second radar units 208, 210 and / or the first and second LIDAR units 204, 206 may actively scan their surroundings for potential obstacles and may be similar to the radar unit 130 and / or the laser range finder / LIDAR unit 132 of the vehicle 100. Additionally, the first LIDAR / radar unit 212 and the second LIDAR / radar unit 214 may actively scan their surroundings for potential obstacles and may be similar to the radar unit 130 and / or the laser range finder / LIDAR unit 132 of the vehicle 100.

[0067] In some examples, the LIDAR unit may be one of two different types of LIDAR units. The first type of LIDAR unit may be a rotating LIDAR that can continuously scan the entire area of ​​the LIDAR unit's field of view. The second type of LIDAR unit may be a rotatable LIDAR that can be steered to scan a specific area of ​​the LIDAR unit's field of view. The first type of LIDAR unit may have a smaller range than the second type of LIDAR unit. The second type of LIDAR unit may have a narrower field of view during operation compared to the first LIDAR unit. In some examples, one or more of the designated LIDAR units of the vehicle 200 may include one or both types of LIDAR units. For example, the LIDAR unit 204 mounted on the top of the vehicle may include both types of LIDAR units. In one example, the second type of LIDAR unit may have a field of view during operation that is 8 degrees wide in the horizontal plane and 15 degrees wide in the vertical plane.

[0068] The sensor unit 202 is mounted on top of the vehicle 200 and includes one or more sensors configured to detect information about the vehicle 200's surrounding environment and output an indication of the information. For example, the sensor unit 202 may include any combination of cameras, radar, LIDAR, range finders, and acoustic sensors. The sensor unit 202 may include one or more movable mounts that may be operable to adjust the orientation of one or more sensors in the sensor unit 202. In one embodiment, the movable mounts may include a rotating platform that can scan the sensors to obtain information from each direction around the vehicle 200. In another embodiment, the movable mounts of the sensor unit 202 may be movable in a scanning manner within a certain range of angles and / or orientations. The sensor unit 202 may be mounted on the roof of the vehicle, although other mounting locations are also possible. In addition, the sensors of the sensor unit 202 may be distributed in various locations and need not be collocated in a single location. Some possible sensor types and mounting locations include two additional locations 216, 218. Additionally, each sensor of the sensor unit 202 may be configured to be moved or scanned in conjunction with or independently of the other sensors of the sensor unit 202.

[0069] In one example configuration, one or more radar scanners (e.g., first and second radar units 208, 210) may be located near the rear of vehicle 200 to actively scan the area behind vehicle 200 for radio-reflective objects. Similarly, first LIDAR / radar unit 212 and second LIDAR / radar unit 214 may be mounted near the front of the vehicle to actively scan the area in front of the vehicle. The radar scanners may be positioned, for example, in a location suitable for illuminating an area including the forward path of vehicle 200 without being blocked by other features of vehicle 200. For example, the radar scanners may be positioned to be embedded and / or mounted in or near a front bumper, front headlights, cowl, and / or hood, etc. Additionally, one or more additional radar scanning devices may be positioned to actively scan the sides and / or rear of vehicle 200 for radio reflective objects, for example, by including such devices in the rear bumper, side panels, rocker panels, and / or chassis.

[0070] In practice, each radar unit may be capable of scanning over a 90 degree beamwidth. If the radar units are located at the corners of the vehicle as shown by radar units 208, 210, 212, and 214, each radar unit may be able to scan a 90 degree field of view in the horizontal plane, providing the vehicle with a radar view of a full 360 degree area around the vehicle. In addition, the vehicle may also include two side-facing radar units. The side-facing radar units may be able to provide additional radar imaging when the other radar units are obstructed, such as when making a right turn without an arrow signal (i.e., a right turn when there is another vehicle in the lane to the left of the vehicle turning right).

[0071] Although not shown in FIG 2, the vehicle 200 may include a wireless communication system. The wireless communication system may include a wireless transmitter and a wireless receiver that may be configured to communicate with devices external or internal to the vehicle 200. In particular, the wireless communication system may include a transceiver configured to communicate with other vehicles and / or computing devices, for example, in a vehicle communication system or road station. Examples of such vehicle communication systems include Dedicated Short Range Communications (DSRC), Radio Frequency Identification (RFID), and other communication standards proposed for intelligent transportation systems.

[0072] Vehicle 200 may optionally include a camera at a location inside sensor unit 202. The camera may be a light-sensitive instrument, such as a still camera, video camera, or the like, configured to capture multiple images of the environment of vehicle 200. To this end, the camera may be configured to detect visible light, and may additionally or alternatively be configured to detect light from other parts of the spectrum, such as infrared or ultraviolet light. In one particular example, sensor unit 202 may include both an optical camera (i.e., a camera that captures human visible light) and an infrared camera. The infrared camera may be capable of capturing thermal images within the camera's field of view.

[0073] The camera may be a two-dimensional detector, and may optionally have a sensitivity range in three-dimensional space. In some embodiments, the camera may include a range detector configured to generate a two-dimensional image, for example, indicative of the distance from the camera to some points in the environment. To this end, the camera may use one or more range detection techniques. For example, the camera may provide range information by using structured light techniques, in which the vehicle 200 illuminates objects in the environment with a predetermined light pattern, such as a grid or checkerboard pattern, and uses the camera to detect reflections of the predetermined light pattern from the surroundings of the environment. Based on distortions in the reflected light pattern, the vehicle 200 may determine the distance to a point on the object. The predetermined light pattern may consist of infrared light, or other wavelengths of radiation suitable for such measurements. In some examples, the camera may be mounted inside the windshield of the vehicle 200. Specifically, the camera may be positioned to capture images from a forward view relative to the orientation of the vehicle 200. Other mounting locations and viewing angles of the camera may also be used, and may be either inside or outside the vehicle 200. The camera may also have associated optics operable to provide an adjustable field of view. Furthermore, the camera may be mounted to the vehicle 200 with a moveable mount to vary the pointing angle of the camera, such as via a pan / tilt mechanism.

[0074] Additionally, the camera sensor may be configured with a rolling shutter. A rolling shutter generally repetitively samples a light sensor to capture image data. The data from the camera sensor may form an image, multiple images, or a video. For example, in a conventional image sensor, a rolling shutter may repetitively sample one row of cells of a light sensor at a time. When sampling a camera sensor with a rolling shutter, fast moving objects in the field of view of the sensor may appear distorted. Such distortion is caused by the repetitive sampling. Because the line of cells is sampled repetitively, the object being imaged moves slightly between each sampling. Thus, each line is sampled at a slightly later time than the previous line. Due to the delay in sampling each line, objects with horizontal motion may have horizontal skew. For example, a vehicle moving across the field of view of the sensor may have horizontal skew and vertical compression (or expansion), which distorts the vehicle. This skew may be troublesome for processing based on the horizontal position of the object in the image. The present system may help identify camera distortions that may be caused by a rolling shutter.

[0075] 3 is a conceptual diagram of wireless communication between various computing systems associated with an autonomous vehicle, according to an example implementation. In particular, wireless communication may occur between a remote computing system 302 and the vehicle 200 over a network 304. Wireless communication may occur between a server computing system 306 and the remote computing system 302, and also between the server computing system 306 and the vehicle 200. During operation of the vehicle 200, the vehicle may send and receive data from both the server computing system 306 and the remote computing system 302 to assist in the operation of the vehicle 200. The vehicle 200 may communicate data related to the operation of the vehicle 200 and data from the sensors of the vehicle 200 to the server computing system 306 and the remote computing system 302. Additionally, the vehicle 200 may receive operation instructions and / or data related to objects sensed by the sensors of the vehicle from the server computing system 306 and the remote computing system 302.

[0076] Vehicle 200 may correspond to various types of vehicles capable of transporting passengers or objects between locations and may take the form of any one or more of the vehicles discussed above.

[0077] The remote computing system 302 may represent any type of device associated with remote assistance and operation techniques, including but not limited to those described herein. In an example, the remote computing system 302 may represent any type of device configured to (i) receive information related to the vehicle 200, (ii) provide an interface through which a human operator or a computer operator can then perceive the information and enter a response related to the information, and (iii) transmit the response to the vehicle 200 or to another device. The remote computing system 302 may take a variety of forms, such as a workstation, a desktop computer, a laptop, a tablet, a mobile phone (e.g., a smartphone), and / or a server. In some examples, the remote computing system 302 may include multiple computing devices operating together in a network configuration.

[0078] The remote computing system 302 may include one or more subsystems and components similar to or identical to those of the vehicle 200. At a minimum, the remote computing system 302 may include a processor configured to perform various operations described herein. In some implementations, the remote computing system 302 may also include a user interface including input / output devices such as a touch screen and speakers. Other examples are possible as well.

[0079] The network 304 represents an infrastructure that enables wireless communication between the remote computing system 302 and the vehicle 200. The network 304 also enables wireless communication between the server computing system 306 and the remote computing system 302, and between the server computing system 306 and the vehicle 200.

[0080] The location of the remote computing system 302 may vary within the examples. For example, the remote computing system 302 may be at a location remote from the vehicle 200 having wireless communication over the network 304. In another example, the remote computing system 302 may correspond to a computing device within the vehicle 200 that is separate from the vehicle 200 but that allows a human operator to interact with a passenger or driver of the vehicle 200. In some examples, the remote computing system 302 may be a computing device equipped with a touch screen operable by a passenger of the vehicle 200.

[0081] In some implementations, operations described herein performed by remote computing system 302 may additionally or instead be performed by vehicle 200 (i.e., by any system or subsystem of vehicle 200). In other words, vehicle 200 may be configured to provide remote assistance mechanisms with which a driver or passengers of the vehicle can interact.

[0082] The server computing system 306 may be configured to wirelessly communicate with the remote computing system 302 and the vehicle 200 over the network 304 (or, in some cases, directly with the remote computing system 302 and / or the vehicle 200). The server computing system 306 may represent any computing device configured to receive, store, determine, and / or transmit information regarding the vehicle 200 and its remote assistance. As such, the server computing system 306 may be configured to perform any operation or a portion of such operations described herein as being performed by the remote computing system 302 and / or the vehicle 200. Some implementations of wireless communication related to remote assistance may utilize the server computing system 306, while others may not.

[0083] The server computing system 306 may include one or more subsystems and components similar to or identical to the subsystems and components of the remote computing system 302 and / or the vehicle 200, such as a processor configured to perform various operations described herein, as well as a wireless communication interface for receiving information from and providing information to the remote computing system 302 and the vehicle 200.

[0084] The various systems described above may perform various operations, and these operations and associated features will now be described.

[0085] In line with the above discussion, a computing system (e.g., the remote computing system 302, or perhaps the server computing system 306, or even a computing system local to the vehicle 200) may operate to use a camera to capture images of the autonomous vehicle's environment. Generally, at least one computing system can analyze the images and possibly control the autonomous vehicle.

[0086] In some implementations, to facilitate autonomous operation, a vehicle (e.g., vehicle 200) may receive data representative of objects in the environment in which the vehicle operates (also referred to herein as "environmental data") in a variety of ways. The vehicle's sensor system may provide the environmental data representative of the objects in the environment. For example, the vehicle may have a variety of sensors including cameras, radar units, laser range finders, microphones, radio units, and other sensors. Each of these sensors may communicate environmental data regarding the information each respective sensor receives to a processor within the vehicle.

[0087] In one example, the radar unit may be configured to transmit an electromagnetic signal that reflects off one or more objects near the vehicle. The radar unit may then capture the electromagnetic signal that reflects off the object. The captured reflected electromagnetic signal may enable the radar system (or processing system) to make various determinations about the object that reflected the electromagnetic signal. For example, the distance and location to various reflecting objects may be determined. In some implementations, the vehicle may have two or more radars at different orientations. In practice, the vehicle has six different radar units. In addition, each radar unit may be configured to steer a beam to one of four different sectors of that radar unit. In various examples, the radar unit may be capable of scanning a beam over a range of 90 degrees by scanning each of the four different sectors of the radar unit. The radar system may be configured to store the captured information in a memory for later processing by the processing system of the vehicle. The information captured by the radar system may be environmental data.

[0088] In another example, a laser range finder (e.g., a LIDAR unit) may be configured to transmit an electromagnetic signal (e.g., light such as from a gas or diode laser, or other possible light source) that may be reflected by one or more target objects near the vehicle. The laser range finder may be able to capture the reflected electromagnetic (e.g., laser) signal. The captured reflected electromagnetic signal may enable the ranging system (or processing system) to determine ranges to various objects, e.g., objects that reflected the electromagnetic signal back to the laser range finder. The ranging system may also determine the velocity or speed of the target objects, which may be stored as environmental data.

[0089] In some implementations, the processing system may be able to combine information from various sensors to further determine the vehicle's environment. For example, the processing system may combine data from both radar information and captured images to determine whether another vehicle or pedestrian is in front of the autonomous vehicle. In other implementations, other combinations of sensor data may be used by the processing system to make determinations about the environment.

[0090] While operating in an autonomous mode, the vehicle may control its operation with little or no human input. For example, if a human operator inputs an address into the vehicle, the vehicle may be able to drive to the specified destination without further input from the human (e.g., without the human having to steer or touch the brake / accelerator pedals). Additionally, while the vehicle is operating autonomously, the sensor system may receive environmental data. The vehicle's processing system may alter control of the vehicle based on the environmental data received from the various sensors. In some examples, the vehicle may vary the speed of the vehicle in response to the environmental data from the various sensors. The vehicle may vary speed to avoid obstacles, obey traffic laws, etc. When the vehicle's processing system identifies an object near the vehicle, the vehicle may be able to change speed or otherwise alter its movement.

[0091] If the vehicle detects an object but is not sufficiently confident in detecting the object, the vehicle may request a human operator (or a more powerful computer) to perform one or more remote assistance tasks, such as (i) verifying whether the object is actually present in the environment (e.g., is there actually a stop sign or is there actually no stop sign), (ii) verifying whether the vehicle's identification of the object is correct, (iii) correcting the identification if it was incorrect, and / or (iv) providing supplemental instructions (or correcting current instructions) to the autonomous vehicle.

[0092] The vehicle may detect objects in the environment in a variety of ways, depending on the source of the environmental data. In some implementations, the environmental data may come from a camera and be image or video data. The vehicle may analyze captured image or video data to identify objects in the image or video data. In other implementations, the environmental data may come from a LIDAR unit. The method and apparatus may be configured to monitor the image and / or video data for objects in the environment. In other implementations, the environmental data may be radar, audio, or other data. The vehicle may be configured to identify objects in the environment based on the radar, audio, or other data.

[0093] In some implementations, the technique used by the vehicle to detect the object may be based on a set of known data. For example, data related to environmental objects may be stored in a memory located in the vehicle. The vehicle may compare the received data to the stored data to determine the object. In other implementations, the vehicle may be configured to determine the object based on the context of the data. For example, construction-related street signs may generally have an orange color. Thus, the vehicle may be configured to detect an orange object located near the side of the road as a construction-related street sign. In addition, as the vehicle's processing system detects objects in the captured data, it may also calculate a confidence level for each object.

[0094] III. Examples of Vehicle Sensor Field of View FIG. 4 illustrates an example of an autonomous vehicle 400 having various sensor fields of view. As previously described with respect to FIG. 2, the vehicle 400 may include multiple sensors. The locations of the various sensors may correspond to the locations of the sensors disclosed in FIG. 2. However, in some cases, the sensors may be at other locations. To simplify the drawing, the sensor locations have been omitted from FIG. 4A. FIG. 4A illustrates the respective fields of view of each sensor unit of the vehicle 400. The field of view of the sensor may include a range corresponding to an angular region in which the sensor can detect an object, and a maximum distance from the sensor at which the sensor can reliably detect an object.

[0095] As discussed above, the vehicle 400 may include six radar units. A first radar unit may be located at the left front of the vehicle and may have an angular field of view corresponding to an angular portion of the field of view 402A. A second radar unit may be located at the right front of the vehicle and may have an angular field of view corresponding to an angular portion of the field of view 402B. A third radar unit may be located at the left rear of the vehicle and may have an angular field of view corresponding to an angular portion of the field of view 402C. A fourth radar unit may be located at the right rear of the vehicle and may have an angular field of view corresponding to an angular portion of the field of view 402D. A fifth radar unit may be located at the left side of the vehicle and may have an angular field of view corresponding to an angular portion of the field of view 402E. A sixth radar unit may be located at the right side of the vehicle and may have an angular field of view corresponding to an angular portion of the field of view 402F. Each of the six radar units may be configured to have a scannable beamwidth of 90 degrees. Although the radar beam width is less than 90 degrees, each radar unit may be capable of steering a radar beam over a 90 degree field of view.

[0096] A first LIDAR unit of the vehicle 400 may be configured to scan a full 360 degree region around the vehicle, as indicated by an angular field of view corresponding to an angular portion of the field of view 404. A second LIDAR unit of the vehicle 400 may be configured to scan an area less than the 360 ​​degree region around the vehicle. In one example, the second LIDAR unit may have a field of view of 8 degrees in the horizontal plane, as indicated by an angular field of view corresponding to an angular portion of the field of view 404.

[0097] In addition, the vehicle may also include at least one camera. The camera may be an optical camera and / or an infrared camera. The camera may have an angular field of view that corresponds to an angular portion of the field of view 408.

[0098] In addition to the field of view of each of the various sensors of the vehicle 400, each sensor may also have a corresponding range. In one example, the range of the radar units may be greater than the range of either LIDAR unit, as shown by the field of view of the radar units 402A-402E extending further than the field of view of the LIDAR units 404 and 406. Additionally, the first LIDAR unit may have a greater range than the range of the second LIDAR unit, as shown by the field of view 404 extending further than the field of view 406. The cameras may have a range, as shown by the width of the field of view 408. In various examples, the range of the cameras may be greater or less than the range of the other sensors. It should be understood that the sensor fields of view, radar units, etc. of FIG. 4 are shown as exemplary diagrams and are not to scale.

[0099] IV. EXAMPLE SYSTEMS AND METHODS Examples of the systems and methods of the present disclosure are described in more detail below.

[0100] 5 is a flow chart of a method 500 according to an example embodiment. Method 500 may include one or more operations, functions, or actions, as illustrated by one or more of blocks 502-506. Although the blocks of each method are shown in sequential order, these blocks may in some cases be performed in parallel and / or in a different order than described herein. Additionally, various blocks may be combined into fewer blocks, divided into additional blocks, and / or removed based on the desired implementation.

[0101] In addition, for method 500 and other processes and methods disclosed herein, a flowchart illustrates the functions and operations of one possible implementation of the present embodiment. In this regard, each block may represent a module, a segment, a part of a manufacturing or operating process, or a part of a program code, which includes one or more instructions executable by a processor to implement a particular logical function or step in the process. The program code may be stored in any type of computer-readable medium, such as a storage device, including a disk or a hard drive. The computer-readable medium may include non-transitory computer-readable medium, such as a register memory, a processor cache, and a computer-readable medium that stores data for a short period of time, such as a random access memory (RAM). The computer-readable medium may also include non-transitory computer-readable medium, such as a secondary storage or a permanent long-term storage, such as a read-only memory (ROM), an optical or magnetic disk, a compact disk read-only memory (CD-ROM). The computer-readable medium may also be any other volatile or non-volatile storage system. The computer-readable medium may be considered to be, for example, a computer-readable storage medium, or a tangible storage device.

[0102] Additionally or alternatively, for method 500, as well as other processes and methods disclosed herein, one or more blocks in a flowchart may represent circuitry hardwired to perform specific logical functions within the process.

[0103] In some examples, for method 500, and other processes and methods disclosed herein, the functions represented in the flowcharts may be performed by a single vehicle (e.g., vehicles 100, 200, etc.), may be distributed among multiple vehicles, may be performed by a remote server / external computing system (e.g., systems 302 and 306), and / or may be performed by a combination of one or more external computing systems and one or more vehicles, among other possibilities.

[0104] At block 502, the method 500 includes determining an operating environment for the autonomous vehicle. The autonomous vehicle includes one or more sensors, each sensor of the one or more sensors configured to operate according to a field of view volume.

[0105] At block 504, the method includes adjusting a field of view volume of at least one sensor of the one or more sensors from a first field of view volume to an adjusted field of view volume that differs from the first field of view volume based on the determined operating environment of the autonomous vehicle.

[0106] At block 506, the method includes controlling the autonomous vehicle to operate using at least one sensor having the adjusted field of view volume.

[0107] As noted above, the one or more sensors of an autonomous vehicle may include a set of one or more LIDAR sensors, a set of one or more radar sensors, and / or a set of one or more cameras (operating in various wavelength bands including visible and infrared), among other possible sensor types. In practice, all sensors of a particular type may be configured to have the same maximum field of view volume, and the vehicle software that receives and processes the sensor data is configured to treat all sensors of that particular type as having that maximum field of view volume. For example, the maximum field of view range of all radar sensors of the vehicle may be 220 meters, and the maximum field of view range of all LIDAR sensors of the vehicle may be 200 meters. In line with this, the act of adjusting the field of view volume of at least one of the sensors of the vehicle may include making the same field of view adjustment to each sensor of the particular sensor type. For example, if the vehicle system determines an adjustment to make to a LIDAR sensor based on the vehicle's operating environment, the vehicle system may make that adjustment to all LIDAR sensors of the vehicle. Other examples are possible as well. Additionally, in alternative embodiments, the sensor field of view volumes may be individually configured, such that a sensor of a particular sensor type may be configured to have a different maximum field of view volume than another sensor of the same sensor type.

[0108] At a given time, the vehicle may be configured to operate in one of a number of different operating environments, each of which may include one or more conditions inside and / or outside the vehicle. (Hereinafter, the term "state" or "operating state" refers to an operating environment defined by at least one condition and may be used synonymously with the term "operating environment.") For example, the operating environment of a vehicle may include (i) a default state (e.g., a state defined to include an operating environment distinct from other environments enumerated herein, in which the vehicle operates according to predefined default parameters such as speed, steering, navigation, and / or sensor field of view), (ii) a clear weather condition (e.g., sunny, not overcast, and without rain, snow, or fog), (iii) a daytime operating state (e.g., the period from sunrise to sunset), (iv) a nighttime operating state (e.g., the period from sunset to sunrise), (v) a rainy weather condition, (vi) a snowy weather condition, (v) a foggy weather condition, (viii) a condition for a particular road type on which the autonomous vehicle is traveling (e.g., city, suburban road, country road, local road, highway, gravel, brick, asphalt, and / or road surface), ... or concrete), (ix) a condition where at least a threshold quantity of vehicles are present on the roadway within a threshold distance of the autonomous vehicle (e.g., seven or more vehicles within 16 meters of the vehicle), (x) overcast conditions (e.g., more clouds, less light), (xi) a sensor cleanliness condition where the vehicle detects that one or more sensors of the vehicle have dust, water droplets, ice / frost, hit insects, oil, road grime, or other material blocking the sensor cover / window or other surface, (xii) a light traffic condition, (xiii) a medium traffic condition, (xiv) a heavy traffic condition, or (xv) a sensor error condition for at least one of the one or more sensors (e.g., one or more LIDAR sensors are emitting an error code indicating that the sensor is inoperable or another indication that may affect the reliability of measurements obtained via the sensor). Other operating environments are possible.

[0109] As mentioned above, the operating environment can be or include a combination of conditions. For example, the operating environment can be a clear, sunny, daytime operating condition (e.g., 11:00 a.m., sunny). As another example, the operating environment can be a clear, daytime, uphill terrain, asphalt road, heavy traffic condition. In some examples, the default state can consist of a clear weather condition and one or more other conditions, such as a condition in which the vehicle is running on asphalt or concrete. Furthermore, in some examples, the default state may have the maximum field of view volume of each sensor type as the corresponding field of view volume. Furthermore, in some examples, there may be operating environments having various degrees of weather conditions, such as heavy snowstorms and / or winds, heavy rain and / or winds, or fog thickness exceeding a predefined threshold, among other possibilities.

[0110] In some situations, an operating environment that affects the field of view volume of one type of sensor may or may not affect the field of view volume of another type of sensor. For example, a vehicle system may reduce the field of view range of a LIDAR sensor in foggy weather at night, and may use the fog / nighttime operating environment information to reduce the field of view range at which one or more of the vehicle's cameras can detect objects with a particular contrast (e.g., a gray car in fog). As another example, a vehicle system may adjust the field of view range of a LIDAR sensor in a particular way due to a large reflection from a highway sign (a retroreflector), but the vehicle system may not adjust the field of view range at which the camera can detect an object due to the large reflection. Other examples are possible as well.

[0111] To facilitate the act of adjusting the sensor field of view volume of at least one sensor of the one or more sensors, the vehicle system may store in memory (e.g., data storage 114) a plurality of operating environments and a mapping between each operating environment of the plurality of operating environments and a corresponding adjusted field of view volume of the at least one sensor of the one or more sensors. In such an embodiment, the act of adjusting the field of view volume based on the operating environment of the autonomous vehicle may include selecting an adjusted field of view volume that corresponds to a determined operating environment of the autonomous vehicle. In some embodiments, the adjusted field of view volume that corresponds to a particular operating environment may be part of a set of adjusted field of view volumes that correspond to that operating environment and that include a respective adjusted field of view volume for each of a plurality of sensor types (e.g., LIDAR, radar, and / or camera, etc.). The stored operating environments and mappings may take various forms, such as a table.

[0112] FIG. 6 illustrates an example mapping of sensor field of view parameters (i.e., ranges in this example) to example operating environments in the form of a table 600. As illustrated, for each of eight representative examples of operating environments, the table 600 includes a respective field of view range corresponding to each of the three sensor types (LIDAR, radar, and camera). In some examples, using the table 600, a vehicle system can detect foggy weather, determine that the vehicle is operating in a foggy weather operating environment, and accordingly select for use a field of view range value that is mapped to the foggy weather operating environment. Specifically, a vehicle system having a set of LIDAR sensors, a set of radar sensors, and a set of cameras may have all LIDAR sensors with a field of view range of 50 meters, all radar sensors with a field of view range of 70 meters, and all cameras with a field of view range of 120 meters. In foggy weather during daytime operation, the camera may be more reliable than the LIDAR sensor, but at night, the LIDAR sensor may be more reliable due to headlights backscattering onto the vehicle.

[0113] As another example, the vehicle system may detect an error in at least one camera of the set of cameras and select one or more field of view volume parameters (e.g., range, azimuth, and / or elevation) based on the detection. For example, the vehicle system may detect a camera error based on determining that one or more images acquired by the at least one camera fall below expected acutance, roughness, and / or other measures of ability to discern detail beyond a certain distance (e.g., unable to discern in the images a reference road sign that is expected to be visible in high contrast from a distance of more than 50 meters). Thus, the vehicle system may responsively switch to determining that the vehicle system's operating environment is in an error state in which the vehicle system may ignore all readings from all cameras. Alternatively, there may be variations in the operating environment that make the sensors less trustworthy, and instead of ignoring all readings from such sensors, the vehicle system may use reduced field of view ranges, azimuth, and / or elevation angles for such sensors. For example, if the vehicle system estimates that the camera images have low contrast beyond about 10 meters and the LIDAR readings are anomalous beyond 50 meters, the vehicle system may reduce the field of view range of all cameras to 70 meters and further reduce the field of view of all LIDAR sensors to 50 meters. To facilitate this and other examples, sensor error operating environments may exist in addition to or in place of the sensor error operating environments shown in Figure 6. Other examples are possible as well.

[0114] In alternative embodiments, the vehicle system may only use field of view volume parameter values ​​from the mapping for a subset of sensors of a particular type. For example, in foggy weather, the vehicle system may use a first field of view range of 50 meters for the vehicle's first LIDAR unit shown in FIG. 4, but a default field of view range of 200 meters for the vehicle's second LIDAR unit. In other alternative embodiments, the vehicle system may only use sensor field of view volume parameter values ​​for a subset of sensor types. For example, in foggy weather, the vehicle system may use reduced field of view ranges for the LIDAR and camera sensors, but may not change the radar field of view range (e.g., the default 220 meters). Other examples are possible, including mappings of more or fewer sensor types.

[0115] In addition to or instead of mapping sensor types to corresponding adjusted field of view volume parameter values, the above mapping or a separate mapping stored in memory may map sensor types to other information related to the sensor field of view and affecting the sensor data that the sensor may acquire. For example, a stored mapping may map sensor types to power levels that a particular sensor may use to acquire sensor data.

[0116] As a more specific example, for a LIDAR sensor, the stored mapping may map LIDAR sensor types to power levels of laser pulses transmitted by the LIDAR sensor when acquiring LIDAR data. In embodiments in which the mapping is stored, and / or in other embodiments in which the stored mapping may not be used, the act of adjusting the field of view volume of at least one sensor from a first field of view volume to an adjusted field of view volume different from the first field of view volume may include adjusting a power level of laser pulses transmitted by the LIDAR sensor when acquiring sensor data from a first power level to an adjusted power level different from the first power level and associated with the adjusted field of view volume. Additionally, the act of controlling an autonomous vehicle to operate using at least one sensor having an adjusted field of view volume may include controlling the LIDAR sensor to acquire sensor data by transmitting one or more laser pulses having an adjusted power level associated with the adjusted field of view volume. For example, a power level for transmitting laser pulses may be reduced when lasing relatively large retroreflector objects after those retroreflector objects are detected. As another example, in foggy conditions, for example, when the vehicle system is observing a narrow field of view, the power level may be increased. Other examples are possible as well. A particular adjusted power level may be associated with an adjusted field of view volume in various ways. For example, a mapping stored by the vehicle system may map, for a particular adjusted range, azimuth angle, or elevation angle of the adjusted field of view volume, a respective corresponding adjusted power level of a transmission that may occur to acquire (or acquire and ignore, discard, identify, etc.) sensor data corresponding to the adjusted range, azimuth angle, or elevation angle. Other examples are possible as well.

[0117] As another particular example, in the case of a radar sensor, the stored mapping may map a radar sensor type to a particular radio wave characteristic (e.g., shape, amplitude, bandwidth, duration) of radio waves transmitted by the radar sensor when acquiring radar data. For illustrative purposes, the transmit power of the transmitted radio waves and the transmit or receive beamforming performed by the vehicle's radar system may also be considered radio wave characteristics. In embodiments in which stored mappings such as these are stored, and / or in other embodiments in which stored mappings may not be used, the act of adjusting the field of view volume of at least one sensor from a first field of view volume to an adjusted field of view volume different from the first field of view volume may include adjusting the radio wave characteristic of the radio waves transmitted by the radar sensor when acquiring the sensor data, for example, by adjusting a characteristic (e.g., transmit power) from a first value to an adjusted value different from the first value, or by otherwise adjusting the characteristic. Additionally, the act of controlling the autonomous vehicle to operate using at least one sensor having an adjusted field of view volume may include controlling the radar sensor to acquire the sensor data by transmitting one or more radio waves having adjusted radio wave characteristics associated with the adjusted field of view volume. Other examples are possible as well. A particular adjusted radio wave characteristic may be associated with an adjusted field of view volume in various ways. For example, a mapping stored by the vehicle system may map, for a particular adjusted range, azimuth angle, or elevation angle of the adjusted field of view volume, respective corresponding adjusted radio wave characteristics of transmissions that may occur to acquire (or acquire and ignore, discard, identify, etc.) sensor data corresponding to the adjusted range, azimuth angle, or elevation angle. Other examples are possible as well.

[0118] In a more specific example of how the vehicle system adjusts the field of view volume of the radar sensor, in a heavy rain operating environment, the vehicle system (or the sensor chip for controlling the radar) may reduce the field of view volume by reducing the azimuth scan of the radar, and may point more antenna gain in the forward direction to improve penetration in the rain. As another example, in a dense urban environment, the vehicle system (or the sensor chip for controlling the radar) may cause the radar sensor to transmit less power at a particular angle corresponding to an object such as a large retroreflector, thereby improving the radar sensor's ability to detect a small object near the large retroreflector. As another example, if there are water droplets on the vehicle's radome, the vehicle system (or the sensor chip for controlling the radar) may cause the radar sensor to transmit more power, thereby compensating for the water droplets and reaching the range the radar sensor would be able to reach if the radome was dry. Other examples are possible as well.

[0119] In some embodiments, the range, azimuth, and / or elevation of a sensor's field of view volume may be adjusted to a value lower than the maximum field of view value for that sensor and parameter, but the sensor may nevertheless be configured to obtain and transmit to a vehicle system (e.g., a processor configured to process the sensor data) sensor data corresponding to ranges, azimuth, and / or elevation angles that exceed the respective ranges, azimuth, and / or elevation angles associated with the adjusted field of view volume. In such embodiments, for example, when controlling a vehicle to operate using a sensor with an adjusted field of view volume, the vehicle system may ignore (e.g., discard or store but not make decisions regarding the vehicle's environment, such as object detection, based thereon) sensor data corresponding to ranges, azimuth, and / or elevation angles that are greater than the respective ranges, azimuth, and / or elevation angles associated with the adjusted field of view volume. For example, if the range of a LIDAR sensor is reduced from 200 meters to 150 meters, the vehicle system may ignore sensor data corresponding to distances from the vehicle that exceed 150 meters. Other examples are possible as well. Additionally or alternatively, the vehicle system may identify sensor data corresponding to parameter values ​​greater than the maximum parameter value of the adjusted field of view volume (e.g., flag or otherwise store in memory an indication that the data is likely suspect). In alternative embodiments, such sensors may be configured to allow the sensors to configure themselves not to acquire sensor data corresponding to ranges, azimuth angles, and / or elevation angles beyond the respective ranges, azimuth angles, and / or elevation angles associated with the adjusted field of view volume. Additionally or alternatively, the sensors may be configured to acquire sensor data corresponding to ranges, azimuth angles, and / or elevation angles beyond the respective ranges, azimuth angles, and / or elevation angles associated with the adjusted field of view volume, but may be further configured to discard such sensor data to reduce the amount of data transmitted from the sensors to other computing devices of the vehicle system.

[0120] The act of controlling the vehicle to operate using at least one sensor with an adjusted field of view volume may include controlling the vehicle to operate in an autonomous mode using at least one sensor with an adjusted field of view volume, i.e., controlling the vehicle to acquire sensor data using the at least one sensor based on the adjusted field of view volume when operating in the autonomous mode. To facilitate this in some embodiments, a local computing system on board the vehicle may configure itself to ignore sensor data readings acquired during operation of the vehicle that exceed the respective range, azimuth, and / or elevation angle associated with the adjusted field of view volume of each of the at least one sensor. Additionally or alternatively, the remote system may transmit instructions to the local computing system of the vehicle that, upon receipt by the local computing system, cause the local computing system to control the vehicle to operate in an autonomous mode in which the local computing system ignores sensor data readings that exceed the respective range, azimuth, and / or elevation angle associated with the adjusted field of view volume of each of the at least one sensor. Other examples are possible as well.

[0121] In some embodiments, the vehicle's sensors and associated computing devices, such as chips (e.g., microchips) that control the operation of one or more sensors, may perform operations before the sensors transmit acquired sensor data to an on-board computer or a remote computer, which may affect how the on-board computer or remote computer controls the operation of the vehicle. In particular, such sensor chips may perform one or more operations of method 500 (or method 800 described in more detail later herein). For example, the sensor chip may be configured to determine the vehicle's operating environment based on the acquired sensor data (in the same or similar manner as described in more detail below) and adjust the field of view volume of one or more sensors accordingly. In this regard, the act of adjusting the field of view volume may include the sensor chip ignoring or flagging sensor data corresponding to ranges, azimuth angles, and / or elevation angles greater than the respective ranges, azimuth angles, and / or elevation angles associated with the adjusted field of view volume. Additionally or alternatively, the act of adjusting the field of view volume may include the sensor chip (i) adjusting a power level of a laser pulse transmitted by one or more LIDAR sensors during acquisition of the sensor data from a first power level to an adjusted power level different from the first power level, and / or (ii) acquiring the sensor data by transmitting one or more laser pulses at an adjusted power level associated with the adjusted field of view volume. Additionally or alternatively, the act of adjusting the field of view volume may include the sensor chip (i) adjusting a characteristic of a radio wave transmitted by one or more radar sensors during acquisition of the sensor data (e.g., from a first value to an adjusted value different from the first value), and / or (ii) acquiring the sensor data by transmitting one or more radio waves having adjusted radio wave characteristics associated with the adjusted field of view volume. Other examples are possible as well.

[0122] The act of the vehicle system determining the vehicle's operating environment may occur in a variety of ways. In general, this act may include the vehicle system receiving information related to the vehicle's surroundings (e.g., detected objects on the road, weather data detected by various sensors, etc.), information related to ongoing operation of the vehicle and vehicle components (e.g., sensor error codes), and / or information input by a user (e.g., the driver of the vehicle) via the user interface 112. For example, one or more of the vehicle's sensors may obtain sensor data, and the vehicle system may use the sensor data to determine weather conditions at a particular location along the vehicle's travel path. The vehicle system may then determine the vehicle's operating environment based on the determined weather conditions. As a more specific example, one or more of the vehicle's sensors may obtain sensor data that the vehicle system is configured to interpret as indicative of sunlight (thus indicating daytime) and rainy weather, and in response to obtaining and analyzing the sensor data, the vehicle system may determine that the vehicle is operating in rainy weather conditions. For example, the vehicle system may be configured to determine weather conditions by reviewing backscattered light from LIDAR laser pulses hitting raindrops, snowflakes, or foggy water droplets. Similarly, the vehicle's radar sensor may acquire sensor data from which the vehicle system can infer the type of rain conditions (e.g., light, moderate, heavy) present in the environment based on the amount / distribution / Doppler shift of backscattered radar energy from the droplets in the air. As another example of determining the operating environment, the vehicle's camera may acquire one or more images from which the vehicle system can infer fog conditions (e.g., dense, fine, etc.) based on the amount of contrast reduction to known objects (e.g., prior information stored in the vehicle) at a particular range.

[0123] As another specific example, a driver, remote assistant, or passenger of the vehicle may know (e.g., based on a weather forecast) that a snowstorm will soon begin and provide input data (e.g., via a touch screen GUI carried by the vehicle) indicating instructions for the vehicle to begin operating in snowy weather conditions. Thus, in response to receiving the input data, the vehicle system may control the vehicle to begin operating in snowy weather conditions, which may take the form of the vehicle system determining that the vehicle is operating in snowy weather conditions. As a more specific variation of this example, the vehicle system may access a predefined 3D map of the vehicle's surrounding environment, for example, a 3D map showing a stop sign that the vehicle is approaching. The vehicle system may be configured to compare the predefined 3D map with sensor data acquired in real time by the vehicle's sensors and determine the vehicle's operating environment based on the comparison. For example, the 3D map may clearly show a stop sign, but the vehicle's camera or LIDAR sensor may acquire sensor data in which the stop sign is not as clearly visible. The vehicle system may be configured to interpret the difference determined in the comparison as a particular type of weather condition, such as snow, depending on the nature of the difference. Other examples are possible as well.

[0124] As yet another specific example, as described above, the vehicle system may receive sensor data acquired by one or more of the vehicle's sensors and determine a sensor error for at least one of the vehicle's sensors based on the sensor data (e.g., an abnormal sensor reading outside a predefined threshold sensor reading range). Using the sensor error, the vehicle system can determine that the vehicle is operating in a particular sensor error condition. Other examples are possible as well.

[0125] In some embodiments, the vehicle system may be configured to determine the operating environment of the vehicle based on weather conditions associated with a particular location along the vehicle's travel path. To facilitate this, the vehicle system may receive weather data indicative of weather conditions at a particular location along the vehicle's travel path, and based on the weather conditions indicated by the received weather data, the vehicle system may determine the operating environment of the vehicle. The weather conditions may take the form of any one or more weather conditions described herein and / or other possible weather conditions. The particular location may be represented in the weather data in various forms. In general, the location may be dynamic (e.g., the vehicle's current location along the travel path) or static (e.g., the vehicle's destination or a location en route to the destination). Furthermore, the location may be a circular area having a particular radius and centered on a particular landmark (e.g., a circular area with a radius of 8 kilometers and centered on the urban center of a city). Other boundaries of the area are also possible, such as a city and its boundaries shown on a given map.

[0126] In some embodiments, the vehicle system may receive weather data from a weather station server or other type of server. The weather station server may be a weather station server local to a particular location, i.e., a weather station server dedicated to a particular location and configured to obtain weather data corresponding to the particular location and transmit the weather data to one or more vehicle systems. Additionally or alternatively, the weather station server may be a global weather station server configured to obtain weather data corresponding to multiple locations, such as an entire state, an entire county, an entire country, etc. The global weather station server may also operate as a server configured to collect weather data from multiple local weather station servers and transmit the collected weather data to one or more vehicle systems. In some embodiments, the weather station server may be configured to estimate weather conditions in various ways and include various types of information in the weather data. For example, the weather station server may estimate weather conditions in the form of a "donut" or other shaped representation of fog, mist, snow, and / or rain, the distribution, density, and diameter of water droplets of clouds, fog, and haze, and / or other forms. Such acts of weather condition estimation may include a weather station server (or vehicle) monitoring and analyzing indicators of doughnut quality such as fog, mist, rain, etc. Other examples of functionality of a local or global weather station server are also possible.

[0127] To facilitate receiving weather data from a weather station server, the vehicle system may select a weather station server from a plurality of possible weather station servers and send a query for weather data to the selected weather station server before determining the vehicle's operating environment. Then, in response to the query, the vehicle system may receive weather data from the selected weather station. The vehicle system may be configured to select the weather station server based on various criteria. In some examples, the vehicle system may select a weather station server that is within a threshold distance (e.g., within 16 kilometers) of the vehicle's current location. In other examples, the vehicle system may select a weather station server or other weather station data transmitter that is within a threshold distance of the vehicle's estimated future location (e.g., if the vehicle is en route to a city, the vehicle system may select a weather station server that is within 8 kilometers of the city's boundaries). Other examples are possible as well.

[0128] In some embodiments, the weather station server may be configured to deliver weather data updates for a particular location to a fleet of vehicles (e.g., multiple different vehicle systems associated with multiple different vehicles) or to multiple individual vehicles. Additionally, the weather station server may be configured to transmit weather data to the vehicle systems in response to receiving a query for weather data from the vehicle systems and / or without being specifically requested by the vehicle systems (e.g., configured to deliver weather data updates for a particular location every 30 minutes).

[0129] In any of the examples provided herein, the weather data may be time-stamped to allow the vehicle system to use the time-stamp as a reference in determining the vehicle's operating environment. For example, if the time-stamp indicates that a vehicle experienced fog in a particular area 35 minutes ago, another vehicle approaching the same area may determine that there is a high-threshold probability that foggy conditions no longer exist based on the time-stamp providing a time that exceeds a predefined threshold (e.g., 30 minutes). Thus, the other vehicle may not adjust its sensor field of view volume to account for such foggy conditions, or may adjust its accepted sensor field of view volume after one or more other signals corroborating the possibility of foggy conditions.

[0130] 7 illustrates an example communication system in which an example vehicle communicates with a local weather station server 700 and a global weather station server 702. In particular, FIG. 7 illustrates a first vehicle 704 and a second vehicle 706 on a travel path 708 (e.g., a road). Additionally, FIG. 7 illustrates an example area of ​​interest 710 to which the weather data may correspond. Although FIG. 7 illustrates the vehicles 704, 706 in direct communication with the servers 700, 702, such communication may additionally or alternatively be performed via one or more other computing devices, such as the remote computing system 302 of FIG. 3.

[0131] In one example, the first vehicle 704 may query one or more of the servers 700, 702 for weather data corresponding to the region 710 because the region 710 is along the travel path 708 of the first vehicle 704. As described above, the first vehicle 704 may determine that the local weather station server 700 is within a threshold distance of the current location of the first vehicle 740 and / or is within a threshold distance of the region 710, and accordingly select the local weather station server 700 to query for weather data corresponding to the region 710. Additionally or alternatively, the second vehicle 706 may obtain sensor data indicative of at least one weather condition present in the region 710 and transmit the weather data indicative of the weather condition directly to the first vehicle 704, or the first vehicle 704 may receive the weather data via another computing device, such as one or more of the servers 700, 702.

[0132] By configuring the vehicle system and weather station server in the above manner, up-to-date weather information can be efficiently provided to the vehicle, allowing the vehicle to quickly adjust its sensor field of view volume to adapt to changing weather conditions.

[0133] In addition to or instead of a weather station server configured to acquire, collect, manage, and transmit weather data, vehicle systems can receive weather data directly or indirectly from other vehicle systems of other vehicles. For example, one vehicle system may transmit weather data to another vehicle system. As another example, a global system (e.g., server computing system 306 or remote computing system 302) may be configured to receive and transmit weather data associated with various regions to a fleet of vehicles. In this manner, vehicles can beneficially inform each other about weather conditions in areas in which other vehicles are currently traveling or planning to travel, and thus can keep each other informed in real time and quickly adjust their sensor field of view volumes accordingly.

[0134] In line with the above, in some embodiments, a vehicle may be configured to operate as a weather station that collects and transmits weather data to other vehicles (e.g., other vehicles configured to operate in an autonomous mode), a weather station server, and / or another back-end server (e.g., server computing system 306 of FIG. 3). Additionally or alternatively, a vehicle may be configured to operate as a weather station server that facilitates communication of weather data between vehicles, weather stations, and / or other servers and / or collects weather data. For simplicity, a vehicle configured to operate as a weather station and / or weather station server is referred to as a weather station vehicle. In some examples, a fleet of weather station vehicles may share weather data among themselves. This may help to localize the weather data, as the fleet of vehicles may share and / or be informed by the back-end server of each other's locations. An example advantage of a weather station vehicle is reducing the dependency of autonomous vehicles on other weather stations or other resources.

[0135] A vehicle configured to operate as a weather station server may be configured with any one or more of the sensors described herein and may use such sensors to obtain weather data. Additionally or alternatively, such sensors may be specially modified to enable the sensors to obtain additional or more detailed weather information than normal, e.g.

[0136] Additionally or alternatively, such vehicles may include additional sensors, components, and computing devices that may enable the vehicle to obtain and provide the type of weather data that a weather station may provide, but that may not typically be used during standard operation of the vehicle (e.g., when the vehicle is not operating as a weather station server). For example, the vehicle may include a sensor configured to determine the air quality of the air in the vehicle's environment, a sensor configured to determine humidity outside the vehicle, a solar sensor (e.g., for determining solar load on the vehicle's equipment and for calculating expected temperature changes due to the sun that may affect the vehicle and its equipment), a temperature sensor, and / or a rain sensor. Any one or more of the sensors described herein (e.g., LIDAR, radar, camera, heat, humidity, air quality, solar, rain, etc.) may be physically distinct sensors or may be integrated as a single sensor configured to be used to obtain weather data to facilitate the operation of the vehicle as a weather station server. Furthermore, such sensors may be mounted in various locations inside and outside the vehicle, such as on the roof, windshield, mirrors, etc.

[0137] In some embodiments, a vehicle configured to operate as a weather station server may be assigned the role of obtaining weather data for a particular geographic location, such as a city or suburb, Additionally or alternatively, such a vehicle may obtain weather data for one or more specific weather services, such as local, national, or global radio, television, or online weather services.

[0138] In some embodiments, in addition to or instead of the vehicle system adjusting the sensor field of view volume, the vehicle system, or other computing system, such as the remote computing system 302, may be configured to reroute the vehicle based on weather conditions. For example, the second vehicle 706 of FIG. 7 may determine and report to the remote computing system 302 or one of the servers 700, 702 the inclement weather in the region 710 and at least one sensor field of view volume reduction that the vehicle system of the second vehicle 706 has reduced beyond a threshold due to the inclement weather. The threshold may be a value that indicates a field of view volume beyond which the at least one sensor reduced may not be reliable to use in navigating the inclement weather. Thus, if the remote computing system 302, or other computing system associated with the first vehicle 704 (including the vehicle system of the first vehicle) determines that the reduction has exceeded the threshold, the remote computing system 302 may accordingly reroute the first vehicle 704 to take an alternative route (and not reach the region 710). Other examples are also possible, such as scenarios in which it may be desirable for the vehicle to avoid mild inclement weather.

[0139] The decision to reroute the vehicle may be determined based on other factors, such as the size of the area in which the weather condition is occurring. For example, if the area in which fog is occurring is determined to be smaller than a threshold size (e.g., a circular area with a radius of 5 kilometers or less), the vehicle may be rerouted to avoid the area such that the area is small enough that rerouting the vehicle is unlikely to significantly increase the estimated travel time of the vehicle. Other examples are possible as well.

[0140] In some circumstances, it may be desirable for the vehicle system to proactively perform at least one of the disclosed actions before the vehicle system reaches a particular location along the travel path with which the determined operating environment is associated (e.g., an area in the rain, a section of a busy road, etc.). For example, the vehicle system may be configured to perform an act of adjusting the field of view volume of at least one sensor within a threshold period (e.g., 5 minutes) before the vehicle is estimated to reach the particular location. Additionally or alternatively, the vehicle system may be configured to perform an act of adjusting the field of view volume of at least one sensor within a threshold distance (e.g., 1.6 kilometers) before the vehicle is estimated to reach the particular location. To facilitate these determinations, the vehicle system may store and continuously update data representing an estimated travel path and estimated time to one or more points along the travel path, including a final destination and / or one or more intermediate points. Other examples are possible as well.

[0141] 8 is a flowchart of another method 800 according to an example embodiment. The method 800 may include one or more operations, functions, or actions, as illustrated by one or more of blocks 802-806.

[0142] At block 802, the method 800 includes operating a sensor of the autonomous vehicle according to a first field of view volume, where the first field of view volume is associated with a first operating environment of the autonomous vehicle.

[0143] At block 804, the method 800 includes receiving data indicative of a second operating environment of the autonomous vehicle, the second operating environment associated with environmental conditions within the environment of the autonomous vehicle.

[0144] At block 806, the method 800 includes automatically adjusting operation of the sensor to operate according to a second field of view volume, the second field of view volume being associated with a second operating environment.

[0145] In some embodiments, the sensor may be one of a LIDAR, a radar, and a camera.

[0146] In some embodiments, the environmental condition may be a weather condition associated with one of fog, rain, and snow.

[0147] In some embodiments, the environmental conditions may be associated with solar conditions, which may be associated with a time of day, such as daytime, twilight, or nighttime.

[0148] In some embodiments, method 800 may also include controlling the autonomous vehicle using data from a sensor operating in the second field of view volume. The act of controlling the autonomous vehicle using data from a sensor operating in the second field of view volume may include adjusting a speed of the autonomous vehicle.

[0149] In some embodiments, the autonomous vehicle may be a first autonomous vehicle, and the act of receiving data indicative of a second operating environment of the autonomous vehicle may include receiving data of an operating environment of a second autonomous vehicle. The second autonomous vehicle may be operated on a planned route of the first autonomous vehicle.

[0150] In some embodiments, the act of receiving data indicative of a second operating environment of the autonomous vehicle may include receiving data of environmental conditions in an environment of the second autonomous vehicle.

[0151] In some embodiments, the act of automatically adjusting operation of the sensor to operate in accordance with the second field of view volume may include discarding data associated with a field of view range that exceeds a maximum field of view range associated with the second field of view volume.

[0152] In some embodiments, the act of automatically adjusting operation of the sensor to operate in accordance with the second field of view volume may include adjusting an azimuth angle or elevation angle associated with the sensor to a respective azimuth angle or elevation angle associated with the second field of view volume based on a driving direction of the autonomous vehicle.

[0153] As described above, the field of view volume of at least one of the sensors of the vehicle may be adjusted based on the ODD of the vehicle. The vehicle may have multiple ODDs, and each ODD may include at least one of environmental conditions, geographic conditions, time conditions, traffic conditions, or road conditions. Furthermore, each ODD may be associated with a respective predefined field of view volume of at least one of the one or more sensors that represents a space around the vehicle in which the at least one sensor is expected to detect an object with a certain confidence level. In some examples, the vehicle system may store in memory an identifier of each ODD, as well as, for each ODD, the conditions that the ODD includes and the respective predefined field of view volume of at least one of the one or more sensors.

[0154] 9 is a flowchart of another method 900 according to an example embodiment. The method 900 may include one or more operations, functions, or actions as illustrated by one or more of blocks 902-906.

[0155] At block 902, the method 900 includes identifying a plurality of ODDs for the autonomous vehicle, where each ODD includes at least one of an environmental condition, a geographic condition, a time of day condition, a traffic condition, or a road condition, and each ODD is associated with a predetermined field of view volume of at least one sensor of the one or more sensors. In some examples, the act of identifying the plurality of ODDs may include referencing where the ODDs for the vehicle are stored in memory.

[0156] At block 904, the method 900 includes associating the autonomous vehicle with a first ODD of the plurality of ODDs. In some examples, the vehicle system may detect one or more conditions of the vehicle's environment using one or more of the vehicle's sensors and / or one or more sensors off the vehicle (e.g., sensors on another vehicle or sensors attached to road structures such as stop signs, medians, traffic lights, etc.) and select one of the plurality of ODDs, i.e., the ODD that includes the detected condition. The act of associating the vehicle with the first ODD may include the vehicle system configuring itself, or at least one of its subsystems, to operate using one or more parameters associated with the first ODD. Thus, in response to associating the vehicle with the first ODD, the vehicle system may operate at least one of the vehicle's sensors using a predetermined field of view volume associated with the first ODD.

[0157] At block 906, the method 900 includes detecting a change in the operating environment of the autonomous vehicle. The act of detecting the change may include detecting the change using one or more of the vehicle's sensors. Additionally or alternatively, the act of detecting the change may include detecting the change using an external sensor different from the vehicle's sensors. In particular, the external sensor may be located away from the vehicle itself but may be communicatively linked directly to the vehicle system, e.g., through a wireless communication interface, or may be communicatively linked indirectly to the vehicle system, e.g., where the vehicle system receives data from the external sensor via a server or other intermediate computing device. In some examples, the external sensor may be attached to a road structure, such as a stop sign, a traffic light, a median strip, a bridge, a curb, or a reflector, among other possible structures.

[0158] At block 908, the method 900 includes, in response to the detection, associating the autonomous vehicle with a second ODD of the plurality of ODDs. In some examples, the act of associating the vehicle with the second ODD may include configuring the vehicle system itself or at least one of its subsystems to operate using one or more parameters associated with the second ODD.

[0159] At block 910, the method 900 included, in response to the autonomous vehicle being associated with the second ODD, operating at least one sensor using a predetermined field of view volume associated with the second ODD.

[0160] In some implementations, the act of operating the at least one sensor using a predetermined field of view volume associated with the second ODD may include operating the at least one sensor using a smaller field of view volume than when operating the at least one sensor using a predetermined field of view volume associated with the first ODD. For example, the first ODD may include an environmental condition of a clear weather condition, and the second ODD may include one or more environmental conditions of a rainy condition, a foggy condition, or a snowy condition, thus making it beneficial to use a smaller field of view volume for the second ODD. In other implementations, the act of operating the at least one sensor using a predetermined field of view volume associated with the second ODD may include operating the at least one sensor using a larger field of view volume than when operating the at least one sensor using a predetermined field of view volume associated with the first ODD.

[0161] As described herein, the first ODD and the second ODD may include other conditions. For example, the first ODD may include a first time condition for a first time (e.g., morning), and the second ODD may include a second time condition for a second time (e.g., evening or night). As another example, the first ODD may include a first traffic condition (e.g., light or no traffic), and the second ODD may include a second traffic condition (e.g., heavy or jammed traffic). As yet another example, the first ODD may include a first speed limit of a road on which the vehicle is traveling, and the second ODD may include a second speed limit that is higher or lower than the first speed limit. As yet another example, the first ODD may include a first geographically defined area on which the vehicle is traveling (e.g., public roads or private property), and the second ODD may include a second geographically defined area on which the vehicle is traveling (e.g., highways or public roads). Other examples are possible as well.

[0162] As used herein, the terms "substantially," "approximately," or "about" mean that the recited property, parameter, value, or geometric planarity need not be achieved exactly, but that deviations or variations, including, for example, tolerances, measurement errors, limits of measurement accuracy, and other factors known to those of skill in the art, may occur in an amount that does not eliminate the effect intended to be provided by the property.

[0163] While various exemplary aspects and exemplary embodiments are disclosed herein, other aspects and embodiments will be apparent to those of ordinary skill in the art. The various exemplary aspects and exemplary embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Claims

1. 1. A system for controlling operation of an autonomous vehicle, the system comprising: one or more sensors, each sensor of the one or more sensors configured to operate according to a field of view volume, the field of view volume representing a space around the autonomous vehicle in which the sensor is expected to detect objects with a confidence level higher than a predefined confidence threshold; one or more processors coupled to the one or more sensors; and a memory coupled to the one or more processors and having instructions stored thereon, the instructions, when executed by the one or more processors, causing the one or more processors to: identifying a plurality of operational design domains (ODDs) for the autonomous vehicle, each ODD including at least one of an environmental condition, a geographic condition, a time of day condition, a traffic condition, or a road condition, and each ODD associated with a predefined field of view volume of at least one of the one or more sensors; Associating the autonomous vehicle with a first ODD of the plurality of ODDs; Detecting a change in an operating environment of the autonomous vehicle; and In response to the detecting, associating the autonomous vehicle with a second ODD of the plurality of ODDs; and in response to the autonomous vehicle being associated with the second ODD, operating the at least one sensor using the predetermined field of view volume associated with the second ODD.

2. 2. The system of claim 1, wherein detecting the change in the operating environment of the autonomous vehicle includes detecting the change in the operating environment of the autonomous vehicle using at least one sensor of the one or more sensors.

3. 2. The system of claim 1, wherein detecting the change in the operating environment of the autonomous vehicle includes detecting the change in the operating environment of the autonomous vehicle using an external sensor distinct from the one or more sensors.

4. The system of claim 1 , wherein the external sensor is located remotely from the autonomous vehicle and is attached to a road structure.

5. 2. The system of claim 1, wherein operating the at least one sensor using the predetermined field of view volume associated with the second ODD comprises operating the at least one sensor using a field of view volume that is smaller than when operating the at least one sensor using the predetermined field of view volume associated with the first ODD.

6. the first ODD includes an environmental condition of a clear weather condition; The system of claim 5 , wherein the second ODD comprises one or more of the following environmental conditions: a rainy condition, a foggy condition, or a snowy condition.

7. the first ODD includes a first time condition at a first time; The system of claim 1 , wherein the second ODD includes a second time condition for a second time different from the first time.

8. the first ODD includes a first traffic condition; The system of claim 1 , wherein the second ODD includes a second traffic condition that is different from the first traffic condition.

9. 1. A system comprising: one or more sensors, each sensor of the one or more sensors configured to operate according to a field of view volume; one or more processors coupled to the one or more sensors; and a memory coupled to the one or more processors and having instructions stored thereon, the instructions, when executed by the one or more processors, causing the one or more processors to: Determining an operating environment for the autonomous vehicle; and adjusting a field of view volume of at least one of the one or more sensors from a first field of view volume to an adjusted field of view volume that differs from the first field of view volume based on the determined operating environment of the autonomous vehicle.

10. The memory stores a plurality of operating environments of the autonomous vehicle and a mapping between each operating environment of the plurality of operating environments and a corresponding adjusted field of view volume of at least one sensor of the one or more sensors; 10. The system of claim 9, wherein adjusting the field of view volume based on the operating environment of the autonomous vehicle comprises selecting the adjusted field of view volume that corresponds to the determined operating environment of the autonomous vehicle.

11. 11. The system of claim 10, wherein the multiple operating environments of the autonomous vehicle include two or more of: (i) a default state, (ii) a clear weather condition, (iii) a daytime operating state, (iv) a nighttime operating state, (v) a rainy weather condition, (vi) a snowy weather condition, (v) a foggy weather condition, (viii) a condition for a particular type of road on which the autonomous vehicle is traveling, (ix) a state in which at least a threshold quantity of vehicles are present on the road within a threshold distance of the autonomous vehicle, or (x) a state in which at least one of the one or more sensors has a sensor error.

12. the corresponding adjusted field of view volume is part of a set of corresponding adjusted field of view volumes including a respective adjusted field of view volume for each of a plurality of sensor types; The system of claim 10 , wherein the multiple sensor types include two or more of a LIDAR sensor, a radar sensor, or a camera.

13. the one or more sensors include a plurality of sensor sets, each sensor set having a respective sensor type; The system of claim 9 , wherein adjusting the field of view volume of the at least one sensor comprises adjusting a field of view volume of all sensors in at least one of the sensor sets.

14. The system of claim 13 , wherein the plurality of sensor sets includes two or more of a set of LIDAR sensors, a set of radar sensors, or a set of cameras.

15. The operation, 10. The system of claim 9, further comprising controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume.

16. 16. The system of claim 15, wherein controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume includes ignoring sensor data corresponding to a range, azimuth angle, or elevation angle greater than a respective range, azimuth angle, or elevation angle of the adjusted field of view volume.

17. 16. The system of claim 15, wherein controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume includes identifying sensor data corresponding to a parameter value greater than a maximum parameter value for the adjusted field of view volume.

18. the at least one sensor includes a LIDAR sensor; 16. The system of claim 15, wherein controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume comprises controlling the LIDAR sensor to acquire sensor data by transmitting one or more laser pulses having an adjusted power level associated with the adjusted field of view volume.

19. the at least one sensor includes a radar sensor; 16. The system of claim 15, wherein controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume includes controlling the radar sensor to acquire sensor data by transmitting one or more radio waves having adjusted radio wave characteristics associated with the adjusted field of view volume.

20. 1. A method performed by a computing device configured to control operation of an autonomous vehicle, the method comprising: Determining an operating environment for an autonomous vehicle, the autonomous vehicle comprising one or more sensors, each sensor of the one or more sensors configured to operate according to a field of view volume; adjusting a field of view volume of at least one sensor of the one or more sensors from a first field of view volume to an adjusted field of view volume different from the first field of view volume based on the determined operating environment of the autonomous vehicle; and controlling the autonomous vehicle to operate using the at least one sensor having the adjusted field of view volume.

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