SYSTEMS AND METHODS FOR IDENTIFYING A GHOST VEHICLE

Abstract

A procedure includes the detection of an object in a shunting environment, a determination of whether one or more conditions are met in response to the detection of the object, a transmission of a request to data originating from one or more sensors of each automated vehicle of one or more automated vehicles, a receipt of the requested data from the one or more automated vehicles, and the execution of one or more corrective actions based on an analysis of the requested data.

Description

AREA

[0001] The present disclosure relates to the identification of a ghost vehicle. More specifically, the present disclosure relates to the identification of a ghost vehicle in a shunting situation. GENERAL STATE OF THE ART

[0002] The statements in this section merely provide background information relating to the present disclosure and may not represent the state of the art.

[0003] Ghost vehicles, or vehicles that do not physically exist, can be detected by infrastructure sensors during shunting operations. The detection of ghost vehicles can disrupt the shunting of one or more vehicles, as the infrastructure sensors may mistake them for real vehicles and attempt to maneuver the real vehicles accordingly. This detection of ghost vehicles causes the shunting of real vehicles to stop, allowing the ghost vehicles to be properly identified and the shunting of the real vehicles to resume without taking the identified ghost vehicles into account.

[0004] The present disclosure addresses these and other problems relating to the identification of a ghost vehicle. SUMMARY

[0005] This section provides a general summary of the revelation and is not a comprehensive revelation of its full scope or all of its features.

[0006] The present disclosure provides a method comprising: detecting an object in a shunting environment; determining whether one or more conditions are met in response to the detection of the object; transmitting a request to one or more automated vehicles based on data from one or more sensors of each automated vehicle in response to the one or more conditions being met; receiving the requested data from the one or more automated vehicles; and performing one or more corrective actions based on an analysis of the requested data, wherein the one or more conditions include one or more of the following: an unexpected location of the object;an inability to identify a historical path associated with each automated vehicle of the one or more automated vehicles; an inconsistency between the data originating from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof; unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to prove a location of each automated vehicle of the one or more automated vehicles; and an inconsistency between a total number of automated vehicles of the one or more automated vehicles and an expected total number of automated vehicles of the one or more automated vehicles;wherein the analysis of the requested data comprises: determining whether a sensor output associated with one or more sensors of an infrastructure system matches the requested data; wherein the execution of one or more corrective actions is further based on a determination that the sensor output does not match the requested data; wherein the execution of one or more corrective actions comprises one of the following: initiating one or more reset routines; switching from a first set of one or more sensors of an infrastructure system to a second set of one or more sensors of the infrastructure system;and causing each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles, and causing an automated lead vehicle of the one or more automated vehicles to follow a historical route; and wherein the implementation of the one or more corrective actions includes: collecting metadata associated with the object; determining one or more object-vulnerable areas within the shunting environment; and generating a recommendation to replace one or more sensors of an infrastructure system or to install a second set of one or more sensors, based on the determination of the one or more object-vulnerable areas.

[0007] The present disclosure provides another method comprising: detecting an object in a shunting environment; determining whether one or more conditions are met in response to the detection of the object; transmitting one or more instructions to one or more automated vehicles to analyze data originating from one or more sensors of each automated vehicle in response to the one or more conditions being met; receiving from the one or more automated vehicles one or more results associated with an analysis of the data performed by the one or more automated vehicles; and performing one or more corrective actions based on the one or more results associated with the analysis of the data.wherein the one or more conditions include one or more of the following: an unexpected location of the object; an inability to identify a historical path associated with each automated vehicle of the one or more automated vehicles; an inconsistency between the data originating from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof; an unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to prove a location of each automated vehicle of the one or more automated vehicles;and an inconsistency between the total number of automated vehicles of the one or more automated vehicles and the expected total number of automated vehicles of the one or more automated vehicles; wherein the analysis of the data by the one or more automated vehicles further comprises: analyzing one or more video recordings of the maneuvering environment from each automated vehicle of the one or more automated vehicles; and verifying a location of the object based on the analysis of the one or more video recordings; wherein the execution of the one or more corrective actions comprises one or more of the following: initiating one or more reset routines; switching from a first set of one or more sensors of an infrastructure system to a second set of one or more sensors of the infrastructure system;and causing each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles, and causing an automated lead vehicle of the one or more automated vehicles to follow a historical route; and wherein the implementation of the one or more corrective actions includes: collecting metadata associated with the object; determining one or more object-vulnerable areas within a shunting environment; and generating a recommendation to replace one or more sensors of an infrastructure system or to install a second set of one or more sensors, based on the determination of the one or more object-vulnerable areas.

[0008] The present disclosure provides a system comprising: an infrastructure system configured to: detect an object in a shunting environment; determine whether one or more conditions are met in response to the detection of the object; transmit a request for data originating from one or more sensors of each automated vehicle of one or more automated vehicles in response to the one or more conditions being met; receive the requested data; and perform one or more corrective actions based on an analysis of the requested data; and one or more automated vehicles configured to: receive the request for the data originating from the one or more sensors of each automated vehicle of the one or more automated vehicles; and transmit the requested data.wherein the one or more automated vehicles are further configured to: receive one or more instructions to analyze the data originating from the one or more sensors of each automated vehicle of the one or more automated vehicles in response to the fulfillment of one or more conditions; and transmit one or more results associated with the analysis of the data performed by the one or more automated vehicles; wherein the infrastructure system is further configured to: transmit the one or more instructions to analyze the data originating from the one or more sensors of each automated vehicle of the one or more automated vehicles; and receive the one or more results;wherein the performance of the data analysis by the one or more automated vehicles comprises: analyzing one or more video recordings of the maneuvering environment from each automated vehicle of the one or more automated vehicles; and verifying a location of the object based on the analysis of the one or more video recordings; wherein the one or more conditions include one or more of the following: an unexpected location of the object; an inability to identify a historical path associated with each automated vehicle of the one or more automated vehicles; an inconsistency between the data originating from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof;unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to prove the location of each automated vehicle of the one or more automated vehicles; and an inconsistency between the total number of automated vehicles of the one or more automated vehicles and the expected total number of automated vehicles of the one or more automated vehicles; wherein the analysis of the requested data by the infrastructure system includes: determining whether a sensor output associated with one or more sensors of the infrastructure system matches the requested data; wherein the execution of the one or more corrective actions is further based on a determination that the sensor output does not match the requested data;wherein the implementation of one or more corrective actions by the infrastructure system comprises one of the following: initiating one or more reset routines; switching from a first set of one or more sensors of the infrastructure system to a second set of one or more sensors of the infrastructure system; and causing each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles, and causing an automated lead vehicle of the one or more automated vehicles to follow a historical path; and wherein the implementation of one or more corrective actions by the infrastructure system comprises: collecting metadata associated with the object; determining one or more object-vulnerable areas within the shunting environment;and generating a recommendation to replace one or more sensors of the infrastructure system or to install a second set of one or more sensors, based on the identification of one or more vulnerable areas.

[0009] Further areas of application will become apparent from the description provided herein.

[0010] It is understood that the description and specific examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. DRAWINGS

[0011] To fully understand the revelation, various forms of it will now be described by way of example with reference to the attached drawings, in which the following applies: Fig. Figure 1 illustrates a system for automated vehicle maneuvering according to one or more embodiments of the present disclosure; Fig. Figure 2 illustrates an exemplary vehicle, which is characterized by the in Fig. 1 system shown is ranked according to one or more embodiments of the present disclosure; Fig. Figure 3 is a process flow diagram illustrating an exemplary method for identifying a ghost vehicle according to one or more embodiments of the present disclosure; Fig. Figure 4 is a flowchart illustrating another exemplary method for identifying a ghost vehicle according to one or more embodiments of the present disclosure; Fig. Figure 5 is a process flow diagram illustrating another exemplary method for identifying a ghost vehicle according to one or more embodiments of the present disclosure; Fig. Figure 6 is a flowchart illustrating another exemplary method for identifying a ghost vehicle according to one or more embodiments of the present disclosure; and Fig. Figure 7 is a block diagram illustrating an exemplary computer system according to one or more embodiments of the present disclosure.

[0012] The drawings described herein serve only for illustration and are not intended to limit the scope of the present disclosure in any way. DETAILED DESCRIPTION

[0013] The following description is merely exemplary and is not intended to limit the present disclosure, application, or uses. It is understood that in all drawings, corresponding reference numerals indicate identical or corresponding parts and features.

[0014] One or more of the examples described herein provide a means for identifying a ghost vehicle in a shunting situation. In one or more embodiments, a means for validating a ghost vehicle is provided by a self-diagnostic software process. In one or more embodiments, a method utilizes one or more discrete systems that are independent of each other and serve different use cases. This method, coupled with a self-diagnostic software process configured to analyze shunting performance, allows for a software-based assessment that is independently redundant in both the analysis performed and the data collected. This method is capable of identifying the output of unique results associated with an infrastructure system relative to other inputs and / or a software routine.In one or more embodiments, a system is provided that relies on independent confirmation of data, searching for differences in one or more assessments performed by unique and independent systems, which may include one or more systems that are themselves observed and / or assessed. Thus, improved identification of which data sets are genuine and which are not is provided by comparing an operational system, an independent system, and / or a device associated with one or more other systems with an independent analysis performed by a software routine.

[0015] Fig. Figure 1 shows a schematic block diagram illustrating an automated vehicle marshaling system (AVM system) 100. In one or more examples, the AVM system 100 marshals one or more vehicles (e.g., a vehicle 102) moving at a low speed. However, it is understood that the AVM system 100 can marshal the one or more vehicles moving at any speed. Furthermore, it is understood that the AVM system 100 can marshal semi-autonomous and / or fully autonomous vehicles.

[0016] The AVM system 100 generally includes the vehicle 102, a vehicle manufacturing cloud system 104, a vehicle delivery manager cloud system 106, a web portal account cloud system 108 for vehicle customers, and an infrastructure system 110. The vehicle manufacturing cloud system 104 acts as the central cloud system that manages and / or facilitates any manufacturing process associated with the vehicle 102. The vehicle manufacturing cloud system 104 is configured to communicate wirelessly with the vehicle delivery manager cloud system 106 and / or the infrastructure system 110. The vehicle manufacturing cloud system 104 is also configured to communicate wirelessly with the vehicle 102.

[0017] The vehicle manufacturing cloud system 104 can include an infrastructure-side AVM algorithm 112. The infrastructure-side AVM algorithm 112 processes status information associated with at least vehicle 102 of the one or more vehicles. It is understood that in one or more embodiments, the infrastructure-side AVM algorithm 112 processes status information associated with each vehicle of the one or more vehicles (e.g., vehicle 102). The vehicle manufacturing cloud system 104 is configured to cause the infrastructure system 110 to monitor the progress of the one or more vehicles (e.g., vehicle 102) as the vehicle(s) progress through a shunting environment. For example, the shunting environment can be a factory shunting situation, an automated loading situation, a depot shunting situation, or an underground parking situation.As an example, the factory shunting scenario might involve a case where newly built vehicles are moved through end-of-line testing in a vehicle assembly plant using overhead vision capture (e.g., one or more sensors 114). Another example, the automated charging scenario might involve the correct assignment of vehicles to automated charging modalities, whether indoors or outdoors. A further example, the depot shunting scenario might involve a commercial vehicle fleet moving through warehouses and depots to automatically load and / or process items. Finally, the underground parking scenario might involve vehicles moving through underground or covered parking environments with a potentially inconsistent communication network, such as a global navigation satellite system.

[0018] The vehicle manufacturing cloud system 104 is also configured to instruct the infrastructure system 110 to communicate with the one or more vehicles. For example, the vehicle manufacturing cloud system 104 uses the infrastructure-side AVM algorithm 112 to send instructions to the infrastructure system 110 and / or to process information received from the infrastructure system 110. The vehicle manufacturing cloud system 104 is also configured to instruct the vehicle delivery manager cloud system 106 to facilitate the delivery of the one or more vehicles (e.g., vehicle 102) to different locations.For example, the vehicle manufacturing cloud system 104 uses the infrastructure-side AVM algorithm 112 to send instructions to the vehicle delivery manager cloud system 106 and / or to process information received from the vehicle delivery manager cloud system 106.

[0019] The vehicle manufacturing cloud system 104 is further configured to communicate directly with the one or more vehicles to cause them to start, stop, or interrupt their progress through the shunting environment. The vehicle manufacturing cloud system 104 is also configured to control the shunting speed of the one or more vehicles as they move through (e.g., traverse) the shunting environment. For example, the vehicle manufacturing cloud system 104 uses the infrastructure-side AVM algorithm 112 to send instructions to the vehicle 102 and / or to process information received from the vehicle 102.

[0020] The infrastructure system 110 includes one or more sensors 114, a wireless communication component 116, a multi-access edge computing (MEC) system 118, and one or more traffic signals 120. It is understood that the MEC system 118 is configured to support communication between the wireless communication component 116 and the vehicle 102. It is further understood that the MEC system 118 is also configured to support communication between the wireless communication component 116 and any of the vehicle manufacturing cloud systems 104, the vehicle delivery manager cloud systems 106, and / or the web portal account cloud systems 108 for vehicle customers. For example, the wireless communication component 116 can use GPS, Wi-Fi, satellite, 3G / 4G / 5G, and / or Bluetooth. ® Use it to communicate with one or more vehicles.

[0021] The wireless communication component 116 also communicates with the one or more sensors 114, which are configured to manage and / or include, for example, one or more cameras, lidar, radar, and / or ultrasonic devices. The one or more sensors 114 monitor the movement of the one or more vehicles while the vehicle(s) are being maneuvered through the shunting environment. Additionally, the wireless communication component 116 communicates with the traffic signals 120. For example, the wireless communication component 116 can cause the traffic signals 120 to direct the traffic of the one or more vehicles while the one or more vehicles are being maneuvered through the shunting environment. It is understood that the infrastructure system 110 can forward instructions received from the vehicle manufacturing cloud system 104 to the vehicle 102.Furthermore, it is understood that the infrastructure system 110, for example, can send instructions directly to the vehicle 102 by using the MEC system 118.

[0022] The vehicle 102 includes a vehicle-side AVM algorithm 122, a wireless transmission module 124, a central vehicle gateway module 126, a vehicle infotainment system 128, one or more vehicle sensors 130, a vehicle battery 132, a vehicle GNSS 134, a vehicle navigation mapping system 136, and a controller area network (CAN) vehicle bus 138. The wireless transmission module 124 can be a transmission control unit (TCU) and / or can be supported by telematics-enabled subsystems. The wireless transmission module 124 includes one or more sensors configured to collect data and transmit signals to other components of the vehicle 102.The one or more sensors of the wireless transmission module 124 may include a vehicle speed sensor (not shown) configured to determine the current speed of the vehicle 102; a wheel speed sensor (not shown) configured to determine whether the vehicle 102 is traveling on an incline or decline; a throttle position sensor (not shown) that determines whether downshifting or upshifting of one or more gears associated with the vehicle 102 is required based on the current status of the vehicle 102; and / or a turbine speed sensor (not shown) configured to transmit data associated with the rotational speed of a torque converter of the vehicle 102.

[0023] The wireless transmission module 124 communicates information acquired by the one or more sensors to the vehicle-side AVM algorithm 122. In one embodiment, the vehicle-side AVM algorithm 122 can be arranged as a component within the wireless transmission module 124. For example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information acquired by the one or more sensors and to send it to the infrastructure system 110. In another example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information acquired by the one or more sensors and to send it directly to the vehicle manufacturing cloud system 104.The vehicle-side AVM algorithm 122 is configured to communicate information and / or instructions to the wireless transmission module 124, which is received from the infrastructure system 110 and / or the vehicle manufacturing cloud system 104.

[0024] The central vehicle gateway module 126 operates as an interface between various vehicle domain bus systems, such as an engine compartment bus (not shown), an interior bus (not shown), an optical multimedia bus (not shown), a diagnostic bus for maintenance (not shown), or the vehicle CAN bus 138. The central vehicle gateway module 126 is configured to distribute data communicated to it by each of the various domain bus systems to other components of the vehicle 102. The central vehicle gateway module 126 is also configured to distribute information received by the vehicle-side AVM algorithm 122 to the various domain bus systems. Furthermore, the central vehicle gateway module 126 is configured to send information received by the various domain bus systems to the vehicle-side AVM algorithm 122.For example, vehicle 102 uses the AVM algorithm 122 to process information received from the central vehicle gateway module 126 and send it to the infrastructure system 110. In another example, vehicle 102 uses the vehicle-side AVM algorithm 122 to process information received from the central vehicle gateway module 126 and send it directly to the vehicle manufacturing cloud system 104. The vehicle-side AVM algorithm 122 is configured to communicate information and / or instructions received from the infrastructure system 110 and / or the vehicle manufacturing cloud system 104 to the central vehicle gateway module 126.

[0025] The vehicle infotainment system 128 provides a user 140 of the vehicle 102 with a combination of information and entertainment content and / or services. It is understood that in some examples, the vehicle infotainment system 128 can only provide entertainment content to the user 140 of the vehicle 102. It is also understood that in other examples, the vehicle infotainment system 128 can provide information services to anyone associated with the vehicle 102. For example, the vehicle infotainment system 128 includes built-in car computers that combine one or more functions, such as digital radios, built-in cameras, and / or televisions. The vehicle infotainment system 128 communicates information associated with the built-in car computers or processors to the vehicle's AVM algorithm 122.For example, vehicle 102 uses the vehicle-side AVM algorithm 122 to process information received from the vehicle infotainment system 128 and send it to the infrastructure system 110. In another example, vehicle 102 uses the vehicle-side AVM algorithm 122 to process information received from the vehicle infotainment system 128 and send it directly to the vehicle manufacturing cloud system 104. The vehicle-side AVM algorithm 122 is configured to communicate information and / or instructions received from the infrastructure system 110 and / or the vehicle manufacturing cloud system 104 to the vehicle infotainment system 128.

[0026] The one or more vehicle sensors 130 can be, for example, one or more cameras, lidar, radar, and / or ultrasonic devices. For example, ultrasonic devices used as the one or more vehicle sensors 130 emit a high-frequency sound wave that strikes a wall or another vehicle and is then reflected back to the vehicle 102. Based on the time it takes for the sound wave to return to the vehicle 102, the vehicle 102 can determine the distance between the one or more vehicle sensors 130 and the wall or the other vehicle. As another example, camera devices used as the one or more vehicle sensors 130 provide a visual indication of the space around the vehicle 102.As a further example, radar devices, used as one or more vehicle sensors 130, emit electromagnetic wave signals that strike the wall or the other vehicle and are then reflected back to the vehicle 102. Based on the time it takes for the electromagnetic waves to return to the vehicle 102, the vehicle 102 can determine its distance, speed, and angle relative to the wall or the other vehicle.

[0027] The one or more vehicle sensors 130 communicate information related to the position and / or distance of the vehicle 102 relative to the wall or the other vehicle to the vehicle-side AVM algorithm 122. For example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information received from the one or more vehicle sensors 130 and send it to the infrastructure system 110. In another example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information received from the one or more vehicle sensors 130 and send it directly to the vehicle manufacturing cloud system 104.The vehicle-side AVM algorithm 122 is configured to communicate information and / or instructions to the one or more vehicle sensors 130, which are received from the infrastructure system 110 and / or the vehicle manufacturing cloud system 104.

[0028] The vehicle battery 132 is controlled by a battery management system (not shown) that provides instructions to the vehicle battery 132. For example, the battery management system provides instructions to the vehicle battery 132 based on its temperature. However, it is understood that the battery management system can provide instructions to the vehicle battery 132 based on any measured value associated with the vehicle battery 132, such as the state of charge of the vehicle 102, a period of at least one day during which the vehicle 102 has been in a switched-off state, or a combination thereof. The battery management system ensures acceptable current modes for the vehicle battery 132. For example, the acceptable current modes protect the vehicle battery 132 from overvoltage, overcharging, and / or overheating.As another example, the temperature of the vehicle battery 132 indicates to the battery management system whether any of the acceptable current modes are within acceptable temperature ranges. The battery management system associated with the vehicle battery 132 communicates information related to the temperature of the vehicle battery 132 to the vehicle-side AVM algorithm 122. For example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information received concerning the vehicle battery 132 and send it to the infrastructure system 110. As another example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information concerning the vehicle battery 132 and send it directly to the vehicle manufacturing cloud system 104.The vehicle-side AVM algorithm 122 is configured to communicate information and / or instructions to the vehicle battery 132, which are received from the infrastructure system 110 and / or the vehicle manufacturing cloud system 104.

[0029] The vehicle GNSS 134 is configured to communicate with satellites, allowing the vehicle 102 to determine its specific location. The vehicle navigation mapping system 136 can display the specific location of the vehicle 102 to the user 140 via a display screen (not shown). The vehicle GNSS 134 communicates geographic information associated with the vehicle 102 to the vehicle-side AVM algorithm 122. For example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information received from the vehicle GNSS 134 and send it to the infrastructure system 110. As another example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information from the vehicle GNSS 134 and send it directly to the vehicle manufacturing cloud system 104.The vehicle-side AVM algorithm 122 is configured to communicate information and / or instructions to the vehicle GNSS 134, which are received from the infrastructure system 110 and / or the vehicle manufacturing cloud system 104. In another example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information associated with the vehicle navigation map system 136 and send it to the infrastructure system 110. As yet another example, the vehicle 102 uses the vehicle-side AVM algorithm 122 to process information from the vehicle navigation map system 136 and send it directly to the vehicle manufacturing cloud system 104. The vehicle-side AVM algorithm 122 is configured to communicate information and / or instructions to the vehicle navigation map system 136, which are received from the infrastructure system 110 and / or the vehicle manufacturing cloud system 104.

[0030] Vehicle 102 is configured to communicate any information associated with any of the components contained within Vehicle 102 to one or more additional Vehicles 142. Vehicle 102 is also configured to communicate (e.g., forward) any instructions received from Infrastructure System 110 and / or Vehicle Manufacturing Cloud System 104 to any one of the additional Vehicles 142. For example, communication between Vehicle 102 and the additional Vehicles 142 can assist Infrastructure System 110 and / or Vehicle Manufacturing Cloud System 104 in maneuvering the additional Vehicles 142.It is understood that each of the one or more additional vehicles 142 may contain any of the components described as being included in the vehicle 102, such as the vehicle-side AVM algorithm 122, the wireless transmission module 124, the central vehicle gateway module 126, the vehicle infotainment system 128, the one or more vehicle sensors 130, the vehicle battery 132, the vehicle GNSS 134, the vehicle navigation map system 136, and / or the CAN vehicle bus 138. It is also understood that any one of the one or more additional vehicles 142 is configured to communicate information associated with any of the components it contains to the vehicle 102. Furthermore, it is understood that the one or more additional vehicles 142 may also be configured to establish a direct line of wireless communication (e.g.,via a communication link) with the infrastructure system 110 and / or the vehicle manufacturing cloud system 104, enabling information to be exchanged directly between the one or more additional vehicles 142 and the infrastructure system 110 and / or the vehicle manufacturing cloud system 104.

[0031] The Vehicle Delivery Manager Cloud System 106 communicates wirelessly (e.g., receives and / or sends instructions and / or information) with one or more Rental Agency Cloud Systems 144, Parking Service Agency Cloud Systems 146, Insurance Agency Cloud Systems 148, and / or Dealer System 150. The Vehicle Delivery Manager Cloud System 106 is configured to facilitate the delivery of one or more vehicles to any one Rental Agency (not shown) associated with Rental Agency Cloud System 144, Parking Service Agency (not shown) associated with Parking Service Agency Cloud System 146, Insurance Agency (not shown) associated with Insurance Agency Cloud System 148, and / or Dealer System 150. The Vehicle Delivery Manager Cloud System 106 also communicates wirelessly with the Web Portal Account Cloud System 108 for vehicle customers.It is understood that other cloud systems may be included in one or more examples.

[0032] The vehicle delivery manager cloud system 106 communicates wirelessly with a user device 152, such as a mobile device, a display panel, and / or a computer. The vehicle 102 is also configured to communicate directly with the user device 152 wirelessly. For example, the user 140 interacts with the user device 152 via an application that organizes any information and / or instructions received from the web portal account cloud system 108 for vehicle customers and / or the vehicle 102.As another example, user 140 can send one or more instructions to the web portal account cloud system 108 for vehicle customers, such as making a selection as to which vehicle user 140 would like to receive from any of the rental agency associated with the rental agency cloud system 144, the parking service agency associated with the parking service agency cloud system 146, the insurance agency associated with the insurance agency cloud system 148, and / or the dealer system 150.

[0033] With reference to Fig. 2. The vehicle(s) 102 can be powered in various ways, for example, by an electric motor and / or an internal combustion engine. It is also understood that the vehicle(s) 102 can be any type of vehicle powered by an electric motor and / or an internal combustion engine, such as a car, a truck, a robot, an aircraft, and / or a boat. The vehicle(s) 102 generally includes the vehicle control unit 200, one or more actuators 202, a variety of onboard sensors 204, a human-machine interface (HMI) 206, and a vehicle system 208. The vehicle(s) 102 also has a reference point 210, that is, a specified point within a space defined by a vehicle body, which identifies the location of the vehicle(s) 102.For example, reference point 210 is a geometric center point where the respective longitudinal and lateral center axes of vehicle(s) 102 intersect. Alternatively, reference point 210 is a point where vehicle(s) 102 is located while navigating towards a waypoint.

[0034] In some examples, the vehicle control unit 200 is configured or programmed to control the operation of one or more of the vehicle's brakes, drive system (e.g., controlling the acceleration of the vehicle(s) 102 by controlling one or more internal combustion engines, electric motors, hybrid motors, etc.), steering, air conditioning, interior and / or exterior lighting, etc. In other examples, the vehicle control unit 200 is further configured or programmed to determine whether and when it should control such operations concerning the vehicle(s) 102 instead of a human driver. It is understood that any of the operations associated with the vehicle(s) 102 can be facilitated by an automated, a semi-automated, or a manual mode.For example, the automated mode can facilitate the complete control of any operation by the vehicle control unit 200 without the assistance of the human driver. Similarly, the semi-automated mode can facilitate the at least partial control of any operation by the human driver in combination with the vehicle control unit 200. Finally, the manual mode can facilitate the complete control of operations by the human driver without the assistance of the vehicle control unit 200.

[0035] The vehicle control unit 200 includes one or more processors (not shown) or may be communicatively coupled to them (e.g., via a vehicle communication bus). For example, the one or more processors may be a controller or the like, which is included in the vehicle(s) 102 for monitoring and / or controlling various vehicle controls, such as a powertrain control, a brake control, a steering control, etc. The vehicle control unit 200 is generally arranged for communication in a vehicle communication network (not shown), which may include a bus in the vehicle(s) 102, such as a Controller Area Network (CAN) or the like, and / or other wired and / or wireless mechanisms.

[0036] The vehicle control unit 200 transmits messages via a vehicle network to various devices in the vehicle(s) 102 and / or receives messages from the various devices, for example, the one or more actuators 202, the MMS 206, etc. Alternatively or additionally, in cases where the vehicle control unit 200 includes multiple devices, the vehicle communication network is used for communication between devices that are referred to in this disclosure as the vehicle control unit 200. Furthermore, as discussed below, various other controllers and / or sensors of the vehicle control unit 200 provide data via the vehicle communication network.

[0037] Additionally, the vehicle controller 200 is configured via a vehicle-side AVM algorithm 122 to communicate through a vehicle-to-infrastructure communication network, such as to communicate with an infrastructure controller (not shown). The vehicle controller 200 is also configured via the vehicle-side AVM algorithm 122 to communicate through a wireless vehicle communication interface with other traffic entities (e.g., vehicles, infrastructure, etc.), such as via a vehicle-to-vehicle communication network. The vehicle communication network represents one or more mechanisms through which the vehicle controller 200 of the vehicle(s) 102 communicates with other traffic entities. As an example, the vehicle communication network could consist of one or more wireless communication mechanisms that support any desired combination of wireless (e.g.,Vehicle communication networks include cellular, wireless, satellite, microwave, and / or radio frequency communication mechanisms and any desired network topology (or topologies if multiple communication mechanisms are used). Examples of vehicle communication networks include, but are not limited to, cellular, Bluetooth®, IEEE 802.11, dedicated short-range communications (DSRC), and / or wide-area networks (WANs), including the internet, which provide data communication services.

[0038] The one or more 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. The one or more actuators 202 can be used to control the braking, acceleration, and / or steering of the vehicle(s) 102. The vehicle control unit 200 can be programmed to activate the one or more actuators 202, which include drive, steering, and / or braking, based on the planned acceleration or deceleration of the vehicle(s) 102.

[0039] The multitude of onboard sensors 204 includes a variety of devices to provide data to the vehicle control system 200. For example, the multitude of onboard sensors 204 may include detection sensors (e.g., lidar sensor(s)) located on or in the vehicle(s) 102, providing relative locations, sizes, and / or shapes of one or more entities surrounding the vehicle(s), such as additional vehicles, bicycles, robots, drones, etc., moving alongside, in front of, and / or behind the vehicle(s). As another example, one or more of the multitude of onboard sensors 204 may be radar sensors mounted on one or more bumpers of the vehicle(s), providing locations of the entities relative to the location of each of the vehicles.

[0040] The multiple onboard sensors 204 can include a camera sensor, for example, to provide a front view, side view, rear view, etc., which provide images from an area surrounding the vehicle(s) 102. As another example, the vehicle control unit 200 can be programmed to receive sensor data from a camera sensor and implement image processing techniques to detect a road, infrastructure elements, etc. The vehicle control unit 200 can also be programmed to determine a current vehicle location based on location coordinates (e.g., GPS coordinates) received by the vehicle(s) 102 and indicating a location of the vehicle 102 determined by a GPS sensor (not shown).

[0041] The MMS 206 is configured to receive information from the human driver(s) during the operation of the vehicle(s) 102. Furthermore, the MMS 206 is configured to display information to the human driver, such as an occupant of the vehicle(s) 102. In some variations, the vehicle control unit 200 is programmed to receive target data (e.g., location coordinates) from the MMS 206.

[0042] The vehicle system 208 is configured to control each of the subsystems within the vehicle(s) 102 and to facilitate requests via each of the components described above (e.g., the vehicle controller 200, the one or more actuators 202, the multitude of onboard sensors 204, and / or the MMS 206). Accordingly, the vehicle(s) 102 can be autonomously guided toward a waypoint using at least the multitude of onboard sensors 204. Route guidance can be performed using the vehicle's location, the distance to be traveled, a queue for vehicle maneuvering, etc.

[0043] Fig. Figure 3 depicts a process flow illustrating an example process 300 for identifying one or more ghost vehicles within the shunting environment. In process 302, infrastructure system 110 is configured to detect whether an object (e.g., a ghost vehicle) is present within the shunting environment. It is understood that, although the object is a ghost vehicle, it could also be any other object that does not physically exist. Infrastructure system 110 continues to monitor the shunting environment to detect the presence of the object within it.

[0044] In a case where the infrastructure system 110 detects the presence of the object within the shunting environment, the infrastructure-side AVM algorithm 112 can determine at operation 304 whether one or more conditions relative to the identification of any ghost vehicles are met. In one or more examples, the one or more conditions may include one or more of the following: an unexpected location of the object; an inability to identify a historical path associated with any of the automated vehicles; an inconsistency between data from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof;unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to prove the location of each automated vehicle of the one or more automated vehicles; and an inconsistency between the total number of automated vehicles of the one or more automated vehicles and the expected total number of automated vehicles of the one or more automated vehicles. In a case where none of the conditions are met, process 300 returns to operation 302 and infrastructure system 110 continues to monitor the shunting environment to detect the presence of the object within the shunting environment.

[0045] However, in a case where one or more of the conditions are met, the infrastructure system 110 transmits a static real-time data verification request to the one or more automated vehicles during operation 306. In one or more examples, the requested data is raw data originating from one or more sensors (e.g., the plurality of onboard sensors 204) of each automated vehicle of the one or more automated vehicles. In one or more examples, the data may consist of one or more image files and / or one or more video files.In another example, other data files can be transferred between the one or more automated vehicles and the infrastructure system 110, such as, but not limited to, a visual signature, a triangulated location file, an ultra-wideband-related file, a radar signature, an ultrasonic signature, and others.

[0046] In process 308, the infrastructure system 110 is configured to receive the requested data from the one or more automated vehicles. In one or more embodiments, the infrastructure-side AVM algorithm 112 is configured to analyze the requested data by determining whether a sensor output associated with the one or more sensors 114 of the infrastructure system 110 matches a sensor output associated with the requested data in order to identify any ghost vehicles.In one or more other embodiments, the infrastructure-side AVM algorithm 112 is also configured to analyze the requested data by determining whether an isolated software routine specific to the infrastructure system 110 matches an analysis performed by the vehicle-side AVM algorithm 122, which is associated with the requested data, to identify any ghost vehicles. In one or more examples, the determination of whether the isolated software routine matches the analysis performed by the vehicle-side AVM algorithm 122, which is associated with the requested data, is based on the use of one or more algorithmic image matching techniques, such as pixel matching, object matching, feature matching, density matching, and others.

[0047] In process 310, one or more corrective actions are performed by the infrastructure system 110 and / or the one or more automated vehicles. In one or more embodiments, one or more reset routines can be initiated in a case where the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 rule out any hardware problems that cause the detection of (a) ghost vehicle(s). For example, the one or more reset routines can be initiated for hardware and software aspects relating to the infrastructure system 110 and / or the one or more automated vehicles.

[0048] In one or more embodiments, if an alternative (e.g., redundant) sensor suite (e.g., the one or more sensors 114) is available for use by the infrastructure system 110, the infrastructure-side AVM algorithm 112 can cause the infrastructure system 110 to switch from the one or more sensors 114 to the alternative sensor suite, while the one or more sensors 114 are reset and correct behavior within the shunting environment is validated. For example, the validation of correct behavior within the shunting environment is performed by the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122, both of which can perform one or more validation routines.

[0049] In one or more embodiments, a shunting system that provides routing for one or more automated vehicles can switch to a historical route routing system, causing each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles. For example, an automated lead vehicle of the one or more automated vehicles is caused to follow a historical path based on historical data obtained from one or more routes followed by multiple sets of automated vehicles that may be moving through the shunting environment.

[0050] In one or more embodiments, the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 are configured to analyze metadata associated with each object appearance from the requested data. For example, the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 are also configured to determine whether certain areas of the shunting environment are more prone to object appearance. As another example, the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 are further configured to provide a recommendation for replacing any one or more sensors 114 or any of the multiple on-board sensors 204 based on the metadata analysis.As another example, the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 are also configured to recommend the installation of a redundant sensor suite in certain areas of the shunting environment to support at least one or more sensors 114, based on the analysis of the metadata.

[0051] Fig. Figure 4 is a flowchart illustrating another exemplary procedure 400 for identifying one or more ghost vehicles within the shunting environment. In procedure 402, an object (e.g., a ghost vehicle) is detected in the shunting environment.

[0052] Operation 404 involves determining whether one or more conditions are met relative to the identification of any ghost vehicles. For example, the determination of whether one or more conditions are met is made by an infrastructure system (e.g., infrastructure system 110). As another example, the determination of whether one or more conditions are met is made in response to the detection of the object. As yet another example, one or more conditions include one or more being located at an unexpected location of the object; an inability to identify a historical path associated with each of the one or more automated vehicles;an inconsistency between the data originating from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof; unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to prove a location of each automated vehicle of the one or more automated vehicles; and an inconsistency between a total number of automated vehicles of the one or more automated vehicles and an expected total number of automated vehicles of the one or more automated vehicles.

[0053] In Operation 406, a request for data originating from one or more sensors (e.g., the plurality of onboard sensors 204) of each automated vehicle (e.g., vehicle 102) is transmitted to the one or more automated vehicles. For example, the request for data is transmitted in response to one or more conditions being met (e.g., any of the conditions being present). In Operation 408, the requested data is received by the one or more automated vehicles.

[0054] In process 410, one or more corrective actions are carried out. For example, the one or more corrective actions are carried out based on an analysis of the requested data. In one or more examples, the analysis of the requested data includes determining whether a sensor output associated with one or more sensors (e.g., the one or more sensors 114) of an infrastructure system matches the requested data in order to identify any ghost vehicles. For example, carrying out the one or more corrective actions is further based on a determination that the sensor output does not match the requested data.In one or more examples, the execution of one or more corrective actions involves initiating one or more reset routines, switching from a first set of one or more sensors of an infrastructure system to a second set of one or more sensors of the infrastructure system, and causing each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles, and causing an automated lead vehicle of the one or more automated vehicles to follow a historical path.In one or more examples, the implementation of one or more corrective actions involves collecting metadata associated with the object, identifying one or more object-vulnerable areas within the shunting environment, and / or generating a recommendation to replace one or more sensors of an infrastructure system or to install a second set of one or more sensors, based on the identification of the one or more object-vulnerable areas.

[0055] Fig. Figure 5 depicts a process flow illustrating an additional example process 500 for identifying one or more ghost vehicles within the shunting environment. In process 502, infrastructure system 110 is configured to detect whether an object (e.g., a ghost vehicle) is present within the shunting environment. Infrastructure system 110 continues to monitor the shunting environment to detect the presence of the object within it.

[0056] In a case where the infrastructure system 110 detects the presence of the object within the shunting environment, the infrastructure-side AVM algorithm 112 can determine at operation 504 whether one or more conditions relative to the identification of any ghost vehicles are met. In one or more examples, the one or more conditions may include one or more of the following: an unexpected location of the object; an inability to identify a historical path associated with each of the one or more automated vehicles; an inconsistency between data from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof;unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to prove the location of each automated vehicle of the one or more automated vehicles; and an inconsistency between the total number of automated vehicles of the one or more automated vehicles and the expected total number of automated vehicles of the one or more automated vehicles. In a case where none of the conditions are met, process 500 returns to operation 502 and infrastructure system 110 continues to monitor the shunting environment to detect the presence of the object within the shunting environment.

[0057] However, in a case where one or more of the conditions are met, the infrastructure system 110 transmits a static real-time test request at operation 506, which may include one or more instructions to analyze data originating from one or more sensors (e.g., the multitude of onboard sensors 204) of each of the automated vehicles. As an example, the data may consist of one or more image files and / or one or more video files.

[0058] In one or more examples, the one or more instructions can direct the vehicle-side AVM algorithm 122 of each automated vehicle to perform a verification process to determine whether the object is located at the location specified by the one or more sensors 114 of the infrastructure system 110 and / or the multitude of onboard sensors 204 of each automated vehicle. In one or more examples, the verification process can include each automated vehicle analyzing stored video clips originating from the multitude of onboard sensors 204 of each automated vehicle.The stored video clips can be of any length and can be stored within a database, which is located, for example, internally within each automated vehicle of the one or more automated vehicles, or externally in a cloud system (e.g. the vehicle manufacturing cloud system 104 or the web portal account cloud system 108 for vehicle customers) or the infrastructure system 110.

[0059] In one or more embodiments, it is understood that the process described as part of Operation 506 may be an isolated requirement as part of Operation 500 or may provide additional processes to those described as part of Operation 406.

[0060] In Operation 508, the infrastructure system 110 is configured to receive one or more results associated with the analysis of the stored video (e.g., the verification process) from the one or more automated vehicles. In one or more examples, the one or more results may include information associated with whether each of the automated vehicles is able to verify whether the object is at the location, as indicated by the one or more sensors 114 of the infrastructure system 110 and / or the multitude of onboard sensors 204 of each of the automated vehicles. In other words, the one or more results may indicate that the object is at the location or that the object is not at the location.In process 510, one or more corrective actions are performed by the infrastructure system 110 and / or the one or more automated vehicles. In one or more embodiments, one or more reset routines may be initiated in a case where the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 rule out any hardware problems that cause the detection of (a) ghost vehicle(s). For example, the one or more reset routines may be initiated for hardware and software aspects relating to the infrastructure system 110 and / or the one or more automated vehicles. In one or more examples, the one or more reset routines may be a hard reset, a soft reset, or any other type of reset.

[0061] In one or more embodiments, if an alternative (e.g., redundant) sensor suite (e.g., the one or more sensors 114) is available for use by the infrastructure system 110, the infrastructure-side AVM algorithm 112 can cause the infrastructure system 110 to switch from the one or more sensors 114 to the alternative sensor suite, while the one or more sensors 114 are reset and correct behavior within the shunting environment is validated. For example, the validation of correct behavior within the shunting environment is performed by the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122, both of which can perform one or more validation routines.

[0062] In one or more embodiments, a shunting system that provides routing for one or more automated vehicles can switch to a historical route routing system, causing each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles. For example, an automated lead vehicle of the one or more automated vehicles is caused to follow a historical path based on historical data obtained from one or more routes followed by many sets of automated vehicles that may be passing through the shunting environment.

[0063] In one or more embodiments, the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 are configured to analyze metadata associated with each object appearance from the requested data. For example, the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 are also configured to determine whether the appearance of objects is more likely in certain areas of the shunting environment. As another example, the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 are further configured to provide a recommendation for replacing any one or more sensors 114 or any of the plurality of on-board sensors 204 based on the metadata analysis.As another example, the infrastructure-side AVM algorithm 112 and / or the vehicle-side AVM algorithm 122 are also configured to recommend the installation of a redundant sensor suite in certain areas of the shunting environment to support at least one or more sensors 114, based on the analysis of the metadata.

[0064] Fig. Figure 6 is a flowchart illustrating another exemplary procedure 600 for identifying one or more ghost vehicles within the shunting environment. In process 602, an object (e.g., a ghost vehicle) is detected in the shunting environment.

[0065] Operation 604 involves determining whether one or more conditions are met relative to the identification of any ghost vehicles. For example, the determination of whether one or more conditions are met is made by an infrastructure system (e.g., infrastructure system 110). As another example, the determination of whether one or more conditions are met is made in response to the detection of the object. As yet another example, one or more conditions include one or more being located at an unexpected location of the object; an inability to identify a historical path associated with each of the one or more automated vehicles;an inconsistency between the data originating from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof; unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to prove a location of each automated vehicle of the one or more automated vehicles; and an inconsistency between a total number of automated vehicles of the one or more automated vehicles and an expected total number of automated vehicles of the one or more automated vehicles.

[0066] In operation 606, one or more instructions for analyzing data originating from one or more sensors (e.g., the multitude of onboard sensors 204) of each automated vehicle (e.g., vehicle 102) of the one or more automated vehicles are transmitted to that one or more automated vehicles. For example, the one or more instructions are transmitted in response to the one or more conditions being met (e.g., any one of the conditions being present).In one or more examples, the one or more instructions can direct the vehicle-side AVM algorithm 122 of each automated vehicle to perform a verification process to determine whether the object is located at the location specified by the one or more sensors 114 of the infrastructure system 110 and / or the multitude of onboard sensors 204 of each automated vehicle. In one or more examples, the verification process can include each automated vehicle analyzing stored video clips originating from the multitude of onboard sensors 204 of each automated vehicle.The stored video clips can be of any length and can be stored within a database, which is located, for example, internally within each automated vehicle of the one or more automated vehicles, or externally in a cloud system (e.g. the vehicle manufacturing cloud system 104 or the web portal account cloud system 108 for vehicle customers) or the infrastructure system 110.

[0067] In operation 608, one or more results are received from the one or more automated vehicles. For example, the one or more results are related to an analysis of the data (e.g., the verification process) performed by the one or more automated vehicles. In one or more examples, the data analysis includes an analysis of one or more video recordings of the maneuvering environment from each of the automated vehicles and / or a verification of the object's location based on the analysis of the one or more video recordings.In one or more examples, the one or more results may include information related to whether each automated vehicle of the one or more automated vehicles is able to verify whether the object is at the location or not, as indicated by the one or more sensors 114 of the infrastructure system 110 and / or the multitude of onboard sensors 204 of each automated vehicle of the one or more automated vehicles. In other words, the one or more results may indicate that the object is at the location or that the object is not at the location.

[0068] In operation 610, one or more corrective actions are performed. For example, the one or more corrective actions are performed based on the one or more results associated with the analysis of the data. In one or more examples, performing the one or more corrective actions includes initiating one or more reset routines, switching from a first set of one or more sensors of an infrastructure system to a second set of one or more sensors of the infrastructure system, and causing each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles, and causing an automated lead vehicle of the one or more automated vehicles to follow a historical path.In one or more examples, the one or more reset routines can be a hard reset, a soft reset, or any other type of reset. In one or more examples, performing the one or more corrective actions involves collecting metadata associated with the object, identifying one or more object-vulnerable areas within a shunting environment, and / or generating a recommendation to replace one or more sensors of an infrastructure system or to install a second set of one or more sensors, based on the identification of the one or more object-vulnerable areas.

[0069] Fig. Figure 7 illustrates an operating environment that facilitates the execution of one or more of the systems and procedures described herein. More specifically, the systems and procedures described herein may be implemented using a computing device 702. For example, the computing device 702 may be a personal computer, a desktop computer, a laptop computer, a tablet, a handheld computer, a server, a workstation, a mainframe computer, a wearable computer, a supercomputer, or a combination thereof. However, it is understood that the foregoing examples of computing device 702 are not exhaustive, and the computing device 702 may be any type of processing or computing device.The computing device 702 generally includes a processor 704, a display adapter 706, one or more input / output ports 708, one or more input / output components 710, a network adapter 712, a power supply 714, and a memory 716. However, it is understood that the computing device 702 may include any additional components and need not include any of the listed components (e.g., the processor 704, the display adapter 706, the one or more input / output ports 708, the one or more input / output components 710, the network adapter 712, the power supply 714, and the memory 716).

[0070] The processor 704 is configured to provide instructions to the computing device 702, enabling the computing device 702 to process one or more tasks, including the execution of a software program to perform one or more operations, as described in more detail herein. It is also understood that the computing device 702 can contain any number of processors 704. The display adapter 706 can be a graphics card or a video card, providing the computing device 702 with the ability to display content on a display device 718.For example, the Display Device 718 may be any screen, monitor, and / or light-emitting component associated with any personal computer, desktop, laptop, tablet, handheld computer, server, workstation, mainframe, wearable computer, supercomputer, or a combination thereof. It is understood, however, that the foregoing examples of Display Device 718 are not exhaustive and that Display Device 718 may be any type of device capable of providing a visual display.

[0071] The input / output port(s) 708 provides a number of interfaces (e.g., sockets) for one or more cables to be connected to the computing device 702. It is understood that any number of input / output ports 708 may be present on the computing device 702. For example, the input / output port(s) 708 provides a means for the computing device 702 to receive signals and / or data from an external device connected to the computing device 702 by one or more cables. As another example, the input / output port(s) 708 provides a means for the computing device 702 to send signals and / or data to an external device connected to the computing device 702 by one or more cables.The input / output component(s) 710 may include one or more components that support the input / output port(s) 708, such as, but not limited to, a switch, a push button, a pressure pad, a float switch, a keypad, a radio receiver, or a combination thereof.

[0072] The network adapter 712 can be any type of network interface controller configured to provide a means of communication over a network 720 with another computing device, such as a remote computing device 722. For example, the remote computing device 722 can be a user device, such as a mobile phone, smartphone, tablet, laptop, or a combination thereof. The power supply 714 is configured to convert high-voltage alternating current (e.g., AC) to direct current (e.g., DC) to provide power to the other components (e.g., the processor 704, the display adapter 706, the one or more input / output port(s) 708, the one or more input / output component(s) 710, the network adapter 712, and the memory 716) of the computing device 702.

[0073] Additionally, the memory 716 can be a mass storage device and / or system memory, such as a hard disk drive, a memory card, a solid-state drive, RAM, or a combination thereof. The memory 716 is configured to provide memory for instructions and data associated with the operation of the computing device 702. The memory 716 can generally include an operating system 724, identification software 726, and identification data 728 to perform one or more operations, which are described in more detail herein. For example, the operating system 724 is configured to manage and / or process any of the data and / or instructions associated with the identification software 726 and / or the identification data 728, as described in more detail herein.

[0074] Furthermore, the computing device 702 includes a system bus 730, which is configured to connect each of the various components (e.g., the processor 704, the display adapter 706, the one or more input / output ports 708, the one or more input / output component(s) 710, the network adapter 712, the power supply 714, and the memory 716) of the computing device 702. It is also understood that each component of the computing device 702 and the functionality assigned to each component of the computing device 702 can be implemented within the remote computing device 722. While the operating environment, which is in Fig. Figure 7 illustrates a specific configuration that is associated with at least the computing device 702, the network 720 and the remote computing device 722; it is understood that the operating environment can be configured in any way.

[0075] Thus, one or more examples of the present disclosure provide a means for identifying a ghost vehicle by considering data analysis associated with a perception of a shunting environment from the perspective of an infrastructure system and / or one or more vehicles. Based on the data analysis, one or more corrective actions can be taken to ensure a seamless and rapid resolution for detecting the ghost vehicle within the shunting environment without disrupting a shunting process associated with the one or more vehicles.

[0076] Unless expressly stated otherwise herein, all numerical values ​​indicating mechanical / thermal properties, percentages of compositions, dimensions and / or tolerances, or other parameters are to be understood as modified by the word "approximately" or "about" when describing the scope of this disclosure. This modification is desirable for various reasons, including industrial practice, material, manufacturing and assembly tolerances, and testability.

[0077] As used in this document, the phrase "at least one of A, B and C" should be interpreted as meaning a logical (A OR B OR C) using a non-exclusive logical OR, and should not be interpreted as meaning "at least one of A, at least one of B and at least one of C".

[0078] In this application, the terms "controller" and / or "module" may refer to, be part of, or include: an application-specific integrated circuit (ASIC); a digital, analog, or mixed analog / digital discrete circuit; a digital, analog, or mixed analog / digital integrated circuit; a combinational logic circuit; a field-programmable gate array (FPGA); a processor circuit (shared, dedicated, or group) that executes code; a memory circuit (shared, dedicated, or group) that stores code executed by the processor circuit; other suitable hardware components that provide the described functionality; or a combination of some or all of the foregoing, such as in a system-on-a-chip.

[0079] The term storage is a subset of the term computer-readable medium. The term computer-readable medium, as used here, does not include transitory electrical or electromagnetic signals that propagate through a medium (such as a carrier wave); the term computer-readable medium can therefore be considered tangible and non-transient.Non-restrictive examples of a non-transient, tangible, computer-readable medium include non-volatile memory circuits (such as a flash memory circuit, a wipeable programmable read-only memory circuit, or a mask read-only memory circuit), volatile memory circuits (such as a static random access memory circuit or a dynamic random access memory circuit), magnetic storage media (such as an analog or digital magnetic tape or a hard disk drive), and optical storage media (such as a CD, a DVD, or a Blu-ray Disc).

[0080] The devices and procedures described in this application may be implemented in whole or in part by a specialized computer created by configuring a general-purpose computer to perform one or more specific functions contained in computer programs. The functional blocks, flowchart components, and other elements described above serve as software specifications that can be translated into computer programs through the routine work of an experienced technician or programmer.

[0081] The description of the revelation is purely exemplary, and thus variations that do not deviate from the content of the revelation are intended to fall within its scope. Such variations are not to be considered a deviation from the nature and scope of the revelation.

[0082] According to the present invention, a method comprises: detecting an object in a shunting environment; determining whether one or more conditions are met in response to the detection of the object; transmitting one or more instructions to one or more automated vehicles to analyze data originating from one or more sensors of each automated vehicle of the one or more automated vehicles in response to the one or more conditions being met; receiving one or more results associated with an analysis of the data performed by the one or more automated vehicles from the one or more automated vehicles; and performing one or more corrective actions based on the one or more results associated with the analysis of the data.

[0083] In one aspect of the invention, the one or more conditions include one or more of the following: an unexpected location of the object; an inability to identify a historical path associated with each automated vehicle of the one or more automated vehicles; an inconsistency between the data originating from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof; an unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to prove a location of each automated vehicle of the one or more automated vehicles;and an inconsistency between the total number of automated vehicles of the one or more automated systems and the expected total number of automated vehicles of the one or more automated systems.

[0084] In one aspect of the invention, the analysis of the data by the one or more automated vehicles further comprises: analyzing one or more video recordings of the maneuvering environment from each automated vehicle of the one or more automated vehicles; and verifying a location of the object based on the analysis of the one or more video recordings.

[0085] In one aspect of the invention, the execution of one or more corrective actions includes one of the following: initiating one or more reset routines; switching from a first set of one or more sensors of an infrastructure system to a second set of one or more sensors of the infrastructure system; and causing each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles, and causing an automated lead vehicle of the one or more automated vehicles to follow a historical path.

[0086] In one aspect of the invention, carrying out the one or more corrective measures includes: collecting metadata associated with the object; determining one or more object-vulnerable areas within a shunting environment; and generating a recommendation to replace one or more sensors of an infrastructure system or to install a second set of one or more sensors, based on the determination of the one or more object-vulnerable areas.

Claims

[1] Procedure, encompassing: Detecting an object in a shunting environment; Determine whether one or more conditions are met in response to the detection of the object; Transmitting a request to one or more automated vehicles based on data originating from one or more sensors of each automated vehicle of the one or more automated vehicles, in response to the one or more conditions being met; Receiving the requested data from the one or more automated vehicles; and Implementing one or more corrective actions based on an analysis of the requested data. [2] The method of claim 1, wherein one or more conditions comprise one or more of the following: an unexpected location of the object; an inability to identify a historical path associated with each automated vehicle of the one or more automated vehicles; an inconsistency between the data originating from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof; unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to determine the location of each automated vehicle of the one or more automated vehicles to demonstrate the existence of multiple automated vehicles; and an inconsistency between the total number of automated vehicles of the one or more automated vehicles and the expected total number of automated vehicles of the one or more automated vehicles. [3] Method according to claim 1, wherein the analysis of the requested data comprises: Determine whether a sensor output associated with one or more sensors of an infrastructure system matches the requested data. [4] Method according to claim 3, wherein the execution of one or more corrective measures is further based on a determination that the sensor output does not match the requested data. [5] The method of claim 1, wherein the performance of one or more corrective measures comprises one of the following: Initiating one or more reset routines; Switching from a first set of one or more sensors of an infrastructure system to a second set of one or more sensors of the infrastructure system; and To cause each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles, and to cause an automated lead vehicle of the one or more automated vehicles to follow a historical path. [6] The method of claim 1, wherein the implementation of one or more corrective measures comprises: Collecting metadata associated with the object; Identifying one or more object-prone areas within the shunting environment; and Generating a recommendation to replace one or more sensors of an infrastructure system or to install a second set of one or more sensors, based on the identification of one or more object-prone areas. [7] System, comprehensive: an infrastructure system configured to do the following: Detecting an object in a shunting environment, Determine whether one or more conditions are met in response to the detection of the object, Transmitting a request to data originating from one or more sensors of each automated vehicle of one or more automated vehicles, in response to one or more conditions being met, Receiving the requested data and Implementing one or more corrective actions based on an analysis of the requested data; and one or more automated vehicles configured to: Receiving the request for data originating from the one or more sensors of each automated vehicle of the one or more automated vehicles, and Transferring the requested data. [8] System according to claim 7, wherein the one or more automated vehicles are further configured to: Receiving one or more instructions to analyze the data originating from the one or more sensors of each automated vehicle of the one or more automated vehicles, in response to the one or more conditions being met; and Transmitting one or more results associated with the analysis of data performed by the one or more automated vehicles. [9] System according to claim 8, wherein the infrastructure system is further configured as follows: Transmitting the one or more instructions for analyzing the data originating from the one or more sensors of each automated vehicle; and Receiving one or more results. [10] System according to claim 8, wherein performing the analysis of the data by the one or more automated vehicles comprises: Analyzing one or more video recordings of the shunting environment from each automated vehicle of the one or more automated vehicles; and Verifying the location of the object based on the analysis of one or more video recordings. [11] System according to claim 7, wherein one or more conditions comprise one or more of the following: an unexpected location of the object; an inability to identify a historical path associated with each automated vehicle of the one or more automated vehicles; an inconsistency between the data originating from the one or more sensors and a controlled location of the one or more automated vehicles, a relative position of the one or more automated vehicles, or a combination thereof; unexpected spacing between each automated vehicle of the one or more automated vehicles; an inability to determine the location of each automated vehicle of the one or more automated vehicles to demonstrate the existence of multiple automated vehicles; and an inconsistency between the total number of automated vehicles of the one or more automated vehicles and the expected total number of automated vehicles of the one or more automated vehicles. [12] System according to claim 7, wherein the analysis of the requested data by the infrastructure system comprises: Determine whether a sensor output associated with one or more sensors of the infrastructure system matches the requested data. [13] System according to claim 12, wherein the execution of one or more corrective measures is further based on a determination that the sensor output does not match the requested data. [14] System according to claim 7, wherein the implementation of one or more corrective measures by the infrastructure system comprises one of the following: Initiating one or more reset routines; Switching from a first set of one or more sensors of the infrastructure system to a second set of one or more sensors of the infrastructure system; and To cause each automated vehicle of the one or more automated vehicles to follow one or more movements of a preceding automated vehicle of the one or more automated vehicles, and to cause an automated lead vehicle of the one or more automated vehicles to follow a historical path. [15] System according to claim 7, wherein the implementation of one or more corrective actions by the infrastructure system comprises: Collecting metadata associated with the object; Identifying one or more object-prone areas within the shunting environment; and Generating a recommendation to replace one or more sensors of the infrastructure system or to install a second set of one or more sensors, based on the identification of one or more object-prone areas.

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