System and method for positioning and navigation of a vehicle to a charging station

Abstract

The present disclosure provides a system and method for positioning and navigation of a vehicle to a charging station. A method of grouping an autonomously operated vehicle includes manipulating a vehicle toward a location and orientation associated with a charging station, receiving location data associated with a first location of the vehicle, and initiating a method of engaging the charging station based on updated location data associated with a second location of the vehicle.

Description

Technical Field

[0001] The present disclosure relates to the automatic positioning and navigation of vehicles. More specifically, the present disclosure relates to systems and methods for positioning a vehicle and navigating it towards a charging station. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] Marshaling systems associated with the automated transportation of vehicles are becoming increasingly popular. However, such marshaling systems can face various logistical problems that reduce the distance over which one or more vehicles can be marshaled without human intervention. Automated charging stations within the marshaling system have been used to address these problems. However, even with automated charging stations, at least some form of human intervention is still required to support the charging of the vehicles.

[0004] The present disclosure addresses these and other problems related to the positioning of vehicles and (in particular) navigation to a charging station. Summary of the Invention

[0005] This section provides a general overview of the present disclosure and is not a full disclosure of its entire scope or all of its features.

[0006] The present disclosure provides a method for marshaling an autonomously operating vehicle, the method comprising: causing the vehicle to be maneuvered by a central server towards a location and orientation associated with a charging station; receiving, by the central server, position data associated with the positioning of the vehicle from one or more positioning sensors; and sending, by the central server, updated position data associated with the positioning of the vehicle to the vehicle based on the position data, thereby causing the vehicle to initiate a method of engaging the charging station based on the updated position data; further comprising: determining, based on the position data, that the vehicle has not been caused to be maneuvered towards the charging station; and repositioning the vehicle towards the charging station based on the vehicle not being caused to be maneuvered towards the charging station; wherein the positioning is global coordinate positioning, and the one or more positioning sensors include a pressure sensor, a magnet, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or a combination thereof; wherein the position data and the updated position data are received via one or more of: ultra-wideband, WIFI, CV2X, a public cellular network, or a private cellular network; wherein the method for causing the vehicle to initiate engagement with the charging station further comprises: receiving positioning data associated with the positioning of the vehicle from the vehicle; wherein the positioning data is based on one or more of: a deep learning model for detecting the charging station via a camera associated with the vehicle; a reference associated with the charging station identified via a camera, ultrasonic, or radar associated with the vehicle; or an alignment of the wheel chocks and at least one wheel of the vehicle; and wherein the alignment of the wheel chocks and the at least one wheel of the vehicle guides the vehicle within a pre-specified distance from the charging station, and wherein the pre-specified distance from the charging station is within an acceptable range for an arm associated with the charging station to insert a charger into the charging port of the vehicle.

[0007] The present disclosure provides a marshalling system for positioning an autonomously operating vehicle, the marshalling system comprising: one or more positioning sensors configured to send position data associated with the positioning of the vehicle; a central server configured to: cause the vehicle to be maneuvered towards a positioning and orientation associated with a charging station; receive the position data from one or more ground sensors; and send updated position data associated with the positioning of the vehicle to the vehicle based on the position data, thereby causing the vehicle to initiate a method of engaging the charging station based on the updated position data; and the vehicle configured to: receive the updated position data; determine positioning data associated with the vehicle based on a deep learning model, wherein the deep learning model is used to detect the charging station via a camera associated with the vehicle; identify the charging station based on the positioning data; and initiate a method of engaging the charging station; wherein the central server is further configured to: determine, based on the position data, that the vehicle has not been maneuvered towards the charging station; and reposition the vehicle towards the charging station based on the vehicle not being maneuvered towards the charging station; wherein the positioning is global coordinate positioning, and the one or more positioning sensors include pressure sensors, magnets, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or a combination thereof; wherein the position data and the updated position data are received via one or more of: ultra-wideband, WIFI, CV2X, a public cellular network, or a private cellular network; wherein the vehicle is further configured to determine the positioning data based on one or more of the following: a reference associated with the charging station identified via a camera, ultrasonic, or radar associated with the vehicle; or the alignment of the wheel chocks and at least one wheel of the vehicle; and wherein the alignment of the wheel chocks and the at least one wheel of the vehicle guides the vehicle to within a pre-specified distance of the charging station, and wherein the pre-specified distance from the charging station is within an acceptable range for an arm associated with the charging station to insert a charger into the charging port of the vehicle.

[0008] The present disclosure provides one or more non-transitory computer-readable media storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: cause the vehicle to be maneuvered towards a position and orientation associated with a charging station; receive position data associated with the positioning of the vehicle from one or more ground sensors; and send updated position data associated with the positioning of the vehicle to the vehicle based on the position data, thereby causing the vehicle to initiate a method of engaging the charging station based on the updated position data; wherein the at least one processor is further caused to: determine, based on the position data, that the vehicle is not being maneuvered towards the charging station; and reposition the vehicle towards the charging station based on not causing the vehicle to be maneuvered towards the charging station; wherein the positioning is global coordinate positioning, and the one or more positioning sensors include pressure sensors, magnets, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or a combination thereof; wherein the position data and the updated position data are received via one or more of the following: ultra-wideband, Wi-Fi, CV2X, a public cellular network, or a private cellular network; wherein the processor-executable instructions, when executed by at least one processor, cause the vehicle to initiate a method of engaging the charging station, and further cause the at least one processor to: receive positioning data associated with the positioning of the vehicle from the vehicle; wherein the positioning data is based on one or more of the following: a deep learning model for detecting the charging station via a camera associated with the vehicle; a reference associated with the charging station identified via a camera, ultrasonic, or radar associated with the vehicle; or the alignment of the wheel chocks and at least one wheel of the vehicle; and wherein the alignment of the wheel chocks and the at least one wheel of the vehicle guides the vehicle to within a pre-specified distance of the charging station, and wherein the pre-specified distance from the charging station is within an acceptable range for an arm associated with the charging station to insert a charger into the charging port of the vehicle.

[0009] Additional applicable fields will become apparent from the description provided herein. It should be understood that the description and specific examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] To better understand the present disclosure, various forms of the present disclosure will now be described by way of example with reference to the accompanying drawings, in which:

[0011] Figure 1 An overall system for automated marshalling is shown according to various implementations;

[0012] Figure 2 An example vehicle associated with the system shown in Figure 1 is shown according to various implementations;

[0013] Figure 3 The general engagement of the vehicle shown in Figure 2 with a hands-free charging station is shown according to various implementations;

[0014] Figures 4 - 6 An example alignment process experienced by the vehicle shown in Figure 2 and Figure 3 when the vehicle approaches the hands-free charging station shown in Figure 3 is shown according to various implementations;

[0015] Figure 7 is a flowchart of an example method for positioning and navigating the vehicle shown in Figure 2 and Figure 3 to within an engagable distance of the hands-free charging station shown in Figures 3 - 6 according to various implementations; and

[0016] Figure 8 is a flowchart of another example method for positioning and navigating the vehicle shown in Figure 2 and Figure 3 to within an engagable distance of the hands-free charging station shown in Figures 3 - 6 according to various implementations.

[0017] The drawings described herein are for illustrative purposes only and are not intended to limit the scope of the present disclosure in any way. DETAILED DESCRIPTION

[0018] The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, or uses. It should be understood that throughout the drawings, corresponding reference numerals indicate the same or corresponding parts and features.

[0019] The present disclosure provides an apparatus for automatically charging one or more vehicles when they are marshaled across an environment such as, for example, a parking lot. In some examples, automatic alignment of the vehicles provides a hands-free charging method and system. This capability reduces the manual labor required by a human operator and also reduces the cycle time associated with one or more vehicles prior to parking.

[0020] Specifically, the present disclosure provides a system for allowing efficient and precise positioning of one or more vehicles when they engage a hands-free charging (HFC) system. The system reduces the cycle time for aligning one or more vehicles with the HFC system. The system further reduces the reliance on human operator intervention and / or orientation of one or more vehicles to place them in a proper position for automatic charging. The system also provides more robust outdoor movement of one or more vehicles through the environment relative to the otherwise limited travel prior to the required human interaction.

[0021] Figure 1 A schematic block diagram of an autonomous vehicle marshalling (AVM) system 100 is shown. In one or more examples, the AVM system 100 marshals one or more autonomous vehicles traveling at low speed, including an HFC system 110. However, it should be understood that the AVM system 100 can marshal one or more vehicles traveling at any speed. It should also be understood that the AVM system 100 can marshal semi-autonomous vehicles and / or fully autonomous vehicles.

[0022] The AVM system 100 generally includes a vehicle original equipment manufacturer (OEM) / supplier cloud backend system 102, an infrastructure system 104, a vehicle control system 106, an environment 108, and an HFC system 110. The OEM / supplier cloud backend system 102 operates as a central cloud system that manages and / or facilitates the positioning and navigation of one or more autonomous vehicles (e.g., autonomous vehicle 200). The OEM / supplier cloud backend system 102 is configured to communicate wirelessly with the infrastructure system 104.

[0023] The OEM / supplier cloud backend system 102 includes a vehicle start / stop component 112. The vehicle start / stop component 112 is configured to cause one or more instructions to be sent to a server wireless connectivity roadside unit (RSU) 114 associated with the infrastructure system 104. For example, the one or more instructions sent to the server wireless connectivity RSU 114 are sent via a cellular vehicle-to-everything (CV2X) messaging protocol. However, it should be understood that the one or more instructions sent to the server wireless connectivity RSU 114 can be sent via any form of messaging, such as but not limited to private and / or public cellular protocols, Wi-Fi protocols, long range (LoRA) signal protocols, Bluetooth protocols, and / or UWB protocols.

[0024] As another example, one or more instructions sent to the server wireless connectivity RSU 114 relate to start / stop operations associated with the autonomous vehicle 200. However, it should be understood that the vehicle start / stop component 112 can send any type of instruction to the server wireless connectivity RSU 114. The vehicle start / stop component 112 is also configured to wirelessly exchange (e.g., send / receive) data regarding start / stop operations associated with the autonomous vehicle 200 with the server wireless connectivity RSU 114. However, it should be understood that the vehicle start / stop component 112 can exchange any type of data with the server wireless connectivity RSU 114.

[0025] For example, based on instructions and / or data exchanged between the vehicle OEM / supplier cloud backend system 102 and the infrastructure system 104 (e.g., via the server wireless connectivity RSU 114), the autonomous vehicle 200 is caused to start, stop, or pause its progress through the environment 108 (e.g., a parking lot). As another example, based on instructions and / or data exchanged between the vehicle OEM / supplier cloud backend system 102 and the infrastructure system 104, the platooning speed of the autonomous vehicle 200 is controlled as the autonomous vehicle 200 traverses the environment 108. As a further example, the instructions and / or data exchanged between the vehicle OEM / supplier cloud backend system 102 and the infrastructure system 104 are based on whether the autonomous vehicle 200 is powered on or off.

[0026] The infrastructure system 104 can include the server wireless connectivity RSU 114, a data component 116, a local database 118, and one or more sensors 120. The infrastructure system 104 is configured to wirelessly broadcast one or more instructions directly to the vehicle telematics on-board unit (OBU) 122 of the autonomous vehicle 200 via the CV2X protocol. However, it should be understood that the server wireless connectivity RSU 114 can be configured to wirelessly broadcast one or more instructions directly to the vehicle telematics OBU 122 via any form of messaging, such as but not limited to private and / or public cellular protocols, Wi-Fi protocols, long range (LoRA) signal protocols, Bluetooth protocols, and / or UWB protocols.

[0027] For example, one or more of the broadcast instructions may be a forwarding of one or more instructions associated with a start / stop operation originating from the vehicle OEM / supplier cloud backend system 102. As an example, the server wireless connectivity RSU 114 may additionally be configured to wirelessly exchange (e.g., send / receive) data with the vehicle telematics OBU 122 via the CV2X protocol. However, it should be understood that the server wireless connectivity RSU 114 may additionally be configured to wirelessly exchange data with the vehicle telematics OBU 122 via any messaging means. For example, the exchanged data may be associated with a start / stop operation of the autonomous vehicle 200 originating from the vehicle OEM / supplier cloud backend system 102. It should be understood that although the server wireless connectivity RSU 114 is a dedicated short-range communication transceiver, one or more RSUs may be utilized throughout the platooning area such that the communication range between the server wireless connectivity RSU 114 and the vehicle telematics OBU 122 may be extended. As an example, the infrastructure system 104 (e.g., via the server wireless connectivity RSU 114) may utilize ultra-wideband, WIFI, CV2X, a public cellular network, or a dedicated cellular network to communicate with the autonomous vehicle 200 (e.g., via the vehicle telematics OBU 122).

[0028] The data component 116 is configured to process one or more instructions and / or data received from the vehicle OEM / supplier cloud backend system 102. As an example, one or more instructions and / or data received from the vehicle OEM / supplier cloud backend system 102 may be one or more various signals that may pertain to anything associated with the platooning of the autonomous vehicle 200. The data component 116 is also configured to process data received from one or more sensors 120. For example, the data received from one or more sensors 120 may be related to vehicle attitude data, obstacle data, route selection data, or a combination thereof. However, it should be understood that the data received from one or more sensors 120 may pertain to anything associated with the platooning of the autonomous vehicle 200.

[0029] The local database 118 is a volatile memory storage component of the infrastructure system 104, which may be but is not limited to random access memory (RAM). It should be understood that the local database 118 may be any type of memory and / or may be non-volatile memory that stores data persistently. The local database 118 is configured to store any of the data and / or one or more instructions received from the vehicle OEM / supplier cloud backend system 102, one or more sensors 120, and / or the autonomous vehicle 200. However, it should be understood that the local database 118 may also store data associated with the platooning of the autonomous vehicle 200 received from any source. One or more sensors 120 may be, for example, one or more of a camera, lidar, radar, and / or ultrasonic device. When the autonomous vehicle 200 traverses the environment 108, the one or more sensors 120 monitor the movement of the autonomous vehicle 200.

[0030] Additionally, the infrastructure system 104 includes an infrastructure controller 115. The infrastructure controller 115 is configured to centrally control the operation of the autonomous vehicle 200. For example, the operation of the autonomous vehicle 200 includes the propulsion, braking, and steering of the autonomous vehicle 200. It should be understood that the infrastructure controller 115 may be provided within the infrastructure system 104 or be located external to the infrastructure system 104. For example, in a platooning environment, the infrastructure system 104 wirelessly broadcasts platooning infrastructure messages to the autonomous vehicle 200. As another example, the platooning infrastructure messages are broadcast via a vehicle-to-everything (V2X) protocol. However, it should be understood that any communication means may be used to broadcast the platooning infrastructure messages.

[0031] The vehicle control system 106 associated with the autonomous vehicle 200 generally includes a vehicle telematics control unit (TCU) 124 and a driver assistance module 126. The vehicle TCU 124 includes a vehicle telematics on-board unit (OBU) 122, which, for example, receives broadcast data and / or one or more instructions from a server wireless connectivity roadside unit (RSU) 114. In some examples, the vehicle TCU 124 further includes a vehicle wireless connectivity interface 128 and a global navigation satellite system (GNSS) receiver 130. The vehicle wireless connectivity interface 128 is configured to receive one or more signals from one or more location tags 132 associated with the environment 108 via cellular means. However, it should be understood that the vehicle wireless connectivity interface 128 may wirelessly receive one or more signals from one or more location tags 132 via any messaging means. Although the vehicle wireless connectivity interface 128 is a logical interface, it should be understood that the vehicle wireless connectivity interface 128 may be any type of interface.

[0032] The GNSS receiver 130 is communicatively coupled (e.g., wired) to the vehicle telematics OBU 122 and is configured to communicate with one or more satellites (not shown) such that the vehicle control system 106 can determine the specific location of the autonomous vehicle 200. The GNSS receiver 130 is further configured to transmit geographic information associated with the autonomous vehicle 200 to the vehicle telematics OBU 122. For example, the vehicle control system 106 utilizes the vehicle telematics OBU 122 to process the information received from the GNSS receiver 130 and send it to the infrastructure system 104.

[0033] The driver assistance module 126 includes a vehicle-to-HFC positioning stack component 134 and an environment-to-vehicle input component 136. The vehicle-to-HFC positioning stack component 134 is configured to assist in positioning the autonomous vehicle 200 within an engagable distance relative to an HFC station (e.g., HFC station 300). The vehicle-to-HFC positioning stack component 134 is communicatively coupled (e.g., wired) to both the vehicle TCU 124 and the environment-to-vehicle input component 136. For example, the vehicle-to-HFC positioning stack component 134 is configured to communicate (e.g., exchange data) with both the environment-to-vehicle input component 136 and / or the vehicle TCU 124. As another example, the exchanged data may be associated with information received from any one of the vehicle sensing components 138, one or more vehicle controls 140, the vehicle infotainment system 142, the vehicle CAN bus 144, or a combination thereof.

[0034] The driver assistance module 126 is communicatively coupled (e.g., wired) to the vehicle sensing component 138 and collects data from the vehicle sensing component 138. For example, the data received from the vehicle sensing component 138 may be associated with environmental conditions such as temperature, amount of light, and / or distance from any object in terms of the positioning and / or orientation of the autonomous vehicle 200. As a further example, the vehicle sensing component 138 may include one or more of a camera, lidar, radar, and / or ultrasonic device. For example, an ultrasonic device used as the vehicle sensing component 138 emits high-frequency sound waves that strike an object (e.g., a wall or another vehicle) and then are reflected back to the autonomous vehicle 200. Based on the amount of time it takes for the sound waves to return to the autonomous vehicle 200, the vehicle control system 106 can determine the distance between the autonomous vehicle 200 and the object.

[0035] As another example, a camera device serving as the vehicle sensing component 138 provides a visual indication of the space around the autonomous vehicle 200. As an additional example, a radar device serving as the vehicle sensing component 138 emits an electromagnetic wave signal that strikes an object and is then reflected back to the autonomous vehicle 200. Based on the amount of time it takes for the electromagnetic wave to return to the autonomous vehicle 200, the vehicle control system 106 can determine the range, speed, and / or angle of the autonomous vehicle 200 relative to the object. For example, the vehicle control system 106 utilizes the driver assistance module 126 to process the information received from the vehicle sensing component 138 and / or send it to the infrastructure system 104 via the vehicle TCU 124. As another example, the driver assistance module 126 is configured to transmit one or more instructions received from the infrastructure system 104 via the vehicle TCU 124 to the vehicle sensing component 138.

[0036] The driver assistance module 126 is also communicatively coupled (e.g., wired) to each of one or more vehicle controls 140, the vehicle infotainment system 142, and the vehicle CAN bus 144, and collects data from each. The one or more vehicle controls 140 may include a hybrid turbo engine, electronic engine and gearbox controls, cruise control, antilock brakes, differential braking, active and / or semi-active suspension, or a combination thereof. However, it should be understood that the one or more vehicle controls 140 may include any control-related systems associated with the autonomous vehicle 200. For example, the vehicle control system 106 utilizes the driver assistance module 126 to process the information received from the one or more vehicle controls 140 and / or send it to the infrastructure system 104 via the vehicle TCU 124. As another example, the driver assistance module 126 is configured to transmit one or more instructions received from the infrastructure system 104 via the vehicle TCU 124 to the one or more vehicle controls 140.

[0037] The vehicle infotainment system 142 is a system that delivers a combination of information, entertainment content, and / or services to the user of the autonomous vehicle 200. It should be understood that in other examples, the vehicle infotainment system 142 may deliver information services to anyone associated with the autonomous vehicle 200. As an example, the vehicle infotainment system 142 includes an in-vehicle computer that combines one or more functions, such as a digital radio, an in-built camera, and / or a television. For example, the vehicle control system 106 utilizes the driver assistance module 126 to process the information received from the vehicle infotainment system 142 and / or send it to the infrastructure system 104 via the vehicle TCU 124. As another example, the driver assistance module 126 is configured to transmit one or more instructions received from the infrastructure system 104 via the vehicle TCU 124 to the vehicle infotainment system 142.

[0038] The vehicle CAN bus 144 communicates with the driver assistance module 126 and is configured to allow any device within the network of the autonomous vehicle 200 to create transmissions such as data frames transmitted in sequence. For example, the vehicle CAN bus 144 is configured to prioritize the further distribution of transmissions received from different components within the autonomous vehicle 200. As another example, the vehicle CAN bus 144 organizes the transmissions received from different components within the autonomous vehicle 200 such that a limited amount of transmission data is allocated at a time. Although the vehicle CAN bus 144 is communicatively coupled to the driver assistance module 126, it should be understood that the vehicle CAN bus 144 can communicate with any number of components within the autonomous vehicle 200. For example, the vehicle control system 106 utilizes the driver assistance module 126 to process information received from the vehicle CAN bus 144 and / or transmit it to the infrastructure system 104 via the vehicle TCU 124. As another example, the driver assistance module 126 is configured to transmit one or more instructions received from the infrastructure system 104 via the vehicle TCU 124 to the vehicle CAN bus 144.

[0039] The environment 108 includes, but is not limited to, for example, a parking lot. The environment 108 also includes one or more location tags 132. The one or more location tags 132 can include any type of geographical location sensing device, such as a pressure sensor, a magnet, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or a combination thereof, etc. As a further example, the one or more location tags 132 are configured to sense the autonomous vehicle 200 when the autonomous vehicle 200 is traveling above and / or within the proximity of any one of the one or more location tags 132. The environment 108 communicates wirelessly with the vehicle wireless connectivity interface 128 via cellular means. However, it should be understood that the environment 108 can communicate wirelessly with the vehicle wireless connectivity interface 128 via any messaging means. For example, the one or more location tags 132 can cause the environment 108 to transmit location data to the vehicle wireless connectivity interface 128, which causes the vehicle control system 106 to make one or more directional adjustments to the driving direction associated with the autonomous vehicle 200 when the autonomous vehicle 200 is guided to the HFC station 300 associated with the HFC system 110.

[0040] The HFC system 110 includes an HFC-to-vehicle micro-positioning component 148. In some examples, the HFC system 110 communicates wirelessly with the autonomous vehicle 200 via cellular means. However, it should be understood that the HFC system 110 can communicate wirelessly with the autonomous vehicle 200 via any messaging means (such as, for example, the CV2X protocol). It should be understood that the HFC system 110 can also communicate wirelessly with the infrastructure system 104 and / or the environment 108 via cellular means. However, it should be understood that the HFC system 110 can communicate with the infrastructure system 104 and / or the environment 108 via any messaging means. The HFC-to-vehicle micro-positioning component 148 is configured to communicate with the HFC station 300. For example, the HFC-to-vehicle micro-positioning component 148 is configured to guide the autonomous vehicle 200 to an engagable distance relative to the HFC station 300 based on the communication with the HFC station 300. As another example, the engagable distance relative to the HFC station 300 indicates a distance sufficient for the autonomous vehicle 200 to interact (e.g., charge) with the HFC station 300. As a further example, the HFC system 110 is further configured to wirelessly transmit one or more repositioning instructions to the vehicle TCU 124 and / or the vehicle wireless connectivity interface 128, which causes the vehicle control system 106 to perform one or more directional adjustments to the driving direction associated with the autonomous vehicle 200 when the autonomous vehicle 200 is guided to the HFC station 300.

[0041] Reference Figure 2 , in various forms, the autonomous vehicle 200 can be powered in various ways (e.g., using an electric motor and / or an internal combustion engine). As a non-limiting example, the autonomous vehicle 200 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 airplane, and / or a boat. The autonomous vehicle 200 can include a vehicle controller 201, one or more actuators 202, a plurality of on-vehicle sensors 204, and a human-machine interface (HMI) 206. The autonomous vehicle 200 also has a reference point 208, that is, a designated point within the space defined by the vehicle body, for example, the geometric center point where the corresponding longitudinal and lateral central axes of the autonomous vehicle 200 intersect. The reference point 208 identifies the position of the autonomous vehicle 200, for example, using the point where the autonomous vehicle 200 is located when the autonomous vehicle 200 is navigating towards a waypoint.

[0042] In some examples, the vehicle controller 201 is configured or programmed to control the operation of the autonomous vehicle 200's braking, propulsion (e.g., controlling the acceleration of the autonomous vehicle 200 by controlling one or more of an internal combustion engine, an electric motor, a hybrid engine, etc.), steering, climate control, interior lights, and / or exterior lights, etc., and to determine whether and when the vehicle controller 201 will control such operations as compared to a human operator. It should be understood that any operations associated with the autonomous vehicle 200 can be facilitated via an automated, semi-automated, or manual mode. For example, the automated mode can facilitate any operation being fully controlled by the vehicle controller 201 without user assistance. As another example, the semi-automated mode can facilitate any operation being controlled at least in part by the vehicle controller 201 and / or the user. As another example, the manual mode can facilitate the user fully controlling any operation.

[0043] The vehicle controller 201 includes or is communicatively coupled to (e.g., via a vehicle communication bus) one or more processors, such as a controller included in the autonomous vehicle 200, etc., for monitoring and / or controlling various vehicle controllers, such as a powertrain controller, a braking controller, a steering controller, etc. The vehicle controller 201 is typically arranged to communicate over a vehicle communication network (which can include a bus in the autonomous vehicle 200, such as CAN, etc.) and / or other wired and / or wireless mechanisms.

[0044] The vehicle controller 201 transmits messages to and / or receives messages from various devices (e.g., one or more actuators 202, HMI 206, etc.) in the autonomous vehicle 200 via the vehicle network. Alternatively or additionally, in cases where the vehicle controller 201 includes multiple devices, the vehicle communication network is used for communication between the devices represented as the vehicle controller 201. In addition, as discussed below, various other controllers and / or sensors provide data to the vehicle controller 201 via the vehicle communication network.

[0045] Additionally, the vehicle controller 201 is configured to communicate with other traffic objects (e.g., vehicles, infrastructure, pedestrians, etc.) via a wireless vehicle communication interface, such as via a vehicle-to-vehicle communication network. The vehicle controller 201 is also configured to communicate via a vehicle-to-infrastructure communication network, such as communicating with the infrastructure controller 115 of the infrastructure system 104. The vehicle communication network represents one or more mechanisms through which the vehicle controller 201 of the autonomous vehicle 200 communicates with other traffic objects, and can be one or more of wireless communication mechanisms, which include any desired combination of wireless (e.g., cellular, wireless, satellite, microwave, and radio frequency) communication mechanisms and any desired network topology (or multiple topologies when using multiple communication mechanisms). Examples of the vehicle communication network include cellular that provides data communication services. IEEE 802.11, dedicated short-range communication (DSRC), and / or wide area network (WAN) (including the Internet), etc.

[0046] The vehicle actuator 202 is implemented via a circuit, chip, or other electronic and / or mechanical components that can actuate various vehicle subsystems according to appropriate control signals. The vehicle actuator 202 can be used to control the braking, acceleration, and / or steering of the autonomous vehicle 200. The vehicle controller 201 can be programmed to actuate the vehicle actuator 202 based on the planned acceleration or deceleration of the autonomous vehicle 200, including propulsion, steering, and / or braking.

[0047] The plurality of on-vehicle sensors 204 includes a variety of devices for providing data to the vehicle controller 201. For example, the plurality of on-vehicle sensors 204 can include object detection sensors, such as lidar sensors disposed on or in the autonomous vehicle 200, which provide the relative position, size, and shape of one or more targets (e.g., additional vehicles, bicycles, pedestrians, robots, drones, etc.) traveling beside, in front of, and / or behind the autonomous vehicle 200. As another example, one or more of the sensors can be radar sensors fixed to one or more bumpers of the autonomous vehicle 200, which can provide the position of a target relative to the position of the autonomous vehicle 200.

[0048] The object detection sensor can include a camera sensor, for example, to provide a front view, side view, rear view, etc., and the sensor provides images from the area around the autonomous vehicle 200. For example, the vehicle controller 201 can be programmed to receive sensor data from the camera sensor and implement image processing techniques to detect roads, infrastructure elements, etc. The vehicle controller 201 can also be programmed to determine the current vehicle position based on position coordinates (e.g., GPS coordinates) received from the autonomous vehicle 200 and indicating the position of the autonomous vehicle 200 from the GPS sensor.

[0049] The HMI 206 is configured to receive information from a user (such as a human operator) during the operation of the autonomous vehicle 200. In addition, the HMI 206 is configured to present information to a user (such as an occupant of the autonomous vehicle 200). In some variations, the vehicle controller 201 is programmed to receive destination data, such as position coordinates, from the HMI 206.

[0050] Accordingly, a combination of one or more sensors 120 and vehicle sensors (e.g., multiple on-vehicle sensors 204) can be used to autonomously guide the autonomous vehicle 200 towards a waypoint. Route selection can be accomplished using vehicle position, travel distance, queuing vehicle platoons, etc. In instances where the autonomous vehicle 200 requires additional charging / fuel, the autonomous vehicle 200 can be prepared before joining a queue of multiple autonomous vehicles. In instances where the autonomous vehicle 200 and each of the multiple autonomous vehicles are destined towards the same waypoint, the autonomous vehicle 200 and each of the multiple autonomous vehicles operate in the same manner such that the movement of the entire convoy can be coordinated. The movement of the entire convoy is coordinated by a central convoy management system that guides all traffic and logistics from an assembly plant to a waypoint. For example, the entire convoy can be organized in a pre-sorted order.

[0051] In various examples, the centralized convoy management application has complete knowledge of controlling the autonomous vehicle 200 and each of the multiple autonomous vehicles (e.g., current location, destination, special notes, etc.), which increases the responsibility and traceability of the allocation process. Convoy management is coordinated within and / or across sites to optimize the delivery timing of the autonomous vehicle 200 and / or each of the multiple autonomous vehicles to a waypoint. Several logistics applications can be used, which can involve a combination of infrastructure systems (e.g., infrastructure system 104) integrated with traffic management algorithms to queue vehicles in real-time and eliminate conflicts. Accordingly, the convoy management application queues the autonomous vehicle 200 and the multiple autonomous vehicles based on unique characteristics (how far a particular vehicle among the multiple autonomous vehicles needs to travel, what traffic is along the route, when a particular vehicle among the multiple autonomous vehicles needs to reach a particular location to queue in the correct order, etc.).

[0052] Figure 3 A general engagement of the autonomous vehicle 200 with the HFC station 300 is shown. The HFC station 300 generally consists of one or more charging hubs 302, arms 304 associated with the one or more charging hubs 302, and a mobile base 306.

[0053] Reference Figure 4, the autonomous vehicle 200 is depicted in an example where the autonomous vehicle 200 has left a manufacturing facility (e.g., a warehouse) and has entered the environment 108. For example, when the autonomous vehicle 200 leaves the manufacturing facility, the infrastructure system 104 transmits one or more marshaling instructions to the autonomous vehicle 200. However, it should be understood that the infrastructure system 104 can transmit one or more marshaling instructions to the autonomous vehicle 200 at any time, as frequently as needed, and / or whenever the autonomous vehicle 200 is within the range of the infrastructure system 104. As another example, one or more marshaling instructions direct the autonomous vehicle 200 towards the approximate location of the HFC station 300. As a further example, before the autonomous vehicle 200 enters the communication range of any of the one or more location tags 132, the autonomous vehicle 200 uses the GNSS receiver 130 to navigate the environment 108. However, it should be understood that the GNSS receiver 130 can navigate the environment 108 whether or not the autonomous vehicle 200 is within the communication range of the one or more location tags 132. As yet another example, the GNSS receiver 130 is capable of navigating the environment 108 based on one or more marshaling instructions received from the infrastructure system 104.

[0054] In this particular example 400, the environment 108 shows one or more paths 402a - 402c that the autonomous vehicle 200 can follow to reach one or more charging hubs 404a - 404c (e.g., one or more charging hubs 302). For example, path 402a shows that the autonomous vehicle 200 will travel over one or more in - ground location tags 406a - 406d (e.g., one or more location tags 132) to reach the charging hub 404a. In an instance where the autonomous vehicle 200 follows path 402a, the autonomous vehicle 200 first travels over the in - ground location tag 406a and continues to travel in a straight line past the in - ground location tag 406a. When the autonomous vehicle 200 travels within the proximity of the in - ground location tag 406b, the in - ground location tag 406b senses the presence of the autonomous vehicle 200 and transmits location data to the autonomous vehicle 200. Based on the location data received from the in - ground location tag 406b, the vehicle control system 106 makes one or more direction adjustments to the travel direction associated with the autonomous vehicle 200 such that the autonomous vehicle 200 eventually travels over the in - ground location tag 406b and continues to travel in a straight line past the in - ground location tag 406b.

[0055] When the autonomous vehicle 200 travels within the proximity of the ground-based position tag 406c, the ground-based position tag 406c senses the presence of the autonomous vehicle 200 and transmits position data to the autonomous vehicle 200. Based on the received position data from the ground-based position tag 406c, the vehicle control system 106 makes one or more direction adjustments to the driving direction associated with the autonomous vehicle 200 such that the autonomous vehicle 200 ultimately travels over the ground-based position tag 406c and continues to travel in a straight line after passing the ground-based position tag 406c. The autonomous vehicle 200 continues to travel in a straight line after passing the ground-based position tag 406d and travels toward an engagable distance from the charging hub 404a. For example, the engagable distance is a distance relative to the charging hub 404a that is sufficient for the autonomous vehicle 200 to interact (e.g., charge) with the charging hub 404a.

[0056] As another example, the path 402b shows that the autonomous vehicle 200 will travel over one or more ground-based position tags 406a, 406e, and 406f (e.g., one or more position tags 132) to reach the charging hub 404b. In an instance where the autonomous vehicle 200 follows the path 402b, the autonomous vehicle first travels over the ground-based position tag 406a and continues to travel in a straight line after passing the ground-based position tag 406a. The autonomous vehicle 200 continues to travel in a straight line after passing both the ground-based position tags 406e and 406f and travels toward an engagable distance from the charging hub 404b. For example, the engagable distance is a distance relative to the charging hub 404b that is sufficient for the autonomous vehicle 200 to interact (e.g., charge) with the charging hub 404b.

[0057] As an additional example, the path 402c shows that the autonomous vehicle 200 will travel over one or more ground-based position tags 406a and 406g - 406l (e.g., one or more position tags 132) to reach the charging hub 404c. In an instance where the autonomous vehicle 200 follows the path 402c, the autonomous vehicle 200 first travels over the ground-based position tag 406a. When the autonomous vehicle 200 travels within the proximity of the ground-based position tag 406g, the ground-based position tag 406g senses the presence of the autonomous vehicle 200 and transmits position data to the autonomous vehicle 200. Based on the received position data from the ground-based position tag 406g, the vehicle control system 106 makes one or more direction adjustments to the driving direction associated with the autonomous vehicle 200 such that the autonomous vehicle 200 ultimately travels over the ground-based position tag 406g and continues to travel in a straight line after passing the ground-based position tag 406g.

[0058] When the autonomous vehicle 200 travels within the proximity of the ground mid-position tag 406h, the ground mid-position tag 406h senses the presence of the autonomous vehicle 200 and transmits position data to the autonomous vehicle 200. Based on the received position data from the ground mid-position tag 406h, the vehicle control system 106 makes one or more directional adjustments to the driving direction associated with the autonomous vehicle 200 such that the autonomous vehicle 200 ultimately travels over the ground mid-position tag 406h and continues to travel in a straight line after passing the ground mid-position tag 406h. The autonomous vehicle 200 continues to travel in a straight line until the ground mid-position tag 406i and travels towards an engagable distance from the charging hub 404c. In an instance where the autonomous vehicle 200 is positioned within the proximity of the ground mid-position tag 406i, the autonomous vehicle 200 is also within the proximity of the ground mid-position tags 406j - 406l. It should be understood that the position information received from multiple ground mid-position tags (e.g., ground mid-position tags 406j - 406l) set within close proximity to each other allows for a finer alignment of the autonomous vehicle 200 compared to the case where the ground mid-position tags are set farther apart from each other. In this particular example, the cluster of ground mid-position tags 406i–406l is configured to transmit position data to the autonomous vehicle 200 such that the vehicle control system 106 can make one or more directional adjustments so that the autonomous vehicle 200 can be precisely positioned within an engagable distance from the charging hub 404c. For example, the engagable distance is a defined or required distance relative to the charging hub 404c that is sufficient for the autonomous vehicle 200 to interact (e.g., charge) with the charging hub 404c.

[0059] In each instance where the autonomous vehicle 200 follows any one of the paths 402a - 402c, it should be understood that all of the ground mid-position tags 406a - 406l are configured to transmit to the autonomous vehicle 200 one or more instructions associated with the positioning, repositioning, and navigation of the autonomous vehicle 200 relative to any one of the charging hubs 404a - 404c.

[0060] It should also be understood that any one of the position tags 406a - 406l in the ground may represent a checkpoint along any one of the paths 402a - 402c. It should also be understood that each of the position tags 406a - 406c in the ground provides a decision point to the vehicle control system 106 regarding whether to cause the autonomous vehicle 200 to change direction (e.g., at any angle such as a 45 - degree angle or a 60 - degree angle) or to remain straight relative to each of the paths 402a - 402c. It should further be understood that when the autonomous vehicle 200 approaches any one of the charging hubs 404a - 404c, each of the position tags 406a - 406l in the ground may assist the autonomous vehicle 200 in making both longitudinal and lateral adjustments. As another example, the autonomous vehicle 200 may be navigated (e.g., given one or more instructions) to follow a specific path along any one of the paths 402a - 402c. Along a specific path, when the autonomous vehicle 200 travels past any one of the checkpoints (e.g., any one of the position tags 406a - 406l in the ground), the autonomous vehicle 200 may be positioned and / or re - positioned.

[0061] Figure 5 One or more arms 500a - 500c (e.g., arm 304) associated with each of the charging hubs 404a - 404c are depicted. Each of the one or more arms 500a - 500c is disposed within the HFC station 300. Additionally, the HFC station 300 further includes one or more sensing components (not shown) that provide sensing capabilities to the HFC station 300, and the sensing capabilities provide additional feedback to the autonomous vehicle 200 regarding whether the autonomous vehicle 200 should move laterally and / or longitudinally with respect to the attitude (e.g., positioning and orientation) of the HFC station 300 and a specific charging hub among the one or more charging hubs 404a - 404c. For example, the HFC station 300 may include ultrasonic sensing components, sensors with radar capabilities, cameras, or a combination thereof. The HFC station 300 may utilize WiFi and / or to provide additional feedback to the autonomous vehicle 200. For example, the HFC station 300 may via ultrasonic, WiFi and / or proximity sensors are used to implement a trilateration method to provide position data of a specific charging hub relative to the HFC station 300 and one or more charging hubs 404a - 404c to the autonomous vehicle 200. Once the autonomous vehicle 200 is caused to move within a pre-specified distance of a specific charging hub of the HFC station 300 (e.g., one or more of the charging hubs 404a - 404c), a specific arm among the one or more arms 500a - 500c can insert a charger (e.g., charger 312) into the charging port (e.g., charging port 310) of the autonomous vehicle 200. For example, the defined (e.g., pre-specified) distance from the specific charging hub is an acceptable range within which a specific arm among the one or more arms 500a - 500c can insert the charger 312 into the charging port 310 of the autonomous vehicle 200.

[0062] Figure 6 A exploded view depicting a set of wheel chocks 600a, 600b of the charging hub 404a is shown. The autonomous vehicle 200 can implement one or more methods to detect the charging hub 404a associated with the HFC station 300. More specifically, in some examples, the autonomous vehicle 200 employs the use of a deep learning model to detect the charging hub 404a associated with the HFC station 300. For example, the autonomous vehicle 200 employs the use of a deep learning model to detect the charging hub 404a associated with the HFC station 300 based on the front windshield camera (not shown) and / or the rear windshield camera (not shown) of the autonomous vehicle 200. It should also be understood that the autonomous vehicle 200 can also employ the use of a deep learning model to detect any one of the charging hubs 404b and 404c.

[0063] The autonomous vehicle 200 can also employ the use of one or more sensing components (e.g., multiple on-vehicle sensors 204), including ultrasonic sensing components, sensors with radar capabilities, and / or cameras on the autonomous vehicle 200, to identify one or more landmarks associated with the charging hub 404a associated with the HFC station 300. The autonomous vehicle 200 can also employ the use of ultrasonic sensing components, sensors with radar capabilities, and / or cameras on the autonomous vehicle 200 to identify one or more references associated with the charging hub 404a associated with the HFC station 300. It should also be understood that the autonomous vehicle 200 can also employ the use of one or more sensing components to detect any one of the charging hubs 404b and 404c.

[0064] The autonomous vehicle 200 may alternatively employ the use of a strategy in which at least two of the wheels of the autonomous vehicle 200 may be aligned with the set of wheel chocks 600a, 600b to navigate the autonomous vehicle to within a close proximity (e.g., 5 - 10 centimeters) of the charging hub 404a. However, it should be understood that, for example, any number of wheels of the autonomous vehicle 200 (e.g., one wheel) may be aligned with either of the set of wheel chocks 600a, 600b. Further, it should be understood that each of the charging hubs 404b and 404c may also utilize a set of wheel chocks to assist in the alignment of the autonomous vehicle 200.

[0065] In some examples, the utilization of the deep learning model, one or more sensing components, and the set of wheel chocks 600a, 600b may each be used as a process for aligning the autonomous vehicle 200 within an acceptable range in which the arm 500a associated with the charging hub 404a of the HFC station 300 can insert the charger 312 into the charging port 310 of the autonomous vehicle 200. However, it should also be understood that each of the deep learning model, one or more sensing components, and the set of wheel chocks 600a, 600b may be used separately for aligning the autonomous vehicle 200 within an acceptable range in which the arm 500a associated with the charging hub 404a of the HFC station 300 can insert the charger 312 into the charging port 310 of the autonomous vehicle 200.

[0066] As an example alignment process, the deep learning model is configured to identify the charging hub 404a of the HFC station 300. One or more sensing components are configured to identify the center of a reference (e.g., a visual marker or a physical marker or other identifier) associated with the charging hub 404a of the HFC station 300. The center of the reference is transformed from the HFC coordinate system to the vehicle coordinate system. The vehicle coordinate system is used to estimate the positioning of the autonomous vehicle 200 relative to the charging hub 404a. The estimated positioning of the autonomous vehicle 200 is used to route the autonomous vehicle 200 to a specific destination (e.g., the charging hub 404a).

[0067] Return reference Figure 3 , the autonomous vehicle 200 is depicted as being positioned within an engagable distance of a specific charging hub of the HFC station 300. In instances in which the autonomous vehicle 200 is positioned within an engagable distance of a specific charging hub by using Figures 4 - 6 any one or combination of the methods described in, the mobile base 306 is configured to move across the support structure 308 of the HFC station 300 to position the arm 304 adjacent to the charging port 310 of the autonomous vehicle 200. Once the arm 304 is positioned adjacent to the charging port 310, the arm 304 is configured to place the charger 312 within the charging port 310 of the autonomous vehicle 200 using any suitable hands - free charging process or technique.

[0068] Figure 7 is a flowchart showing an example method 700 for facilitating the positioning and navigation of a marshaled vehicle (e.g., autonomous vehicle 200). At operation 702, the vehicle is maneuvered toward a positioning and / or orientation associated with a charging station (e.g., HFC station 300). For example, the vehicle is maneuvered toward a positioning and / or orientation associated with a charging station by a vehicle control system (e.g., vehicle control system 106). As another example, maneuvering the vehicle toward a positioning and / or orientation associated with a charging station is based on one or more instructions received from an infrastructure system (e.g., infrastructure system 104). As an additional example, the positioning is global coordinate positioning.

[0069] In one example, positioning data associated with a first positioning and / or a second positioning of the vehicle is determined. For example, the positioning data is based on one or more of the following: a deep learning model for detecting a charging station via a camera associated with the vehicle; a reference associated with a charging station identified via a camera, ultrasonic, or radar associated with the vehicle; or an alignment of a wheel chock and at least one wheel of the vehicle. As another example, the alignment of the wheel chock and at least one wheel of the vehicle guides the vehicle within a pre-specified distance from the charging station, and wherein the pre-specified distance from the charging station is within an acceptable range for a charger (e.g., charger 312) associated with the charging station to be inserted into a charging port (e.g., charging port 310) of the vehicle.

[0070] At operation 704, position data associated with a first positioning of the vehicle is received. For example, the position data is received from one or more positioning sensors (e.g., one or more position tags 132). As another example, the one or more positioning sensors include pressure sensors, magnets, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or a combination thereof.

[0071] At operation 706, a method for engaging a charging station is initiated. For example, initiating the method to engage a charging station is based on updated position data associated with a second positioning of the vehicle. As another example, the updated position data is received from one or more positioning sensors. As yet another example, the updated position data is based on the position data. As a further example, the position data and the updated position data are received via one or more of the following: ultra-wideband, WIFI, CV2X, a public cellular network, or a private cellular network.

[0072] In some examples, it is determined whether a driving direction associated with the vehicle is outside an engagable distance from the charging station. For example, determining whether a driving direction associated with the vehicle is outside an engagable distance from the charging station is based on location data. For example, the vehicle is repositioned based on determining that the driving direction associated with the vehicle is outside an engagable distance from the charging station. For example, the repositioned vehicle is redirected towards an engagable distance from the charging station.

[0073] Figure 8 FIG. 800 is a flow chart illustrating another example method for facilitating the positioning and navigation of a platooned vehicle (e.g., autonomous vehicle 200). At operation 802, location data is received from one or more positioning sensors (e.g., one or more location tags 132). For example, the one or more positioning sensors include pressure sensors, magnets, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or combinations thereof. As another example, location data is received via one or more of the following: ultra-wideband, WIFI, CV2X, a public cellular network, or a dedicated cellular network.

[0074] At operation 804, positioning data associated with the vehicle is determined. For example, the determination of positioning data associated with the vehicle is based on a deep learning model and / or location data. As another example, the deep learning model is used to detect a charging station (e.g., HFC station 300) via a camera associated with the vehicle (e.g., multiple in-vehicle sensors 204). As yet another example, the positioning data associated with the vehicle charging station is determined by a vehicle control system (e.g., vehicle control system 106). As a further example, the positioning data is associated with the positioning of the vehicle. As an additional example, the positioning is global coordinate positioning.

[0075] At operation 806, a method for engaging the charging station is initiated. For example, a method for engaging the charging station is initiated based on the positioning data and / or the identification of the charging station. In an embodiment, the positioning data is determined based on one or more of the following: a reference associated with the charging station identified via a camera, ultrasonic, radar associated with the vehicle; and / or the alignment of a wheel chock and at least one wheel of the vehicle. For example, the alignment of the wheel chock and at least one wheel of the vehicle guides the vehicle within a pre-specified distance from the charging station, and wherein the pre-specified distance from the charging station is within an acceptable range for an arm associated with the charging station (e.g., arm 304) to insert a charger (e.g., charger 312) into a charging port of the vehicle (e.g., charging port 310). In some examples, a database stores vehicle-specific information, such as charging port location, charger requirements, charging configuration, etc., which is used to determine the pre-specified distance or other parameters for automated charging using the HFC station.

[0076] In summary, one or more examples provide an apparatus for positioning and navigating one or more autonomous vehicles when the one or more autonomous vehicles are marshaled towards an HFC station. For example, positioning and navigation of one or more autonomous vehicles can generally be achieved by: enhanced cross-environment vehicle maneuvering via physical environment feedback for precise positioning of the one or more autonomous vehicles, HFC-to-vehicle communication for guiding the one or more autonomous vehicles, and / or vehicle-to-HFC detection for initiating positioning of the one or more autonomous vehicles closer to the HFC station.

[0077] Unless expressly indicated otherwise herein, all numerical values indicating mechanical / thermal properties, percentage compositions, dimensions, and / or tolerances or other characteristics should be understood to be modified by the word "about" or "approximately" when describing the scope of the present disclosure. This type of modification is desired for various reasons, including: industrial practice; material, manufacturing, and assembly tolerances; and test capabilities.

[0078] As used herein, the phrase "at least one of A, B, and C" should be interpreted to represent the logical (A or B or C) using non-exclusive logic "or", and should not be interpreted to mean "at least one of A, at least one of B, and at least one of C".

[0079] 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 circuits; digital, analog, or mixed analog / digital integrated circuits; combinational logic circuits; field programmable gate arrays (FPGA); processor circuits (shared, dedicated, or grouped) that execute code; memory circuits (shared, dedicated, or grouped) that store code executed by the processor circuits; other suitable hardware components that provide the described functionality (e.g., operational amplifier circuit integrators as part of a heat flux data module); or a combination of some or all of the above, such as in a system-on-chip.

[0080] 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); thus, the term computer-readable medium can be considered tangible and non-transitory. Non-limiting examples of non-transitory tangible computer-readable media are 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 or digital magnetic tape or hard disk drives), and optical storage media (such as CDs, DVDs, or Blu-ray discs).

[0081] The devices and methods described in this application can be implemented, in part or in whole, by a special-purpose computer created by configuring a general-purpose computer to execute one or more specific functions embodied in a computer program. Functional blocks, flowchart components, and other elements described above serve as software specifications that can be translated into a computer program through routine work by a technician or programmer.

[0082] The description of the present disclosure is merely exemplary in nature, and thus, variations that do not depart from the essence of the present disclosure are intended to be within the scope of the present disclosure. Such variations should not be regarded as departing from the spirit and scope of the present disclosure.

[0083] According to the present invention, a method for marshalling autonomously operating vehicles includes: receiving position data from one or more positioning sensors; determining positioning data associated with the vehicle based on a deep learning model and the position data, wherein the deep learning model is used to detect a charging station via a camera associated with the vehicle, and wherein determining the positioning data associated with the vehicle is performed by a vehicle control system; and initiating a method for engaging the charging station based on the positioning data and the identification of the charging station.

[0084] In one aspect of the present invention, the positioning data is associated with the positioning of the vehicle, and the positioning is global coordinate positioning.

[0085] In one aspect of the present invention, one or more positioning sensors include pressure sensors, magnets, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or combinations thereof.

[0086] In one aspect of the present invention, the position data is received via one or more of the following: ultra-wideband, WIFI, CV2X, a public cellular network, or a private cellular network.

[0087] In one aspect of the present invention, the vehicle is further configured to determine positioning data based on one or more of the following: a reference associated with a charging station identified via a camera, ultrasonic, or radar associated with the vehicle; or the alignment of a wheel chock and at least one wheel of the vehicle.

[0088] In one aspect of the present invention, the alignment of the wheel chock and the at least one wheel of the vehicle guides the vehicle within a pre-specified distance from the charging station, and wherein the pre-specified distance from the charging station is within an acceptable range for an arm associated with the charging station to insert a charger into a charging port of the vehicle.

Claims

1. A method for marshalling an autonomously operating vehicle, the method comprising: maneuvering the vehicle by a vehicle control system towards a positioning and orientation associated with a charging station, wherein maneuvering the vehicle towards the positioning and the orientation associated with the charging station is based on one or more instructions received from an infrastructure system; receiving position data associated with a first positioning of the vehicle from one or more positioning sensors; and initiating a method of engaging the charging station based on updated position data associated with a second positioning of the vehicle, wherein the updated position data is received from the one or more positioning sensors and wherein the updated position data is based on the position data.

2. The method of claim 1, further comprising: determining that a driving direction associated with the vehicle is outside an engagable distance from the charging station based on the position data; and repositioning the vehicle based on the determination that the driving direction associated with the vehicle is outside the engagable distance from the charging station.

3. The method of claim 2, wherein the repositioned vehicle is redirected towards the engagable distance from the charging station.

4. The method of claim 1, wherein the positioning is global coordinate positioning and the one or more positioning sensors include a pressure sensor, a magnet, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or a combination thereof.

5. The method according to claim 1, wherein the location data and the updated location data are received via one or more of the following: ultra-wideband, WIFI, CV2X, a public cellular network, or a private cellular network.

6. The method of claim 1, wherein maneuvering the vehicle towards the positioning and the orientation associated with the charging station further comprises: determining positioning data associated with the first positioning and the second positioning of the vehicle.

7. The method of claim 6, wherein the positioning data is based on one or more of: a deep learning model for detecting the charging station via a camera associated with the vehicle; a reference associated with the charging station identified via a camera, ultrasonic, or radar associated with the vehicle; or an alignment of a wheel chock and at least one wheel of the vehicle.

8. The method of claim 7, wherein the alignment of the wheel chock and the at least one wheel of the vehicle guides the vehicle within a pre-specified distance from the charging station, and wherein the pre-specified distance from the charging station is within an acceptable range for an arm associated with the charging station to insert a charger into a charging port of the vehicle.

9. One or more non-transitory computer-readable media storing processor-executable instructions that, when executed by at least one processor, cause the at least one processor to: maneuver a vehicle towards a positioning and orientation associated with a charging station, wherein maneuvering the vehicle towards the positioning and the orientation associated with the charging station is based on one or more instructions received from an infrastructure system; receive position data associated with a first positioning of the vehicle from one or more positioning sensors; and A method of initiating engagement with a charging station based on updated position data associated with a second positioning of the vehicle, wherein the updated position data is received from the one or more positioning sensors, and wherein the updated position data is based on the position data.

10. The one or more non-transitory computer-readable media of claim 9, wherein the at least one processor is further caused to: Determine that a driving direction associated with the vehicle is outside an engagable distance from the charging station based on the position data; and Reposition the vehicle based on the determination that the driving direction associated with the vehicle is outside the engagable distance from the charging station, wherein the repositioned vehicle is redirected toward the engagable distance from the charging station.

11. The one or more non-transitory computer-readable media of claim 9, wherein the positioning is global coordinate positioning and the one or more positioning sensors include a pressure sensor, a magnet, ultrasonic, proximity sensors, ultra-wideband tags, RFID tags, or a combination thereof.

12. The one or more non-transitory computer-readable media of claim 9, wherein the location data and the updated location data are received via one or more of: ultra-wideband, Wi-Fi, CV2X, a public cellular network, or a private cellular network.

13. The one or more non-transitory computer-readable media of claim 9, wherein the processor-executable instructions, when executed by the at least one processor, maneuver the vehicle toward the positioning and orientation associated with the charging station, further causing the at least one processor to: Determine positioning data associated with the first positioning and the second positioning of the vehicle.

14. The one or more non-transitory computer-readable media of claim 9, wherein the positioning data is based on one or more of: a deep learning model for detecting the charging station via a camera associated with the vehicle; a reference associated with the charging station identified via a camera, ultrasonic, or radar associated with the vehicle; or an alignment of a wheel chock and at least one wheel of the vehicle.

15. The one or more non-transitory computer-readable media of claim 14, wherein the alignment of the wheel chock and the at least one wheel of the vehicle guides the vehicle within a pre-specified distance from the charging station, and wherein the pre-specified distance from the charging station is within an acceptable range for an arm associated with the charging station to insert a charger into a charging port of the vehicle.

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