Systems, Methods, and Apparatus for using Remote Assistance to Classify Objects in an Environment

Example embodiments relate to techniques for enabling one or more systems of a vehicle (e.g., an autonomous vehicle) to request remote assistance to help the vehicle navigate in an environment. A computing device may be configured to receive a request for assistance from a vehicle to classify an object and to initiate display of a graphical user interface at a display device. The graphical user interface may be configured to visually represent the object and may comprise one or more graphical user interface elements to enable input to be provided for classifying the object. The computing device may also be configured to generate a response that includes a classification of the object based on detecting a selection of at least one of the one or more graphical user interface elements. Further, the computing device may be configured to transmit the response to the vehicle.

BACKGROUND

This background description is provided for the purpose of generally presenting the context of the disclosure. Unless otherwise indicated herein, material described in this section is neither expressly nor impliedly admitted to be prior art to the present disclosure or the appended claims.

Vehicles may be used to complete various types of tasks, including object and human transportation. With advances in technology, some vehicles are configured with systems that enable the vehicles to operate in a partial or fully autonomous mode. When operating in a partial or fully autonomous mode, some or all of the navigation aspects of vehicle operation are controlled by a vehicle control system rather than a traditional human driver. Autonomous operation of a vehicle may involve systems sensing the vehicle's surrounding environment to enable a computing system to plan and safely navigate.

SUMMARY

Example embodiments described herein relate to techniques for enabling one or more systems of a vehicle (e.g., a partial or fully autonomous vehicle) to request remote assistance to help the vehicle navigate in an environment. The techniques may enable a remote assistant (e.g., a human assistant or a computer assistant) to assist the vehicle systems in navigating conditions and situations that may be encountered along a travel route (e.g., road conditions, traffic conditions, obstacles, etc.). During the operation of the vehicle, a vehicle system may detect objects or obstacles along or near the travel route of the vehicle and may request assistance from a remote assistant for classifying the obstacles or objects. The remote assistant may determine a classification for an object and may send the classification to the vehicle systems to assist the vehicle in navigating the environment.

In one aspect, an example apparatus is provided. The apparatus may include a memory and a computing device. The computing device may be configured to receive a request for assistance from a vehicle to classify an object and to initiate display of a graphical user interface at a display device. The graphical user interface may be configured to visually represent the object and may comprise one or more graphical user interface elements to enable input to be provided for classifying the object. The computing device may also be configured to generate a response that includes a classification of the object based on detecting a selection of at least one of the one or more graphical user interface elements. Further, the computing device may be configured to transmit the response to the vehicle.

In another aspect, an example method is provided. The method may comprise receiving a request for assistance from a vehicle to classify an object and initiating display of a graphical user interface at a display device. The graphical user interface may be configured to visually represent the object and may comprise one or more graphical user interface elements to enable input to be provided for classifying the object. The method may also comprise generating a response that includes a classification of the object based on detecting a selection of at least one of the one or more graphical user interface elements. Further, the method may comprise transmitting the response to the vehicle.

In another aspect, an example method is provided. The method may comprise receiving first sensor data from one or more sensors of a vehicle and determining a first classification for an object at a first time period based on first sensor data. The method may further comprise receiving second sensor data from the one or more sensors of the vehicle and determining a second classification of the object at a second time period based on the second sensor data. The method may also comprise causing a request for assistance to be sent to a remote assistant for object classification and receiving a response from the remote assistant. The response may include a classification of the object. In addition, the method may comprises generating at least one instruction for causing the vehicle to perform the one or more navigation operations based on the received classification of the object.

DETAILED DESCRIPTION

Advancements in computing, sensors, and other technologies have enabled vehicles to navigate autonomously between locations without requiring input from a driver. By processing measurements of the surrounding environment from vehicle sensors in near real-time, an autonomous vehicle can transport passengers or objects between locations while avoiding obstacles, obeying traffic requirements, and performing other necessary actions that are typically conducted by a driver. The shift of control of the vehicle over to a vehicle control system can permit passengers to devote their attention to tasks other than driving.

During operation, a vehicle capable of autonomous or semi-autonomous operations may encounter complex or unexpected situations that can interfere with the vehicle's current navigation strategy. In some cases, a vehicle's sensor system may detect the presence of one or more unexpected situations or obstacles that can limit the current navigation plan of the vehicle. Without a human driver to interpret the situation, the vehicle may remain stopped as a default until obtaining enough measurements of environment changes that enable safely proceeding. In some instances, however, the vehicle may remain stopped for a substantial amount of time if the environment remains static and the vehicle systems are not able to identify a safe strategy for further navigation.

Because autonomous vehicles may navigate in various locations, there are numerous situations that may cause navigation issues for the vehicle navigation system and/or other vehicle systems. For example, a parking lot may include parked vehicles, pedestrians, shopping carts, and other potential obstacles that can interfere with an autonomous vehicle's ability to navigate per the lines and rules of the parking lot. In some cases, the navigation system of an autonomous vehicle may become temporarily stranded if too many obstacles interfere with potential routes. Similarly, encountering an accident between other vehicles or road construction are other example scenarios that may unexpectedly disrupt an autonomous vehicle's path of navigation. These are just a few examples where the current navigation strategy for an autonomous vehicle may be impacted and potentially limited in some way.

When a vehicle encounters a situation or obstacle where the vehicle systems enabling autonomous operation may benefit from additional assistance, the vehicle systems may request remote assistance to help overcome the situation or obstacle. A request for assistance may involve a vehicle communication interface (or another vehicle-based system) transmitting a request for assistance via wireless communication to a computing device positioned remotely from the vehicle. The request for assistance may include sensor data (e.g., images, video, location information, etc.) and other information that can help the remote computing device (or a human operator using the remote computing device) to provide some form of assistance to the vehicle that may help the vehicle overcome the current situation or obstacle.

In some instances, a vehicle navigating in the environment may encounter situations or obstacles where the vehicle's systems may have difficulty classifying or identifying objects. Such a situation may cause the vehicle to attempt to change navigation operations or to stop until enough sensor measurements may be obtained for identifying and/or classifying the object in the environment. In some examples, a vehicle navigating along a travel route may determine different identifications and/or classifications for an object in the environment. For example, a vehicle system may identify and/or classify an object as a scooter, bicycle, plastic bag, sign, or other type of object and subsequently classify the object as a pedestrian when the vehicle travels closer to the object. The change in the classification may occur when different sensors of the vehicle detect the object as the vehicle changes location relative to the object. In some cases, different sensors or perception systems may identify and/or classify the object differently. Further, multiple object identifications and/or classifications for an object may arise from hand-offs between the sensor or perception systems of the vehicle.

Example embodiments described herein relate to techniques for enabling one or more systems of a vehicle (e.g., a partial or fully autonomous vehicle) to obtain remote assistance to help a vehicle navigate in an environment. The techniques may enable a remote assistant (e.g., a human assistant or a computer assistant) to assist one or more systems of a vehicle in navigating obstacles and/or situations that may be encountered along a travel route (e.g., road conditions, traffic conditions, obstacles, etc.). When the vehicle encounters an obstacle or object in an environment, the autonomous vehicle may request remote assistance that may help the vehicle identify and/or classify the obstacle or object. The remote assistant may determine a classification for an object and may send the classification to the vehicle systems to assist the vehicle in navigating the environment. Remote assistance techniques described herein may be utilized for object classification in various situations, such as when a vehicle is temporarily stranded at an intersection, a parking lot, in an area with road constructions, or other potential navigation environments.

To further illustrate, a remotely positioned computing device associated with an assistant may initially receive a request for assistance from a vehicle operating in an environment. For instance, the vehicle may be autonomously navigating a neighborhood or city and encounter an object or obstacle that the vehicle may need assistance in order to classify and/or identify the object. As such, the request for assistance sent to the remote computing device may indicate details related to the vehicle's situation. The request may specify that the vehicle is currently stopped and may need a classification and/or identification of an object or obstacle in the environment.

The vehicle may request remote assistance if the vehicle has determined one or more different classifications for an object in the environment. The vehicle may be configured to provide to the remote computing device one or more object classifications for a human operator to review and to select therefrom. For example, the one or more classifications for the object may be determined locally by one or more systems of the vehicle and provided to the remote computing device. The vehicle may also provide a score indicative of the probability of a correct classification and/or identification. Further, the vehicle may also request remote assistance if multiple classifications appear to be viable for the object and/or if a confidence value associated with a classification of an object is below a threshold confidence level.

Responsive to receiving the request from the vehicle, the remote computing device may provide an interface (e.g., a graphical user interface (GUI)) for an assistant (e.g., a human operator) to review and subsequently provide assistance to the vehicle. Based on one or more inputs from the assistant, the remote computing device may transmit information and/or instructions to the vehicle. The GUI generated by the remote computing device to enable remote assistance can vary within embodiments. The GUI may be used to convey information about an object detected by a vehicle to the remote computer device so that the assistant may provide input to help identify and/or classify the object. For example, the remote computing device may convey information about the object to a human operator of the remote computing device. In some examples, the GUI may display one or more classifications for an object. Further, each classification for the object may be displayed with a score indicative of the probability of a correct classification and/or identification to help the assistant or operator classify and/or identify the object. For instance, a score may be provided for each classification that represents the probability of a correct classification and/or identification of the object (e.g., 68% pedestrian, 12% scooter, X % sign, and Y % unknown). In some examples, the probability may be calculated by the vehicle and provided to the remote computing device. The GUI may also include images or other sensor data to help represent the object or obstacle encountered by the vehicle for review by the assistant. Further, the GUI may represent other information, such as information relating to the vehicle (e.g., location, quantity of passengers, type of vehicle, etc.).

The GUI produced by the remote computing device may enable input from the assistant or operator of the remote computing device. For instance, the remote computing device may provide an interface configured with a single selectable option, which when selected by the assistant or operator, causes the remote computing device to transmit a response to the vehicle that may include a classification of the object. In some examples, the GUI may display a plurality of selectable options. Each selectable option may be associated with a particular classification of an object. Further, a score indicative of the probability of a correct classification and/or identification may be provided for each selected option. The GUI may also request the operator to confirm or deny if the object is associated with a particular object classification (e.g., a pedestrian or a scooter). When the remote computing device detects the input from the assistant or operator, the remote computing device may generate information to be provided to the vehicle.

The remote computing device may send information about the object to the vehicle. Without such information, the vehicle's ability to navigate in the environment may be limited. Upon reception of the information (e.g. a classification for an object), the vehicle may be configured to develop a navigation strategy based on the information while also monitoring the environment for objects and/or changes that may require the vehicle to stop or change navigation operations.

Remote assistance for vehicles may be obtained from a network of remote assistants (e.g., human assistants or operators). For example, a vehicle may submit a request for assistance that is received at an entry point of the network. The entry point may connect the request with an assistant or operator that can provide assistance. The assistant may be selected based on credentials associated with the assistant that indicate the assistant's expertise to handle the type of assistance that is being requested and/or the assistant's availability, among other potential parameters. The entry point may analyze the information within the request to route the request for assistance accordingly. For example, the network of remote operators may be used to provide assistance to an entire fleet of autonomous vehicles.

Example systems within the scope of the present disclosure will now be described in greater detail. An example system may be implemented in or may take the form of an automobile, but other example systems can be implemented in or take the form of other vehicles, such as cars, trucks, motorcycles, buses, boats, airplanes, helicopters, lawn mowers, earth movers, boats, snowmobiles, aircraft, recreational vehicles, amusement park vehicles, farm equipment, construction equipment, trams, golf carts, trains, trolleys, and robot devices. Other vehicles are possible as well.

Referring now to the figures,FIG.1is a functional block diagram illustrating vehicle100, which represents a vehicle capable of operating fully or partially in an autonomous mode. More specifically, vehicle100may operate in an autonomous mode without human interaction (or reduced human interaction) through receiving control instructions from a computing system (e.g., a vehicle control system). As part of operating in the autonomous mode, vehicle100may use sensors (e.g., sensor system104) to detect and possibly identify objects of the surrounding environment to enable safe navigation. In some implementations, vehicle100may also include subsystems that enable a driver (or a remote operator) to control operations of vehicle100.

As shown inFIG.1, vehicle100includes various subsystems, such as propulsion system102, sensor system104, control system106, one or more peripherals108, power supply110, computer system112, data storage114, and user interface116. The subsystems and components of vehicle100may be interconnected in various ways (e.g., wired or secure wireless connections). In other examples, vehicle100may include more or fewer subsystems. In addition, the functions of vehicle100described herein can be divided into additional functional or physical components, or combined into fewer functional or physical components within implementations.

Propulsion system102may include one or more components operable to provide powered motion for vehicle100and can include an engine/motor118, an energy source119, a transmission120, and wheels/tires121, among other possible components. For example, engine/motor118may be configured to convert energy source119into mechanical energy and can correspond to one or a combination of an internal combustion engine, one or more electric motors, steam engine, or Stirling engine, among other possible options. For instance, in some implementations, propulsion system102may include multiple types of engines and/or motors, such as a gasoline engine and an electric motor.

Energy source119represents a source of energy that may, in full or in part, power one or more systems of vehicle100(e.g., engine/motor118). For instance, energy source119can correspond to gasoline, diesel, other petroleum-based fuels, propane, other compressed gas-based fuels, ethanol, solar panels, batteries, and/or other sources of electrical power. In some implementations, energy source119may include a combination of fuel tanks, batteries, capacitors, and/or flywheel.

Transmission120may transmit mechanical power from the engine/motor118to wheels/tires121and/or other possible systems of vehicle100. As such, transmission120may include a gearbox, a clutch, a differential, and a drive shaft, among other possible components. A drive shaft may include axles that connect to one or more wheels/tires121.

Wheels/tires121of vehicle100may have various configurations within example implementations. For instance, vehicle100may exist in a unicycle, bicycle/motorcycle, tricycle, or car/truck four-wheel format, among other possible configurations. As such, wheels/tires121may connect to vehicle100in various ways and can exist in different materials, such as metal and rubber.

Sensor system104can include various types of sensors, such as Global Positioning System (GPS)122, inertial measurement unit (IMU)124, one or more radar units126, laser rangefinder/LIDAR unit128, camera130, steering sensor123, and throttle/brake sensor125, among other possible sensors. In some implementations, sensor system104may also include sensors configured to monitor internal systems of the vehicle100(e.g.,02monitors, fuel gauge, engine oil temperature, condition of brakes).

GPS122may include a transceiver operable to provide information regarding the position of vehicle100with respect to the Earth. IMU124may have a configuration that uses one or more accelerometers and/or gyroscopes and may sense position and orientation changes of vehicle100based on inertial acceleration. For example, IMU124may detect a pitch and yaw of the vehicle100while vehicle100is stationary or in motion.

Radar unit126may represent one or more systems configured to use radio signals to sense objects (e.g., radar signals), including the speed and heading of the objects, within the local environment of vehicle100. As such, radar unit126may include one or more radar units equipped with one or more antennas configured to transmit and receive radar signals as discussed above. In some implementations, radar unit126may correspond to a mountable radar system configured to obtain measurements of the surrounding environment of vehicle100. For example, radar unit126can include one or more radar units configured to couple to the underbody of a vehicle.

Laser rangefinder/LIDAR128may include one or more laser sources, a laser scanner, and one or more detectors, among other system components, and may operate in a coherent mode (e.g., using heterodyne detection) or in an incoherent detection mode. Camera130may include one or more devices (e.g., still camera or video camera) configured to capture images of the environment of vehicle100.

Steering sensor123may sense a steering angle of vehicle100, which may involve measuring an angle of the steering wheel or measuring an electrical signal representative of the angle of the steering wheel. In some implementations, steering sensor123may measure an angle of the wheels of the vehicle100, such as detecting an angle of the wheels with respect to a forward axis of the vehicle100. Steering sensor123may also be configured to measure a combination (or a subset) of the angle of the steering wheel, electrical signal representing the angle of the steering wheel, and the angle of the wheels of vehicle100.

Throttle/brake sensor125may detect the position of either the throttle position or brake position of vehicle100. For instance, throttle/brake sensor125may measure the angle of both the gas pedal (throttle) and brake pedal or may measure an electrical signal that could represent, for instance, the angle of the gas pedal (throttle) and/or an angle of a brake pedal. Throttle/brake sensor125may also measure an angle of a throttle body of vehicle100, which may include part of the physical mechanism that provides modulation of energy source119to engine/motor118(e.g., a butterfly valve or carburetor). Additionally, throttle/brake sensor125may measure a pressure of one or more brake pads on a rotor of vehicle100or a combination (or a subset) of the angle of the gas pedal (throttle) and brake pedal, electrical signal representing the angle of the gas pedal (throttle) and brake pedal, the angle of the throttle body, and the pressure that at least one brake pad is applying to a rotor of vehicle100. In other embodiments, throttle/brake sensor125may be configured to measure a pressure applied to a pedal of the vehicle, such as a throttle or brake pedal.

Control system106may include components configured to assist in navigating vehicle100, such as steering unit132, throttle134, brake unit136, sensor fusion algorithm138, computer vision system140, navigation/pathing system142, and obstacle avoidance system144. More specifically, steering unit132may be operable to adjust the heading of vehicle100, and throttle134may control the operating speed of engine/motor118to control the acceleration of vehicle100. Brake unit136may decelerate vehicle100, which may involve using friction to decelerate wheels/tires121. In some implementations, brake unit136may convert kinetic energy of wheels/tires121to electric current for subsequent use by a system or systems of vehicle100.

Sensor fusion algorithm138may include a Kalman filter, Bayesian network, or other algorithms that can process data from sensor system104. In some implementations, sensor fusion algorithm138may provide assessments based on incoming sensor data, such as evaluations of individual objects and/or features, evaluations of a particular situation, and/or evaluations of potential impacts within a given situation.

Computer vision system140may include hardware and software operable to process and analyze images in an effort to determine objects, environmental objects (e.g., stop lights, road way boundaries, etc.), and obstacles. As such, computer vision system140may use object recognition, Structure From Motion (SFM), video tracking, and other algorithms used in computer vision, for instance, to recognize objects, map an environment, track objects, estimate the speed of objects, etc.

Navigation/pathing system142may determine a driving path for vehicle100, which may involve dynamically adjusting navigation during operation. As such, navigation/pathing system142may use data from sensor fusion algorithm138, GPS122, and maps, among other sources to navigate vehicle100. Obstacle avoidance system144may evaluate potential obstacles based on sensor data and cause systems of vehicle100to avoid or otherwise negotiate the potential obstacles.

As shown inFIG.1, vehicle100may also include peripherals108, such as wireless communication system146, touchscreen148, microphone150, and/or speaker152. Peripherals108may provide controls or other elements for a user to interact with user interface116. For example, touchscreen148may provide information to users of vehicle100. User interface116may also accept input from the user via touchscreen148. Peripherals108may also enable vehicle100to communicate with devices, such as other vehicle devices.

Wireless communication system146may securely and wirelessly communicate with one or more devices directly or via a communication network. For example, wireless communication system146could use 3G cellular communication, such as CDMA, EVDO, GSM/GPRS, or 4G cellular communication, such as WiMAX or LTE. Alternatively, wireless communication system146may communicate with a wireless local area network (WLAN) using WiFi or other possible connections. Wireless communication system146may also communicate directly with a device using an infrared link, Bluetooth, or ZigBee, for example. Other wireless protocols, such as various vehicular communication systems, are possible within the context of the disclosure. For example, wireless communication system146may include one or more dedicated short-range communications (DSRC) devices that could include public and/or private data communications between vehicles and/or roadside stations.

Vehicle100may include power supply110for powering components. Power supply110may include a rechargeable lithium-ion or lead-acid battery in some implementations. For instance, power supply110may include one or more batteries configured to provide electrical power. Vehicle100may also use other types of power supplies. In an example implementation, power supply110and energy source119may be integrated into a single energy source.

Vehicle100may also include computer system112to perform operations, such as operations described therein. As such, computer system112may include at least one processor113(which could include at least one microprocessor) operable to execute instructions115stored in a non-transitory computer readable medium, such as data storage114. In some implementations, computer system112may represent a plurality of computing devices that may serve to control individual components or subsystems of vehicle100in a distributed fashion.

In some implementations, data storage114may contain instructions115(e.g., program logic) executable by processor113to execute various functions of vehicle100, including those described above in connection withFIG.1. Data storage114may contain additional instructions as well, including instructions to transmit data to, receive data from, interact with, and/or control one or more of propulsion system102, sensor system104, control system106, and peripherals108.

In addition to instructions115, data storage114may store data such as roadway maps, path information, among other information. Such information may be used by vehicle100and computer system112during the operation of vehicle100in the autonomous, semi-autonomous, and/or manual modes.

Vehicle100may include user interface116for providing information to or receiving input from a user of vehicle100. User interface116may control or enable control of content and/or the layout of interactive images that could be displayed on touchscreen148. Further, user interface116could include one or more input/output devices within the set of peripherals108, such as wireless communication system146, touchscreen148, microphone150, and speaker152.

Computer system112may control the function of vehicle100based on inputs received from various subsystems (e.g., propulsion system102, sensor system104, and control system106), as well as from user interface116. For example, computer system112may utilize input from sensor system104in order to estimate the output produced by propulsion system102and control system106. Depending upon the embodiment, computer system112could be operable to monitor many aspects of vehicle100and its subsystems. In some embodiments, computer system112may disable some or all functions of the vehicle100based on signals received from sensor system104.

The components of vehicle100could be configured to work in an interconnected fashion with other components within or outside their respective systems. For instance, in an example embodiment, camera130could capture a plurality of images that could represent information about a state of an environment of vehicle100operating in an autonomous mode. The state of the environment could include parameters of the road on which the vehicle is operating. For example, computer vision system140may be able to recognize the slope (grade) or other features based on the plurality of images of a roadway. Additionally, the combination of GPS122and the features recognized by computer vision system140may be used with map data stored in data storage114to determine specific road parameters. Further, radar unit126may also provide information about the surroundings of the vehicle.

In other words, a combination of various sensors (which could be termed input-indication and output-indication sensors) and computer system112could interact to provide an indication of an input provided to control a vehicle or an indication of the surroundings of a vehicle. In some embodiments, computer system112may make a determination about various objects in the environment based on data that is provided by systems of the vehicle. For example, vehicle100may have lasers or other optical sensors configured to sense objects in a field of view of the vehicle. Computer system112may use the outputs from the various sensors to determine information about objects in a field of view of the vehicle, and may determine distance and direction information to the various objects. Computer system112may also determine whether objects are desirable or undesirable based on the outputs from the various sensors. Further, computing system112may also classify and/or identify an object in the environment based on the output of the various sensors. Further computer system112may request remote assistance when different classifications are determined for an object. In addition, vehicle100may also include telematics control unit (TCU)160. TCU160may enable vehicle connectivity and internal passenger device connectivity through one or more wireless technologies.

AlthoughFIG.1shows various components of vehicle100, i.e., wireless communication system146, computer system112, data storage114, and user interface116, as being integrated into the vehicle100, one or more of these components could be mounted or associated separately from vehicle100. For example, data storage114could, in part or in full, exist separate from vehicle100. Thus, vehicle100could be provided in the form of device elements that may be located separately or together. The device elements that make up vehicle100could be communicatively coupled together in a wired and/or wireless fashion.

FIGS.2A,2B,2C,2D, and2Eillustrate different views of a physical configuration of vehicle100. The various views are included to depict example sensor positions202,204,206,208,210on vehicle100. In other examples, sensors can have different positions on vehicle100. Although vehicle100is depicted inFIGS.2A-2Eas a van, vehicle100can have other configurations within examples, such as a truck, a car, a semi-trailer truck, a motorcycle, a bus, a shuttle, a golf cart, an off-road vehicle, robotic device, or a farm vehicle, among other possible examples.

As discussed above, vehicle100may include sensors coupled at various exterior locations, such as sensor positions202-210. Vehicle sensors include one or more types of sensors with each sensor configured to capture information from the surrounding environment or perform other operations (e.g., communication links, obtain overall positioning information). For example, sensor positions202-210may serve as locations for any combination of one or more cameras, radars, LIDARs, range finders, radio devices (e.g., Bluetooth and/or 802.11), and acoustic sensors, among other possible types of sensors.

When coupled at the example sensor positions202-210shown inFIGS.2A-2E, various mechanical fasteners may be used, including permanent or non-permanent fasteners. For example, bolts, screws, clips, latches, rivets, anchors, and other types of fasteners may be used. In some examples, sensors may be coupled to the vehicle using adhesives. In further examples, sensors may be designed and built as part of the vehicle components (e.g., parts of the vehicle mirrors).

In some implementations, one or more sensors may be positioned at sensor positions202-210using movable mounts operable to adjust the orientation of one or more sensors. A movable mount may include a rotating platform that can rotate sensors so as to obtain information from multiple directions around vehicle100. For instance, a sensor located at sensor position202may use a movable mount that enables rotation and scanning within a particular range of angles and/or azimuths. As such, vehicle100may include mechanical structures that enable one or more sensors to be mounted on top the roof of vehicle100. Additionally, other mounting locations are possible within examples. In some situations, sensors coupled at these locations can provide data that can be used by a remote operator to provide assistance to vehicle100.

FIG.3is a simplified block diagram exemplifying computing device300, illustrating some of the components that may be included in a computing device arranged to operate in accordance with the embodiments herein. Computing device300may be implemented as computer system112, which may be located on vehicle100and perform processing operations related to vehicle operations. For example, computing device300may be used to process sensor data received from sensor system104. In some examples, computing system300may be a computing device within the vehicle, but separate from the vehicle, with which a human operator may interact while a passenger or driver of the vehicle, such as a touchscreen interface for displaying remote assistance information. Alternatively, computing device300may be located remotely from vehicle100and communicate via wireless communication. For example, computing device300may operate as a remotely positioned device that a remote human operator can use to communicate with one or more vehicles.

In the example embodiment shown inFIG.3, computing device300includes processor or processing system302, memory304, input/output unit306, and network interface308, all of which may be coupled by a system bus310or a similar mechanism. In some embodiments, computing device300may include other components and/or peripheral devices (e.g., detachable storage, sensors, and so on)

Processor302may be one or more of any type of computer processing device or element, such as a central processing unit (CPU), a co-processor (e.g., a mathematics, graphics, or encryption co-processor), a digital signal processor (DSP), a network processor, and/or a form of integrated circuit or controller that performs processor operations. In some cases, processor302may be one or more single-core processors. In other cases, processor302may be one or more multi-core processors with multiple independent processing units. Processor302may also include register memory for temporarily storing instructions being executed and related data, as well as cache memory for temporarily storing recently-used instructions and data.

Memory304may store program instructions and/or data on which program instructions may operate. By way of example, memory304may store program instructions on a non-transitory, computer-readable medium, such that the instructions are executable by processor302to carry out any of the methods, processes, or operations disclosed in this specification or the accompanying drawings. Memory304may be any form of computer-usable memory, including but not limited to random access memory (RAM), read-only memory (ROM), and non-volatile memory. This may include flash memory, hard disk drives, solid state drives, re-writable compact discs (CDs), re-writable digital video discs (DVDs), and/or tape storage, as just a few examples. Further, Memory304may include fixed memory as well as one or more removable memory units, the latter including but not limited to various types of secure digital (SD) cards. Thus, memory304can represent both main memory units, as well as long-term storage. Other types of memory may include biological memory.

As shown inFIG.3, memory304may include firmware314A, kernel314B, and/or applications314C. Firmware314A may be program code used to boot or otherwise initiate some or all of functions and/or operations of computing device300. Kernel314B may be an operating system, including modules for memory management, scheduling and management of processes, input/output, and communication. Kernel314B may also include device drivers that allow the operating system to communicate with the hardware modules (e.g., memory units, networking interfaces, ports, and busses), of computing device300. Applications314C may be one or more user-space software programs, such as web browsers or email clients, as well as any software libraries used by these programs. In some examples, applications314C may include one or more neural network applications and other deep learning-based applications. Memory304may also store data used by these and other programs and applications.

Input/output unit306may facilitate user and peripheral device interaction with computing device300and/or other computing systems. Input/output unit306may include one or more types of input devices, such as keyboards, one or more touch screens, sensors, biometric sensors, and so on. Similarly, input/output unit306may include one or more types of output devices, such as screens, monitors, printers, speakers, and/or one or more light emitting diodes (LEDs). Additionally or alternatively, computing device300may communicate with other devices using a universal serial bus (USB) or high-definition multimedia interface (HDMI) port interface, for example. In some examples, input/output unit306can be configured to receive data from other devices. For instance, input/output unit306may receive sensor data from vehicle sensors.

As shown inFIG.3, input/output unit306includes graphical user interface (GUI)312, which may be configured to provide information to an operator or another user. GUI312may be displayable one or more display interfaces, or another type of mechanism for conveying information and receiving inputs. In some examples, the representation of GUI312may differ depending on a vehicle situation. For example, computing device300may provide GUI312in a particular format, such as a format with a single selectable option for a remote operator to select from.

Network interface308may take the form of one or more wireline interfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, and so on). Network interface308may also support communication over one or more non-Ethernet media, such as coaxial cables or power lines, or over wide-area media, such as Synchronous Optical Networking (SONET) or digital subscriber line (DSL) technologies. Network interface308may additionally take the form of one or more wireless interfaces, such as IEEE 802.11 interfaces (Wifi), BLUETOOTH® interfaces, global positioning systems (GPS), or wide-area wireless interfaces. However, other forms of physical layer interfaces and other types of standard or proprietary communication protocols may be used over network interface308. Furthermore, network interface308may comprise multiple physical interfaces. For instance, some embodiments of computing device300may include Ethernet, BLUETOOTH®, and Wifi interfaces. In some embodiments, network interface308may enable computing device300to connect with one or more vehicles to allow for remote assistance techniques presented herein.

Referring still toFIG.3, when computing device300determines that a vehicle may encounter a condition or situation along a travel route where navigation progress may be impeded by one or more obstacles or conditions, computing device300may obtain remote assistance. Computing device300may generate a request to obtain remote assistance from an assistant (e.g., a human assistant and/or a computing assistant) that may help resolve a variety of conditions or situations (e.g., obstacles, road conditions, traffic conditions, etc.) that a human driver would typically be able to overcome. For example, remote assistance may be used for assisting computing device300to determine traffic and/or road conditions, avoid obstacles, monitor performance of a route, adjust the navigation of the route, confirm or deny navigation options or maneuvers proposed by a vehicle navigation system, check on passengers, and/or perform other forms of remote assistance.

Further, computing device300may request remote assistance to help identify and/or classify objects in the environment in which the vehicle operates. Such objects may include pedestrians, street signs, vehicles, and other various objects or obstacles detected in the environment. Computing device300may be configured to receive sensor data representative of the objects from a sensor system of the vehicle. For example, a sensor system of the vehicle may detect objects in the environment and provide sensor data representing the objects to computing device300. The sensor system may have various sensors, including one or more cameras, one or more radar units, one or more light detection and ranging (lidar) sensors, one or more microphones, one or more radio units, and other sensors. Each of the sensors may capture sensor data and communicate the sensor data to the computing device300. In some implementations, the sensor data may be received from an image capture device (e.g., a camera) and may include image or video data. In other implementations, the sensor data may include data received from a lidar unit. Further, the sensor data may include radar, audio, or other data.

Computing device300may be configured to detect objects in the environment based on the sensor data and may analyze the sensor data to determine a classification or identification of the object. In some implementations, computing device300may identify or classify objects based on a set of known data. For example, sensor data related to objects may be stored in a memory device. Computing device300may compare the received sensor data to the stored sensor data to classify or identify objects. In other implementations, computing device300may be configured to determine objects based on the context of the data. For example, street signs related to construction may generally have an orange color. As such, computing device300may classify objects as constructed-related street signs when computing device300detects objects that are orange and are located near the side of road ways.

When computing device300determines different classifications for an object and/or a low confidence of the classification for the object, the computing device300can request a remote assistant (or a more powerful computing device) to perform one or more assistance tasks, such as determining which classification of the object is correct. The vehicle may transmit the sensor data to a remote computing device over a network, and in some implementations, via a server. The human operator or assistant associated with the remote computing device may in turn use the sensor data as a basis for responding to the request.

The request for assistance sent by computing device300to the remote computing device may indicate details related to the vehicle's situation. For example, the request for assistance may include the sensor data representing an object (e.g., image data, lidar data, audio data, etc.) and one or more classifications and/or identifications of the object in the environment. The request for assistance may also include a score indicative of the probability of the correctness of each classification and/or identification for the object in order to help the assistant or operator classify and/or identify the object. For instance, a score may be provided for each classification that represents the probability of a correct classification and/or identification of the object (e.g., 68% pedestrian, 12% scooter, X % sign, and Y % unknown). Further, the request may specify that the vehicle is currently stopped and may need assistance for classifying and/or identifying the object or obstacle in the environment.

In some implementations, the remote assistance process may be triggered in response to computing device300determining different classifications and/or identifications for an object. For example, during the operation of the vehicle, computing device300may acquire (e.g., via cameras, lidar sensors, radar sensors, and/or other sensors) sensor data representing an object or obstacle in the vehicle's environment. The computing device300may determine a first classification for an object at a first time, and as the vehicle continues to operate in the environment, computing system300may then determine a different or second classification for the object at a second time. In some examples, computing system300may misclassify and/or misidentify the object.

In response to determining different classifications for an object, computing device300may send, to the remote computing system (e.g. a remote assistant), the first and second classifications along with the sensor data that includes the object, such as in the form of an image of the object, a video of the object, and/or audio from the object. For example, computing system300may be configured to provide the different classifications for the object to the remote computing device for an assistant (e.g., human operator) to review and to select therefrom.

In response, computing system300of the vehicle may receive information and/or instructions from the remote assistant. For example the information from the remote assistant may include a correct classification of the object and/or one or more instructions. Computing system300may use the information from the remote assistant as a basis to control the vehicle. For example, the response may include information or instructions to control the operation of the vehicle (e.g., instruct the vehicle to stop at a stop sign if the human operator determines that the object is a stop sign). In some scenarios, the vehicle itself may control its own operation based on the information from the assistant including the classification of the object.

In some implementations, computing device300may analyze the sensor data representative of the objects in the environment to determine a confidence value associated with the classification of the object. The confidence value may be indicative of a likelihood that the object is correctly identified or classified. The confidence value may be calculated in various ways depending on the implementation. In one example, when detecting objects in the environment, computing device300may compare sensor data to predetermined data relating to an object classification. The closer the match between the sensor data to the predetermined data, the higher the confidence value. In other implementations, computing device300may use a mathematical analysis of the sensor data to determine the confidence value associated with the classification of the object. For example, computing device300may detect objects within sensor data (e.g., image data) and determine a confidence value associated with the object. The computing device300may determine that an object has a low detection confidence through analysis of sensor data of the vehicle. In some examples, computing device300may determine that the classification of the object has a confidence value below a confidence threshold value based on being unable to classify the object with a confidence value above a confidence threshold value. For example, if a result of a classification of the object is inconclusive, then the confidence value may be low or below the confidence threshold value.

In response to determining that an object in the environment has a low confidence value, computing device300may transmit, to the remote computing system, a request for remote assistance with the identification and/or classification of the object. For example, computing device300may determine that a classification of an object has a confidence below a confidence threshold value based on sensor data received from a vehicle system and may request assistance for object classification.

The confidence threshold value may be determined or computed by computing device300and may vary depending on the type of object being detected. For example, the confidence threshold value may be lower for an object that may require a quick responsive action from computing device300, such as brake lights on another vehicle. However, in other implementations, the confidence threshold value may be the same for all detected objects. When the confidence value associated with a detected object is greater than the confidence threshold value, computing device300may assume the object was correctly classified and responsively adjust the control of the vehicle based on that assumption. Further, when the confidence level associated with a detected object is less than the confidence threshold value, computing device300may request a remote assistant to determine the classification for the object.

In some implementations, a remote assistance process may be triggered in response to computing device300determining that a confidence level associated with a classification for an object is below a confident threshold value. As an example of the remote assistance process, computing device300may acquire (e.g., via cameras, lidar sensors, radar sensors, and/or other sensors) sensor data representative of an object in the vehicle's environment. Computing device300may then determine that a confidence value for a classification of the object is below the predefined threshold value, and then send, to a remote computing device, the sensor data representative of the object, such as in the form of an image of the object, a video of the object, and/or audio from the object. An assistant or operator associated with the remote computing device may provide an input to the remote computing system indicative of a correct classification of the object and/or one or more instructions for controlling the vehicle. The remote computing device may transmit the response wirelessly or by some other manner to computing device300of the vehicle.

Further, computing device300may receive the response to the request for assistance from a remote computing device that includes a representation of the input and/or feedback from the assistant (e.g., human operator), whether in the form of an instruction to control the vehicle, a correct classification or identification of the object at issue, and/or some other form of feedback. Computing device300may receive the response wirelessly or by some other manner. Upon receipt of the response, computing device300may provide one or more instructions to the vehicle to operate in a manner that is in accordance with the remote assistance data. For example, the computing system300may provide instruction to alter the vehicle's movement, such as by stopping the vehicle, switching the vehicle to a human-controlled mode, changing a velocity of vehicle (e.g., a speed and/or direction), and/or another movement alteration.

FIG.4is a system for wireless communication between computing devices and a vehicle, according to one or more example embodiments. System400may enable vehicles (e.g., vehicle402) to obtain assistance from remote assistants (e.g., human operators) using computing devices positioned remotely from the vehicles (e.g., remote computing device404). Particularly, system400is shown with vehicle402, remote computing device404, and server406communicating wirelessly via network408. System400may include other components not shown within other embodiments, such as firewalls and multiple networks, among others.

Vehicle402may transport passengers or objects between locations, and may take the form of any one or more of the vehicles discussed above, including passenger vehicles, cargo shipping vehicles, farming and manufacturing vehicles, and dual-purpose vehicles. When operating in an autonomous mode (or semi-autonomous mode), vehicle402may navigate to pick up and drop off passengers (or cargo) between desired destinations. In some embodiments, vehicle402can operate as part of a fleet of vehicles, such as within a fleet of ride-share vehicles.

Remote computing device404may represent any type of device related to enabling remote assistance techniques, including but not limited to those described herein. Within examples, remote computing device404may represent any type of device configured to (i) receive information related to vehicle402, (ii) provide an interface (e.g., a GUI, physical input interfaces) through which a human operator can in turn perceive the information and input a response related to the information, and (iii) transmit the response to vehicle402or to other devices (e.g., storage at server406). As such, remote computing device404may take various forms, such as a workstation, a desktop computer, a laptop, a tablet, a mobile phone (e.g., a smart phone), a wearable device (e.g., a headset) and/or a server. In some examples, remote computing device404may include multiple computing devices operating together in a network configuration. In further embodiments, remote computing device404may resemble a vehicle simulation center with the remote operator positioned as the drive of the simulation center. In addition, remote computing device404may operate as a head mountable device that can simulate the perspective of vehicle402.

The position of remote computing device404relative to vehicle402can vary within examples. For instance, remote computing device404may have a remote position from vehicle402, such as operating inside a physical building. In another example, remote computing device404may be physically separate from vehicle402, but operate inside vehicle402to enable a passenger of vehicle402to act as the human operator. For instance, remote computing device404may be a touchscreen device operably by a passenger of vehicle402. Operations described herein that are performed by remote computing device404may be additionally or alternatively performed by vehicle402(i.e., by any system(s) or subsystem(s) of vehicle100). In other words, vehicle402may be configured to provide a remote assistance mechanism with which a driver or passenger of the vehicle can interact.

Operations described herein may be performed by any of the components communicating via network408. For instance, remote computing device404may determine remote assist options for a human operator to review based on different levels of information provided by vehicle402. In some embodiments, vehicle402may determine potential navigation options for remote computing device404to display for a remote operator to review. Potential options could include routes, vehicle movements, and other navigation parameters for review by remote computing device404and/or a remote operator using remote computing device404.

In other embodiments, remote computing device404may analyze sensor data or other information from vehicle402to determine the situation and potential options for a remote operator to review. For instance, remote computing device404may determine a route and/or operations for vehicle402to execute using information from vehicle402and/or other external sources (e.g., server406). In some embodiments, remote computing device404may generate a GUI to display one or more selectable options for review by a remote operator.

Server406may be configured to wirelessly communicate with remote computing device404and vehicle402via network408(or perhaps directly with remote computing device404and/or vehicle402). As such, server406may represent any computing device configured to receive, store, determine, and/or send information relating to vehicle402and the remote assistance thereof. As such, server406may be configured to perform any operation(s), or portions of such operation(s), that is/are described herein as performed by remote computing system404and/or vehicle402. Some implementations of wireless communication related to remote assistance may utilize server406, while others may not.

Network408represents infrastructure that may enable wireless communication between computing devices, such as vehicle402, remote computing device404, and server406. For example, network408can correspond to a wireless communication network, such as the Internet or a cellular wireless communication network. The various systems described above may perform various operations. These operations and related features will now be described.

In some examples, a remote computing system (e.g., remote computing device404or server406) may operate in one of two modes. The first of these modes may serve, in essence, as a means for a human operator (of the vehicle and/or the remote computing system) to provide remote assistance support for the vehicle. The remote computing system may enable a human operator to provide this support in near real-time or less frequently than real-time.

The second of these two modes may serve, at a minimum, as a means for keeping the human operator alert. The human operator may be a passenger or driver of the vehicle, or may be a third party located remotely from the vehicle but tasked with the responsibility of providing remote assistance to the vehicle (and possibly to other vehicles as well). Regardless of who the human operator is, it is desirable to keep the human operator alert so that the human operator can provide optimal remote assistance with minimal delay.

For instance, there may be scenarios in which the vehicle may not have requested remote assistance in a certain amount of time (e.g., one hour), and therefore the human operator tasked with providing remote assistance to the vehicle may not have taken any remote assistance action in that amount of time, which may be long enough where the human operator may become fatigued or otherwise less attentive than desirable. In these and other types of possible scenarios, it may be desirable to periodically prompt the human operator during this time, via the remote computing system, with alertness data to keep them alert. The alertness data may take various forms, such as archived images, audio, or video having confirmed or unconfirmed object identifications, also including generated natural-language questions regarding the confirmed or unconfirmed object identifications.

Remote assistance tasks may also include the human operator providing an instruction to control operation of the vehicle (e.g., instruct the vehicle to travel to a particular destination associated with an identified passenger). In some scenarios, the vehicle itself may control its own operation based on the human operator's feedback related to an identification of an object. In some instances, upon receiving a confirmation that the occupancy of the vehicle meets a desired occupancy, the vehicle control system may cause the vehicle to safely transport the passengers to a requested destination. For example, a remote operator can enable a vehicle to temporarily perform one or more operations to resolve a situation that the vehicle may normally not be permitted to perform. For instance, remote computing device404may be used to enable vehicle402to back up, navigate with a decreased buffer zone, or travel in a zone that is usually off limits (e.g., over the median or use a driveway).

In some embodiments, remote assistance for vehicles may originate from a network of remote operators. For example, a vehicle may submit a request for assistance that is received at an entry point of the network. The entry point may connect the request with a remote operator that can provide assistance. The remote operator may be selected based on credentials associated with the remote operator that indicate that he or she is able to handle the type of assistance that is being requested and/or the operator's availability, among other potential parameters. The entry point may analyze information within the request to route requests for assistance accordingly. For example, the network of remote operators may be used to provide assistance to an entire fleet of autonomous vehicles.

FIG.5illustrates a computing device for enabling remote assistance to be provided to a vehicle, according to one or more example embodiments. When remote assistance is requested by a vehicle, a remote computing device may be configured to enable an assistant (e.g., a human operator) to provide information to the vehicle (e.g., a classification or identification for an object in an environment). For example, the remote computing device may display, on a touchscreen, a graphical user interface (GUI) including information about a vehicle's environment (e.g, captured images or video of objects in the environment). In some embodiments, a human operator may be presented with a GUI that shows the vehicle's sensor data representation of at least a portion of the environment and one or more classifications for an object Depending on the type of response to be provided to the vehicle, the GUI may allow the operator to input information or guidance to the vehicle in a number of different ways (e.g., selecting from a list of operations and/or classifications, typing in a particular mode of operation, selecting a particular region of focus within an image of the environment, etc.).

As shown inFIG.5, computing device500is displaying GUI502, which may include a representation of the environment504, classification option506A, classification option506B, and contextual information508. In other embodiments, GUI502may include more or less elements in other potential arrangements. GUI502may represent a system of interactive visual components for computer software. As such, GUI502may be used to display objects that convey information to a remote operator and also represent actions that may be taken by the remote operator. Computing device500may generate GUI502based on templates enabling an available remote operator to quickly review and provide assistance to a vehicle. In addition, computing device500may display GUI502on a display interface, such as a touch screen or external monitor. In other examples, computing device500may display GUI502or elements from GUI502via a display interface associated with a head-mounted wearable computing device (e.g., augmented reality).

Computing device500may use GUI502to enable interaction between a human operator and vehicles that request assistance. The human operator may provide inputs to computing device500via touch inputs, buttons or hardware inputs, motion and vocal inputs. For example, computing device500may include a microphone to receive vocal inputs and use speech recognition software to derive operations based on the vocal inputs from the operator. In some examples, computing device500may resemble a vehicle emulator that enables a human operator to experience a simulation that mimics the vehicle's perspective.

Representation of the environment504is an object displayable via GUI502that may represent the current environment (or recent environment) from the perspective of the vehicle. By displaying representation of the environment504, a remote operator may review a sensor perspective of the environment as captured by vehicle sensors. For instance, representation of the environment504may display images and/or video of the environment as captured by vehicle cameras. In other instances, sensor data from different types of sensors may be used to generate and provide representation of the environment504via GUI502. For instance, representation of the environment504may include a point cloud developed using radar and/or lidar. As such, representation of the environment504may show the positions of obstacles or other environment elements that may have disrupted the path of travel of the vehicle that is requesting assistance. For example, representation of the environment504may depict the road, other vehicles, pedestrians, bicycles, traffic signals and signs, road elements, and other features within the vehicle's environment.

In some examples, representation of the environment504may depict the vehicle's environment in real-time. For example, vehicle sensors (e.g., cameras) may capture and provide sensor data (e.g., images) of the environment in near real-time to computing device500enabling a human operator to observe the current state of the vehicle's environment. In some examples, the representation of the environment504may depict one or more objects for the human operator to review and classify as further described below.

Computing device500may use visual indicators, such as arrows, boxes, or a combination to highlight aspects of the environment, such as the obstacles blocking the path of travel of the vehicle. For example, computing device500may use computer vision to detect elements within images and identify elements using different colors, such as red boxes to identify pedestrians, blue boxes for other vehicles, and green boxes for stationary objects.

Computing device500may further obtain map data based on a location of the vehicle. For instance, the vehicle may provide GPS measurements or another indication of the vehicle's location within the request for assistance or during subsequent communication between the vehicle and computing device500. By using the vehicle's location, computing device500may acquire map data and further enhance the information included within representation of the environment504and/or other objects displayed via GUI502. For example, computing device500may determine and display representation of environment504as an elevated view of the vehicle and nearby surroundings based on the map data and the sensor data from the vehicle. In some examples, GUI502may include both a sensor perspective of the vehicle's environment and the elevated view based on one or both of the sensor data and map data.

Classification options506A,506B may represent different classifications and/or identifications for an object that may be displayed by GUI502. A human operator or assistant may review and select classification option506A or classification option506B to cause computing device500to relay a classification and/or identification for the object to the vehicle. In some implementations, the vehicle may receive the instructions from computing device500and perform the selected navigation option while monitoring for changes in the environment that may require modifying or stopping performance of the selected navigation option. For instance, while performing a navigation strategy received from a remote assistant, the vehicle may detect the presence of another vehicle or pedestrian that may alter the performance of the remote assistance strategy.

In the embodiment shown inFIG.5, GUI502shows two classification options (i.e., classification options506A,506B). In some instances, GUI502may show only one classification option or more than two classification options. The number of classification options may depend on the situation that the vehicle is involved in when requesting assistance. In some examples, the number of classification options may also be limited to potentially decrease the amount of time that the human operator uses to provide options. For example, a high number of classification options (e.g., 4 or more) may take too much time to review. In addition, the quality of the proposed classification options may decrease as the quantity increases. In some examples, the autonomous vehicle may be configured to only transmit the best classification option based on sensor data measuring the environment.

In some examples, computing device500may receive a request for assistance that does not include any proposed classification options. Computing device500may display GUI502with an indication that the vehicle systems are requesting the human operator to provide a classification for an object to the vehicle. Further, GUI502may enable a human operator to adjust or change existing classification options or provide different classification options for an object determined by the human operator.

In some examples, classification options506A,506B may be displayed in a visual representation that enables quick review by a human operator. Further, GUI502may display multiple classification options (e.g., both classification option506A and classification option506B) together to enable a human operator to review and compare. In some instances, GUI502may be configured to display only a single classification option at a time to avoid confusion.

In some examples, objects may be displayed in an augmented reality via images and/or video data received by the vehicle by computing device500in near real-time. Particularly, the human operator may watch and monitor the vehicle's environment using video, images, and/or other sensor data from the vehicle as the vehicle awaits and receives remote assistance. For example, GUI502can display images or video received from the vehicle in near real-time to enable the human operator to provide continuous assistance to the vehicle. This technique may enable a human operator to closely review the environment of the vehicle and to provide useful remote assistance based on a clear understanding of the environment.

In the embodiment shown inFIG.5, each classification option506A,506B may be displayed with additional information developed to assist the human operator review, such as score512and score516, respectively. Scores512,516may indicate the probability that each classification and/or identification is correct for the object. For instance, a score may be provided for each classification that represents the probability of a correct classification and/or identification of the object (e.g., 68% pedestrian, 12% scooter, X % sign, and Y % unknown).

As shown inFIG.5, each classification option506A,506B may also indicate maneuver techniques514,518, respectively. Maneuver techniques514,518may convey one or more maneuvers that the vehicle will perform should a particular classification option be selected. For example, classification option506A may include a U-turn, which is represented by maneuver technique514as a description (e.g. “U-turn here”) or a visual representation. In some examples, only maneuver techniques that require human operator approval prior to performance may be represented via maneuver techniques514,518.

GUI502may also include contextual information508, which may convey additional information to supplement a remote operator's understanding of the vehicle's situation. As shown inFIG.5, contextual information508includes vehicle information510and location information522. Vehicle information520may indicate a variety of information about the vehicle, such as the type of vehicle, the vehicle sensors on the vehicle, the quantity of the passengers, and target destination, etc. Location information522may represent information based on the current location of the vehicle, such as map data depicting the environment. Contextual information508may also specify information related to the situation, such as how long has the vehicle been stranded and a reason proposed by the vehicle for the stranding.

Referring still toFIG.5, computing device500may obtain information about objects from vehicles operating in the environment and provide the information to other vehicles in the environment. For example, computing device500may compile information about the current state of various conditions or situations within a geographic area (e.g., road blockages, construction, traffic, etc.). In some examples, computing device500may maintain a persistent, time-ordered database of information about the conditions or situations by combining data or information (e.g., reports) from individual vehicles in the environment. The vehicles may be autonomously detecting possible issues or situations within the environment and generating information to send back to computing device500. The vehicles may send sensor data collected from the environment along with the information about situations encountered by the vehicles. Once the computing device500receives the information, the computing device500may be configured to provide notifications to the vehicles in the environment about the situation.

FIGS.6A,6B, and6Cillustrate a scenario encountered by an autonomous vehicle, according to one or more example embodiments. InFIG.6A, scenario600is shown with an environment perspective from the view point from behind vehicle602. Vehicle602may operate in an autonomous mode along a navigation path or travel route within an environment.

During operation, the vehicle may use remote assistance to determine classifications for obstacles or objects encountered along the navigation path. As shown in scenario600, while the vehicle travels along a roadway approaching a four way intersection with stop sign604, vehicle602may determine that an object606is in the vehicle's current navigation path608. For example, vehicle602may determine that object606prevents vehicle602from navigating straight through the intersection to continue along navigation path608. The vehicle may initially classify or identify object606. For example, the vehicle602may classify object606as a pediatrician. However, as the vehicle continues along its travel path, vehicle606may identify or classify object606differently. For example, a vehicle may identify or classify the object as traffic cones and an open manhole. Vehicle602may be configured to request remote assistance since different classifications have been determined for an object in the environment.

Vehicle602may request remote assistance in order to classify or identify the object or obstacle606. Vehicle602may send information that depicts the situation encountered by vehicle602to a remote computing device (e.g., a remote assistant). For example, the information may include a sensor perspective of the environment as measured from the current location of vehicle602. The sensor perspective may include information and measurements from one or more types of sensors. In some examples, the sensor perspective can be conveyed as a 3D map of the environment generated by the sensor system of the vehicle using one or more types of sensors. The sensor perspective may include images or video from cameras, lidar measurements, radar measurements, GPS measurements, and motion measurements from inertial measurement unit (IMU), among other options.

In some examples, the request for assistance may additionally include one or more proposed classifications or identifications for the object. For example, the vehicle may transmit one or more classification options to the remote computing device to obtain input or feedback from an assistant (e.g., a human operator). In one example, the vehicle may send a single suggested classification for the object, and may hold the vehicle's position until a response is received. In other examples, the vehicle may send a set of two or more proposed classification options for the assistant to select from. In such examples, the assistant may be able to determine a classification for the object. Further, the vehicle may make additional requests for assistance in order to determine how best to proceed based on the classification of the object.

In some situations, the vehicle may identify or classify the object along with a confidence value. The vehicle may request remote assistance when the confidence level of one or more classifications of an object is below a predetermined threshold level. The confidence level may be based on one or more different factors about the vehicle's operation and/or the vehicle's view of the environment. In some embodiments, vehicle602may estimate and associate a probability of a correct classification with each classification option, which can be subsequently used by the remote assistant for providing remote assistance.

When the remote computing device receives a request from the vehicle, the remote computing device may responsively generate a GUI that may allow for the assistant or operator to review the situation and provide assistance. For example, the remote computing device may generate a GUI similar to GUI502shown inFIG.5. The GUI may convey sensor data in different arrangements and other information related to the situation (e.g., map data).

FIG.6Bfurther illustrates vehicle602determining a set of navigation options to navigate past obstacle606as depicted in scenario600illustrated inFIG.6A, according to one or more example embodiments. Vehicle602may determine navigation option610, navigation option612, and navigation option614in response to detecting the presence of or receiving information about obstacle606. In some examples, vehicle602may determine navigation options610-614once the vehicle determines a classification for an object. Vehicle602may receive the classification for an object in response to a request for assistance from a remote computing device (e.g., a remote operator or assistant).

Vehicle602may utilize sensor data from one or more types of vehicle sensors to determine each navigation option610-614. In some examples, vehicle602may utilize map/GPS data and/or object classifications to determine each navigation option610-614. The number of navigation options610-614may vary within embodiments and may depend on aspects of the particular scenario. In particular, scenario600shown inFIGS.6A and6Binvolves an intersection that may offer alternative routes that vehicle602may use to determine navigation options610-614as shown. In other scenarios, vehicle602may be able to determine more or fewer navigation options depending on the environment. For example, a scenario involving a vehicle navigating within a parking lot may have limited navigation options if there are numerous vehicles parked in the parking lot limiting available navigation routes.

Vehicle602may send a request for assistance to the remote computing device that includes navigation options610-614. For instance, vehicle602may transmit a request for assistance to a network configured to receive and subsequently connect vehicle602to a computing device of a remote operator available to provide remote assistance. Vehicle602may include navigation options610-614in an initial request for assistance or may subsequently communicate navigation options610-614after establishing a secure wireless connection with the computing device used by a remote operator.

In some embodiments, vehicle602may estimate and associate a score with each navigation option610-614, which can be subsequently used by the remote assistant or operator providing assistance. Each score may depend on various parameters with each navigation option610-614and may be used to provide the remote operator with a reference system for comparing navigation options610-614. In some instances, the score for a given navigation option depends on the maneuver techniques used to complete the navigation option. For example, navigation option614may have a lower score than navigation options610,612because navigation option614requires vehicle602to execute a U-turn. The U-turn may be considered a difficult maneuver technique that requires remote approval prior to execution.

In addition, the score can also depend on the amount a navigation option deviates from the original path (i.e., navigation path608) of vehicle602. For example, navigation option610may have a higher score than navigation options612,614because navigation option610helps vehicle602resume navigation path608quickly while the other navigation options612,614may result in vehicle602taking a longer detour to reach the desired destination. Thus, in some examples, map data can be used to determine scores for each navigation option610-614. The map data can be used to determine route times and other potential factors that are weighed when determining scores for each navigation option.

The score may also depend on other factors. For instance, each score may depend on whether or not vehicle602would need to temporarily reduce the safety buffer maintained around vehicle602while navigating to complete a particular navigation option. The longer duration (i.e., period of time) that vehicle602may need to reduce its safety buffer to execute a navigation option may reduce that option's score. In addition, when the performance of a navigation option requires vehicle602to temporarily break one or more rules of the road, the score associated with that option might be decreased relative to other navigation options that may not require breaking any rules of the road. In some embodiments, the score for each navigation option can be determined based on weighted analysis of multiple factors, such as the maneuver techniques used for each navigation option. For example, vehicle402may factor and weigh various parameters to develop a score for each navigation option.

When transmitting options to a remote computing device for assistance, vehicle602may provide navigation options610-614in various formats. In some examples, vehicle602may provide navigation options610-614in a visual format, such as virtual representations layered on sensor data as further shown inFIG.6C.

In some embodiments, vehicle602may only convey a top navigation option (e.g., navigation option610) to the human operator to receive confirmation before proceeding. Limiting the options may accelerate the overall remote assistance process since the human operator has less to review and can approve or modify the proposed option (e.g., navigation option610). In some instances, vehicle602may only convey sensor information (e.g., images or video) of the environment including obstacle606and a request for assistance to develop a strategy or identifying obstacle606. Other variations are possible within examples.

FIG.6Cdepicts a GUI for enabling remote assistance for scenario600illustrated inFIGS.6A and6B. Particularly, a computing device may cause GUI620to display on a display interface, such as a touchscreen or a high definition (HD) display similar to computing device500displaying GUI502as illustrated inFIG.5. As shown, GUI620includes environment representation621, contextual information630, map data632, and custom route634. In further examples, GUI620may further include other options. For instance, GUI620may include object classification options and a request more information option, which the remote operator can use to obtain additional sensor data or communicate with a passenger.

Environment representation621may convey a perspective of the environment based on sensor data obtained from vehicle sensors, such as cameras. In other embodiments, environment representation621may display a larger portion of vehicle's602environment to provide additional information for the human operator to use to make a decision. For instance, environment representation621may utilize a combination of sensor measures from areas around the vehicle to portray vehicle602within the environment for the human operator to use when providing remote assistance.

In the embodiment shown inFIG.6C, GUI620shows virtual representation of navigation options as option A622, option B624, and option C626. Particularly, option A622is a virtual representation of navigation option610determined by vehicle602, option B624is a virtual representation of navigation option612determined by vehicle602, and option C626is a virtual representation of navigation option614determined by vehicle602. Each option622-626is shown as an overlay on environment representation621to show how vehicle602may navigate and avoid virtual obstacle628representing obstacle606as shown inFIG.6AandFIG.6B. In some examples, options may be shown in different colors and further segmented to include checkpoints that can enable easier monitoring and modification.

In some examples, GUI620may only show one option at a given time. Alternatively, a human operator can customize which options are shown. In addition, GUI620may enable an operator to adjust one or more aspects of the options as well as provide custom route634for vehicle602to perform. Custom route634may represent a navigation strategy provided by the human operator tasked with providing remote assistance. For example, a human operator may draw custom route634on environment representation621or map data632to customize the route utilized by vehicle602. As such, GUI620may also include map data632, which may correspond to one or more maps that represent the current location of vehicle602. A human operator may use map data632to help route plan for a vehicle requesting remote assistance.

In addition, GUI620may also include contextual information630, which can include additional information or data that can help a human operator (or the computing device) provide remote assistance to vehicle602. In the embodiment shown inFIG.6C, contextual information630includes scores and parameters for each option respectively (i.e., option A622, option B624, and option C626). As discussed above, the parameters associated with performance of an option may influence the score for the option. Particularly, deviation from the desired path (e.g., navigation path608shown inFIG.6A), the difficulty of maneuvers associated with a given option, the time required to complete an option, the quantity and complexity of disfavored maneuvers, and other factors (e.g., how long and the extent of which the vehicle might need to reduce the safety buffer maintained around the vehicle) can impact the score for an option. Contextual information630also includes vehicle information and route information. Route information may indicate a current location of vehicle602and a target destination (e.g., a location where vehicle602is dropping off passengers or objects).

FIG.7illustrates an example scenario700involving vehicle702traveling down roadway704. Vehicle702may be operating in an autonomous mode and may include sensor unit710. In some examples, sensor unit710may have a sensor, such as a camera, that has a field of view706. The field of view706may correspond to a region of where the camera may be able to capture an image. In other implementations, sensor unit710may include a radar unit or a lidar unit. For example, the field of view706may correspond to a region over which the radar unit may send and receive signals. Field of view706may not be limited to a single region in front of the vehicle, but instead may correspond to the entire region (e.g., 360-degrees) around the vehicle.FIG.7illustrates an example scenario700in which the sensor unit uses a camera to obtain data about the environment of the vehicle. The description ofFIG.7can also be used with other sensors and is not limited to optical sensors like cameras.

As one example implementation, as shown inFIG.7, there may be two environmental objects at least partially within field of view706of vehicle702. In this example, it is assumed that the field of view706is that of an optical sensor, such as a camera. The camera of the sensor unit710may take a picture or video. This picture video may be analyzed to determine objects in the environment.

When the camera of the sensor unit710captures a video or image, object714may fall completely within the field of view706. A second object712may only partially be located within the capture picture or video. When a system of vehicle702analyzes the picture or video at a first time period, it may be able to identify or classify object714, such as a first object, and as vehicle702continues to approach object714, vehicle702may determine a different classification or identification for object714at a second time. When the vehicle determines different classifications for an object, the vehicle may communicate sensor data associated with the captured image or video to a remote assistant or human operator for further processing. When a human operator views the resulting image or video, he or she may be able to successfully classify and/or identify object714.

When vehicle702has a difficult or hard time classifying an object because the sensor data does not include enough information to determine a classification of an object, vehicle602may classify the object but with a low confidence value. For example, object712may be a street sign, but an image captured by vehicle702may have a portion of the street sign cut off. As such, a sensor system of vehicle702may not be able to accurately classify the street sign and may provide a classification for the object with a low confidence value. When the classification of the object712has a low confidence level, vehicle702may communicate sensor data representing the object to a remote computing device for classifying and identifying the object.

FIG.8is a flow chart of a method for providing remote assistance to a vehicle, according to example implementations. Method800represents an example method that may include one or more operations, functions, or actions, as depicted by one or more of blocks802-810, each of which may be carried out by any of the systems, devices, and/or vehicles shown inFIGS.1-7, among other possible systems. For instance, system500depicted inFIG.5may enable execution of method800.

Those skilled in the art will understand that the flowchart described herein illustrates functionality and operations of certain implementations of the present disclosure. In this regard, each block of the flowchart may represent a module, a segment, or a portion of program code, which includes one or more instructions executable by one or more processors for implementing specific logical functions or steps in the process. The program code may be stored on any type of computer readable medium, for example, such as a storage device including a disk or hard drive.

In addition, each block may represent circuitry that is wired to perform the specific logical functions in the process. Alternative implementations are included within the scope of the example implementations of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrent or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art.

At block802, method800involves receiving a request for assistance from a vehicle to classify an object, wherein the request indicates that the object is associated with a plurality of classifications. A vehicle (e.g., an autonomous or autonomously driven vehicle (ADV)) may be operating in an autonomous mode in which the vehicle may use a computing device to control the operation of the vehicle with little-to-no human input. For example, a person may enter an address into a system of an autonomous vehicle and determine one or more travel routes to a specified destination. The vehicle may then be able to drive, without further input from the person (e.g., the person does not have to steer or touch the brake/gas pedals), to the specified destination.

During operation, the vehicle may receive information (e.g., sensor data) about objects in the environment. When the vehicle system determines different classifications and/or identifications for an object, the vehicle system may request and obtain remote assistance (e.g., human input) that may help the vehicle classify the object. For example, a system of the vehicle may determine an initial classification for an object in an environment at a first time period and, as the vehicle continues to operate in the environment, the vehicle system may determine a different classification for the object. When the vehicle system classifies the object differently, the vehicle system may send a request for remote assistance. The vehicle system may also determine a confidence value or level for each of the classifications. The vehicle may request remote assistance if multiple classifications appear to be viable for the object and/or if a confidence value associated with a classification of an object is below a threshold confidence level. For example, the vehicle may determine that at least one object in the environment has a detection confidence below a threshold based on sensor data. As such, the request may provide an indication to the remote assistant that an object has a low detection confidence.

A remotely positioned computing device associated with an assistant (e.g., a human operator) may initially receive a request for assistance from the vehicle operating in an environment. For example, a remote computing device (e.g., a computer assistant or device) may receive an assistance request from the vehicle system for classifying and/or identifying the object. The request may seek to obtain a classification for an object in the environment. The request may include one or more object classifications for an assistant (e.g., a human operator) to review and to select therefrom. For example, the classification for the object may be determined locally by one or more systems of the vehicle and provided to the remote computing device. The request may also provide a score indicative of the probability of a correct classification and/or identification for the object. In some examples, the request may indicate that the vehicle is stopped at a location with a sensor perspective of the environment.

At block804, method800involves initiating display of a graphical user interface (GUI) at a display device, wherein the graphical user interface is configured to visually represent the object, and wherein the graphical user interface comprises one or more graphical user interface elements to enable input to be provided for classifying the object. Once the remote computing device receives a request for assistance from a vehicle, the remote computing device may provide an interface (e.g., a graphical user interface (GUI)) to present classification options to an assistant (e.g., a human or computer assistant) so that the assistant may review and select therefrom. The classification options that may help the assistant in determining a classification for the object.

The remote computing device may receive sensor data representing the sensor perspective of the environment. For example, the remote computing device may provide an interface (e.g., GUI) for displaying a sensor perspective that represents the current state of the vehicle. The interface of the remote computing device may include images or other sensor data to help represent the object or obstacle encountered by the vehicle. In addition, the remote computing device may also obtain map data based on the location of the vehicle. Using the map data, the remote computing device may display an elevated view of the vehicle at a location based on the map data and the sensor data. In addition, the interface may represent other information, such as information relating to the vehicle (e.g., location, quantity of passengers, type of vehicle, etc.).

In some examples, the remote computing device may cause one or more selectable elements to be displayed such that each selectable element is associated with an object classification. For example, the computing device may initiate a display of one or more graphical user interface elements on a display device. Each of the selectable elements may display a classification for an object. Further, each classification for the object may be displayed with a score indicative of the probability of a correct classification and/or identification for the object to help the assistant or operator classify and/or identify the object. For instance, a score may be provided for each classification that represents the probability of a correct classification and/or identification of the object (e.g., 68% pedestrian, 12% scooter, X % sign, and Y unknown). The probability may be calculated by the vehicle and provided to the remote computing device.

The interface (e.g., GUI) produced by the remote computing device may enable input from the assistant or operator of the remote computing device to be transmitted to the vehicle. For instance, the remote computing device may provide an interface configured with a single selectable option, which when selected by the assistant or operator, causes the remote computing device to transmit a response to the vehicle that includes a classification of the object. In some examples, the interface may display a plurality of selectable options that are each associated with a particular classification of an object. Further, the interface may also request the operator to confirm or deny if the object is associated with a particular object classification (e.g., a pedestrian or a scooter). Without such information from the assistant or operator, the vehicle's ability to navigate in the environment may be limited. Upon reception of the information (e.g. a classification for an object), the vehicle may be configured to develop a navigation strategy based on the information while also monitoring the environment for changes that may require the vehicle to stop or change navigation operations.

At block806, method800involves, based on detecting a selection of at least one of the one or more graphical user interface elements, generating a response that includes a classification of the object. After presenting classification options to the remote assistant (e.g., a human or computer assistant), the remote assistant may review and select a classification option associated with the object. In response to detecting a selection by the assistant, the remote computing device may generate a response that includes the classification for the object. Further, the response may include one or more instructions for the vehicle and/or other input. In some instances, the remote computing system may determine a command based on the operator's input. Once the command is determined, it may be communicated to the vehicle. The vehicle may responsively perform the action indicated by the command once it is received.

At block808, the method800involves transmitting the response to the vehicle. After generating the response to the request for remote assistance, the remote computing device may send the response to the vehicle. The computing device may transmit the response based on detecting a selection of the selectable option. The computing system may transmit the response over a network so the response is sent to the vehicle wirelessly. When the vehicle receives the response from the computing device, the vehicle may be configured to perform navigation operations based on the response while also monitoring the environment for changes that may require additional input from the assistant at the remote computing device.

FIG.9is a flow chart of a method for enabling a vehicle to request remote assistance, according to example implementations. In some examples, method900may be carried out by a system of a vehicle such as vehicle100and/or vehicle200as illustrated and described in reference toFIGS.1and2, respectively. For example, the processes described herein may be implemented as special-function and/or configured general-function hardware modules, portions of program code executed by a processor (e.g., the processor113within computer system112or computing device300ofFIG.3) for achieving specific logical functions, determinations, and/or steps described in connection with the flowcharts shown inFIG.9. Where used, program code can be stored on any type of computer-readable medium (e.g., computer-readable storage medium or non-transitory media, such as data storage114described above with respect to computer system112, for example, such as a storage device.

In addition, each block of the flowchart shown inFIG.9may represent circuitry that is wired to perform the specific logical functions in the process. Unless specifically indicated, functions in the flowchart shown inFIG.9may be executed out of order from that shown or discussed, including substantially concurrent execution of separately described functions, or even in reverse order in some examples, depending on the functionality involved, so long as the overall functionality of the described method is maintained.

At block902, method900includes receiving first sensor data from one or more sensors of a vehicle. A vehicle may be operating in an autonomous mode in which the vehicle may use a computing device or system to control the operation of the vehicle with little-to-no human input. For example, a person or human operator may enter an address into an autonomous vehicle and determine one or more travel routes to the specified destination. The vehicle may then be able to drive, without further input from the person (e.g., the person does not have to steer or touch the brake/gas pedals), to the specified destination.

While the vehicle is operating autonomously, a sensor system of the vehicle may receive sensor data representative of the environment of the vehicle. In some embodiments, the sensor data representing objects of the environment may be received from a camera and include image (or video) data. In other embodiments, the sensor data representative of the objects in the environment may be lidar data, radar data, audio data, or other data.

A computing device or system of the vehicle may adjust or alter the control of the vehicle based on the sensor data received from the various sensors. When the computing device of the vehicle identifies obstacles or other situations encountered by the autonomous vehicle, the vehicle may be able to autonomously determine how to proceed (e.g., by altering velocity, changing trajectory to avoid an obstacle, and so on). In some examples, the vehicle may alter the velocity of the vehicle in response to the sensor data from the various sensors. For instance, the autonomous vehicle may change velocity in order to avoid obstacles, obey traffic laws, etc.

At block904, method900involves determining a first classification for an object in an environment at a first time period based on first sensor data. When operating in an environment, a system of a vehicle may receive information about obstacles or conditions (e.g., a road condition, a travel condition, an obstacle, etc.) in the environment that may impede the vehicle from navigating according to a planned travel route. For example, the vehicle system may obtain information about obstacles or conditions from the vehicle sensor system, other vehicles, and/or remote sources (e.g., map systems or GPS systems). Once a vehicle system receives information about the object or obstacle in an environment, the vehicle system may determine a classification or identification for the object.

At block906, method900includes receiving second sensor data from one or more sensors of a vehicle. While the vehicle continues to operate in the environment, the sensor system of the vehicle may receive data representative of the environment of the vehicle. As discussed above, the data representing objects of the environment may be received from a camera and include image (or video) data. In other embodiments, the sensor data representing objects of the environment may be lidar data, radar data, audio data, or other data.

At block908, method900includes determining a second classification of the object at a second time period based on the second sensor data. While operating in the environment, the vehicle system continues to receive information about the objects or obstacles in an environment. The vehicle system may determine a classification or identification for the object at a second time period based on the sensor data.

At block910, method900involves causing a request for assistance to be sent to a remote assistant for object classification. After a vehicle system receives information about an object or obstacle in an environment, the vehicle system may determine whether to request remote assistance for determining a classification for the object. When a vehicle system determines that the vehicle may benefit from remote assistance to classify an object, the vehicle system may send a request for remote assistance. In some examples, the vehicle system may send the request for assistance upon an occurrence of a triggering event. For example, the vehicle system may use the information about the object to develop a trigger for requesting remote assistance. For example, a remote assistance process may be triggered in response to the vehicle system identifying different classifications for an object in the environment. The remote assistance may help the vehicle determine a classification for the object, determine a navigation strategy for the vehicle, and/or determine a maneuver for the vehicle in an environment.

At block912, method900involves receiving a response from the remote assistant, wherein the response includes a classification of the object. After the vehicle sends a request for assistance to a remote assistant, the vehicle system may receive a response from the remote assistant. The response may include a classification for the object and/or one or more instructions for performing one or more navigations options. The remote assistance may provide the response to the vehicle via a network. As described above, an assistant or operator may provide an input by way of a user interface of the remote computing device. The input may be an identification and/or classification of an object, one or more instructions for the vehicle, and/or other input. In response to receiving the input, a vehicle system may cause a command to be issued to a system of the vehicle. The computing system may issue the command over a network so the command is sent to the vehicle system wirelessly.

In some examples, a command may be entered by an operator or assistant of the remote computing device. The remote computing device may determine a command based on the operator's input. Once the command is determined, it may be communicated to the vehicle. The vehicle may responsively perform the action described by the command once it is received.

At block914, method900involves generating at least one instruction for causing the vehicle to perform one or more navigation operations based on the received classification of the object. Once the vehicle system receives the response from the remote assistant, the vehicle system may generate instructions based on the response that includes the classification of the object. The vehicle system may execute the instruction to cause the vehicle to perform navigational operations.

FIG.10is a schematic diagram of a computer program, according to an example implementation. In some implementations, the disclosed methods may be implemented as computer program instructions encoded on a non-transitory computer-readable storage media in a machine-readable format, or on other non-transitory media or articles of manufacture.

In the embodiment shown inFIG.10, computer program product1000is provided using signal bearing medium1002, which may include one or more programming instructions1004that, when executed by one or more processors may provide functionality or portions of the functionality described above with respect toFIGS.1-9.

Signal bearing medium1002may encompass a non-transitory computer-readable medium1006, such as, but not limited to, a hard disk drive, a Compact Disc (CD), a Digital Video Disk (DVD), a digital tape, memory, components to store remotely (e.g., on the cloud) etc. In some implementations, signal bearing medium1002may encompass computer recordable medium1008, such as, but not limited to, memory, read/write (R/W) CDs, R/W DVDs, etc.

In some implementations, signal bearing medium1002may encompass communications medium1010, such as, but not limited to, a digital and/or an analog communication medium (e.g., a fiber optic cable, a waveguide, a wired communications link, a wireless communication link, etc.). Similarly, signal bearing medium1002may correspond to a remote storage (e.g., a cloud). A computing system may share information with the cloud, including sending or receiving information. For example, the computing system may receive additional information from the cloud to augment information obtained from sensors or another entity. Thus, for example, signal bearing medium1002may be conveyed by a wireless form of communications medium1010.

One or more programming instructions1004may be, for example, computer executable and/or logic implemented instructions. In some examples, a computing device such as computer system112shown inFIG.1or computing device300shown inFIG.3may be configured to provide various operations, functions, or actions in response to programming instructions1004conveyed to the computer system by one or more of computer readable medium1006, computer recordable medium1008, and/or communications medium1010. The non-transitory computer readable medium could also be distributed among multiple data storage elements and/or cloud (e.g., remotely), which could be remotely located from each other. Computing device that executes some or all of the stored instructions could be a vehicle. Alternatively, the computing device that executes some or all of the stored instructions could be another computing device, such as a server.

The above detailed description describes various features and functions of the disclosed systems, devices, and methods with reference to the accompanying figures. While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope being indicated by the following claims.