Suggesting remote vehicle assist actions

Abstract

There is provided a method for data-driven suggestion of remote vehicle assisted actions, the method may include: in response to receiving a request from a vehicle to request assistance to resolve a scene involving the vehicle; determining a plurality of actions to cope with a plurality of previously resolved scenes involving a plurality of vehicles using data stored in the at least one data structure relating to the scenes; causing an indication of the plurality of actions to be provided on at least one display located at a remote location relative to the vehicle; receiving a user selection of one of the plurality of actions; and sending an instruction to the vehicle based on a selected action of the plurality of actions. Systems and computer program products are also provided.

Description

Technical Field

[0001] This application claims the benefit of priority of U.S. Patent Application No. 17 / 941,608, titled "SUGGESTING REMOTE VEHICLE ASSISTANCE ACTIONS", filed on September 9, 2022, the content of which is hereby incorporated by reference in its entirety. Background Art

[0002] The use of autonomous vehicles has been increasing, potentially creating more efficient movement of passengers and goods through a transportation network. Additionally, the use of autonomous vehicles can result in improved vehicle safety and more efficient communication between vehicles. However, even in cases where individual autonomous vehicles are effective, different factors can lead to scenarios that include disruptions to the normal operation of the autonomous vehicles. The rapid resolution of such scenarios can help provide safe and efficient vehicle performance without adversely affecting the vehicle, the occupant(s) of the vehicle, or the surrounding environment of the vehicle. Brief Description of the Drawings

[0003] Figure 1 is an example environment of a vehicle that can implement one or more components of an autonomous system;

[0004] Figure 2 is a diagram of one or more systems of a vehicle including an autonomous system;

[0005] Figure 3 is Figure 1 and Figure 2 is a diagram of one or more devices and / or components of one or more systems;

[0006] Figure 4A is a diagram of certain components of an autonomous system;

[0007] Figure 4B is a diagram of an implementation of a neural network;

[0008] Figure 4C and Figure 4D is a diagram illustrating an example operation of a CNN;

[0009] Figure 5A is a diagram of an example of a system that generates suggested actions for resolving vehicle scenarios according to some embodiments of the current subject matter;

[0010] Figure 5B is an example of a display of suggested actions for resolving vehicle scenarios according to some embodiments of the current subject matter;

[0011] Figure 5CA diagram that is an example of a process for generating proposed actions for resolving vehicle scenarios according to some embodiments of the present subject matter; and

[0012] Figure 6 A flowchart of a process for proposing actions for resolving vehicle scenarios according to some embodiments of the present subject matter. Detailed Description

[0013] In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent that the embodiments described herein may be practiced without these specific details. In some instances, well-known structures and devices are illustrated in block diagram form to avoid unnecessarily obscuring aspects of the present disclosure.

[0014] In the drawings, for ease of description, a particular arrangement or order of illustrative elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements, etc.) is illustrated. However, those skilled in the art will understand that unless explicitly described, the particular order or arrangement of illustrative elements in the drawings is not intended to imply a required processing order or sequence, or a separation of processes. Additionally, unless explicitly described, the inclusion of illustrative elements in the drawings is not intended to imply that such elements are required in all embodiments, nor that the features represented by such elements cannot be included in some embodiments or combined with other elements in some embodiments.

[0015] Furthermore, in the drawings, connecting elements (such as solid lines, dashed lines, or arrows, etc.) are used to illustrate connections, relationships, or associations between two or more other illustrative elements or within them. The absence of any such connecting element is not intended to imply that no connection, relationship, or association can exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the present disclosure. Additionally, for ease of illustration, a single connecting element may be used to represent multiple connections, relationships, or associations between elements. For example, if a connecting element represents the communication of a signal, data, or instruction (e.g., "software instruction"), those skilled in the art should understand that such an element may represent one or more than one signal path (e.g., a bus) that may be required to affect the communication.

[0016] Although terms such as "first," "second," and / or "third," etc. are used to describe various elements, these elements should not be limited by these terms. The terms "first," "second," and / or "third" are only used to distinguish one element from another. For example, without departing from the scope of the described embodiments, a first contact may be referred to as a second contact, and similarly, a second contact may be referred to as a first contact. Both the first contact and the second contact are contacts, but they are not the same contact.

[0017] The terms used in the specification of the various embodiments described herein are included only for the purpose of describing specific embodiments and are not intended to be limiting. As used in the specification of the various embodiments and the appended claims, the singular forms "a", "an", and "the" are also intended to include the plural forms and may be used interchangeably with "one or more than one" or "at least one", unless the context clearly indicates otherwise. It will also be understood that the term "and / or" as used herein refers to and includes any and all possible combinations of one or more of the associated listed items. It will also be understood that when the terms "comprises", "comprising", "includes", and / or "including" are used in this specification, it specifies the presence of the stated features, integers, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0018] As used herein, the terms "communicate" and "communicating" refer to at least one of receiving, receiving, transmitting, conveying, and / or providing information (or information represented by, for example, data, signals, messages, instructions, and / or commands, etc.). For a unit (e.g., a device, a system, a component of a device or system, and / or a combination thereof, etc.) that is to communicate with another unit, this means that the unit can directly or indirectly receive information from the other unit and / or send (e.g., transmit) information to the other unit. This can refer to a direct or indirect connection that is inherently wired and / or wireless. Additionally, two units can communicate with each other even if the information transmitted between the first unit and the second unit is modified, processed, relayed, and / or routed. For example, even if the first unit receives information passively and does not actively transmit information to the second unit, the first unit can communicate with the second unit. As another example, if at least one intermediate unit (e.g., a third unit located between the first unit and the second unit) processes the information received from the first unit and transmits the processed information to the second unit, the first unit can communicate with the second unit. In some embodiments, a message can refer to a network packet (e.g., a data packet, etc.) that includes data.

[0019] As used herein, depending on the context, the term "if" may optionally be construed to mean "when", "at the time", "in response to determining that", and / or "in response to detecting", etc. Similarly, depending on the context, the phrase "if it has been determined" or "if [stated condition or event] is detected" may optionally be construed to mean "when determining...", "in response to determining that", or "at the time of detecting [stated condition or event]" and / or "in response to detecting [stated condition or event]", etc. Further, as used herein, terms such as "has", "have", or "having", etc. are intended to be open-ended terms. Additionally, unless otherwise explicitly stated, the phrase "based on" is intended to mean "at least partially based on".

[0020] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying drawings. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the various described embodiments. However, it will be apparent to one of ordinary skill in the art that the various described embodiments may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0021] General Overview

[0022] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement actions that can provide suggestions on a display screen to an operator providing remote assistance to a vehicle to address a particular problem. The suggested actions can be based on previously collected data, such as past operator decisions, etc. The operator's action selection can be added to the previously collected data and used when providing subsequent suggested actions to that operator and / or at least one other operator.

[0023] Vehicles (such as autonomous vehicles, etc.) may encounter scenarios that require remote assistance. The vehicle can request assistance to address a scenario involving the vehicle. To solve the scenario, determined data from previously solved scenarios involving the same vehicle or other vehicles is used. By comparing the vehicle's current scenario with past similar scenarios, one or more actions that can be used to solve the scenario can be identified. These actions can be indicated on a display located at a remote location relative to the vehicle. These actions can be indicated on the display of an operator experiencing the scenario being solved, and the operator can provide a selection of what is considered to be the action most suitable for addressing the situation experienced by the vehicle. The action selection can correspond to an instruction sent to the vehicle based on one of the selected actions among the multiple actions.

[0024] Some advantages of these techniques include reducing the amount of time an operator spends providing remote assistance to a vehicle to resolve a scenario. Quick resolution of the scenario can help provide safe and effective vehicle performance without adversely affecting the vehicle, the vehicle's occupant(s), or the vehicle's surrounding environment. Quick resolution of the scenario can reduce operator stress and / or quickly calm the occupant(s) of the vehicle in need of assistance, such that this adverse effect does not occur. A recommendation engine that provides recommended actions to the operator can allow the operator to quickly make a decision regarding which action to take by selecting from the recommended actions. The recommended actions can be based on statistical data, such as data related to past operator actions in resolving scenarios, which can increase the operator's confidence when selecting from the recommended actions. Over time, the recommendation engine may become more effective at providing recommended actions as the engine collects data related to the operator's selection of actions. Many scenarios in which an operator is providing remote assistance to a vehicle require the operator to perform a series of steps to resolve the scenario. The recommendation engine can provide recommendations for each step, which can speed up the remote assistance process until resolution.

[0025] By implementation of the systems, methods, and computer program products described herein, techniques for data-driven recommended remote vehicle assistance actions include reducing the amount of time an operator spends providing remote assistance to a vehicle to resolve a scenario. Quick resolution of the scenario can help provide safe and effective vehicle performance without adversely affecting the vehicle, the vehicle's occupant(s), or the vehicle's surrounding environment. Quick resolution of the scenario can reduce operator stress and / or quickly calm the occupant(s) of the vehicle in need of assistance, such that this adverse effect does not occur. A recommendation engine that provides recommended actions to the operator can allow the operator to quickly make a decision regarding which action to take by selecting from the recommended actions. The recommended actions can be based on statistical data, such as data related to past operator actions in resolving scenarios, which can increase the operator's confidence when selecting from the recommended actions. Over time, the recommendation engine may become more effective at providing recommended actions as the engine collects data related to the operator's selection of actions. Many scenarios in which an operator is providing remote assistance to a vehicle require the operator to perform a series of steps to resolve the scenario. The recommendation engine can provide recommendations for each step, which can speed up the remote assistance process until resolution.

[0026] Now refer to Figure 1, exemplary environment 100 is illustrated, in which vehicles with autonomous systems and vehicles without autonomous systems operate. As illustrated, environment 100 includes vehicles 102a - 102n, objects 104a - 104n, routes 106a - 106n, area 108, vehicle - to - infrastructure (V2I) devices 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118. Vehicles 102a - 102n, vehicle - to - infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 are interconnected via a wired connection, a wireless connection, or a combination of wired and wireless connections (e.g., establishing a connection for communication, etc.). In some embodiments, objects 104a - 104n are interconnected with at least one of vehicles 102a - 102n, vehicle - to - infrastructure (V2I) devices 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 via a wired connection, a wireless connection, or a combination of wired and wireless connections.

[0027] Vehicles 102a - 102n (individually referred to as vehicle 102 and collectively as vehicles 102) include at least one device configured to transport goods and / or people. In some embodiments, vehicle 102 is configured to communicate with V2I device 110, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, vehicles 102 include cars, buses, trucks, and / or trains, etc. In some embodiments, vehicle 102 is the same as or similar to vehicle 200 described herein (see Figure 2 ). In some embodiments, vehicles 200 in the set of vehicles 200 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicle 102 travels along corresponding routes 106a - 106n (individually referred to as route 106 and collectively as routes 106). In some embodiments, one or more than one vehicle 102 includes an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

[0028] Objects 104a - 104n (individually referred to as object 104 and collectively referred to as objects 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, and / or at least one structure (e.g., building, sign, fire hydrant, etc.). Each object 104 (e.g., located at a fixed location and over a period of time) is stationary or (e.g., having a speed and associated with at least one trajectory) moving. In some embodiments, object 104 is associated with a corresponding location in region 108.

[0029] Routes 106a - 106n (individually referred to as route 106 and collectively referred to as routes 106) are each associated with (e.g., define) a sequence of actions (also referred to as a trajectory) that a connected AV can navigate along. Each route 106 begins at an initial state (e.g., a state corresponding to a first spatio - temporal location and / or speed, etc.) and ends at a final goal state (e.g., a state corresponding to a second spatio - temporal location different from the first spatio - temporal location) or a goal region (e.g., a subspace of acceptable states (e.g., a termination state)). In some embodiments, the first state includes a location where one or more individuals will board the AV, and the second state or region includes one or more locations where one or more individuals boarding the AV will disembark. In some embodiments, route 106 includes multiple acceptable sequences of states (e.g., multiple sequences of spatio - temporal locations) that are associated with (e.g., define) multiple trajectories. In an example, route 106 includes only high - level actions or imprecise state locations, such as a series of connected roads indicating a direction change at a roadway intersection. Additionally or alternatively, route 106 can include more precise actions or states, such as, for example, a specific target lane or precise location within a lane region and a target rate at those locations. In an example, route 106 includes multiple precise state sequences along at least one high - level action with a finite look - ahead horizon to reach an intermediate goal, where the combination of successive iterations of the finite - horizon state sequences cumulatively corresponds to multiple trajectories that together form a high - level route terminating at the final goal state or region.

[0030] Region 108 includes a physical region (e.g., a geographic region) in which vehicle 102 can navigate. In an example, region 108 includes at least one state (e.g., a country, a province, an individual state among multiple states included in a country, etc.), at least a portion of a state, at least one city, at least a portion of a city, etc. In some embodiments, region 108 includes at least one named arterial road (referred to herein as a "road"), such as a highway, an interstate highway, a parkway, an urban street, etc. Additionally or alternatively, in some examples, region 108 includes at least one unnamed road, such as a lane, a section of a parking lot, a section of a vacant and / or undeveloped area, a dirt road, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road through which vehicle 102 can pass). In an example, a road includes at least one lane associated with (e.g., identified based on) at least one lane marking line.

[0031] A vehicle-to-infrastructure (V2I) device 110 (sometimes referred to as a vehicle-to-infrastructure or vehicle-to-everything (V2X) device) includes at least one device configured to communicate with vehicle 102 and / or V2I system 118. In some embodiments, V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, queue management system 116, and / or V2I system 118 via network 112. In some embodiments, V2I device 110 includes a radio frequency identification (RFID) device, a sign, a camera (e.g., a two-dimensional (2D) and / or three-dimensional (3D) camera), a lane marking, a streetlight, a parking meter, etc. In some embodiments, V2I device 110 is configured to communicate directly with vehicle 102. Additionally or alternatively, in some embodiments, V2I device 110 is configured to communicate with vehicle 102, remote AV system 114, and / or queue management system 116 via V2I system 118. In some embodiments, V2I device 110 is configured to communicate with V2I system 118 via network 112.

[0032] Network 112 includes one or more wired and / or wireless networks. In an example, network 112 includes a cellular network (e.g., a Long-Term Evolution (LTE) network, a third-generation (3G) network, a fourth-generation (4G) network, a fifth-generation (5G) network, a Code Division Multiple Access (CDMA) network, etc.), a Public Land Mobile Network (PLMN), a Local Area Network (LAN), a Wide Area Network (WAN), a Metropolitan Area Network (MAN), a telephone network (e.g., a Public Switched Telephone Network (PSTN)), a private network, an ad hoc network, an intranet, the Internet, a fiber-optic-based network, a cloud computing network, etc., and / or a combination of some or all of these networks.

[0033] The remote AV system 114 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the network 112, the queue management system 116, and / or the V2I system 118 via the network 112. In an example, the remote AV system 114 includes a server, a server group, and / or other similar devices. In some embodiments, the remote AV system 114 is co-located with the queue management system 116. In some embodiments, the remote AV system 114 participates in the installation of some or all of the components of the vehicle (including autonomous systems, autonomous vehicle computing, and / or software implemented by autonomous vehicle computing, etc.). In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the life of the vehicle.

[0034] The queue management system 116 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the V2I system 118. In an example, the queue management system 116 includes a server, a server group, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ridesharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including autonomous systems and / or vehicles not including autonomous systems), etc.).

[0035] In some embodiments, the V2I system 118 includes at least one device configured to communicate with the vehicle 102, the V2I device 110, the remote AV system 114, and / or the queue management system 116 via the network 112. In some examples, the V2I system 118 is configured to communicate with the V2I device 110 via a connection different from the network 112. In some embodiments, the V2I system 118 includes a server, a server group, and / or other similar devices. In some embodiments, the V2I system 118 is associated with a municipal authority or a private institution (e.g., a private institution for maintaining the V2I device 110, etc.).

[0036] Provide Figure 1 The number and arrangement of the illustrated elements are provided as examples. Compared with Figure 1 the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements with different arrangements. Additionally or alternatively, at least one element of the environment 100 may perform one or more functions described as being performed by Figure 1 at least one different element. Additionally or alternatively, at least one set of elements of the environment 100 may perform one or more functions described as being performed by at least one different set of elements of the environment 100.

[0037] Now refer to Figure 2 , vehicle 200 (which may be the same as or similar to Figure 1 vehicle 102) includes an autonomous system 202, a powertrain control system 204, a steering control system 206, and a braking system 208, or is associated with the autonomous system 202, the powertrain control system 204, the steering control system 206, and the braking system 208. In some embodiments, vehicle 200 is the same as or similar to vehicle 102 (see Figure 1 ). In some embodiments, the autonomous system 202 is configured to endow vehicle 200 with autonomous driving capabilities (e.g., implement at least one of the following driving automatic or maneuver-based functions, features, and / or devices, etc., where the at least one driving automatic or maneuver-based function, feature, and / or device enables vehicle 200 to operate partially or completely without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that dispense with reliance on human intervention, such as level 5 ADS-operated vehicles, etc.), highly autonomous vehicles (e.g., vehicles that dispense with reliance on human intervention in certain situations, such as level 4 ADS-operated vehicles, etc.), and / or conditionally autonomous vehicles (e.g., vehicles that dispense with reliance on human intervention in limited situations, such as level 3 ADS-operated vehicles, etc.), etc.). In one embodiment, the autonomous system 202 includes the operational or tactical functionality required to operate vehicle 200 in road traffic and continuously perform part or all of the dynamic driving task (DDT). In another embodiment, the autonomous system 202 includes an advanced driver assistance system (ADAS) that includes driver support features. The autonomous system 202 supports various levels of driving automation ranging from no driving automation (e.g., level 0) to full driving automation (e.g., level 5). For a detailed description of fully autonomous vehicles and highly autonomous vehicles, reference may be made to SAE International Standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire content of which is incorporated by reference. In some embodiments, vehicle 200 is associated with an autonomous queue manager and / or a ridesharing company.

[0038] The autonomous system 202 includes a sensor suite that includes one or more devices such as a camera 202a, a LiDAR sensor 202b, a Radar sensor 202c, and a microphone 202d. In some embodiments, the autonomous system 202 may include more or fewer devices and / or different devices (e.g., ultrasonic sensors, inertial sensors, GPS receivers (discussed below), and / or odometer sensors for generating data associated with an indication of the distance the vehicle 200 has traveled, etc.). In some embodiments, the autonomous system 202 uses one or more devices included in the autonomous system 202 to generate data associated with the environment 100 described herein. The data generated by one or more devices of the autonomous system 202 can be used by one or more systems described herein to observe the environment (e.g., environment 100) in which the vehicle 200 is located. In some embodiments, the autonomous system 202 includes a communication device 202e, an autonomous vehicle computing 202f, a drive-by-wire (DBW) system 202h, and a safety controller 202g.

[0039] The camera 202a includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., a bus 302 that is the same as or similar to Figure 3 the bus). The camera 202a includes at least one camera (e.g., a digital camera using an optical sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, and / or an event camera, etc.) for capturing images including physical objects (e.g., cars, buses, curbs, and / or people, etc.). In some embodiments, the camera 202a generates camera data as an output. In some examples, the camera 202a generates camera data including image data associated with the image. In this example, the image data may specify at least one parameter corresponding to the image (e.g., image characteristics such as exposure, brightness, etc., and / or an image timestamp, etc.). In such an example, the image may be in a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a includes a plurality of independent cameras configured (e.g., positioned) on the vehicle for capturing images for the purpose of stereovision (stereo vision). In some examples, the camera 202a includes generating image data and transmitting the image data to the autonomous vehicle computing 202f and / or a queue management system (e.g., the same as Figure 1a plurality of cameras of the same or similar queue management system as the queue management system 116. In such an example, the autonomous vehicle computing 202f determines the depth to one or more objects in the fields of view of at least two of the plurality of cameras based on image data from at least two cameras. In some embodiments, the camera 202a is configured to capture images of objects within a distance relative to the camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Thus, the camera 202a includes features such as sensors and lenses optimized for sensing objects at one or more distances relative to the camera 202a.

[0040] In an embodiment, the camera 202a includes at least one camera configured to capture one or more images associated with one or more traffic lights, street signs, and / or other physical objects that provide visual navigation information. In some embodiments, the camera 202a generates traffic light data associated with one or more images. In some examples, the camera 202a generates TLD (Traffic Light Detection) data associated with one or more images including a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, the camera 202a that generates TLD data is different from other systems incorporating cameras described herein in that the camera 202a may include one or more cameras having a wide field of view (e.g., a wide-angle lens, a fish-eye lens, and / or a lens having a viewing angle of about 120 degrees or greater, etc.) to generate images related to as many physical objects as possible.

[0041] The Light Detection and Ranging (LiDAR) sensor 202b includes being configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., with Figure 3at least one device configured to communicate via a bus (e.g., a bus identical or similar to bus 302). The LiDAR sensor 202b includes a system configured to emit light from a light emitter (e.g., a laser emitter). The light emitted by the LiDAR sensor 202b includes light outside the visible spectrum (e.g., infrared light, etc.). In some embodiments, during operation, the light emitted by the LiDAR sensor 202b encounters a physical object (e.g., a vehicle) and is reflected back to the LiDAR sensor 202b. In some embodiments, the light emitted by the LiDAR sensor 202b does not penetrate the physical object it encounters. The LiDAR sensor 202b further includes at least one light detector that detects the light after the light emitted from the light emitter encounters a physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., a point cloud and / or a combined point cloud, etc.) representing the objects included in the field of view of the LiDAR sensor 202b. In some examples, at least one data processing system associated with the LiDAR sensor 202b generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In such examples, the image is used to determine the boundary of the physical object in the field of view of the LiDAR sensor 202b.

[0042] A Radio Detection and Ranging (Radar) sensor 202c includes at least one device configured to communicate with a communication device 202e, an autonomous vehicle computer 202f, and / or a safety controller 202g via a bus (e.g., a bus identical or similar to Figure 3 bus 302). The Radar sensor 202c includes a system configured to emit (pulsed or continuous) radio waves. The radio waves emitted by the Radar sensor 202c include radio waves within a pre-determined spectrum. In some embodiments, during operation, the radio waves emitted by the Radar sensor 202c encounter a physical object and are reflected back to the Radar sensor 202c. In some embodiments, the radio waves emitted by the Radar sensor 202c are not reflected by some objects. In some embodiments, at least one data processing system associated with the Radar sensor 202c generates a signal representing the objects included in the field of view of the Radar sensor 202c. For example, at least one data processing system associated with the Radar sensor 202c generates an image representing the boundary of a physical object and / or the surface of a physical object (e.g., the topology of the surface), etc. In some examples, the image is used to determine the boundary of the physical object in the field of view of the Radar sensor 202c.

[0043] The microphone 202d includes at least one device configured to communicate with the communication device 202e, the autonomous vehicle computing 202f, and / or the safety controller 202g via a bus (e.g., a bus the same or similar to Figure 3 bus 302). The microphone 202d includes one or more than one microphone (e.g., an array microphone and / or an external microphone, etc.) that captures an audio signal and generates data associated with (e.g., representing) the audio signal. In some examples, the microphone 202d includes a transducer device and / or a similar device. In some embodiments, one or more than one system described herein can receive the data generated by the microphone 202d and determine the position (e.g., distance, etc.) of an object relative to the vehicle 200 based on the audio signal associated with the data.

[0044] The communication device 202e includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the autonomous vehicle computing 202f, the safety controller 202g, and / or the DBW (drive-by-wire) system 202h. For example, the communication device 202e can include a device the same or similar to Figure 3 communication interface 314. In some embodiments, the communication device 202e includes a vehicle-to-vehicle (V2V) communication device (e.g., a device for enabling wireless communication of data between vehicles).

[0045] The autonomous vehicle computing 202f includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the safety controller 202g, and / or the DBW system 202h. In some examples, the autonomous vehicle computing 202f includes devices such as a client device, a mobile device (e.g., a cellular phone and / or a tablet, etc.), and / or a server (e.g., a computing device including one or more than one central processing unit and / or a graphics processing unit, etc.). In some embodiments, the autonomous vehicle computing 202f is the same or similar to the autonomous vehicle computing 400 described herein. Additionally or alternatively, in some embodiments, the autonomous vehicle computing 202f is configured to communicate with an autonomous vehicle system (e.g., an autonomous vehicle system the same or similar to Figure 1 the remote AV system 114), a queue management system (e.g., a queue management system the same or similar to Figure 1 queue management system 116), a V2I device (e.g., a V2I device the same or similar to Figure 1 V2I device 110), and / or a V2I system (e.g., a V2I system the same or similar to Figure 1communicate with a V2I system 118 that is the same as or similar to it.

[0046] The safety controller 202g includes at least one device configured to communicate with the camera 202a, the LiDAR sensor 202b, the Radar sensor 202c, the microphone 202d, the communication device 202e, the autonomous vehicle computing 202f, and / or the DBW system 202h. In some examples, the safety controller 202g includes one or more controllers (such as an electrical controller and / or an electromechanical controller, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 200 (such as the powertrain control system 204, the steering control system 206, and / or the braking system 208, etc.). In some embodiments, the safety controller 202g is configured to generate control signals that take precedence over (e.g., override) the control signals generated and / or transmitted by the autonomous vehicle computing 202f.

[0047] The DBW system 202h includes at least one device configured to communicate with the communication device 202e and / or the autonomous vehicle computing 202f. In some examples, the DBW system 202h includes one or more controllers (such as an electrical controller and / or an electromechanical controller, etc.) configured to generate and / or transmit control signals to operate one or more devices of the vehicle 200 (such as the powertrain control system 204, the steering control system 206, and / or the braking system 208, etc.). Additionally or alternatively, one or more controllers of the DBW system 202h are configured to generate and / or transmit control signals to operate at least one different device of the vehicle 200 (such as turn signals, headlights, door locks, and / or windshield wipers, etc.).

[0048] The powertrain control system 204 includes at least one device configured to communicate with the DBW system 202h. In some examples, the powertrain control system 204 includes at least one controller and / or actuator, etc. In some embodiments, the powertrain control system 204 receives control signals from the DBW system 202h, and the powertrain control system 204 causes the vehicle 200 to perform longitudinal vehicle movements (such as starting to move forward, stopping moving forward, starting to move backward, stopping moving backward, accelerating in a certain direction, decelerating in a certain direction, etc.) or perform lateral vehicle movements (such as making a left turn and / or making a right turn, etc.). In an example, the powertrain control system 204 increases, maintains the same, or decreases the energy (such as fuel and / or electricity, etc.) provided to the motor of the vehicle, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0049] The steering control system 206 includes at least one device configured to rotate one or more wheels of the vehicle 200. In some examples, the steering control system 206 includes at least one controller and / or actuator, etc. In some embodiments, the steering control system 206 rotates two front wheels and / or two rear wheels of the vehicle 200 left or right to turn the vehicle 200 left or right. In other words, the steering control system 206 causes the activities required to regulate the y-axis component of the vehicle's movement.

[0050] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate the vehicle 200 and / or keep it stationary. In some examples, the braking system 208 includes at least one controller and / or actuator configured to close one or more calipers associated with one or more wheels of the vehicle 200 on the corresponding rotors of the vehicle 200. Additionally or alternatively, in some examples, the braking system 208 includes an automatic emergency braking (AEB) system and / or a regenerative braking system, etc.

[0051] In some embodiments, the vehicle 200 includes at least one platform sensor (not explicitly illustrated) for measuring or inferring the nature of the state or condition of the vehicle 200. In some examples, the vehicle 200 includes platform sensors such as a global positioning system (GPS) receiver, an inertial measurement unit (IMU), a wheel speed sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor, etc. Although the braking system 208 is illustrated as being located Figure 2 proximal to the vehicle 200 in, the braking system 208 can be located anywhere in the vehicle 200.

[0052] Now refer to Figure 3, a schematic diagram of an exemplary device 300. As illustrated, device 300 includes a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, a communication interface 314, and a bus 302. In some embodiments, device 300 corresponds to: at least one device of vehicle 102 (e.g., at least one device of a system of vehicle 102); at least one device of V2I device 110 (e.g., at least one device of a system of V2I device 110); at least one device of AV system 114 (e.g., at least one device of a system of AV system 114); at least one device of queue management system 116 (e.g., at least one device of a system of queue management system 116); at least one device of V2I system 118 (e.g., at least one device of a system of V2I system 118); at least one device of camera 202a (e.g., at least one device of a system of camera 202a); at least one device of LiDAR sensor 202b (e.g., at least one device of a system of LiDAR sensor 202b); at least one device of Radar sensor 202c (e.g., at least one device of a system of Radar sensor 202c); at least one device of microphone 202d (e.g., at least one device of a system of microphone 202d); at least one device of communication device 202e (e.g., at least one device of a system of communication device 202e); at least one device of autonomous vehicle computing 202f (e.g., at least one device of a system of autonomous vehicle computing 202f); at least one device of safety controller 202g (e.g., at least one device of a system of safety controller 202g); at least one device of DBW system 202h (e.g., at least one device of a system of DBW system 202h); at least one device of powertrain control system 204 (e.g., at least one device of a system of powertrain control system 204); at least one device of steering control system 206 (e.g., at least one device of a system of steering control system 206); at least one device of braking system 208 (e.g., at least one device of a system of braking system 208); at least one device of platform sensors; and / or one or more devices of network 112 (e.g., one or more devices of a system of network 112).In some embodiments, one or more devices of vehicle 102 (e.g., one or more devices of the system of vehicle 102), one or more devices of V2I device 110 (e.g., one or more devices of the system of V2I device 110), one or more devices of AV system 114 (e.g., one or more devices of the system of AV system 114), one or more devices of queue management system 116 (e.g., one or more devices of the system of queue management system 116), one or more devices of V2I system 118 (e.g., one or more devices of the system of V2I system 118), one or more devices of camera 202a (e.g., one or more devices of the system of camera 202a), one or more devices of LiDAR sensor 202b (e.g., one or more devices of the system of LiDAR sensor 202b), one or more devices of Radar sensor 202c (e.g., one or more devices of the system of Radar sensor 202c), one or more devices of microphone 202d (e.g., one or more devices of the system of microphone 202d), one or more devices of communication device 202e (e.g., one or more devices of the system of communication device 202e), one or more devices of autonomous vehicle computing 202f (e.g., one or more devices of the system of autonomous vehicle computing 202f), one or more devices of safety controller 202g (e.g., one or more devices of the system of safety controller 202g), one or more devices of DBW system 202h (e.g., one or more devices of the system of DBW system 202h), one or more devices of powertrain control system 204 (e.g., one or more devices of the system of powertrain control system 204), one or more devices of steering control system 206 (e.g., one or more devices of the system of steering control system 206), one or more devices of braking system 208 (e.g., one or more devices of the system of braking system 208), one or more devices of platform sensors (e.g., one or more devices of the system of platform sensors), and / or one or more devices of network 112 (e.g., one or more devices of the system of network 112) include at least one device 300 and / or at least one component of device 300. As... Figure 3 As shown, device 300 includes bus 302, processor 304, memory 306, storage component 308, input interface 310, output interface 312, and communication interface 314.

[0053] The bus 302 includes components that permit communication among the components of the device 300. In some embodiments, the processor 304 is implemented in hardware, software, or a combination of hardware and software. In some examples, the processor 304 includes a processor (e.g., a central processing unit (CPU), a graphics processing unit (GPU), and / or an accelerated processing unit (APU), etc.), a microphone, a digital signal processor (DSP), and / or any processing component that can be programmed to perform at least one function (e.g., a field programmable gate array (FPGA) and / or an application specific integrated circuit (ASIC), etc.). The memory 306 includes random access memory (RAM), read only memory (ROM), and / or another type of dynamic and / or static storage device that stores data and / or instructions for use by the processor 304 (e.g., flash memory, magnetic memory, and / or optical memory, etc.).

[0054] The storage component 308 stores data and / or software related to the operation and use of the device 300. In some examples, the storage component 308 includes a hard disk (e.g., a magnetic disk, an optical disk, a magneto-optical disk, and / or a solid state disk, etc.), a compact disc (CD), a digital versatile disc (DVD), a floppy disk, a cassette tape, a magnetic tape, a CD-ROM, RAM, PROM, EPROM, FLASH-EPROM, NV-RAM, and / or another type of computer-readable medium, and corresponding drives.

[0055] The input interface 310 includes components that permit the device 300 to receive information such as via user input (e.g., a touch screen display, a keyboard, a keypad, a mouse, a button, a switch, a microphone, and / or a camera, etc.). Additionally or alternatively, in some embodiments, the input interface 310 includes sensors for sensing information (e.g., a global positioning system (GPS) receiver, an accelerometer, a gyroscope, and / or an actuator, etc.). The output interface 312 includes components for providing output information from the device 300 (e.g., a display, a speaker, and / or one or more light emitting diodes (LEDs), etc.).

[0056] In some embodiments, the communication interface 314 includes transceiver-like components that permit the device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection (e.g., a transceiver and / or separate receiver and transmitter, etc.). In some examples, the communication interface 314 permits the device 300 to receive information from another device and / or provide information to another device. In some examples, the communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, an interface, and / or a cellular network interface, etc.

[0057] In some embodiments, apparatus 300 performs one or more of the processes described herein. Apparatus 300 performs these processes based on software instructions executed by a processor 304 that are stored by a computer-readable medium such as memory 306 and / or storage component 308. A computer-readable medium (e.g., a non-transitory computer-readable medium) is defined herein as a non-transitory memory device. A non-transitory memory device includes storage space located within a single physical storage device or storage space distributed across multiple physical storage devices.

[0058] In some embodiments, software instructions are read into memory 306 and / or storage component 308 from another computer-readable medium or from another apparatus. When executed, the software instructions stored in memory 306 and / or storage component 308 cause processor 304 to perform one or more of the processes described herein. Additionally or alternatively, hardwired circuitry is used in place of or in combination with software instructions to perform one or more of the processes described herein. Accordingly, unless otherwise explicitly stated, embodiments described herein are not limited to any specific combination of hardware circuitry and software.

[0059] Memory 306 and / or storage component 308 includes a data store or at least one data structure (e.g., a database, etc.). Apparatus 300 is capable of receiving information from the data store or at least one data structure in memory 306 or storage component 308, storing information in the data store or at least one data structure, communicating information to the data store or at least one data structure, or searching for information stored in the data store or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0060] In some embodiments, apparatus 300 is configured to execute software instructions stored in memory 306 and / or the memory of another apparatus (e.g., another apparatus that is the same as or similar to apparatus 300). As used herein, the term “module” refers to at least one instruction stored in memory 306 and / or the memory of another apparatus that, when executed by processor 304 and / or the processor of another apparatus (e.g., another apparatus that is the same as or similar to apparatus 300), causes apparatus 300 (e.g., at least one component of apparatus 300) to perform one or more of the processes described herein. In some embodiments, a module is implemented in software, firmware, and / or hardware, etc.

[0061] Provided Figure 3 The number and arrangement of the illustrated components are provided as an example. In some embodiments, Figure 3Compared with the illustrated components, device 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a set of components of device 300 (e.g., one or more than one component) may perform one or more than one function described as being performed by another component or another set of components of device 300.

[0062] Now referring to Figure 4A , an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack") is illustrated. As illustrated, the autonomous vehicle computing 400 includes a perception system 402 (sometimes referred to as a perception module), a planning system 404 (sometimes referred to as a planning module), a positioning system 406 (sometimes referred to as a positioning module), a control system 408 (sometimes referred to as a control module), and a database 410. In some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in and / or implemented in an automatic navigation system of a vehicle (e.g., the autonomous vehicle computing 202f of vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in one or more than one independent systems (e.g., one or more than one system the same as or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, the planning system 404, the positioning system 406, the control system 408, and the database 410 are included in one or more than one independent system located in a vehicle and / or at least one remote system as described herein. In some embodiments, any and / or all of the systems included in the autonomous vehicle computing 400 are implemented in software (e.g., software instructions stored in a memory), computer hardware (e.g., via a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), and / or a field programmable gate array (FPGA), etc.), or a combination of computer software and computer hardware. It will also be understood that, in some embodiments, the autonomous vehicle computing 400 is configured to communicate with remote systems (e.g., an autonomous vehicle system the same as or similar to the remote AV system 114, a queue management system 116 the same as or similar to the queue management system 116, and / or a V2I system 118 the same as or similar to the V2I system, etc.).

[0063] In some embodiments, the perception system 402 receives data associated with at least one physical object in the environment (e.g., data used by the perception system 402 to detect the at least one physical object), and classifies the at least one physical object. In some examples, the perception system 402 receives image data captured by at least one camera (e.g., camera 202a), the image being associated with one or more physical objects within the field of view of the at least one camera (e.g., representing the one or more physical objects). In such examples, the perception system 402 classifies the at least one physical object based on one or more groupings of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.). In some embodiments, based on the classification of the physical objects by the perception system 402, the perception system 402 transmits data associated with the classification of the physical objects to the planning system 404.

[0064] In some embodiments, the planning system 404 receives data associated with a destination, and generates data associated with at least one route (e.g., route 106) along which a vehicle (e.g., vehicle 102) can travel towards the destination. In some embodiments, the planning system 404 periodically or continuously receives data from the perception system 402 (e.g., the data associated with the classification of the physical objects described above), and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the perception system 402. In other words, the planning system 404 can perform tasks related to the tactical functions required to operate the vehicle 102 in road traffic. Tactical efforts involve maneuvering the vehicle in traffic during the journey, which includes but is not limited to deciding whether and when to overtake another vehicle, change lanes, or select an appropriate speed, acceleration, deceleration, etc. In some embodiments, the planning system 404 receives data associated with the updated position of the vehicle (e.g., vehicle 102) from the positioning system 406, and the planning system 404 updates at least one trajectory or generates at least one different trajectory based on the data generated by the positioning system 406.

[0065] In some embodiments, the positioning system 406 receives data associated with (e.g., representing) the location of a vehicle (e.g., vehicle 102) in an area. In some examples, the positioning system 406 receives LiDAR data associated with at least one point cloud generated by at least one LiDAR sensor (e.g., LiDAR sensor 202b). In certain examples, the positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and the positioning system 406 generates a combined point cloud based on the respective point clouds. In these examples, the positioning system 406 compares the at least one point cloud or combined point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in the database 410. Then, based on the positioning system 406 comparing the at least one point cloud or combined point cloud with the map, the positioning system 406 determines the position of the vehicle in the area. In some embodiments, the map includes a combined point cloud of the area generated prior to the navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of roadway geometry, a map describing the connectivity of the road network, a map describing the physical properties of roadways (such as traffic speed, traffic flow, the number of vehicle and bicycle traffic lanes, lane width, lane traffic direction or the type and location of lane markings, or a combination thereof, etc.), and a map describing the spatial location of road features (such as crosswalks, traffic signs or various types of other driving signal lights, etc.). In some embodiments, the map is generated in real time based on the data received by the perception system.

[0066] In another example, the positioning system 406 receives global navigation satellite system (GNSS) data generated by a global positioning system (GPS) receiver. In some examples, the positioning system 406 receives GNSS data associated with the location of a vehicle in an area, and the positioning system 406 determines the latitude and longitude of the vehicle in the area. In such examples, the positioning system 406 determines the position of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, the positioning system 406 generates data associated with the position of the vehicle. In some examples, based on the positioning system 406 determining the position of the vehicle, the positioning system 406 generates data associated with the position of the vehicle. In such examples, the data associated with the position of the vehicle includes data associated with one or more semantic properties corresponding to the position of the vehicle.

[0067] In some embodiments, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle. In some examples, the control system 408 receives data associated with at least one trajectory from the planning system 404, and the control system 408 controls the operation of the vehicle by generating and transmitting control signals to cause the powertrain control system (e.g., the DBW system 202h and / or the powertrain control system 204, etc.), the steering control system (e.g., the steering control system 206), and / or the braking system (e.g., the braking system 208) to operate. For example, the control system 408 is configured to perform operational functions such as lateral vehicle motion control or longitudinal vehicle motion control. Lateral vehicle motion control causes activities required to regulate the y-axis component of the vehicle motion. Longitudinal vehicle motion control causes activities required to regulate the x-axis component of the vehicle motion. In an example, in the case where the trajectory includes a left turn, the control system 408 transmits a control signal to cause the steering control system 206 to adjust the steering angle of the vehicle 200, thereby causing the vehicle 200 to turn left. Additionally or alternatively, the control system 408 generates and transmits control signals to cause other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.) to change states.

[0068] In some embodiments, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model (e.g., at least one multi-layer perceptron (MLP), at least one convolutional neural network (CNN), at least one recurrent neural network (RNN), at least one autoencoder, and / or at least one transformer, etc.). In some examples, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model individually or in combination with one or more of the above systems. In some examples, the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408 implement at least one machine learning model as part of a pipeline (e.g., a pipeline for identifying one or more objects located in the environment, etc.). The following are examples of Figures 4B to 4D implementations including machine learning models.

[0069] The database 410 stores data transmitted to, received from, and / or updated by the perception system 402, the planning system 404, the positioning system 406, and / or the control system 408. In some examples, the database 410 includes a storage component for storing operation-related data and / or software and using at least one system of the autonomous vehicle computing 400 (e.g., associated with Figure 3the same or similar storage components as the storage component 308). In some embodiments, the database 410 stores data associated with 2D and / or 3D maps of at least one area. In some examples, the database 410 stores data associated with 2D and / or 3D maps of a part of a city, multiple parts of multiple cities, multiple cities, counties, states, and / or countries (e.g., nations), etc. In such examples, a vehicle (e.g., a vehicle the same or similar to the vehicle 102 and / or the vehicle 200) can drive along one or more drivable areas (e.g., single-lane roads, multi-lane roads, highways, back roads, and / or off-road paths, etc.), and cause at least one LiDAR sensor (e.g., a LiDAR sensor the same or similar to the LiDAR sensor 202b) to generate data associated with an image representing the objects included in the field of view of the at least one LiDAR sensor.

[0070] In some embodiments, the database 410 can be implemented across multiple devices. In some examples, the database 410 is included in a vehicle (e.g., a vehicle the same or similar to the vehicle 102 and / or the vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system the same or similar to the remote AV system 114), a queue management system (e.g., a queue management system the same or similar to Figure 1 the queue management system 116), and / or a V2I system (e.g., a V2I system the same or similar to Figure 1 the V2I system 118), etc.

[0071] Now refer to Figure 4B , a diagram illustrating the implementation of a machine learning model. More specifically, a diagram illustrating the implementation of a convolutional neural network (CNN) 420. For illustrative purposes, the following description of the CNN 420 will be with respect to implementing the CNN 420 by the perception system 402. However, it will be understood that in some examples, the CNN 420 (e.g., one or more components of the CNN 420) is implemented by other systems different from or in addition to the perception system 402, such as the planning system 404, the positioning system 406, and / or the control system 408, etc. Although the CNN 420 includes certain features as described herein, these features are provided for illustrative purposes and are not intended to limit the present disclosure.

[0072] The CNN 420 includes a plurality of convolutional layers including a first convolutional layer 422, a second convolutional layer 424, and a convolutional layer 426. In some embodiments, the CNN 420 includes a subsampling layer 428 (sometimes referred to as a pooling layer). In some embodiments, the subsampling layer 428 and / or other subsampling layers have dimensions smaller than the dimensions of the upstream system (i.e., the amount of nodes). By means of the subsampling layer 428 having dimensions smaller than the dimensions of the upstream layer, the CNN 420 combines the amount of data associated with the initial input and / or output of the upstream layer, thereby reducing the amount of computation required for the CNN 420 to perform downstream convolutional operations. Additionally or alternatively, by means of the subsampling layer 428 being associated with at least one subsampling function (e.g., being configured to perform at least one subsampling function) (as described below with respect to Figure 4C and Figure 4D ), the CNN 420 combines the amount of data associated with the initial input.

[0073] Based on the perception system 402 providing corresponding inputs and / or outputs associated with the first convolutional layer 422, the second convolutional layer 424, and the convolutional layer 426 respectively to generate corresponding outputs, the perception system 402 performs convolutional operations. In some examples, based on the perception system 402 providing data as inputs to the first convolutional layer 422, the second convolutional layer 424, and the convolutional layer 426, the perception system 402 implements the CNN 420. In such examples, based on the perception system 402 receiving data from one or more different systems (e.g., one or more systems of a vehicle the same or similar to the vehicle 102, a remote AV system the same or similar to the remote AV system 114, a queue management system the same or similar to the queue management system 116, and / or a V2I system the same or similar to the V2I system 118, etc.), the perception system 402 provides the data as inputs to the first convolutional layer 422, the second convolutional layer 424, and the convolutional layer 426. The following is a detailed description of Figure 4C including convolutional operations.

[0074] In some embodiments, the perception system 402 provides data associated with an input (referred to as an initial input) to the first convolutional layer 422, and the perception system 402 uses the first convolutional layer 422 to generate data associated with an output. In some embodiments, the perception system 402 provides the output generated by the convolutional layer as an input to a different convolutional layer. For example, the perception system 402 provides the output of the first convolutional layer 422 as an input to the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426. In such an example, the first convolutional layer 422 is referred to as an upstream layer, and the subsampling layer 428, the second convolutional layer 424, and / or the convolutional layer 426 are referred to as downstream layers. Similarly, in some embodiments, the perception system 402 provides the output of the subsampling layer 428 to the second convolutional layer 424 and / or the convolutional layer 426, and in this example, the subsampling layer 428 will be referred to as an upstream layer, and the second convolutional layer 424 and / or the convolutional layer 426 will be referred to as downstream layers.

[0075] In some embodiments, before the perception system 402 provides an input to the CNN 420, the perception system 402 processes data associated with the input provided to the CNN 420. For example, based on the perception system 402 normalizing sensor data (such as, for example, image data, LiDAR data, and / or Radar data, etc.), the perception system 402 processes data associated with the input provided to the CNN 420.

[0076] In some embodiments, based on the perception system 402 performing convolutional operations associated with each convolutional layer, the CNN 420 generates an output. In some examples, based on the perception system 402 performing convolutional operations associated with each convolutional layer and the initial input, the CNN 420 generates an output. In some embodiments, the perception system 402 generates an output and provides the output to the fully connected layer 430. In some examples, the perception system 402 provides the output of the convolutional layer 426 to the fully connected layer 430, where the fully connected layer 430 includes data associated with a plurality of eigenvalues referred to as F1, F2,..., FN. In this example, the output of the convolutional layer 426 includes data associated with a plurality of output eigenvalues representing predictions.

[0077] In some embodiments, the perception system 402 identifies the eigenvalue associated with the highest likelihood of being the correct prediction among a plurality of predictions, and the perception system 402 identifies the prediction from the plurality of predictions. For example, in the case where the fully connected layer 430 includes eigenvalues F1, F2,..., FN and F1 is the largest eigenvalue, the perception system 402 identifies the prediction associated with F1 as the correct prediction among the plurality of predictions. In some embodiments, the perception system 402 trains the CNN 420 to generate predictions. In some examples, the perception system 402 trains the CNN 420 to generate predictions based on the perception system 402 providing training data associated with the prediction to the CNN 420.

[0078] Now refer to Figure 4C and Figure 4D , a diagram illustrating an example operation of the CNN 440 that utilizes the perception system 402. In some embodiments, the CNN 440 (e.g., one or more components of the CNN 440) is the same as or similar to the CNN 420 (e.g., one or more components of the CNN 420) (see Figure 4B ).

[0079] In step 450, the perception system 402 provides data associated with the image as input to the CNN 440 (step 450). For example, as illustrated, the perception system 402 provides data associated with the image to the CNN 440, where the image is a grayscale image represented as values stored in a two-dimensional (2D) array. In some embodiments, the data associated with the image may include data associated with a color image, which is represented as values stored in a three-dimensional (3D) array. Additionally or alternatively, the data associated with the image may include data associated with an infrared image and / or a Radar image, etc.

[0080] In step 455, the CNN 440 performs a first convolution function. For example, based on the CNN 440 providing the values representing the image as input to one or more neurons (not explicitly illustrated) included in the first convolutional layer 442, the CNN 440 performs the first convolution function. In this example, the values representing the image may correspond to the values of a region (sometimes referred to as a receptive field) representing the image. In some embodiments, each neuron is associated with a filter (not explicitly illustrated). The filter (sometimes referred to as a kernel) can be represented as an array of values corresponding in size to the values provided as input to the neuron. In one example, the filter can be configured to identify edges (e.g., horizontal lines, vertical lines, and / or straight lines, etc.). In successive convolutional layers, the filters associated with the neurons can be configured to successively identify more complex patterns (e.g., arcs and / or objects, etc.).

[0081] In some embodiments, based on the CNN 440, the values provided as input to each neuron among one or more neurons included in the first convolutional layer 442 are multiplied by the values of the filters corresponding to each neuron among the same one or more neurons, and the CNN 440 performs a first convolutional function. For example, the CNN 440 may multiply the values provided as input to each neuron among one or more neurons included in the first convolutional layer 442 by the values of the filters corresponding to each neuron among the same one or more neurons to generate a single value or an array of values as output. In some embodiments, the collective output of the neurons of the first convolutional layer 442 is referred to as the convolutional output. In some embodiments, when each neuron has the same filter, the convolutional output is referred to as the feature map.

[0082] In some embodiments, the CNN 440 provides the output of each neuron of the first convolutional layer 442 to the neurons of a downstream layer. For clarity, an upstream layer may be a layer that transmits data to a different layer (referred to as the downstream layer). For example, the CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the subsampling layer. In an example, the CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the first subsampling layer 444. In some embodiments, the CNN 440 adds a bias value to the aggregated set of all values provided to each neuron of the downstream layer. For example, the CNN 440 adds a bias value to the aggregated set of all values provided to each neuron of the first subsampling layer 444. In such an example, the CNN 440 determines the final value to be provided to each neuron of the first subsampling layer 444 based on the aggregated set of all values provided to each neuron and the activation function associated with each neuron of the first subsampling layer 444.

[0083] In step 460, the CNN 440 performs a first subsampling function. For example, based on the CNN 440 providing the values output by the first convolutional layer 442 to the corresponding neurons of the first subsampling layer 444, the CNN 440 may perform a first subsampling function. In some embodiments, the CNN 440 performs the first subsampling function based on an aggregation function. In an example, based on the CNN 440 determining the maximum input among the values provided to a given neuron (referred to as the max pooling function), the CNN 440 performs the first subsampling function. In another example, based on the CNN 440 determining the average input among the values provided to a given neuron (referred to as the average pooling function), the CNN 440 performs the first subsampling function. In some embodiments, based on the CNN 440 providing values to each neuron of the first subsampling layer 444, the CNN 440 generates an output, which is sometimes referred to as the subsampled convolutional output.

[0084] At step 465, CNN 440 performs a second convolution function. In some embodiments, CNN 440 performs the second convolution function in a manner similar to how CNN 440 performs the first convolution function as described above. In some embodiments, based on the values output by the first subsampling layer 444 being provided as inputs to one or more neurons (not explicitly illustrated) included in the second convolutional layer 446, CNN 440 performs the second convolution function. In some embodiments, as described above, each neuron of the second convolutional layer 446 is associated with a filter. As described above, the (one or more) filters associated with the second convolutional layer 446 may be configured to identify more complex patterns compared to the filters associated with the first convolutional layer 442.

[0085] In some embodiments, based on CNN 440 multiplying the values provided as inputs to each of the one or more neurons included in the second convolutional layer 446 by the values of the filters corresponding to each of the one or more neurons, CNN 440 performs the second convolution function. For example, CNN 440 may multiply the values provided as inputs to each of the one or more neurons included in the second convolutional layer 446 by the values of the filters corresponding to each of the one or more neurons to generate a single value or an array of values as output.

[0086] In some embodiments, CNN 440 provides the output of each neuron of the second convolutional layer 446 to the neurons of the downstream layer. For example, CNN 440 may provide the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the subsampling layer. In an example, CNN 440 provides the output of each neuron of the first convolutional layer 442 to the corresponding neurons of the second subsampling layer 448. In some embodiments, CNN 440 adds a bias value to the aggregated set of all values provided to each neuron of the downstream layer. For example, CNN 440 adds a bias value to the aggregated set of all values provided to each neuron of the second subsampling layer 448. In such an example, CNN 440 determines the final value provided to each neuron of the second subsampling layer 448 based on the aggregated set of all values provided to each neuron and the activation function associated with each neuron of the second subsampling layer 448.

[0087] At step 470, the CNN 440 performs a second subsampling function. For example, based on the values output by the second convolutional layer 446 being provided to the respective neurons of the second subsampling layer 448 by the CNN 440, the CNN 440 may perform the second subsampling function. In some embodiments, based on the CNN 440 using an aggregation function, the CNN 440 performs the second subsampling function. In an example, as described above, based on the CNN 440 determining the maximum input or average input among the values provided to a given neuron, the CNN 440 performs the first subsampling function. In some embodiments, based on the CNN 440 providing values to the respective neurons of the second subsampling layer 448, the CNN 440 generates an output.

[0088] At step 475, the CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449. For example, the CNN 440 provides the output of each neuron of the second subsampling layer 448 to the fully connected layer 449 such that the fully connected layer 449 generates an output. In some embodiments, the fully connected layer 449 is configured to generate an output associated with a prediction (sometimes referred to as classification). The prediction may include an indication that the objects included in the image provided as input to the CNN 440 include an object and / or a collection of objects, etc. In some embodiments, the perception system 402 performs one or more operations and / or provides data associated with the prediction to different systems described herein.

[0089] Now refer to Figure 5A , an example of an illustrative environment 500, where an autonomous system is configured to receive suggested actions for solving a scenario. As illustrated, the environment 500 includes an autonomous vehicle (AV) computing (stack system) 502, a remote vehicle assist (RVA) side component 504, a vehicle communication component 506, a network 510 (e.g., the network 112 described with reference to Figure 1 ), cloud / Internet services 512, an operations center system 514, operations center computing 516, an operations center human machine interface (HMI) 518, and an operator computing system 520.

[0090] The AV computing 502, RVA side component 504, vehicle communication component 506, operations center system 514, operations center computing 516, operations center HMI 518, and operator computing system 520 are interconnected via a wired connection, a wireless connection, or a combination of a wired connection and a wireless connection (e.g., establishing a connection for communication, etc.).

[0091] In some embodiments, any system and / or all systems included in the AV computing 502 are implemented in software (e.g., software instructions stored in a memory), computer hardware (e.g., via a microprocessor, a microcontroller, an application specific integrated circuit (ASIC), and / or a field programmable gate array (FPGA), etc.), or a combination of computer software and computer hardware. In some embodiments, the AV computing 502 includes one or more components (e.g., components of the autonomous vehicle computing 400 described with reference to Figure 4A ), and is configured to communicate with the RVA side components 504 (e.g., an autonomous vehicle system identical or similar to the remote AV system 114) and the operator computing system 520 (e.g., a queue management system identical or similar to the queue management system 116 and / or a V2I system identical or similar to the V2I system 118, etc.).

[0092] The RVA side components 504 may be configured to receive signals from the AV computing 502, process the received signals (formatting for transmission via the vehicle communication component 506), and send the processed signals to the vehicle communication component 506. In some embodiments, the RVA side components 504 are involved in the installation of some or all components of the vehicle (including autonomous systems, autonomous vehicle computing, and / or software implemented by the autonomous vehicle computing, etc.). In some embodiments, the RVA side components 504 maintain (e.g., update and / or replace) such components and / or software (e.g., scenario management and resolution software) during the life of the vehicle.

[0093] The vehicle communication component 506 includes transceiver-like components (e.g., a transceiver, a separate receiver, and / or a transmitter, etc.) that permit the AV computing 502 to communicate with the remote operator computing system 520 via the network 510. In some examples, the vehicle communication component 506 permits the vehicle to receive information from another device and / or provide information to another device. In some examples, the communication interface 314 includes an Ethernet interface, an optical interface, a coaxial interface, an infrared interface, a radio frequency (RF) interface, a universal serial bus (USB) interface, interface, and / or a cellular network interface, etc.

[0094] The operation center 514 may include a database of existing operations. The operation center 514 stores operation data sent to the operator computing system 520, operation data received from the operator computing system 520, and / or operation data updated by the operator computing system 520. In some examples, the operation center 514 includes storage components (e.g., associated with Figure 3a storage component 308 that is the same as or similar to the storage component), and uses at least one system of the autonomous vehicle computing 502. In some embodiments, the operation center 514 stores data associated with a 2D map and / or a 3D map of at least one area. In some examples, the operation center 514 stores data associated with a 2D map and / or a 3D map of a part of a city, multiple parts of multiple cities, multiple cities, counties, governments, and / or states (e.g., countries), etc. In such examples, a vehicle (e.g., a vehicle that is the same as or similar to vehicle 102 and / or vehicle 200) can perform one or more operations (e.g., drive along one or more drivable areas and cause at least one LiDAR sensor to generate data associated with an image representing objects included in the field of view of at least one LiDAR sensor).

[0095] The operation center computing 516 can include, but is not limited to: data center computing nodes for a data center, such as servers, server racks, multiple server racks, etc.; cloud computing nodes, which can be distributed across one or more data centers; or a combination of data center computing nodes and cloud computing nodes, etc. The operation center computing 516 can be configured to receive one or more candidate operations from the operation center 514 and (e.g., as described with reference to Figure 4B generate a recommended action by using a machine learning model). The recommended action can be based on statistical data, such as data related to past selected actions in solving scenarios, which can increase the confidence when selecting from the recommended actions. Over time, the recommendation engine can become more effective in providing recommended actions because the engine collects data related to the preferred selection of actions in relation to the scenarios. The operation center computing 516 can provide the recommended action to the operation center HMI 518, and the operation center HMI 518 can filter based on a confidence level indicator whether the action selection can be automated or requires user input. For scenarios that require manual input, the operation center HMI 518 can send the action and scenario data to the selected operator computing system 520. The operator computing system 520 is configured to display (as described with reference to Figure 5B an action panel so that the user of the operator computing system 520 can quickly make a decision regarding which action to take by selecting from the recommended actions).

[0096] The operator computing system 520 may include a graphical user interface that enables communication with a user. The term "graphical user interface" or "GUI" may be used in the singular or plural to describe one or more than one graphical user interface and the respective displays of a particular graphical user interface. Thus, the GUI may represent any graphical user interface, including but not limited to a web browser, a touch screen, or a command line interface (CLI) that processes information and effectively presents the information results to the user. Generally, the GUI may include a plurality of user interface (UI) elements, and some or all of the UI elements are associated with scenario-based operation selections, such as interactive fields, drop-down lists, and buttons that can be operated by the user. These UI elements and other UI elements may be related to the functions of scenario-based operation selections or represent the functions of scenario-based operation selections. For example, as referenced Figure 5B As described, the graphical user interface may be configured to display an action panel. As referenced Figure 5C and Figure 6 As described, in many scenarios where the operator computing system 520 is providing remote assistance to the AV computing 502, the operator computing system 520 is required to perform a series of steps to resolve the scenario.

[0097] Provide Figure 5A The number and arrangement of the illustrated elements are provided as an example. Compared with the Figure 5A illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements with a different arrangement. Additionally or alternatively, at least one element of the environment 500 may perform one or more than one operation- and scenario-related functions described as being performed by at least one different element of Figure 5A . Additionally or alternatively, at least one set of elements of the environment 500 may perform one or more than one operation- and scenario-related functions described as being performed by at least one different set of elements of the environment 500.

[0098] Now refer to Figure 5B , an example of an action panel 530 that may be displayed by an operator computing system (e.g., the operator computing system 520 described in reference Figure 5A ). The action panel 530 depicts examples of recommended action buttons 532a, 532b, 532c for a selected number (e.g., 3, 4, or 5) of the top-ranked recommended actions that can be sent to the operator computing system for update and / or approval by an operator (a user of the operator computing system).

[0099] In some implementations, each suggested action button includes a ranking indicator (e.g., a percentage marker) 534a, 534b, 534c indicating a confidence ranking determined based on previously captured data, where the previously captured data includes or excludes the vehicle involved in the currently processed scenario. The ranking indicators 534a, 534b, 534c can be determined based on processing historical data (including past scenarios and selected actions for specific scenarios) using a machine learning model (as described in references Figure 4B and Figure 5A ), and the machine learning model is configured to predict the operator's tendency to select a specific action for a specific scenario.

[0100] The action panel 530 includes a drop-down button 536 that enables a user to view more actions (e.g., lower-ranked actions associated with the scenario) and potentially select an action from the additional action list. In some implementations, each suggested (or listed) action button 532a, 532b, 532c includes an action symbol 536a, 536b, 536c representing the respective action.

[0101] Now refer to Figure 5C for an example of a process 540 that classifies data to evaluate a scenario and generate a display of suggested actions for remote vehicle assistance (RVA). The process 540 can associate use cases 542a, 542b,... 542n of assistance capabilities with scenarios that a vehicle may encounter. The use cases 542a, 542b,... 542n of assistance capabilities can include dealing with temporary traffic control area issues, dealing with special-purpose vehicles, dealing with obstacles, dealing with law enforcement officers, dealing with traffic lights, dealing with highway conditions, dealing with pick-up and drop-off, dealing with AV stack uncertainties, dealing with collision events, remotely dealing with vehicle problems, dealing with extreme weather conditions, or other traffic or vehicle usage events.

[0102] Each use case 542a, 542b,... 542n of assistance capabilities can be associated with one of a plurality of RVA intervention modes 544a, 544b,... 544m. The RVA intervention modes 544a, 544b,... 544m can include: waypoints defining a trajectory, speed constraints, setting intervals for waiting and moving, release of constraints, editing of constraint clearance, no assistance required, request for path replanning, call RCA, editing of traffic light status, editing (reclassifying or deleting) object traces, cleaning or resetting sensors, controlling auxiliary devices, reporting semantic map deviations, contacting RCA, contacting operations, and other modes of RVA intervention.

[0103] Each RVA intervention mode 544a, 544b, … 544m can be associated with one or more secondary actions 546a, 546b, 546c, … 546p of each intervention. The secondary actions 546a, 546b, 546c, … 546p of each intervention can include plotting waypoints, editing waypoints, selecting a path, waiting for a set time, traveling, stopping, detecting a false alarm, requesting a replan of the path, performing a replan of the path, canceling a replan of the path, making an audio call, making a video call, sending a message alert, and other potential actions.

[0104] Some secondary actions 546a, 546b, 546c, … 546p of each intervention can be associated with one or more subsequent actions 548a, 548b, 548c, … 548r. For example, multiple secondary actions 546a, 546b, 546c, … 546p of each intervention can be combined into a single subsequent action 548a, 548b, 548c, … 548r, or a single secondary action 546a, 546b, 546c, … 546p of an intervention can cause multiple subsequent actions 548a, 548b, 548c, … 548r. The subsequent actions 548a, 548b, 548c, … 548r can include merging into an existing path, turning (right or left), moving to the next (right or left) lane, removing an obstruction and continuing the path, alerting a convoy of an obstruction.

[0105] As referenced Figure 5B As described, the secondary actions 546a, 546b, 546c, … 546p of each intervention and the associated one or more subsequent actions 548a, 548b, 548c, … 548r can be used to generate suggested actions 550 to be displayed in an action panel.

[0106] Now refer Figure 6 , a flowchart of a process 600 for providing suggested actions for a scenario of a vehicle is illustrated. In some embodiments, as referenced Figures 1 to 4D and Figure 5A As described, one or more steps described for process 600 are performed by an autonomous system (e.g., fully and / or partially, etc.). Additionally or alternatively, in some embodiments, as referenced Figures 1 to 4D and Figure 5A As described, one or more steps described for process 600 are performed by another device or group of devices (e.g., fully and / or partially, etc.) separate from or including the autonomous system.

[0107] At 602, receive a request from a vehicle (e.g., an autonomous vehicle) for assistance in dealing with a real-time (ongoing) scenario involving the vehicle (e.g., use cases for assistance capabilities such as dealing with a temporary traffic control area, dealing with a special-purpose vehicle, dealing with an obstacle, etc.).

[0108] At 604, in response to receiving a request from the vehicle for assistance in dealing with a scenario involving the vehicle, determine an action for dealing with the scenario by using historical scenario data including previously selected actions. The action can be determined by executing a recommendation engine stored in at least one data structure. As referenced Figure 5C as described, the action can include a pattern of RVA intervention for dealing with the scenario.

[0109] At 606, provide an indication of one or more actions for selection (display) by a computing system (including a human-machine interface) located at a remote location relative to the vehicle. In some implementations, the indication of the action is automatically processed, and if at least one indication of the action exceeds a set confidence threshold, the subsequent steps of process 600 are automatically executed without human intervention.

[0110] At 608, receive the selection of an action. If at least one indication of the action exceeds a set confidence threshold, automatically select the (one or more) actions with a high confidence level. If the scenario is relatively new and the action indicator is below the set threshold, request a manual selection of one or more actions. The user action selection can be used to select one or more second actions (e.g., secondary actions for each intervention) for dealing with the scenario from a data structure. Second actions with respective indicators can be provided for display to enable a user selection of one of the second actions. The indicator for each proposed second action can indicate the ranking of the relevance between the second action and the scenario (e.g., the percentage of times the proposed second action was selected in previously resolved scenarios of the same vehicle or other vehicles). In some implementations, the user selection of one of the second actions can be used to determine a third action (e.g., a follow-up action) for dealing with the scenario from a data structure. Third actions with respective indicators can be provided for display to enable a user selection of one of the third actions.

[0111] At 610, send an instruction to the vehicle based on the selected action among the multiple actions. In some implementations, the vehicle that receives the instruction automatically causes the vehicle to execute the selected action. The automatic implementation of the action can enable a real-time response for resolving the ongoing scenario.

[0112] According to some non - limiting embodiments or examples, a method is provided. The method includes: in response to receiving a request from a vehicle for assistance in dealing with a scenario involving the vehicle, using data stored in at least one data structure related to a plurality of previously resolved scenarios involving a plurality of vehicles to determine a plurality of actions for dealing with the scenario; causing an indication of the plurality of actions to be provided on at least one display located at a remote location relative to the vehicle; receiving a user selection of one of the plurality of actions; and causing an instruction to be sent to the vehicle based on the selected action among the plurality of actions.

[0113] According to some non - limiting embodiments or examples, a system is provided. The system includes: at least one processor and at least one non - transitory storage medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations including: in response to receiving a request from a vehicle for assistance in dealing with a scenario involving the vehicle, using data stored in at least one data structure related to a plurality of previously resolved scenarios involving a plurality of vehicles to determine a plurality of actions for dealing with the scenario; causing an indication of the plurality of actions to be provided on at least one display located at a remote location relative to the vehicle; receiving a user selection of one of the plurality of actions; and causing an instruction to be sent to the vehicle based on the selected action among the plurality of actions.

[0114] According to some non - limiting embodiments or examples, at least one non - transitory computer - readable medium is provided, which includes one or more instructions that, when executed by at least one processor, cause the at least one processor to perform operations including: in response to receiving a request from a vehicle for assistance in dealing with a scenario involving the vehicle, using data stored in at least one data structure related to a plurality of previously resolved scenarios involving a plurality of vehicles to determine a plurality of actions for dealing with the scenario; causing an indication of the plurality of actions to be provided on at least one display located at a remote location relative to the vehicle; receiving a user selection of one of the plurality of actions; and causing an instruction to be sent to the vehicle based on the selected action among the plurality of actions.

[0115] Further non - limiting aspects or embodiments are set forth in the numbered clauses below:

[0116] Clause 1: A method includes: in response to receiving a request from a vehicle for assistance in dealing with a scenario involving the vehicle, using at least one processor and the at least one processor using data stored in at least one data structure related to multiple previously resolved scenarios involving multiple vehicles to determine multiple actions for dealing with the scenario; the at least one processor causing an indication of the multiple actions to be provided on at least one display located at a remote location relative to the vehicle; receiving a user selection of one of the multiple actions at the at least one processor; and the at least one processor causing an instruction to be sent to the vehicle based on the selected action among the multiple actions.

[0117] Clause 2: The method according to Clause 1 further includes: the at least one processor causing an indicator for each of the proposed actions among the proposed actions to be provided on the at least one display, the indicator being used to indicate the percentage of the number of times the proposed action was selected by an operator in the previously resolved scenario.

[0118] Clause 3: The method according to Clause 1 or 2 further includes: based on a user selection of one of the multiple actions, at the at least one processor and the at least one processor using the data stored in the at least one data structure to determine multiple second actions for dealing with the scenario; the at least one processor causing an indication of the multiple second actions to be provided on the at least one display; and receiving a user selection of one of the multiple second actions at the at least one processor, wherein the instruction sent is based on the selected second action among the multiple second actions.

[0119] Clause 4: The method according to Clause 3 further includes: the at least one processor causing an indicator for each of the proposed second actions among the proposed second actions to be provided on the at least one display, the indicator being used to indicate the percentage of the number of times the proposed second action was selected by an operator in the previously resolved scenario.

[0120] Clause 5: The method according to Clause 3 or 4 further includes: based on a user selection of one of the multiple second actions, at the at least one processor and the at least one processor using the data stored in the at least one data structure to determine multiple third actions for dealing with the scenario; the at least one processor causing an indication of the multiple third actions to be provided on the at least one display; and receiving a user selection of one of the multiple third actions at the at least one processor, wherein the instruction sent is based on the selected third action among the multiple third actions.

[0121] Clause 6: The method according to Clause 5 further includes: the at least one processor causes an indicator for each of the third actions proposed in the third action to be provided on the at least one display, and the indicator is used to indicate the percentage of the number of times the proposed third action is selected by the operator in the previously resolved scenario.

[0122] Clause 7: The method according to any one of the preceding clauses, wherein the data related to the plurality of previously resolved scenarios involving the plurality of vehicles includes the actions previously selected by the operator when resolving the scenarios.

[0123] Clause 8: The method according to any one of the preceding clauses, wherein the data related to the plurality of previously resolved scenarios involving the plurality of vehicles includes statistical data related to the results of the previously resolved scenarios.

[0124] Clause 9: The method according to any one of the preceding clauses, wherein the plurality of actions are provided on the at least one display via a human-machine interface, i.e., HMI.

[0125] Clause 10: The method according to any one of the preceding clauses, wherein the vehicle that receives the instruction automatically causes the vehicle to execute one of the plurality of actions.

[0126] Clause 11: The method according to any one of the preceding clauses, wherein the determination includes: the at least one processor executes a suggestion engine stored in the at least one data structure.

[0127] Clause 12: The method according to any one of the preceding clauses, wherein the at least one processor and the at least one data structure are located at a remote location relative to the vehicle.

[0128] Clause 13: The method according to any one of the preceding clauses, wherein the vehicle is among the plurality of vehicles.

[0129] Clause 14: The method according to any one of the preceding clauses, wherein the vehicle is not among the plurality of vehicles.

[0130] Clause 15: A system includes: at least one computer-readable medium storing computer-executable instructions; and at least one processor configured to execute the computer-executable instructions, and the execution implements the method according to any one of Clauses 1 to 14.

[0131] Clause 16: A non-transitory computer-readable medium comprising at least one program for execution by at least one processor of a system, the at least one program including instructions that, when executed by the at least one processor, cause the system to perform the method according to any one of Clauses 1 to 14.

[0132] In the foregoing description, aspects and embodiments of the present disclosure have been described with reference to numerous specific details, which may vary depending on the implementation. Accordingly, the specification and drawings are to be regarded as illustrative rather than restrictive in a limiting sense. The sole and exclusive indication of the scope of the invention, and what the applicant desires to be the scope of the invention, is the literal and equivalent scope of the claims that issue from this application in the specific form of the granted claims, including any subsequent amendments. Any definition expressly set forth herein for terms to be included in such claims shall govern the meaning of such terms as used in the claims. Additionally, when the term "further comprises" is used in the foregoing specification or the appended claims, the text following that phrase may be additional steps or entities, or sub-steps / sub-entities of the previously recited steps or entities.

Claims

1. A method, comprising: In response to receiving, from a vehicle, a request for assistance in dealing with a scenario involving the vehicle, using at least one processor and the at least one processor using data stored in at least one data structure related to a plurality of previously resolved scenarios involving a plurality of vehicles, to determine a plurality of actions for dealing with the scenario; The at least one processor causes an indication of the plurality of actions to be provided on at least one display located at a remote location relative to the vehicle; Receiving, at the at least one processor, a user selection of one of the plurality of actions; And The at least one processor causes an instruction to be sent to the vehicle based on the selected action among the plurality of actions.

2. The method according to claim 1 further comprises: The at least one processor causes an indicator for each of the proposed actions among the proposed actions to be provided on the at least one display, the indicator being used to indicate the percentage of the number of times the proposed action was selected by an operator in the previously resolved scenario.

3. The method according to claim 1 or 2, further comprising: Based on a user selection of one of the plurality of actions, at the at least one processor and the at least one processor using the data stored in the at least one data structure to determine a plurality of second actions for dealing with the scenario; The at least one processor causes an indication of the plurality of second actions to be provided on the at least one display; And Receiving, at the at least one processor, a user selection of one of the plurality of second actions, wherein the instruction sent is based on the selected second action among the plurality of second actions.

4. The method according to claim 3 further comprises: The at least one processor causes an indicator for each of the proposed second actions among the proposed second actions to be provided on the at least one display, the indicator being used to indicate the percentage of the number of times the proposed second action was selected by an operator in the previously resolved scenario.

5. The method according to claim 3 or 4, further comprising: Based on a user selection of one of the plurality of second actions, at the at least one processor and the at least one processor using the data stored in the at least one data structure to determine a plurality of third actions for dealing with the scenario; The at least one processor causes an indication of the plurality of third actions to be provided on the at least one display; And Receiving, at the at least one processor, a user selection of one of the plurality of third actions, wherein the instruction sent is based on the selected third action among the plurality of third actions.

6. The method according to claim 5 further comprises: The at least one processor causes an indicator for each of the proposed third actions among the proposed third actions to be provided on the at least one display, the indicator being used to indicate the percentage of the number of times the proposed third action was selected by an operator in the previously resolved scenario.

7. The method according to any one of the preceding claims, wherein, The data related to the plurality of previously resolved scenarios involving the plurality of vehicles includes the actions previously selected by the operator in resolving the scenario.

8. The method according to any one of the preceding claims, wherein, Data related to the plurality of previously resolved scenarios involving the plurality of vehicles includes statistical data related to the outcomes of the previously resolved scenarios.

9. The method according to any one of the preceding claims, wherein The plurality of actions are provided via a human-machine interface, i.e., HMI, on the at least one display.

10. The method according to any one of the preceding claims, wherein, The vehicle that receives the instruction automatically causes the vehicle to perform the one action among the plurality of actions.

11. The method according to any one of the preceding claims, wherein, The determining includes: the at least one processor executing a recommendation engine stored in the at least one data structure.

12. The method according to any one of the preceding claims, wherein, The at least one processor and the at least one data structure are located at a remote location with respect to the vehicle.

13. The method according to any one of the preceding claims, wherein, The vehicle is among the plurality of vehicles.

14. The method according to any one of the preceding claims, wherein, The vehicle is not among the plurality of vehicles.

15. A system, comprising: at least one computer-readable medium storing computer-executable instructions; and at least one processor configured to execute the computer-executable instructions, the execution implementing the method according to any one of claims 1 to 14.

16. A non-transitory computer-readable medium comprising at least one program for execution by at least one processor of a system, the at least one program including instructions that, when executed by the at least one processor, cause the system to perform the method according to any one of claims 1 to 14.

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