System and method for monitoring and recalibrating infrastructure and vehicle sensors

Abstract

The invention provides a system and method for monitoring and recalibrating infrastructure and vehicle sensors. A method includes identifying a current trajectory of an automated vehicle based on movement of the automated vehicle through a marshalling environment; calculating a distance-based difference between a current trajectory of the automated vehicle and an expected trajectory of the automated vehicle; determining whether the distance-based difference exceeds a change threshold; and recalibrating one or more sensors of the infrastructure system in response to the distance-based difference exceeding the change threshold.

Description

Technical Field

[0001] This disclosure relates to identifying problems associated with infrastructure sensor suites and / or vehicle sensor suites. More specifically, this disclosure relates to identifying these problems and recalibrating the sensors of infrastructure sensor suites and / or vehicle sensor suites based on the identification of these problems. Background Technology

[0002] The statements in this section are provided only as background information in connection with this disclosure and may not constitute prior art.

[0003] Infrastructure-guided grouping of one or more vehicles in a grouping environment typically relies on communication between the sensor suite associated with the infrastructure and the sensor suite associated with each of the one or more vehicles. However, in some cases, this communication may be unreliable, which can lead to inefficiencies in the grouping process. For example, identifying such communication problems is time-consuming and often difficult, and therefore, due to late identification, potential recalibration of the infrastructure sensor suite and / or vehicle sensor suites occurs too late during the grouping process. This disclosure addresses these and other problems related to identifying and / or recalibrating the infrastructure sensor suite and / or vehicle sensor suite. Summary of the Invention

[0004] This section provides a general overview of this disclosure and is not a full disclosure of its entire scope or all its features.

[0005] This disclosure provides a method comprising: identifying the current trajectory of an automated vehicle based on its movement through a grouping environment, wherein the current trajectory is based on the current position and current speed of the automated vehicle; calculating a distance-based difference between the current trajectory and a desired trajectory of the automated vehicle, wherein the desired trajectory is based on a target path and a target vehicle speed; determining whether the distance-based difference exceeds a change threshold; and recalibrating one or more sensors of an infrastructure system in response to the distance-based difference exceeding the change threshold, wherein the recalibration of the one or more sensors is based on vehicle perception analysis; the method further comprising: causing one or more sensors of the automated vehicle to be recalibrated in response to the distance-based difference exceeding the change threshold; wherein the recalibration of one or more sensors of the infrastructure system or one or more sensors of the automated vehicle includes inherent calibration, external calibration, color calibration, frequency calibration, and angle calibration. The method includes calibration, power calibration, or a combination thereof; wherein vehicle perception analysis includes field-of-view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or a combination thereof; the method further includes: transmitting one or more instructions to an automated vehicle in response to a distance-based difference satisfying a change threshold; and causing the automated vehicle to move from one workstation in a grouped environment to another workstation in the grouped environment based on the one or more instructions; the method further includes: aggregating distance-based differences for each of a plurality of automated vehicles; generating a statistical distribution of the expected trajectory of each of the plurality of automated vehicles based on the aggregated distance-based differences; and determining, based on the statistical distribution, whether the distance-based difference for each of the plurality of automated vehicles exceeds a change threshold; and the method further includes: transmitting an alarm in response to unsuccessful recalibration of one or more sensors, wherein the alarm is a service request.

[0006] This disclosure provides a system comprising: an infrastructure system configured to: identify the current trajectory of an automated vehicle based on its movement through a grouping environment, wherein the current trajectory is based on the current position and current speed of the automated vehicle; calculate a distance-based difference between the current trajectory and a desired trajectory of the automated vehicle, wherein the desired trajectory is based on a target path and a target vehicle speed; determine whether the distance-based difference exceeds a change threshold; and recalibrate one or more sensors of the infrastructure system in response to the distance-based difference exceeding the change threshold, wherein the recalibration of the one or more sensors is based on vehicle perception analysis; and an automated vehicle configured to: recalibrate one or more sensors of the automated vehicle in response to the distance-based difference exceeding the change threshold; wherein the recalibration of the one or more sensors of the infrastructure system or the one or more sensors of the automated vehicle includes inherent calibration, external calibration, color calibration, frequency calibration, etc. The system includes calibration, angle calibration, power calibration, or a combination thereof; wherein vehicle perception analysis includes field-of-view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or a combination thereof; wherein the infrastructure system is further configured to: transmit one or more instructions to an automated vehicle in response to a distance-based difference satisfying a change threshold; and cause the automated vehicle to move from one workstation in a grouped environment to another workstation in the grouped environment based on the one or more instructions; wherein the infrastructure system is further configured to: aggregate distance-based differences for each of the multiple automated vehicles; generate a statistical distribution of the expected trajectory of each of the multiple automated vehicles based on the aggregated distance-based differences; and determine, based on the statistical distribution, whether the distance-based difference for each of the multiple automated vehicles exceeds a change threshold; and wherein the infrastructure system is further configured to: transmit an alarm in response to unsuccessful recalibration of one or more sensors, wherein the alarm is a service request.

[0007] This disclosure provides one or more non-transitory computer-readable media storing processor-executable instructions that, when executed by at least one processor, cause at least one processor to: identify the current trajectory of an automated vehicle based on the movement of the automated vehicle through a grouping environment, wherein the current trajectory is based on the current position and current speed of the automated vehicle; calculate a distance-based difference between the current trajectory of the automated vehicle and a desired trajectory of the automated vehicle, wherein the desired vehicle trajectory is based on a target path and a target vehicle speed; determine whether the distance-based difference exceeds a change threshold; and recalibrate one or more sensors of an infrastructure system in response to the distance-based difference exceeding the change threshold, wherein the recalibration of the one or more sensors is based on vehicle perception analysis; wherein the at least one processor is further caused to: cause one or more sensors of the automated vehicle to be recalibrated in response to the distance-based difference exceeding the change threshold; wherein the recalibration of one or more sensors of the infrastructure system or one or more sensors of the automated vehicle... This includes inherent calibration, external calibration, color calibration, frequency calibration, angle calibration, power calibration, or combinations thereof; wherein vehicle perception analysis includes field of view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or combinations thereof; wherein at least one processor is further caused to: transmit one or more instructions to an automated vehicle in response to a distance-based difference satisfying a change threshold; and to cause the automated vehicle to move from one workstation in a grouped environment to another workstation in the grouped environment based on the one or more instructions; wherein at least one processor is further caused to: aggregate distance-based differences for each of the plurality of automated vehicles; generate a statistical distribution of the expected trajectory of each of the plurality of automated vehicles based on the aggregated distance-based differences; and determine, based on the statistical distribution, whether the distance-based difference for each of the plurality of automated vehicles exceeds a change threshold; and wherein at least one processor is further caused to: transmit an alarm in response to unsuccessful recalibration of one or more sensors, wherein the alarm is a service request.

[0008] Further applicability will become apparent from the description provided herein. It should be understood that the descriptions and specific examples are intended for illustrative purposes only and are not intended to limit the scope of this disclosure. Attached Figure Description

[0009] To better understand this disclosure, various forms of the disclosure will now be described by way of example with reference to the accompanying drawings, in which: Figure 1 A system for automated vehicle grouping according to one or more embodiments of the present disclosure is shown; Figure 2One or more embodiments of the present disclosure are shown. Figure 1 Example vehicles grouped by the system shown; Figure 3 This is a flowchart illustrating an example method for monitoring and / or recalibrating one or more sensors associated with an automated vehicle and / or infrastructure system according to one or more embodiments of this disclosure; Figure 4 This is a flowchart illustrating another example method for monitoring and / or recalibrating one or more sensors associated with automated vehicle and / or infrastructure systems according to one or more embodiments of this disclosure; and Figure 5 This is a block diagram illustrating an example computer system according to one or more embodiments of the present disclosure.

[0010] The accompanying drawings described herein are for illustrative purposes only and are not intended to limit the scope of this disclosure in any way. Detailed Implementation

[0011] The following description is merely exemplary in nature and is not intended to limit this disclosure, its application, or its uses. It should be understood that throughout the drawings, corresponding reference numerals indicate the same or corresponding parts and features.

[0012] One or more examples described herein provide a means for identifying one or more problems (e.g., performance and / or operational issues) associated with an infrastructure sensor suite and / or a vehicle sensor suite, and for recalibrating the infrastructure sensor suite and / or the vehicle sensor suite (e.g., recalibrating one or more sensors) based on the identified one or more problems. In one or more embodiments, identifying problems and / or recalibrating the infrastructure sensor suite and / or the vehicle sensor suite based on communication between the infrastructure sensor suite and / or the vehicle sensor suite can be implemented during the manufacturing process without requiring a human operator to identify any problems with the infrastructure sensor suite and / or the vehicle sensor suite. In the case of manufacturing a large number of vehicles, one or more embodiments thereby provide a more efficient and time-saving process with a more secure communication link between the infrastructure sensor suite and / or the vehicle sensor suite.

[0013] Now for reference Figure 1An Automated Vehicle Grouping (AVM) system 100 is illustrated for maneuvering one or more automated and / or semi-automated vehicles 102 (e.g., one or more vehicles 102a, 102b) within a grouping environment (e.g., a manufacturing facility or parking lot). The AVM system 100 includes an infrastructure system 104. The infrastructure system 104 includes sensor components 106 that communicate with a set of infrastructure sensors 108 (such as, for example, one or more cameras, lidar, radar, and / or ultrasonic devices). The set of infrastructure sensors 108 is configured to monitor the movement of the vehicles 102 as they move through the grouping environment. In one or more examples, the set of infrastructure sensors 108 is configured to utilize a shared global coordinate system for monitoring the movement of the vehicles 102 as they move through the grouping environment. In one or more examples, the set of infrastructure sensors 108 is also configured to detect, identify, and / or verify behaviors (e.g., operational characteristics or conditions) corresponding to each of the vehicles 102a, 102b as they move through a grouped environment, as described herein.

[0014] Infrastructure system 104 also includes a wireless communication component 110 providing communication between infrastructure system 104 and vehicle 102. Additionally, infrastructure system 104 includes infrastructure controller 112. Infrastructure controller 112 is configured to centrally control the operation of each of vehicles 102a and 102b in a closed-loop control system. However, it should be understood that infrastructure controller 112 is configured to centrally control the operation of each of vehicles 102a and 102b within the functional and / or technical limits of any system. For example, the operation of each of vehicles 102a and 102b includes propulsion, braking, and / or steering of vehicle 102. It should be understood that infrastructure controller 112 may be located within infrastructure system 104 or externally relative to infrastructure system 104.

[0015] Infrastructure controller 112 includes infrastructure-side AVM algorithm 114 (e.g., AVM software module), which is configured to perform vehicle translation analysis and / or vehicle perception analysis using one or more algorithmic learning models, as described herein. It should be understood that vehicle-side AVM algorithms (e.g., vehicle-side AVM algorithm 212) are also configured to support the vehicle translation analysis and / or vehicle perception analysis performed by infrastructure-side AVM algorithm 114 using one or more algorithmic learning models. However, it should also be understood that vehicle-side AVM algorithm 212 is also configured to perform vehicle translation analysis and / or vehicle perception analysis itself using one or more algorithmic learning models (e.g., via vehicle-side AVM algorithm 212). In one or more examples, one or more algorithmic learning models can be trained dynamically (e.g., in real-time) via supervised or unsupervised learning processes. It should be understood that the one or more algorithmic learning models can be, for example, neural network models or any other type of learning model. It should also be understood that the infrastructure-side AVM algorithm 114 and / or the vehicle-side AVM algorithm 212 can execute one or more algorithm learning models to perform vehicle translation analysis and / or vehicle perception analysis on each of the vehicles 102a and 102b, respectively.

[0016] The infrastructure-side AVM algorithm 114 is also configured to facilitate the association of the infrastructure controller 112 with the vehicle controller (e.g., as...) associated with each of the vehicles 102a, 102b. Figure 2 Communication between the infrastructure controller 112 and the vehicle controller 200 (as shown). In one or more examples, communication between the infrastructure controller 112 and the vehicle controller 200 may take the form of an exchange of one or more infrastructure grouping messages and / or one or more vehicle grouping messages.

[0017] In one or more embodiments, the infrastructure-side AVM algorithm 114 may execute one or more algorithm learning models to perform vehicle translation analysis and / or vehicle perception analysis on each of the vehicles 102a, 102b, respectively, based on the exchange of one or more infrastructure grouping messages and / or one or more vehicle grouping messages.

[0018] In one or more embodiments, infrastructure system 104 is configured to store anticipated behaviors associated with any vehicle configured to move through the marshalling environment. For example, the anticipated behaviors are stored in a database (not shown) associated with infrastructure system 104. As another example, the database may be located internally or externally relative to infrastructure system 104. As an example, the stored anticipated behaviors may represent historical data used as the basis for performing vehicle translation analysis and / or vehicle perception analysis. As another example, the stored anticipated behaviors may relate to the anticipated behaviors of vehicles approaching a specific workstation among one or more workstations associated with the marshalling environment.

[0019] In one or more embodiments, the infrastructure-side AVM algorithm 114 is further configured to perform one or more analyses to support the detection, identification, and / or verification of behavior corresponding to each of vehicles 102a, 102b. In one or more examples, the infrastructure-side AVM algorithm 114 is configured to verify the behavior corresponding to each of vehicles 102a, 102b based on whether the identified behavior detected in each of vehicles 102a, 102b matches the expected behavior of the vehicle at a specific location within the grouping environment.

[0020] In one or more embodiments, one or more analyses used to support the detection, identification, and / or verification of behavior corresponding to each of vehicles 102a, 102b can also serve as the basis for performing vehicle translation analysis. In one or more examples, one or more analyses can compare the target path and / or target speed of each of vehicles 102a, 102b with the current position and / or current speed of each of vehicles 102a, 102b. As another example, the set of infrastructure sensors 108 is configured to monitor each of vehicles 102a, 102b to determine the current position and / or current speed of each of vehicles 102a, 102b. As yet another example, the set of infrastructure sensors 108 is configured to monitor any angle of each of the vehicles 102a, 102b, such as, but not limited to, the front of each of the vehicles 102a, 102b, the rear of each of the vehicles 102a, 102b, and / or one or more sides of each of the vehicles 102a, 102b. As a further example, the set of infrastructure sensors 108 is also configured to monitor the left-right positioning associated with each of the vehicles 102a, 102b, which can be the center of any vehicle (e.g., as shown in the image). Figure 2 The distance between reference point 210 shown and the center of the specific driving lane in which the vehicle is moving.

[0021] In one or more examples, vehicle translation analysis may include calculating a distance-based difference between the current trajectory and the expected trajectory of each of vehicles 102a and 102b. It should be understood that the expected trajectory of each of vehicles 102a and 102b is based on the expected position and / or the expected velocity of each of vehicles 102a and 102b. It should also be understood that the current trajectory of each of vehicles 102a and 102b is based on the current position and / or the current velocity of each of vehicles 102a and 102b.

[0022] In one or more embodiments, vehicle translation analysis may further include generating (e.g., creating) a statistical distribution of the expected trajectory of each of vehicles 102a, 102b in real time based on generating distance-based differences specific to each of vehicles 102a, 102b. As an example, the statistical distribution associated with each of vehicles 102a, 102b serves as the basis for determining whether the distance-based differences specific to each of vehicles 102a, 102b exceed a variation threshold. For example, the variation threshold may represent a predefined range of acceptable distance-based differences determined not to impede the movement of vehicles 102a, 102b through the grouping environment. It should be understood that the predefined range can be any range, for example, based on any grouping-related considerations or other factors.

[0023] In one or more embodiments, vehicle translation analysis can be used to identify vehicle positioning skew associated with the movement of each of vehicles 102a, 102b over time as each of the vehicles 102a, 102b traverses (e.g., moves through) a grouped environment. In one or more examples, and where vehicle 102 is fully or primarily controlled by infrastructure system 104, vehicle translation analysis can be used to identify any problems with the infrastructure-based sensor suite. For example, the infrastructure-based sensor suite may include the set of infrastructure sensors 108 and / or any components within infrastructure system 104 that support the set of infrastructure sensors 108, such as infrastructure controller 112, infrastructure-side AVM algorithm 114, sensor component 106, and / or wireless communication component 110. In one or more examples, and where vehicle 102 is fully or primarily controlled by the vehicle itself (e.g., vehicle 102), vehicle translation analysis can be used to identify any problems with the vehicle sensor suite. For example, a vehicle-based sensor suite may include multiple onboard sensors 204 and / or any component within the vehicle that supports multiple onboard sensors 204, such as Figure 2As shown, such as vehicle controller 200, one or more actuators 202, human-machine interface (HMI) 206, vehicle system 208 and / or vehicle-side AVM algorithm 212.

[0024] In one or more embodiments, vehicle translation analysis is further configured to determine one or more trends associated with different sizes of vehicle groups moving through certain locations within a formation environment. In other words, different sizes of vehicle groups can constitute different-sized vehicle groups formed by different numbers of vehicles in different groups. As an example, one or more trends can be determined based on historical data associated with the average movement of each vehicle group in one or more previous vehicle groups. As another example, one or more determined trends can indicate changes associated with a change threshold. In other words, one or more determined trends can indicate higher-than-expected changes or lower-than-expected changes associated with a change threshold. As yet another example, one or more determined trends can indicate a change-related deviation relative to a change threshold associated with a specific location within the formation environment. For example, a change-related deviation can include situations where a vehicle may turn right or left over time as it moves through a specific location within the formation environment.

[0025] In one or more examples, vehicle perception analysis may include field-of-view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or a combination thereof. As another example, vehicle perception analysis may be performed at any point in time and at any location within the grouping environment as each of vehicles 102a, 102b traverses the grouping environment.

[0026] In one or more embodiments, the infrastructure-side AVM algorithm 114 can create a bounding box 116 associated with the vehicle 102 (e.g., as shown in the image). Figure 1 One or more bounding boxes 116a, 116b are shown. As an example, bounding box 116 (e.g., a virtual vehicle layout) defines a vehicle 102 within a matrix grid. As another example, and in relation to the grouping of more than one vehicle 102 in the infrastructure system 104, bounding boxes 116a, 116b define each vehicle 102a, 102b, respectively. As a further example, the creation (e.g., generation) of bounding boxes 116 can help accurately group vehicles 102 through the grouping environment, and thus can support the operational functionality of the infrastructure sensor suite and / or vehicle sensor suite.

[0027] In one or more embodiments, the AVM system 100 also includes a vehicle manufacturing cloud system 118, which can operate as a central cloud system for managing and / or facilitating the manufacturing processes associated with the vehicle 102 described herein. In one or more examples, the infrastructure system 104 is configured to communicate wirelessly with the vehicle manufacturing cloud system 118, and in some cases, the vehicle manufacturing cloud system 118 is configured to cause the infrastructure system 104 to monitor the movement of the vehicle 102 as it moves through a grouping environment.

[0028] Further reference Figure 2 In various forms, vehicle 102 can be powered in various ways (e.g., using electric motors and / or internal combustion engines). It should be understood that vehicle 102 can be any type of vehicle powered by electric motors and / or internal combustion engines, such as cars, trucks, robots, aircraft, and / or boats. Vehicle 102 typically includes a vehicle controller 200, one or more actuators 202, multiple onboard sensors 204, an HMI 206, and a vehicle system 208. Vehicle 102 also has a reference point 210, i.e., a designated point within the space defined by the vehicle body, which identifies the position of vehicle 102. For example, reference point 210 is the geometric center point where the respective longitudinal and lateral center axes of vehicle 102 intersect. As another example, reference point 210 is the point where vehicle 102 is located when navigating toward a waypoint.

[0029] In some examples, vehicle controller 200 is configured or programmed to control one or more of the following: vehicle braking, propulsion (e.g., controlling the acceleration of vehicle 102 by controlling one or more of an internal combustion engine, electric motor, hybrid engine, etc.), steering, climate control, interior and / or exterior lights, etc. In other examples, vehicle controller 200 is also configured or programmed to determine whether and when vehicle controller 200 (rather than a human operator) controls such operations associated with vehicle 102. It should be understood that any operation associated with vehicle 102 can be facilitated via automated, semi-automated, or manual modes. For example, an automated mode can facilitate complete control of any operation by vehicle controller 200 without the assistance of a human operator. As another example, a semi-automated mode can facilitate at least partial control of any operation by a human operator in combination with vehicle controller 200. As a further example, a manual mode can facilitate complete control of operation by a human operator without the assistance of vehicle controller 200.

[0030] Vehicle controller 200 includes one or more processors (not shown), or can be communicatively coupled to one or more processors (e.g., via a vehicle communication bus). For example, the one or more processors may be controllers included in vehicle 102, used to monitor and / or control various vehicle controllers, such as powertrain controllers, brake controllers, steering controllers, etc. Vehicle controller 200 is typically arranged for various communications over a vehicle communication network (not shown) (which may include buses in vehicle 102, such as Controller Area Network (CAN)) and / or other wired and / or wireless mechanisms.

[0031] Vehicle controller 200 transmits messages to and / or receives messages from various devices (e.g., one or more actuators 202, HMI 206, etc.) in vehicle 102 via a vehicle network. Alternatively or additionally, where vehicle controller 200 includes multiple device generators, a vehicle communication network is used for communication between the device generators of vehicle controller 200, as represented herein. Furthermore, as discussed below, various other controllers and / or sensors provide data to vehicle controller 200 via the vehicle communication network.

[0032] Additionally, the vehicle controller 200 is configured via a vehicle-side AVM algorithm 212 to communicate with the infrastructure communication network via the vehicle, such as identifying the trajectory of the vehicle 102 relative to a target travel path. It should be understood that, depending on the assembly level of the vehicle 102, the movement of the vehicle 102 may utilize the vehicle sensor suite to a lesser or greater extent, and will therefore rely on the infrastructure sensor suite to move around the grouped environment (e.g., via the grouping device).

[0033] The vehicle controller 200 is also configured via the vehicle-side AVM algorithm 212 to communicate with other traffic objects (e.g., vehicles, infrastructure, etc.) through a wireless vehicle communication interface, such as via a vehicle-to-vehicle communication network. The vehicle communication network refers to one or more mechanisms by which the vehicle controller 200 of vehicle 102 communicates with other traffic objects. As an example, the vehicle communication network can be one or more wireless communication mechanisms, including any desired combination of wireless (e.g., cellular, wireless, satellite, microwave, and / or radio frequency) communication mechanisms, and any desired network topology (or multiple topologies utilizing multiple communication mechanisms). Examples of vehicle communication networks include cellular, Bluetooth®, IEEE 802.11, Dedicated Short Range Communication (DSRC), and / or Wide Area Network (WAN) (including the Internet) providing data communication services.

[0034] Vehicle actuators 202 are implemented via circuits, chips, or other electronic and / or mechanical components that can actuate various vehicle subsystems according to appropriate control signals. Actuators 202 can be used to control the braking, acceleration, and / or steering of vehicle 102. Vehicle controller 200 can be programmed to activate vehicle actuators 202 (including propulsion, steering, and / or braking actuators) based on planned acceleration or deceleration of vehicle 102.

[0035] The multiple vehicle-mounted sensors 204 include various means for providing data to the vehicle controller 200. For example, the multiple vehicle-mounted sensors 204 may include object detection sensors (e.g., lidar sensors) disposed on or in the vehicle 102, which provide the relative position, size, and / or shape of one or more objects (such as attached vehicles, bicycles, robots, drones, etc.) traveling beside, in front of, and / or behind the vehicle 102. As another example, one or more of the multiple vehicle-mounted sensors 204 may be radar sensors fixed to one or more bumpers of the vehicle 102, which can provide the position of an object relative to each of the objects in the vehicle 102.

[0036] Multiple onboard sensors 204 may include camera sensors that provide images from the area surrounding vehicle 102, such as providing front, side, and rear views. As another example, vehicle controller 200 may be programmed to receive sensor data from camera sensors and implement image processing techniques to detect roads, infrastructure elements, etc. Vehicle controller 200 may also be programmed to determine the current vehicle position based on location coordinates (e.g., GPS coordinates) received from vehicle 102 indicating the position of vehicle 102 from a GPS sensor (not shown).

[0037] HMI 206 is configured to receive information from a human operator during operation of vehicle 102. Additionally, HMI 206 is configured to present information to a human operator, such as an occupant of vehicle 102. In some variations, vehicle controller 200 is programmed to receive destination data (e.g., location coordinates) from HMI 206.

[0038] Vehicle system 208 is configured to control each of the subsystems within vehicle 102 and facilitate requests across each of the aforementioned components (e.g., vehicle controller 200, one or more actuators 202, multiple onboard sensors 204, and / or HMI 206). Therefore, at least multiple onboard sensors 204 can be used to autonomously guide vehicle 102 to waypoints. Route selection can be performed using vehicle location, distance traveled, queuing for waiting vehicles, etc.

[0039] Figure 3This is a flowchart illustrating an example method 300 for monitoring and / or recalibrating one or more sensors associated with an automated vehicle (e.g., vehicle 102) and / or an infrastructure system (e.g., infrastructure system 104). At operation 302, the current trajectory of the automated vehicle is identified. For example, the current trajectory of the automated vehicle is identified based on the movement of the automated vehicle through a grouped environment. As another example, the current trajectory is based on the current position and / or the current speed of the automated vehicle. As yet another example, the identification of the current trajectory of the automated vehicle is performed by the infrastructure system.

[0040] At operation 304, a distance-based difference is calculated between the current trajectory of the automated vehicle and its expected trajectory. For example, the expected trajectory is based on the target path and target speed of the automated vehicle. At operation 306, a determination is made as to whether the distance-based difference exceeds a change threshold (e.g., a distance change limit).

[0041] At operation 308, one or more sensors of the infrastructure system (e.g., the set of infrastructure sensors 108) are recalibrated in response to a distance-based difference exceeding a change threshold. For example, the recalibration of one or more sensors is based on vehicle perception analysis. In one or more embodiments, the infrastructure system is configured to cause one or more sensors of the automated vehicle (e.g., multiple onboard sensors 204) to be recalibrated. In one or more examples, the infrastructure system is configured to cause one or more sensors of the automated vehicle to be recalibrated in response to a distance-based difference exceeding a change threshold. For example, recalibration of one or more sensors of the infrastructure system and / or one or more sensors of the automated vehicle includes inherent calibration, external calibration, color calibration, frequency calibration, angle calibration, power calibration, or combinations thereof. As yet another example, vehicle perception analysis includes field-of-view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or combinations thereof.

[0042] In one or more embodiments, the infrastructure system is configured to transmit one or more instructions to the automated vehicle. For example, the transmission of one or more instructions may be in response to a change threshold being met based on a distance difference. The infrastructure system is also configured to cause the automated vehicle to move from one workstation in a troop environment to another workstation in the troop environment based on one or more instructions. In one or more embodiments, the infrastructure system is configured to transmit an alarm in response to unsuccessful recalibration of one or more sensors. For example, the alarm may be a service request. However, it should be understood that the alarm can be any type of request associated with functionality related to the automated vehicle and / or the infrastructure system.

[0043] In one or more embodiments, the infrastructure system is configured to aggregate distance-based differences for each of a plurality of automated vehicles. The infrastructure system is also configured to generate a statistical distribution of the expected trajectory for each of the plurality of automated vehicles. For example, the generation of the statistical distribution of the expected trajectory for each of the plurality of automated vehicles is based on the aggregated distance-based differences. The infrastructure system is also configured to determine whether the distance-based differences for each of the plurality of automated vehicles exceed a change threshold. For example, the determination of whether the distance-based differences for each of the plurality of automated vehicles exceed the change threshold is based on a statistical distribution.

[0044] Figure 4 This is a flowchart illustrating another example method 400 for monitoring and / or recalibrating one or more sensors associated with an automated vehicle (e.g., vehicle 102) and / or an infrastructure system (e.g., infrastructure system 104). At operation 402, the current trajectory of the automated vehicle is identified. For example, the current trajectory of the automated vehicle is identified based on the movement of the automated vehicle through a grouped environment. As another example, the current trajectory is based on the current position and / or current speed of the automated vehicle. As yet another example, the identification of the current trajectory of the automated vehicle is performed by the infrastructure system. At operation 404, a distance-based difference between the current trajectory of the automated vehicle and the expected trajectory of the automated vehicle is calculated. For example, the expected vehicle trajectory is based on the target path and target speed of the automated vehicle.

[0045] At operation 406, a determination is made regarding whether the distance-based difference exceeds a change threshold. In one or more examples, and where such a determination is made that the distance-based difference exceeds the change threshold, one or more sensors of the infrastructure system (e.g., the set of infrastructure sensors 108) are recalibrated at operation 408 in response to the distance-based difference exceeding the change threshold. For example, the recalibration of one or more sensors is based on vehicle perception analysis. As another example, the recalibration of one or more sensors of the infrastructure system includes inherent calibration, external calibration, color calibration, frequency calibration, angle calibration, power calibration, or a combination thereof. As yet another example, vehicle perception analysis includes field-of-view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or a combination thereof.

[0046] In one or more examples, determining that a distance-based difference exceeds a change threshold can indicate anomalous functionality associated with the operational behavior of the infrastructure sensor suite and / or vehicle sensor suite. As an example, infrastructure system 104 is then configured to transmit one or more operational commands to a relevant sensor suite system (e.g., another infrastructure sensor suite and / or vehicle sensor suite) to dynamically identify, verify, and / or diagnose problems affecting the infrastructure sensor suite and / or vehicle sensor suite in real time. In one or more examples, sensor recalibration for the infrastructure sensor suite and / or vehicle sensor suite can occur within a closed-loop system. However, it should be understood that sensor recalibration for the infrastructure sensor suite and / or vehicle sensor suite can occur within the functional and / or technical limits of any system. For example, sensor recalibration can include camera calibration, radar calibration, etc. As another example, camera calibration can include inherent calibration associated with the camera's internal parameters, external calibration associated with the field of view related to the camera's orientation, and / or color calibration. As yet another example, radar calibration can include frequency calibration, angle calibration, and / or power calibration.

[0047] However, in other examples, and where a determination is made at operation 406 that the distance-based difference does not exceed a change threshold, the infrastructure system is configured at operation 410 to cause an automated vehicle to move from one workstation in the grouping environment to another workstation in the grouping environment. For example, this is based on one or more instructions to cause the automated vehicle to move from one workstation in the grouping environment to another workstation in the grouping environment. As another example, the infrastructure system is configured to transmit one or more instructions to the automated vehicle. As yet another example, one or more instructions are transmitted in response to a distance-based difference meeting (not exceeding) a change threshold. In one or more examples, making a determination that the distance-based difference does not exceed the change threshold may indicate that the infrastructure sensor suite and / or the vehicle sensor suite are outputting sensor data within the expected change range, and therefore, remedial action is not necessary.

[0048] Figure 5An operating environment, such as a computer system, is shown that facilitates the execution of one or more systems and methods described herein. More specifically, the systems and methods described herein can be implemented using computing device 502. For example, computing device 502 can be a personal computer, desktop computer, laptop computer, tablet computer, handheld computer, server, workstation, mainframe, wearable computer, supercomputer, or a combination thereof. However, it should be understood that the foregoing examples of computing device 502 are not exhaustive, and computing device 502 can be any type of processing or computing device. Computing device 502 typically includes a processor 504, a display adapter 506, one or more input / output ports 508, one or more input / output components 510, a network adapter 512, a power supply 514, and memory 516. However, it should be understood that computing device 502 can include any of the listed components, and is not required to include any of them.

[0049] Processor 504 is configured to provide instructions to computing device 502, enabling computing device 502 to perform one or more tasks, including implementing software programs to perform one or more operations as described in more detail herein. It should also be understood that computing device 502 may include any number of processors 504. Display adapter 506 may be a graphics card or video board that provides computing device 502 with the ability to display content on display device 518. For example, display device 518 may be any screen, monitor, and / or light-emitting component associated with any of a personal computer, desktop computer, laptop computer, tablet computer, handheld computer, server, workstation, mainframe, wearable computer, supercomputer, or a combination thereof. However, it should be understood that the foregoing examples of display device 518 are not exhaustive, and display device 518 may be any type of device capable of providing visual display.

[0050] Input / output port 508 provides multiple interfaces (e.g., jacks) for one or more cables to connect to computing device 502. It should be understood that any number of input / output ports 508 may be present on computing device 502. For example, input / output port 508 provides computing device 502 with a means to receive signals and / or data from external devices connected to computing device 502 via one or more cables. As another example, input / output port 508 provides computing device 502 with a means to transmit signals and / or data to external devices connected to computing device 502 via one or more cables. Input / output component 510 may include one or more components supporting input / output port 508, such as, but not limited to, switches, buttons, pressure pads, float switches, keyboards, radio receivers, or combinations thereof.

[0051] Network adapter 512 can be any type of network interface controller configured to provide means for communicating with another computing device (such as remote computing device 522) via network 520. For example, remote computing device 522 can be a user device such as a cellular phone, smartphone, tablet computer, laptop computer, or a combination thereof. Power supply 514 is configured to convert alternating high-voltage current (e.g., AC) into direct current (e.g., DC) to provide power to other components of computing device 502 (e.g., processor 504, display adapter 506, one or more input / output ports 508, one or more input / output components 510, network adapter 512, and memory 516).

[0052] Additionally, memory 516 may be a mass storage device and / or system memory, such as a hard disk drive, memory card, solid-state drive, random access memory (RAM), or a combination thereof. Memory 516 is configured to provide storage for instructions and data associated with the operation of computing device 502. Memory 516 may typically include operating system 524, calibration software 526, and calibration data 528. For example, operating system 524 is configured to manage and / or process any of the data and / or instructions associated with calibration software 526 and / or calibration data 528, as described in more detail herein.

[0053] Furthermore, a system bus 530 is also included within the computing device 502, configured to couple each of the various components of the computing device 502 (e.g., processor 504, display adapter 506, one or more input / output ports 508, one or more input / output components 510, network adapter 512, power supply 514, and memory 516). It should also be understood that the functions associated with each component of the computing device 502 and with each component of the computing device 502 can be implemented within a remote computing device 522. Although Figure 5The operating environment shown herein depicts a specific configuration associated with at least computing device 502, network 520, and remote computing device 522; however, it should be understood that the operating environment can be configured in any manner.

[0054] Therefore, one or more examples of this disclosure provide a means for monitoring an infrastructure sensor suite and / or a vehicle sensor suite based on the exchange of one or more messages between the infrastructure sensor suite and / or the vehicle sensor suite. This disclosure also provides a means for recalibrating an infrastructure sensor suite and / or a vehicle sensor suite based on the detection of one or more problems with any of them.

[0055] Unless otherwise expressly indicated herein, all numerical values ​​indicating mechanical / thermal properties, percentages of composition, dimensions and / or tolerances or other characteristics should be understood as being modified by the words “about” or “approximately” when describing the scope of this disclosure. Such modification is expected for various reasons, including: industrial practice; material, manufacturing and assembly tolerances; and testing capabilities.

[0056] As used herein, the phrases A, B, and C at least one should be interpreted as representing logic (A or B or C) using the non-exclusive logic "or", and should not be interpreted as representing "at least one of A, at least one of B, and at least one of C".

[0057] In this application, the terms “controller” and / or “module” may refer to, be part of, or include the following: application-specific integrated circuit (ASIC); digital, analog, or mixed analog / digital discrete circuit; digital, analog, or mixed analog / digital integrated circuit; composable logic circuit; field-programmable gate array (FPGA); processor circuitry (shared, dedicated, or grouped) that executes code; memory circuitry (shared, dedicated, or grouped) that stores code executed by the processor circuitry; other suitable hardware components that provide the described functionality; or combinations of some or all of the foregoing, such as in a system-on-a-chip.

[0058] The term memory is a subset of the term computer-readable medium. As used herein, the term computer-readable medium does not cover transient electrical or electromagnetic signals propagated through a medium (such as on a carrier wave); therefore, the term computer-readable medium can be considered tangible and non-transient. Non-limiting examples of non-transient tangible computer-readable media include non-volatile memory circuits (such as flash memory circuits, erasable programmable read-only memory circuits, or mask read-only circuits), volatile memory circuits (such as static random access memory circuits or dynamic random access memory circuits), magnetic storage media (such as analog magnetic tape or digital magnetic tape or hard disk drives), and optical storage media (such as CDs, DVDs, or Blu-ray discs).

[0059] The apparatus and methods described in this application can be implemented, in part or in whole, by a dedicated computer created by configuring a general-purpose computer to perform one or more specific functions embodied in a computer program. Function blocks, flowchart components, and other elements described above serve as software specifications that can be translated into computer programs through the routine work of a technician or programmer.

[0060] The description in this disclosure is merely exemplary in nature, and therefore, variations without departing from the spirit and scope of this disclosure are intended to be made within its scope. Such variations should not be considered as departing from the spirit and scope of this disclosure.

[0061] According to the present invention, one or more non-transitory computer-readable media are provided, the non-transitory computer-readable media storing processor-executable instructions, which, when executed by at least one processor, cause at least one processor to: identify the current trajectory of an automated vehicle through a grouping environment based on the movement of the automated vehicle, wherein the current trajectory is based on the current position and current speed of the automated vehicle; calculate a distance-based difference between the current trajectory of the automated vehicle and a desired trajectory of the automated vehicle, wherein the desired vehicle trajectory is based on a target path and a target vehicle speed; determine whether the distance-based difference exceeds a change threshold; and recalibrate one or more sensors of an infrastructure system in response to the distance-based difference exceeding the change threshold, wherein the recalibration of the one or more sensors is based on vehicle perception analysis.

[0062] According to an embodiment, at least one processor is also caused to recalibrate one or more sensors of the automated vehicle in response to a distance-based difference exceeding a change threshold.

[0063] According to an embodiment, recalibration of one or more sensors of an infrastructure system and / or one or more sensors of an automated vehicle includes inherent calibration, external calibration, color calibration, frequency calibration, angle calibration, power calibration, or a combination thereof.

[0064] According to an embodiment, vehicle perception analysis includes field of view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or a combination thereof.

[0065] According to an embodiment, at least one processor is further caused to: transmit one or more instructions to the automated vehicle in response to a distance-based difference satisfying a change threshold; and to cause the automated vehicle to move from one workstation in the grouping environment to another workstation in the grouping environment based on the one or more instructions.

[0066] According to an embodiment, at least one processor is further caused to: aggregate distance-based differences for each of the plurality of automated vehicles; generate a statistical distribution of the expected trajectory of each of the plurality of automated vehicles based on the aggregated distance-based differences; and determine, based on the statistical distribution, whether the distance-based differences for each of the plurality of automated vehicles exceed a change threshold.

[0067] According to an embodiment, at least one processor is also caused to transmit an alarm in response to unsuccessful recalibration of one or more sensors, wherein the alarm is a service request.

Claims

1. A method comprising: The current trajectory of the automated vehicle is identified by the movement of the automated vehicle in the group environment, wherein the current trajectory is based on the current position and the current speed of the automated vehicle; Calculate a distance-based difference between the current trajectory of the automated vehicle and the expected trajectory of the automated vehicle, wherein the expected vehicle trajectory is based on the target path and target speed of the automated vehicle; Determine whether the distance-based difference exceeds a change threshold; as well as One or more sensors of the infrastructure system are recalibrated in response to the distance-based difference exceeding the change threshold, wherein the recalibration of the one or more sensors is based on vehicle perception analysis.

2. The method of claim 1, further comprising: One or more sensors of the automated vehicle are recalibrated in response to the distance-based difference exceeding the change threshold.

3. The method of claim 2, wherein the recalibration of the one or more sensors of the infrastructure system and / or the one or more sensors of the automated vehicle includes inherent calibration, external calibration, color calibration, frequency calibration, angle calibration, power calibration, or a combination thereof.

4. The method of claim 2, wherein the vehicle perception analysis includes field of view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or a combination thereof.

5. The method of claim 1, further comprising: One or more instructions are transmitted to the automated vehicle in response to the distance-based difference satisfying the change threshold; as well as The automated vehicle is moved from one workstation in the grouping environment to another workstation in the grouping environment based on one or more instructions.

6. The method of claim 1, further comprising: Aggregate distance-based differences for each of the multiple automated vehicles.

7. The method of claim 6, further comprising: The statistical distribution of the expected trajectory of each of the plurality of automated vehicles is generated based on aggregated distance-based differences. as well as Based on the statistical distribution, it is determined whether the distance-based difference for each of the plurality of automated vehicles exceeds the change threshold.

8. The method of claim 1, further comprising: An alarm is transmitted in response to an unsuccessful recalibration of the one or more sensors, wherein the alarm is a service request.

9. A system comprising: Infrastructure system, the infrastructure system being configured as follows: The current trajectory of the automated vehicle is identified by the movement of the convoy environment, wherein the current trajectory is based on the current position and current speed of the automated vehicle. Calculate the distance-based difference between the current trajectory of the automated vehicle and the expected trajectory of the automated vehicle, where the expected vehicle trajectory is based on the target path and the target vehicle speed. Determine whether the distance-based difference exceeds a change threshold, and One or more sensors of the infrastructure system are recalibrated in response to the distance-based difference exceeding the change threshold, wherein the recalibration of the one or more sensors is based on vehicle perception analysis; as well as The automated vehicle is configured to: One or more sensors of the automated vehicle are recalibrated in response to the distance-based difference exceeding the change threshold.

10. The system of claim 9, wherein the recalibration of the one or more sensors of the infrastructure system and / or the one or more sensors of the automated vehicle includes inherent calibration, external calibration, color calibration, frequency calibration, angle calibration, power calibration, or a combination thereof.

11. The system of claim 9, wherein the vehicle perception analysis includes field of view analysis, power analysis, signal strength of reflected beams, identification of one or more objects, distance measurement, detection accuracy, or a combination thereof.

12. The system of claim 9, wherein the infrastructure system is further configured to: One or more instructions are transmitted to the automated vehicle in response to the distance-based difference satisfying the change threshold; and The automated vehicle is moved from one workstation in the grouping environment to another workstation in the grouping environment based on one or more instructions.

13. The system of claim 10, wherein the infrastructure system is further configured to: Aggregate distance-based differences for each of the multiple automated vehicles.

14. The system of claim 13, wherein the infrastructure system is further configured to: The statistical distribution of the expected trajectory of each of the plurality of automated vehicles is generated based on aggregated distance-based differences; and Based on the statistical distribution, it is determined whether the distance-based difference for each of the plurality of automated vehicles exceeds the change threshold.

15. The system of claim 10, wherein the infrastructure system is further configured to: An alarm is transmitted in response to an unsuccessful recalibration of the one or more sensors, wherein the alarm is a service request.

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