Systems and methods for vehicles and storage media

Abstract

This invention provides systems and methods for vehicles, as well as storage media. A method for accessing an autonomous driving mode is provided. Some of the described systems receive at least one autonomous vehicle operation task output associated with driving the vehicle in autonomous driving mode in an environment while operating the vehicle in manual driving mode. The at least one autonomous vehicle operation task output is compared with a corresponding threshold, wherein the corresponding threshold is adaptive based on the current operating state of the vehicle in manual driving mode. A transition indicator is set based on this comparison, and a transition from manual driving mode to autonomous driving mode is rejected in response to a transition indicator indicating that a smooth transition from manual driving mode to autonomous driving mode is unavailable. Methods and computer program products are also provided.

Description

Technical Field

[0001] This invention relates to a system and method for a vehicle, as well as a storage medium. Background Technology

[0002] A vehicle can operate in one or more modes. Operating modes include autonomous driving mode, manual driving mode, and non-driving modes such as parking or disabling, or any other mode of operation or non-operation occurring at the vehicle's location. Transitions occur between the various operating modes of the vehicle. Summary of the Invention

[0003] According to one aspect of the invention, a system for a vehicle includes: at least one processor; and at least one memory storing instructions that, when executed by the at least one processor, cause the at least one processor to: receive one or more autonomous vehicle operation task outputs associated with driving the vehicle in an autonomous driving mode in an environment while operating the vehicle in a manual driving mode; compare the one or more autonomous vehicle operation task outputs with the corresponding thresholds, wherein the corresponding thresholds are adaptive based on the current operating state of the vehicle in the manual driving mode; set a transition indicator based on the comparison, wherein the transition indicator indicates the availability of a smooth transition from the manual driving mode to the autonomous driving mode; and refuse a transition from the manual driving mode to the autonomous driving mode in response to the transition indicator indicating that a smooth transition from the manual driving mode to the autonomous driving mode is unavailable.

[0004] According to another aspect of the invention, a method for a vehicle includes: using at least one processor, during operation of the vehicle in a manual driving mode in an environment, receiving one or more autonomous vehicle operation task outputs associated with driving the vehicle in an autonomous driving mode in the same environment; using the at least one processor, comparing the one or more autonomous vehicle operation task outputs with the corresponding threshold, wherein the corresponding threshold is adaptive based on the current operating state of the vehicle in the manual driving mode; using the at least one processor, setting a transition indicator based on the comparison, wherein the transition indicator indicates the availability of a smooth transition from the manual driving mode to the autonomous driving mode; and using the at least one processor, rejecting a transition from the manual driving mode to the autonomous driving mode in response to the transition indicator indicating that a smooth transition from the manual driving mode to the autonomous driving mode is unavailable.

[0005] According to another aspect of the invention, at least one non-transitory storage medium stores instructions that, when executed by at least one processor, cause the at least one processor to perform the method described above. Attached Figure Description

[0006] Figure 1 It is an example environment that can realize a vehicle that includes one or more components of an autonomous system;

[0007] Figure 2 It is a diagram of one or more systems that include autonomous vehicles;

[0008] Figure 3 yes Figure 1 and Figure 2 A diagram of one or more devices and / or one or more system components;

[0009] Figure 4 It is a diagram of some components of an autonomous system;

[0010] Figure 5 This is a diagram illustrating the implementation of processing for autonomous driving mode access;

[0011] Figure 6 This is a block diagram of the autonomous driving mode access;

[0012] Figure 7 This is a block diagram of an adaptive threshold generation system.

[0013] Figure 8 This is a block diagram of the process used to enable autonomous driving mode access. Detailed Implementation

[0014] In the following description, numerous specific details are set forth for purposes of explanation in order to provide a thorough understanding of this disclosure. However, it will be apparent that the embodiments described herein can be practiced without these specific details. In some instances, well-known constructions and apparatuses are illustrated in block diagram form to avoid unnecessarily obscuring aspects of this disclosure.

[0015] In the accompanying drawings, for ease of description, specific arrangements or orders of schematic elements (such as those representing systems, devices, modules, instruction blocks, and / or data elements) are illustrated. However, those skilled in the art will understand that, unless explicitly described, the specific order or arrangement of schematic elements in the drawings is not intended to imply a requirement for a particular processing order or sequence, or separation of processes. Furthermore, unless explicitly described, the inclusion of schematic elements in the drawings is not intended to imply that such elements are required in all embodiments, nor is it intended to imply that features represented by such elements cannot be included in some embodiments or cannot be combined with other elements in some embodiments.

[0016] Furthermore, in the accompanying drawings, connecting elements (such as solid or dashed lines or arrows) are used to illustrate connections, relationships, or associations between or among two or more other schematic elements. The absence of any such connecting element does not imply that connections, relationships, or associations cannot exist. In other words, some connections, relationships, or associations between elements are not illustrated in the drawings so as not to obscure the content of this 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 communication of signals, data, or instructions (e.g., "software instructions"), those skilled in the art will understand that such an element may represent one or more signal paths (e.g., a bus) that may be necessary to influence the communication.

[0017] Although the terms "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 used only 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.

[0018] The terminology used in the description of the various embodiments described herein is included for the purpose of describing particular embodiments only and is not intended to be limiting. As used in the description of the various embodiments described 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” 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 “comprising,” “including,” “possessing,” and / or “having” are used in this specification, they specifically indicate the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0019] As used herein, the terms "communication" and "to communicate" refer to at least one of the following: receiving, receiving, transmitting, conveying, and / or providing information (or information represented by, for example, data, signals, messages, instructions, and / or commands). For a unit (e.g., an apparatus, system, component of an apparatus or system, and / or combinations thereof) that wants to communicate with another unit, this means that the unit is able to receive information directly or indirectly 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 essentially wired and / or wireless. Furthermore, two units can communicate with each other even if the transmitted information can be modified, processed, relayed, and / or routed between the first and second units. For example, the first unit can communicate with the second unit even if it passively receives information and does not actively transmit information to the second unit. As another example, the first unit can communicate with the second unit if at least one intermediary unit (e.g., a third unit located between the first and second units) processes information received from the first unit and transmits the processed information to the second unit. In some embodiments, a message may refer to a network packet that includes data (e.g., a data packet, etc.).

[0020] As used herein, depending on the context, the term "if" may optionally be interpreted as "when," "in," "in response to being determined," and / or "in response to being detected," etc. Similarly, depending on the context, the phrases "if determined" or "if [the stated condition or event] is detected" may optionally be interpreted as "in response to being determined," "in response to being determined," "or" "in response to being detected," and / or "in response to being detected," etc. Furthermore, as used herein, the terms "have," "possess," or "own," etc., are intended to be open-ended terms. Additionally, unless explicitly stated otherwise, the phrase "based on" is intended to mean "at least partially based on."

[0021] Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. Numerous specific details are set forth in the following detailed description in order to provide a thorough understanding of the various embodiments described. However, it will be apparent to those skilled in the art that the various embodiments described can be practiced without these specific details. In other instances, well-known methods, processes, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0022] General Overview

[0023] In some aspects and / or embodiments, the systems, methods, and computer program products described herein include and / or implement autonomous driving mode access. Vehicles (such as autonomous vehicles) have multiple operating modes with different levels of autonomy. In some cases, the vehicle enables both autonomous and manual driving modes. For example, in autonomous driving mode, the vehicle navigates its environment without human assistance. In manual driving mode, a human driver controls the vehicle as it navigates through its environment. During manual driving mode, for one or more autonomous vehicle tasks, health information and task information (e.g., autonomous vehicle task output) are obtained. The health information and task information are compared to at least one adaptive threshold. Based on this comparison, a transition indicator is set, where the transition indicator indicates that a smooth transition from manual to autonomous driving mode is available for the corresponding task. As a result, autonomous driving mode access is enabled. When a smooth transition is not available for the corresponding task, the transition from manual to autonomous driving mode is rejected. When a smooth transition from manual to autonomous driving mode is unavailable, autonomous driving mode access is disabled. In one embodiment, an AutoReady indicator indicates the availability of a transition to autonomous driving mode.

[0024] By implementing the systems, methods, and computer program products described herein, the technology for autonomous driving mode access provides a guided switch between autonomous vehicle operation and manual vehicle operation. Some advantages of autonomous driving mode access include a smoother and more comfortable transition from manual to autonomous driving mode. This smoother and more comfortable transition increases confidence in the capabilities of the vehicle's autonomous system. Furthermore, the resulting operation of the vehicle is more coordinated because the transition to autonomous driving mode is based on instructions that the vehicle can make such a transition at a predetermined level of comfort for both individuals inside and outside the vehicle (e.g., pedestrians, other vehicles, etc.). This technology also ensures that the transition to autonomous driving mode is safe and stable. Moreover, this technology enables corrective actions to be taken to re-engage autonomous driving mode in response to its unavailability.

[0025] Now for reference Figure 1Example environment 100 is illustrated, in which vehicles including autonomous systems and vehicles not including autonomous systems operate. As illustrated, environment 100 includes vehicles 102a-102n, objects 104a-104n, routes 106a-106n, area 108, vehicle-to-infrastructure (V2I) device 110, network 112, remote autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118. Vehicles 102a-102n, vehicle-to-infrastructure (V2I) device 110, network 112, autonomous vehicle (AV) system 114, queue management system 116, and V2I system 118 are interconnected via wired connections, wireless connections, or a combination of wired and wireless connections (e.g., establishing connections 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 wired connection, wireless connection, or a combination of wired and wireless connection.

[0026] Vehicles 102a-102n (specifically referred to as vehicle 102 and collectively as vehicle 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, vehicle 102 includes cars, buses, trucks and / or trains, etc. In some embodiments, vehicle 102 is associated with vehicle 200 described herein (see Figure 2 The vehicles 102 are the same as or similar to autonomous vehicles 202. In some embodiments, vehicles 200 in a group of vehicles 200 are associated with an autonomous queue manager. In some embodiments, as described herein, vehicles 102 travel along corresponding routes 106a-106n (each individually referred to as route 106 and collectively as route 106). In some embodiments, one or more vehicles 102 include an autonomous system (e.g., an autonomous system that is the same as or similar to autonomous system 202).

[0027] Objects 104a-104n (each individually referred to as object 104 and collectively as object 104) include, for example, at least one vehicle, at least one pedestrian, at least one cyclist, and / or at least one structure (e.g., a building, a sign, a fire hydrant, etc.). Each object 104 (e.g., located at a fixed location and for a period of time) is either 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 area 108.

[0028] Routes 106a-106n (each individually referred to as Route 106 and collectively as Route 106) are each associated with (e.g., defining) a series of actions (also referred to as trajectories) along which the connecting AV can navigate. Each Route 106 begins with an initial state (e.g., a state corresponding to a first spatiotemporal location and / or speed, etc.) and ends with a final target state (e.g., a state corresponding to a second spatiotemporal location different from the first spatiotemporal location) or a target area (e.g., a subspace of an acceptable state (e.g., a termination state)). In some embodiments, a first state includes a location where one or more individuals will board the AV, and a second state or area includes a location where one or more individuals boarding the AV will disembark. In some embodiments, Route 106 includes multiple acceptable state sequences (e.g., multiple spatiotemporal location sequences) associated with multiple trajectories (e.g., defining multiple trajectories). In the example, Route 106 includes only high-level actions or imprecise state locations, such as a series of connecting roads indicating a change of direction at a roadway intersection. Additionally or alternatively, route 106 may include more precise actions or states, such as, for example, specific target lanes or precise locations within a lane area and target rates at those locations. In the example, route 106 includes multiple precise state sequences along at least one high-level action with a finite look-ahead horizon leading to an intermediate target, wherein the cumulative combination of successive iterations of the finite horizon state sequences corresponds to multiple trajectories that collectively form a high-level route terminating at a final target state or region.

[0029] Region 108 includes a physical area (e.g., a geographic region) that the vehicle 102 can navigate. In the example, region 108 includes at least one state (e.g., a country, a province, a single 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, interstate highway, park road, city street, etc. Additionally or alternatively, in some examples, region 108 includes at least one unnamed road, such as a driving lane, a section of a parking lot, a section of vacant land and / or undeveloped area, dirt road, etc. In some embodiments, a road includes at least one lane (e.g., a portion of the road that the vehicle 102 can traverse). In the example, a road includes at least one lane associated with at least one lane marking (e.g., identified based on at least one lane marking).

[0030] The Vehicle-to-Infrastructure (V2I) device 110 (sometimes referred to as a Vehicle-to-Everything (V2X) device) includes at least one device configured to communicate with vehicle 102 and / or V2I infrastructure system 118. In some embodiments, the 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, the V2I device 110 includes radio frequency identification (RFID) devices, signs, cameras (e.g., two-dimensional (2D) and / or three-dimensional (3D) cameras), lane markings, streetlights, parking meters, etc. In some embodiments, the V2I device 110 is configured to communicate directly with vehicle 102. Additionally or alternatively, in some embodiments, the 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.

[0031] Network 112 includes one or more wired and / or wireless networks. In the example, network 112 includes cellular networks (e.g., Long Term Evolution (LTE) networks, third-generation (3G) networks, fourth-generation (4G) networks, fifth-generation (5G) networks, Code Division Multiple Access (CDMA) networks, etc.), Public Land Mobile Networks (PLMNs), Local Area Networks (LANs), Wide Area Networks (WANs), Metropolitan Area Networks (MANs), telephone networks (e.g., Public Switched Telephone Networks (PSTN)), private networks, self-organizing networks, intranets, the Internet, fiber-based networks, cloud computing networks, etc., and / or combinations of some or all of these networks.

[0032] The remote AV system 114 includes at least one device configured to communicate with vehicle 102, V2I device 110, network 112, queue management system 116, and / or V2I system 118 via network 112. In examples, the remote AV system 114 includes a server, server group, and / or other similar devices. In some embodiments, the remote AV system 114 is located in the same location as 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. In some embodiments, the remote AV system 114 maintains (e.g., updates and / or replaces) these components and / or software during the lifespan of the vehicle.

[0033] The queue management system 116 includes at least one device configured to communicate with vehicle 102, V2I device 110, remote AV system 114, and / or V2I infrastructure system 118. In examples, the queue management system 116 includes servers, server groups, and / or other similar devices. In some embodiments, the queue management system 116 is associated with a ride-sharing company (e.g., an organization for controlling the operation of multiple vehicles (e.g., vehicles including and / or not including autonomous systems)).

[0034] 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 a 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 municipality or private entity (e.g., a private entity maintaining the V2I device 110).

[0035] supply Figure 1 The number and arrangement of the elements are shown as examples. (and) Figure 1 Compared to the illustrated elements, there may be additional elements, fewer elements, different elements, and / or elements arranged differently. Additionally or alternatively, at least one element of environment 100 may be described as being composed of… Figure 1 One or more functions performed by at least one different element of environment 100. Additionally or alternatively, at least one group of elements of environment 100 may perform one or more functions described as performed by at least one different group of elements of environment 100.

[0036] Now for reference Figure 2 The vehicle 200 includes an autonomous system 202, a powertrain control system 204, a steering control system 206, and a braking system 208. In some embodiments, the vehicle 200 and the vehicle 102 (see...) Figure 1 The vehicle 200 is similar to or the same as the vehicle in question. In some embodiments, the vehicle 200 has autonomous capabilities (e.g., implementing at least one function, feature, and / or device that enables the vehicle 200 to operate partially or fully without human intervention, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon human intervention) and / or highly autonomous vehicles (e.g., vehicles that abandon human intervention in certain situations)). For a detailed description of fully autonomous and highly autonomous vehicles, refer to SAE International's standard J3016: Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems, the entire contents of which are incorporated herein by reference. In some embodiments, the vehicle 200 is associated with an autonomous queue manager and / or a ride-sharing company. Typically, the vehicle 200 includes systems, sensors, devices, and controllers for generating data associated with various tasks (e.g., autonomous vehicle operation task outputs). In some cases, these tasks implement at least one function, feature, and / or device that enables vehicle 200, including but not limited to fully autonomous vehicles (e.g., vehicles that abandon human intervention), to operate partially or completely without human intervention.

[0037] Autonomous system 202 includes a sensor suite comprising one or more devices such as camera 202a, LiDAR sensor 202b, radar sensor 202c, and microphone 202d. In some embodiments, 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 traveled by vehicle 200). In some embodiments, autonomous system 202 uses one or more devices included in autonomous system 202 to generate data associated with environment 100 as described herein. The data generated by one or more devices of autonomous system 202 may be used by one or more systems as described herein to observe the environment in which vehicle 200 is located (e.g., environment 100). In some embodiments, autonomous system 202 includes communication device 202e, autonomous vehicle computing 202f, and safety controller 202g.

[0038] Camera 202a includes components configured to communicate with communication device 202e, autonomous vehicle computing 202f, and / or safety controller 202g via a bus (e.g., with...). Figure 3 At least one means of communicating with the same or similar bus as bus 302. Camera 202a includes at least one camera (e.g., a digital camera using a light sensor such as a charge-coupled device (CCD), a thermal camera, an infrared (IR) camera, and / or an event camera, etc.) for capturing images of physical objects (e.g., cars, buses, curbs, and / or people, etc.). In some embodiments, camera 202a generates camera data as output. In some examples, camera 202a generates camera data including image data associated with an 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 image timestamp, etc.). In such examples, the image may be in a format (e.g., RAW, JPEG, and / or PNG, etc.). In some embodiments, camera 202a includes multiple independent cameras configured (e.g., positioned on) a vehicle to capture images for stereoscopic imaging (stereoscopic vision). In some examples, camera 202a includes generating image data and transmitting the image data to an autonomous vehicle computing 202f and / or a queue management system (e.g., with...). Figure 1The queue management system 116 (same as or similar to a queue management system) has multiple cameras. In such an example, the autonomous vehicle calculation 202f determines the depth of one or more objects in the fields of view of at least two of the multiple cameras based on image data from at least two cameras. In some embodiments, camera 202a is configured to capture images of objects within a distance relative to camera 202a (e.g., up to 100 meters and / or up to 1 kilometer, etc.). Therefore, camera 202a includes features such as sensors and lenses optimized for sensing objects at one or more distances relative to camera 202a.

[0039] In embodiments, 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 providing visual navigation information. In some embodiments, camera 202a generates traffic light data associated with one or more images. In some examples, camera 202a generates TLD data associated with one or more images, including formats such as RAW, JPEG, and / or PNG. In some embodiments, camera 202a, which generates TLD data, differs from other camera-included systems described herein in that camera 202a may include one or more cameras with a wide field of view (e.g., wide-angle lens, fisheye lens, and / or a lens with an angle of view of about 120 degrees or greater) to generate images associated with as many physical objects as possible.

[0040] The laser detection and ranging (LiDAR) sensor 202b includes components configured to communicate with a communication device 202e, an autonomous vehicle computing unit 202f, and / or a safety controller 202g via a bus (e.g., with...). Figure 3At least one device that communicates with the same or similar bus (bus 302). The LiDAR sensor 202b includes a system configured to emit light from a 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 also includes at least one photosensor that detects the light after it has encountered a physical object. In some embodiments, at least one data processing system associated with the LiDAR sensor 202b generates an image (e.g., point cloud and / or combined point cloud, etc.) representing 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 boundaries of a physical object and / or the surface of the physical object (e.g., the topology of the surface). In such examples, the image is used to determine the boundaries of the physical object within the field of view of the LiDAR sensor 202b.

[0041] The radio detection and ranging (radar) sensor 202c includes components 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 3 At least one device that communicates with the same or similar bus (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 predetermined 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 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 boundaries of physical objects and / or the surfaces of physical objects (e.g., surface topology). In some examples, this image is used to determine the boundaries of physical objects in the field of view of the radar sensor 202c.

[0042] Microphone 202d includes components configured to communicate with communication device 202e, autonomous vehicle computing 202f, and / or safety controller 202g via a bus (e.g., with...). Figure 3At least one device that communicates with the same or similar bus as bus 302. Microphone 202d includes one or more microphones (e.g., array microphones and / or external microphones, etc.) that capture audio signals and generate data associated with (e.g., representing) the audio signals. In some examples, microphone 202d includes transducer devices and / or similar devices. In some embodiments, one or more systems described herein can receive data generated by microphone 202d and determine the position (e.g., distance, etc.) of an object relative to vehicle 200 based on the audio signal associated with the data.

[0043] The communication device 202e includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, an autonomous vehicle computing system 202f, a safety controller 202g, and / or a drive-by-wire (DBW) system 202h. For example, the communication device 202e may include communication with… Figure 3 The communication device 202e is the same as or similar to the 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).

[0044] The autonomous vehicle computing 202f includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, a communication device 202e, a security controller 202g, and / or a DBW system 202h. In some examples, the autonomous vehicle computing 202f includes devices such as client devices, mobile devices (e.g., cellular phones and / or tablets) and / or servers (e.g., computing devices including one or more central processing units and / or graphics processing units). In some embodiments, the autonomous vehicle computing 202f is the same as 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., with...). Figure 1 Remote AV systems 114 are the same as or similar to autonomous vehicle systems), queue management systems (e.g., with...). Figure 1 The queue management system 116 is the same as or similar to the queue management system 116), and V2I devices (e.g., with Figure 1 V2I devices (same as or similar to V2I devices 110) and / or V2I systems (e.g., with V2I devices 110) Figure 1 The V2I system 118 communicates with the same or similar V2I system.

[0045] The safety controller 202g includes at least one device configured to communicate with a camera 202a, a LiDAR sensor 202b, a radar sensor 202c, a microphone 202d, a communication device 202e, an autonomous vehicle computing system 202f, and / or a DBW system 202h. In some examples, the safety controller 202g includes one or more controllers (electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate the vehicle 200 (e.g., powertrain control system 204, steering control system 206, and / or 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 system 202f.

[0046] 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 (e.g., electrical controllers and / or electromechanical controllers, etc.) configured to generate and / or transmit control signals to operate the vehicle 200, including one or more devices (e.g., powertrain control system 204, steering control system 206, and / or 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 (e.g., turn signals, headlights, door locks, and / or windshield wipers, etc.) of the vehicle 200.

[0047] 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 start moving forward, stop moving forward, start moving backward, stop moving backward, accelerate in a certain direction, decelerate in a certain direction, make a left turn and / or make a right turn, etc. In examples, the powertrain control system 204 increases, keeps the same, or decreases the energy (e.g., fuel and / or electricity, etc.) supplied to the motor of the vehicle, thereby causing at least one wheel of the vehicle 200 to rotate or not rotate.

[0048] 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 causes the two front wheels and / or the two rear wheels of the vehicle 200 to turn left or right, thereby causing the vehicle 200 to turn left or right.

[0049] The braking system 208 includes at least one device configured to actuate one or more brakes to decelerate and / or keep the vehicle 200 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 respective rotor 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.

[0050] 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 conditions 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 rate sensor, a wheel brake pressure sensor, a wheel torque sensor, an engine torque sensor, and / or a steering angle sensor.

[0051] Now for reference Figure 3 A schematic diagram of device 300 is illustrated. 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 system of vehicle 102); and / or one or more devices of network 112 (e.g., one or more devices of system of network 112). In some embodiments, one or more devices of vehicle 102 (e.g., one or more devices of system of vehicle 102), and / or one or more devices of network 112 (e.g., one or more devices of system of network 112) include at least one device 300 and / or at least one component of device 300. Figure 3 As shown, the device 300 includes a bus 302, a processor 304, a memory 306, a storage component 308, an input interface 310, an output interface 312, and a communication interface 314.

[0052] Bus 302 includes components for communication between the components of the licensed device 300. In some embodiments, processor 304 is implemented in hardware, software, or a combination of hardware and software. In some examples, processor 304 includes a processor (e.g., a central processing unit (CPU), graphics processing unit (GPU), and / or accelerated processing unit (APU), 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 application-specific integrated circuit (ASIC), etc.). Memory 306 includes random access memory (RAM), read-only memory (ROM), and / or another type of dynamic and / or static storage device (e.g., flash memory, magnetic memory, and / or optical memory, etc.) that stores data and / or instructions for use by processor 304.

[0053] Storage component 308 stores data and / or software related to the operation and use of device 300. In some examples, storage component 308 includes hard disks (e.g., magnetic disks, optical disks, magneto-optical disks, and / or solid-state disks), compact discs (CDs), digital versatile discs (DVDs), floppy disks, cassette tapes, magnetic tapes, CD-ROMs, RAM, PROMs, EPROMs, FLASH-EPROMs, NV-RAMs, and / or other types of computer-readable media, and corresponding drives.

[0054] Input interface 310 includes components that enable the device 300 to receive information, such as via user input (e.g., a touchscreen display, keyboard, keypad, mouse, buttons, switches, microphone, and / or camera). Additionally or alternatively, in some embodiments, input interface 310 includes sensors for sensing information (e.g., a Global Positioning System (GPS) receiver, accelerometer, gyroscope, and / or actuator). Output interface 312 includes components for providing output information from device 300 (e.g., a display, speaker, and / or one or more light-emitting diodes (LEDs)).

[0055] In some embodiments, the communication interface 314 includes transceiver-like components (e.g., a transceiver and / or separate receivers and transmitters) that enable the licensing device 300 to communicate with other devices via a wired connection, a wireless connection, or a combination of wired and wireless connections. In some examples, the communication interface 314 enables the licensing device 300 to receive information from 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, etc. Interfaces and / or cellular network interfaces, etc.

[0056] In some embodiments, device 300 performs one or more of the processes described herein. Device 300 performs these processes based on software instructions stored in a computer-readable medium, such as memory 305 and / or storage component 308, executed by processor 304. Computer-readable medium (e.g., 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.

[0057] In some embodiments, software instructions are read from another computer-readable medium or from another device via communication interface 314 into memory 306 and / or storage component 308. When executed, the software instructions stored in memory 306 and / or storage component 308 cause processor 304 to perform one or more processes described herein. Additionally or alternatively, hard-wired circuitry may be used in place of or in combination with software instructions to perform one or more processes described herein. Therefore, unless explicitly stated otherwise, the embodiments described herein are not limited to any particular combination of hardware circuitry and software.

[0058] The memory 306 and / or storage component 308 include a data storage unit or at least one data structure (e.g., a database). The device 300 is capable of receiving information from the data storage unit or at least one data structure in the memory 306 or storage component 308, storing the information in the data storage unit or at least one data structure, communicating information to the data storage unit or at least one data structure, or searching for information stored in the data storage unit or at least one data structure. In some examples, the information includes network data, input data, output data, or any combination thereof.

[0059] In some embodiments, device 300 is configured to execute software instructions stored in the memory of memory 306 and / or another device (e.g., another device identical or similar to device 300). As used herein, the term "module" refers to at least one instruction stored in the memory of memory 306 and / or the other device, which, when executed by the processor of processor 304 and / or the processor of another device (e.g., another device identical or similar to device 300), causes device 300 (e.g., at least one component of device 300) to perform one or more processes as described herein. In some embodiments, modules are implemented in software, firmware, and / or hardware, etc.

[0060] supply Figure 3 The number and arrangement of components are illustrated as examples. In some embodiments, with Figure 3Compared to the illustrated components, device 300 may include additional components, fewer components, different components, or components arranged differently. Additionally or alternatively, a group of components of device 300 (e.g., one or more components) may perform one or more functions described as being performed by another component or another group of components of device 300.

[0061] Now for reference Figure 4 The diagram illustrates an example block diagram of an autonomous vehicle computing 400 (sometimes referred to as an "AV stack"). 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, planning system 404, positioning system 406, control system 408, and database 410 are included in and / or implemented in the vehicle's automatic navigation system (e.g., the autonomous vehicle computing 202f of vehicle 200). Additionally or alternatively, in some embodiments, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in one or more separate systems (e.g., one or more systems that are the same as or similar to the autonomous vehicle computing 400, etc.). In some examples, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 are included in one or more independent systems located within the 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 memory), computer hardware (e.g., via microprocessors, microcontrollers, application-specific integrated circuits (ASICs), and / or field-programmable gate arrays (FPGAs), 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., autonomous vehicle systems identical or similar to remote AV system 114, queue management systems identical or similar to queue management systems 116, and / or V2I systems identical or similar to V2I system 118, etc.). Furthermore, in embodiments, the perception system 402, planning system 404, positioning system 406, control system 408, and database 410 generate data associated with at least one autonomous vehicle operation task.

[0062] 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 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) that is 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 at least one physical object based on one or more groups of physical objects (e.g., bicycles, vehicles, traffic signs, and / or pedestrians, etc.). In some embodiments, based on the classification of physical objects by the perception system 402, the perception system 402 transmits data associated with the classification of physical objects to the planning system 404. In an example, the classification data output by the perception system 402 is an autonomous vehicle operation task output that enables the setting of a transition indicator as described below. In an embodiment, the transition indicator is an AutoReady indicator associated with the autonomous function of the AV.

[0063] 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 toward the destination. In some embodiments, the planning system 404 periodically or continuously receives data from the sensing system 402 (e.g., the data associated with the classification of 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 sensing system 402. In some embodiments, the planning system 404 receives data associated with the updated location 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. In some embodiments, the output of the planning system is data associated with route planning. For example, updating at least one trajectory or generating at least one different trajectory based on data generated by the sensing system is at least one autonomous vehicle operation task output that enables the setting of a change indicator as described below.

[0064] In some embodiments, positioning system 406 receives data associated with (e.g., representing) a location of a vehicle (e.g., vehicle 102) in an area. In some examples, 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 some examples, positioning system 406 receives data associated with at least one point cloud from multiple LiDAR sensors, and positioning system 406 generates a composite point cloud based on the individual point clouds. In these examples, positioning system 406 compares the at least one point cloud or composite point cloud with a two-dimensional (2D) and / or three-dimensional (3D) map of the area stored in database 410. Then, based on the comparison of the at least one point cloud or composite point cloud with the map, positioning system 406 determines the location of the vehicle in the area. In some embodiments, the map includes a composite point cloud of the area generated prior to navigation of the vehicle. In some embodiments, the map includes, but is not limited to, a high-precision map of the geometry of the roadway, a map describing the connectivity of the road network, a map describing the physical properties of the roadway (such as traffic speed, traffic flow, the number of vehicle and bicycle lanes, lane width, lane traffic direction, or the type and location of lane markings, or combinations thereof), and a map describing the spatial locations of road features (such as pedestrian crossings, traffic signs, or various types of other traffic lights). In some embodiments, the map is generated in real time based on data received by the sensing system.

[0065] In another example, positioning system 406 receives Global Navigation Satellite System (GNSS) data generated by a Global Positioning System (GPS) receiver. In some examples, positioning system 406 receives GNSS data associated with the location of the vehicle in an area, and positioning system 406 determines the latitude and longitude of the vehicle in the area. In such examples, positioning system 406 determines the location of the vehicle in the area based on the latitude and longitude of the vehicle. In some embodiments, positioning system 406 generates data associated with the location of the vehicle. In some examples, based on the location of the vehicle determined by positioning system 406, positioning system 406 generates data associated with the location of the vehicle. In such examples, the data associated with the location of the vehicle includes data associated with one or more semantic properties corresponding to the location of the vehicle. In embodiments, positioning system 406 provides at least one autonomous vehicle operation task output that enables the setting of a transition indicator as described below. The positioning system output compares one or more locations with a desired location to determine that the transition from manual driving mode to autonomous driving mode based on positioning data is smooth.

[0066] 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 operate the powertrain control system (e.g., DBW system 202h and / or powertrain control system 204, etc.), the steering control system (e.g., steering control system 206), and / or the braking system (e.g., braking system 208). In an example, where the trajectory includes a left turn, the control system 408 transmits control signals 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 change the state of other devices of the vehicle 200 (e.g., headlights, turn signals, door locks, and / or windshield wipers, etc.). In one embodiment, the DBW system 202h, powertrain control system 204, steering control system 206, and braking system 208 generate at least one autonomous vehicle operation task output that enables the setting of a transition indicator as described below. In another embodiment, the control system 408 outputs a compared control signal to determine that the transition from manual driving mode to autonomous driving mode based on the control signal is smooth.

[0067] In some embodiments, the perception system 402, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model (e.g., at least one multilayer 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, planning system 404, positioning system 406, and / or control system 408 implement at least one machine learning model individually or in combination with one or more of the aforementioned systems. In some examples, the perception system 402, planning system 404, positioning system 406, and / or 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.).

[0068] Database 410 stores data transmitted to, received from, and / or updated by the sensing system 402, planning system 404, positioning system 406, and / or control system 408. In some examples, database 410 includes storage components for storing operation-related data and / or software, and for computing 400 using autonomous vehicles (e.g., with...). Figure 3(The storage component 308 is the same as or similar to the storage component 308). In some embodiments, database 410 stores data associated with 2D and / or 3D maps of at least one region. In some examples, 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., countries). In such examples, a vehicle (e.g., the same as or similar to vehicle 102 and / or vehicle 200) can drive along one or more drivable zones (e.g., single-lane roads, multi-lane roads, highways, remote roads, and / or off-road roads, etc.) and causes at least one LiDAR sensor (e.g., the same as or similar to LiDAR sensor 202b) to generate data associated with images representing objects included in the field of view of the at least one LiDAR sensor. In some embodiments, database 410 stores data associated with the output of autonomous vehicle operation tasks.

[0069] In some embodiments, database 410 may be implemented across multiple devices. In some examples, database 410 includes a vehicle (e.g., a vehicle identical or similar to vehicle 102 and / or vehicle 200), an autonomous vehicle system (e.g., an autonomous vehicle system identical or similar to remote AV system 114), and a queue management system (e.g., with...). Figure 1 Queue management system 116 (same as or similar to queue management system) and / or V2I system (e.g., with Figure 1 Among the V2I systems (118 similar to or similar V2I systems), etc.

[0070] Now for reference Figure 5 A diagram illustrating an implementation 500 for processing autonomous driving mode access is shown. In some embodiments, implementation 500 includes an AutoReady system 510 and a control system 504b. In some embodiments, control system 504b is the same as or similar to control system 408. In response to systems, sensors, devices, and controllers (such as...) used to generate data associated with various tasks... Figure 2 The system, sensors, devices, and controllers shown receive data and generate control signals 518.

[0071] In an embodiment, the AutoReady system 510 applies adaptive thresholds to the output of the autonomous vehicle operation task. For example, the transition from manual driving mode to autonomous driving mode may produce unusual or unsafe behavior. In particular, the transition from manual driving mode to autonomous driving mode may sometimes result in sudden acceleration, deceleration, or sharp turns. To prevent this, the present technology polls the autonomous vehicle operation task to determine the corresponding health and output quality of the autonomous vehicle operation task. Health and output quality are used to determine whether access to autonomous driving mode is permitted, thereby creating an AutoReady check before transitioning from manual driving mode to autonomous driving mode. If the AutoReady check fails, the reason for the failure can be communicated to a human, allowing the human to resolve the issue and attempt to access autonomous driving mode again. In an embodiment, the human is the vehicle driver operating the vehicle from within the vehicle. In another embodiment, the human is the remote operator of the vehicle operating the vehicle from a remote location relative to the vehicle.

[0072] The adaptive threshold applied to the output of the autonomous vehicle operation task is based at least in part on the current state of the autonomous vehicle operation task. For example, when the vehicle is operating in manual driving mode at a speed of 60 km / hr, the adaptive threshold ranges from 58 km / hr to 62 km / hr. In this example, autonomous vehicle functionality is available for access when the autonomous system (e.g., autonomous system 202) controls the vehicle within the range of 58 km / hr to 62 km / hr. When the vehicle is operating in manual driving mode at a speed of 20 km / hr, an exemplary adaptive threshold range is from 15 km / hr to 25 km / hr. In this example, autonomous vehicle functionality is available for access when the autonomous system (e.g., autonomous system 202) controls the vehicle within the range of 15 km / hr to 25 km / hr. In this example, when the vehicle is traveling at a lower rate of change, the adaptive threshold enables a larger rate range that autonomous vehicle operation can access (e.g., control manual driving). Humans typically tolerate larger rate variations at lower travel speeds. Therefore, adaptive thresholds enable larger variations in the required rates for access at lower travel speeds.

[0073] In some embodiments, the autonomous system (e.g., autonomous system 202) enables AV operation in a fully autonomous driving mode. The autonomous system also enables the AV system to operate, for example, in a fully manual driving mode. This technology is generally applicable to hybrid-mode driving and hybrid-mode driving systems, at least some implementations of which provide the option to drive in a fully or partially autonomous driving mode or in a fully or partially manual driving mode. In some implementations, hybrid-mode driving may sometimes allow the occupant to drive manually. In some instances of hybrid-mode driving, the AV system may permit the occupant to access one or more driving modes or a combination of driving modes. In some cases, the AV system may require the occupant or the AV, or both, to access only one or more specific driving modes while blocking one or more of the selected other driving modes.

[0074] In some implementations, AV systems with hybrid-mode driving capabilities can permit or require their driving modes to change from one mode to another. This change can be based on automatic detection, occupant requests, server commands, or a combination thereof. This flexibility in driving mode switching provides AV system users with more options and allows for more efficient use of the AV system. In embodiments, a hybrid-mode driving system (e.g., one or more AV systems that permit or require hybrid-mode driving) includes one or more AVs and an AV system, where one or more AVs are part of the AV system (although in some implementations, an AV is only part of the AV system, the terms "AV" and "AV system" are sometimes used interchangeably).

[0075] Figure 6 This is a block diagram showing the autonomous driving mode access at point 600 of the AutoReady system. Figure 6 In the example, tasks 602A, 602B, ..., 602N (collectively referred to as task 602) are shown. Additionally, AutoReady checkers 606A, 606B, ..., 606N (collectively referred to as AutoReady checker 606) are shown. Figure 6 In the example, each of the tasks 602 outputs a corresponding output 604A, output 604B, ..., output 604N (collectively referred to as output 604 or autonomous vehicle operation task output 604). In the example, output 604 includes the corresponding health information and autonomous vehicle task output.

[0076] In the example, the autonomous vehicle operates by performing one or more tasks 602. Tasks include, for example, perception, planning, and control. Task 602 is performed separately from other tasks 602. Each task 602 has associated health information and outputs. The task health information and outputs of each task are transmitted to the corresponding AutoReady checker 606. At block 608, if the evaluation of output 604 at the AutoReady checker 606 indicates that a transition to autonomous driving mode is available, then autonomous driving mode access is enabled. If not all tasks indicate that a transition to autonomous driving mode is available, then autonomous access is not enabled at block 610, and the reason for not enabling autonomous access is communicated to the driver or operator at block 612. The reason for not allowing autonomous access is communicated to the driver or operator in the form of mitigation, enabling the driver or operator to take corrective actions that enable the availability of autonomous driving mode.

[0077] Typically, the output of autonomous vehicle operation tasks is generated by the autonomous system. Autonomous vehicle operation task outputs include, for example, data, abnormal conditions, or requests associated with autonomous system information and data. The outputs 604 of each task are independently evaluated at the corresponding AutoReady checker 606. In embodiments, methods such as those used for... Figure 7 The current operating state based on AV is used to generate one or more adaptive thresholds to evaluate the outputs 604 of each task. Then, the AutoReady checker 606 integrates the evaluation results of the outputs 604.

[0078] In the example, output 604 is a status message used to communicate the availability of transition to autonomous driving mode. In an embodiment, task 602, selected for evaluation before autonomous driving mode access, is based on the expected sensitivity of the autonomous driving mode access system. In an embodiment, output 604 communicates metrics of the health of the algorithm associated with task 602 and the quality of the corresponding output of the task to determine whether autonomous driving mode is available for access. If checker 606 of output 604 fails, the reason for the failure is communicated, thereby enabling a human (e.g., driver or remote operator) to resolve the problem. Typically, without checking the availability of autonomous driving mode, an AV may issue commands to perform uncomfortable behaviors (e.g., abrupt steering, acceleration, or deceleration) when transitioning from manual driving mode to autonomous driving mode. Conventional techniques for controlling the transition are limited to reducing control authority or filtering control commands during the transition from manual driving mode to autonomous driving mode. However, reducing control or filtering cannot achieve a smooth transition from manual driving mode to autonomous driving mode, given the current operating state of the vehicle.

[0079] In an embodiment, the evaluation of output 604 by the AutoReady checker 606 includes one or more determinations. For example, the AutoReady checker evaluates output 604 to determine whether a path exists from the planning system, whether longitudinal acceleration and steering commands are within comfort thresholds, or whether the vehicle state is within the controller attraction ratio given a controller reference / constraint. The AutoReady checker 606 also determines whether acceleration and steering rate are within predetermined comfort thresholds. In the example, the corresponding predetermined thresholds for acceleration or steering rate can be defined based on a passenger profile.

[0080] In addition to the evaluation performed on each corresponding task at the AutoReady checker 606, time constraints can govern the availability of the transition from manual driving mode to autonomous driving mode, as determined by the corresponding AutoReady checker. For example, the availability of the transition from manual driving mode to autonomous driving mode is determined by evaluating the age associated with the control signal. In this example, if the human driver manually engages the steering wheel beyond a predefined time limit during a sharp turn, the AutoReady checker 606 indicates that the transition from manual driving mode to autonomous driving mode is unavailable. In another example, if the human driver repeatedly engages the steering wheel within a predefined time window during a sharp turn, the AutoReady checker 606 indicates that the transition from manual driving mode to autonomous driving mode is unavailable. Furthermore, in other examples, if the acceleration command in the driving direction exceeds a predefined acceleration value, the AutoReady checker indicates that the transition from manual driving mode to autonomous driving mode is unavailable. In this example, a human driver operates the vehicle to cause a high rate of change in acceleration, and the autonomous system refuses to transition to autonomous driving mode during this high rate of change to prevent an uncomfortable transition. In this embodiment, the acceleration is the predicted yaw acceleration.

[0081] In an embodiment, upon receiving a request to access autonomous driving mode, at block 608, it is determined whether the transition from manual driving mode to autonomous driving mode is available. If the AutoReady checker 606 indicates that the transition from manual driving mode to autonomous driving mode is unavailable, the process continues to blocks 610 and 612. At block 610, autonomous driving mode is denied. At block 612, one or more reasons for denying autonomous driving mode are communicated to the human driver or operator. In an embodiment, the operator's location is remote from the AV. In some cases, the reason for denying autonomous driving mode is communicated to the driver of the AV. Example communications with the driver or remote operator (if accessing autonomy remotely) include audio feedback indicating the denial of autonomous access attempts and / or visual instructions drawn by a graphical user interface (GUI) to provide the driver or remote operator with actionable information. For example, when attempting autonomous access, audio feedback may be provided at the AV to warn the driver or remote operator that access to autonomous driving mode has been denied via a series of beeps, chirps, or other audio prompts. The audio feedback may also include digital voice for explaining the denial of autonomous mode in human-understood language. In the example, visual feedback includes instructions drawn on one or more displays. At box 608, if all tasks indicate that autonomous driving mode is available according to the AutoReady checker 606 based on health information and output 604, the processing flow continues to box 614. At box 614, autonomous driving mode is enabled (e.g., allowing a transition to autonomous operation).

[0082] In the example, this technology identifies one or more reasons for refusing to switch from manual driving mode to autonomous driving mode. Typically, the reason for refusing to switch from manual driving mode to autonomous driving mode is based on the autonomous vehicle's task output failing to meet an adaptive threshold. Therefore, the reason for refusing autonomous mode access is that the autonomous vehicle's task output (e.g., planning, perception, and localization) is outside the expected value indicated by the adaptive threshold.

[0083] Figure 7 This is a block diagram of an adaptive threshold generation system 700. Information sources used for threshold generation include sensors 704 (e.g., such as…). Figure 2 One or more of the following devices: camera 202a, LiDAR sensor 202b, radar sensor 202c, and microphone 202d; database 706 (e.g., Figure 3 The memory 306 or storage component 308, Figure 4 Database 410), passenger profile 708, autonomous system 702 (e.g., with autonomous system 202 and / or Figure 4The autonomous vehicle computing system 400 is the same as or similar to an autonomous system, the adaptive threshold generator 710 (for monitoring perception, trajectory planning and motion control, etc.), and the AutoReady system 600 or any combination thereof.

[0084] When the autonomous system 702 receives a request 720 to transition to a fully or partially autonomous driving mode, the AutoReady system 600 determines, at least in part, based on one or more adaptive thresholds whether the transition from manual driving mode to autonomous driving mode is available. The adaptive thresholds can be generated based on sensor data 704, database 706, passenger profile 708, the current operating state of the vehicle, or any combination thereof. In the example, the passenger profile 708 is obtained via an adaptive threshold generator 710. The passenger profile may, for example, indicate passenger preferences regarding maximum speed, acceleration, deceleration, and steering angle. These preferences are used to calculate one or more adaptive thresholds, where the adaptive thresholds enable a smooth transition from manual driving to autonomous driving. In the example, data associated with the operation of the autonomous vehicle (e.g., sensor data 704, data stored in database 706) is evaluated to determine one or more adaptive thresholds for comparison with at least one autonomous vehicle task output.

[0085] In some cases, the adaptive threshold generator 710 captures the current operating state of the AV. Typically, when transitioning to autonomous driving mode, the current operating state of the AV is in manual driving mode (e.g., human-controlled operation). The current operating state of the AV includes the state of human-operated sensors, functions, devices, or autonomous systems. In some cases, the current operating state includes the current perception state of an autonomous system (e.g., perception system 402), the current planning state of an autonomous system (e.g., planning system 404), and the current set of control signals of the autonomous system in response to human manual driving (e.g., steering, braking, rate, acceleration, and turning radius, etc.). In the example, the current operating state of the AV includes the driving rate or turning angle, or both, during human manual driving.

[0086] An adaptive threshold generator 710 monitors the current operating state of the AV 700. In response to changes in the current operating state of the AV 700, the adaptive threshold generator 710 updates or modifies the thresholds applied to the autonomous vehicle operation task output by the AutoReady system 600. In some cases, the autonomous vehicle operation task output includes control signals for governing the operation of the AV. In an embodiment, the adaptive threshold is generated during fully or partially manual driving mode. In an embodiment, the adaptive threshold is calculated such that the transition from manual driving mode (e.g., the current operating state) to autonomous driving mode is smooth. In an example, a smooth transition is a change from manual driving mode to autonomous driving mode without abruptness or sudden unintentional acceleration.

[0087] Based on the current operating state, an adaptive threshold is generated that enables the AutoReady system to determine whether a transition from manual driving mode to autonomous driving mode is possible. If the AutoReady system 600's task output is 604... Figure 6 If the adaptive threshold for the corresponding autonomous vehicle operation task is exceeded or otherwise cannot be met, then task 602 or AutoReady checker 606 ( Figure 6 The system indicates that the autonomous driving mode is unavailable. For example, if the AV rate during manual driving (e.g., the current operating state) is 50 km / hr, the adaptive threshold for transitioning from manual to autonomous driving mode is the rate at which the autonomous system can maintain or approach the current AV rate during manual driving. In this example, a preferred AV rate could be 30 km / hr; however, a sudden deceleration from 50 km / hr to 30 km / hr could result in a rear-end collision or other abrupt and uneven transitions. In this example, the AutoReady check fails, and the autonomous driving mode is unavailable for transition. In embodiments, audio prompts, video detection, or occupant confirmation, or a combination thereof, are used to communicate the reason why the autonomous driving mode is unavailable.

[0088] Figure 8 This is a block diagram of the process for implementing autonomous driving mode access. In some embodiments, one or more steps described with respect to process 800 (e.g., wholly and / or partially) are performed by the autonomous system 202. Figure 2 ) to be performed. Additionally or alternatively, in some embodiments, one or more steps described with respect to processing 800 (e.g., wholly and / or partially) are performed by the autonomous system 202 ( Figure 2 (such as the AutoReady system 510) Figure 5 ), AutoReady system 600 ( Figure 6 ) and Adaptive Threshold Generation System 700 ( Figure 7Separation or inclusion of autonomous systems 202 (etc.) Figure 2 (to be carried out by other devices or groups of devices.)

[0089] At box 802, during operation of the vehicle in manual driving mode, at least one autonomous vehicle task output associated with driving the vehicle in autonomous driving mode is received. (Continue to refer to...) Figure 4 The autonomous vehicle mission outputs include, for example, the outputs of the sensing system 402, the planning system 404, the positioning system 406, the control system 408, the database 410, or any combination thereof.

[0090] At box 804, the output of at least one autonomous vehicle operation task is compared with various thresholds. In the example, an adaptive threshold generator 710 is used. Figure 7 This is used to generate at least one adaptive threshold. In some cases, the adaptive threshold is based on the current operating state of the AV.

[0091] At box 806, a transition indicator is set based on comparison, indicating the availability of a smooth transition. In this embodiment, the transition indicator is a Boolean value indicating whether the corresponding task approves the transition from manual driving mode to autonomous driving mode. If any transition indicator indicates that the transition from manual driving mode to autonomous driving mode is unavailable, autonomous driving mode is disabled. In this way, the technology evaluates the availability of autonomous driving mode at the task level to ensure a comfortable access to autonomous driving mode. This eliminates abrupt movements when transitioning from manual driving mode to autonomous driving mode.

[0092] At block 808, in response to a transition indicator indicating that a smooth transition from manual driving mode to autonomous driving mode is unavailable, the transition is rejected. In embodiments, the rejection of the transition is communicated to the driver. For example, light, text indicators, audio messages, or visual messages are provided to the driver or remote operator. In some examples, a Boolean indicator that is always active during manual driving mode, indicating whether the driver can access autonomous mode, is used to communicate the rejection. Typically, the AV uses a ready state to communicate that autonomous driving mode is unavailable. In response to this ready state, the driver or remote operator takes one or more corrective actions. Corrective actions include, for example, steering the AV to bring it closer to the path or rate generated by the AV. Corrective actions also include accelerating or decelerating to better match the autonomous vehicle's mission output. In some examples, messages explaining one or more reasons why autonomous driving mode cannot be accessed are provided. Typically, corrective actions are any actions that cause the AV's current operating state to meet (e.g., fall into) a range corresponding to an adaptive threshold. Therefore, this technology ensures a smooth and comfortable transition from manual driving mode to autonomous driving mode. Furthermore, this technology ensures the safety and stability of the mission. Additionally, the reason for disabling autonomous mode is communicated to the operator, allowing for corrective actions.

[0093] In the preceding description, aspects and embodiments of this disclosure have been described with reference to numerous specific details, which may vary from implementation to implementation. Therefore, the specification and drawings should be considered illustrative rather than restrictive. The sole and exclusive indication of the scope of this invention, and what the applicant expects to be the scope of this invention, is the literal and equivalent scope of the claims published from this application in the specific form of the published claims, including any subsequent amendments. Any definitions of terms expressly set forth herein for inclusion in such claims should be taken as meaning as such terms are used in the claims. Furthermore, when the term “comprising” is used in the preceding specification or appended claims, what follows that phrase may be an additional step or entity, or a sub-step / sub-entity of a previously stated step or entity.

Claims

1. A system for a vehicle, comprising: At least one processor; as well as At least one memory storing instructions that, when executed by the at least one processor, cause the at least one processor to: During operation of the vehicle in manual driving mode in the environment, receive one or more autonomous vehicle operation task outputs associated with driving the vehicle in autonomous driving mode in the same environment; The output of one or more autonomous vehicle operation tasks is compared with a corresponding threshold, wherein the corresponding threshold is adaptive based on the current operating state of the vehicle in the manual driving mode; A transition indicator is set based on the comparison, wherein the transition indicator indicates the availability of a smooth transition from the manual driving mode to the autonomous driving mode, wherein the smooth transition is a change from the manual driving mode to the autonomous driving mode without abruptness or sudden unintentional acceleration; and In response to the transition indicator indicating that a smooth transition from the manual driving mode to the autonomous driving mode is unavailable, the transition from the manual driving mode to the autonomous driving mode is refused.

2. The system according to claim 1, wherein, The output of one or more autonomous vehicle operation tasks includes data associated with path planning, wherein the path planning data is compared with corresponding thresholds to determine the existence of a path from the planner, and the transition to the path is smooth for the planner.

3. The system according to claim 1 or 2, wherein, The output of one or more autonomous vehicle operation tasks includes data associated with acceleration, and wherein comparing the acceleration data with corresponding thresholds determines that the transition from the manual driving mode to the autonomous driving mode based on the acceleration data is smooth.

4. The system according to claim 1 or 2, wherein, The output of one or more autonomous vehicle operation tasks includes steering-related data, and the steering data is compared with corresponding thresholds to determine that the transition from the manual driving mode to the autonomous driving mode is smooth based on the steering data.

5. The system according to claim 1 or 2, wherein, The transition indicator shows that the vehicle is unable to safely follow the path traversed during manual driving mode.

6. The system according to claim 1 or 2, wherein, The operation also includes providing feedback if the switch from manual driving mode to autonomous driving mode is rejected.

7. The system according to claim 6, wherein, The feedback is audio feedback or visual feedback drawn on at least one display.

8. The system according to claim 1 or 2, further comprising generating visual feedback indicating that the transition from manual driving mode to autonomous driving mode has been rejected, wherein, The visual feedback identifies the reason for refusing to switch from the manual driving mode to the autonomous driving mode.

9. The system according to claim 1 or 2, wherein, The operation also includes providing at least one instruction corresponding to a corrective action when the transition from the manual driving mode to the autonomous driving mode is rejected, wherein the corrective action is applied to the current operating state of the vehicle.

10. A method for a vehicle, comprising: Using at least one processor, during operation of the vehicle in manual driving mode in an environment, one or more autonomous vehicle operation task outputs associated with driving the vehicle in autonomous driving mode in the same environment are received; Using the at least one processor, the output of one or more autonomous vehicle operation tasks is compared with a corresponding threshold, wherein the corresponding threshold is adaptive based on the current operating state of the vehicle in the manual driving mode; Using the at least one processor, a transition indicator is set based on the comparison, wherein the transition indicator indicates the availability of a smooth transition from the manual driving mode to the autonomous driving mode, wherein the smooth transition is a change from the manual driving mode to the autonomous driving mode without abruptness or sudden unintentional acceleration; and Using the at least one processor, in response to a transition indicator indicating that a smooth transition from the manual driving mode to the autonomous driving mode is unavailable, the transition from the manual driving mode to the autonomous driving mode is rejected.

11. The method according to claim 10, wherein, The output of one or more autonomous vehicle operation tasks includes data associated with path planning, wherein the path planning data is compared with corresponding thresholds to determine the existence of a path from the planner, and the transition to the path is smooth for the planner.

12. The method according to claim 10 or 11, wherein, The output of one or more autonomous vehicle operation tasks includes data associated with acceleration, and wherein comparing the acceleration data with corresponding thresholds determines that the transition from the manual driving mode to the autonomous driving mode based on the acceleration data is smooth.

13. The method according to claim 10 or 11, wherein, The output of one or more autonomous vehicle operation tasks includes steering-related data, and the steering data is compared with corresponding thresholds to determine that the transition from the manual driving mode to the autonomous driving mode is smooth based on the steering data.

14. The method according to claim 10 or 11, wherein, The transition indicator shows that the vehicle is unable to safely follow the path traversed during manual driving mode.

15. The method according to claim 10 or 11, wherein, The operation also includes providing feedback if the switch from manual driving mode to autonomous driving mode is rejected.

16. The method of claim 10 or 11, further comprising generating visual feedback indicating that the transition from manual driving mode to autonomous driving mode has been rejected, wherein, The visual feedback identifies the reason for refusing to switch from the manual driving mode to the autonomous driving mode.

17. The method according to claim 10 or 11, wherein, The operation also includes providing at least one instruction corresponding to a corrective action when the transition from the manual driving mode to the autonomous driving mode is rejected, wherein the corrective action is applied to the current operating state of the vehicle.

18. At least one non-transitory storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 10-17.

19. A computer program product comprising instructions that, when executed by at least one processor, cause the at least one processor to perform the method according to any one of claims 10-17.

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