System and method for inbound and outbound autonomous vehicle operation

Abstract

A system includes a fleet of autonomous vehicles, a terminal station, and an autonomous freight network (AFN) management device. The AFN management device receives information indicating that the autonomous vehicle is inbound to a terminal station. The AFN management device determines a position of an object within the terminal station based on the sensor data. The AFN management device determines a landing instruction indicating avoidance of the position of the object detected from the sensor data. The AFN management device transmits a landing instruction to the autonomous vehicle. A control device associated with the autonomous vehicle determines a route for reaching a landing pad within the terminal station based on the landing command. The control device instructs the autonomous vehicle to travel along the route.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Application No. 63 / 365,295, filed on May 25, 2022, entitled “SYSTEM AND METHOD FOR INBOUND AND OUTBOUND AUTONOMOUS VEHICLE OPERATIONS,” which is incorporated herein by reference. Technical Field

[0003] The present disclosure relates generally to autonomous vehicles. More specifically, the present disclosure relates to a system and method for inbound and outbound autonomous vehicle operations. Background Art

[0004] One goal of autonomous vehicle technology is to provide a vehicle that can navigate safely with limited or no driver assistance. An autonomous vehicle can travel from an origin to a destination. Several operations need to be performed to receive an oncoming autonomous vehicle at a given location and to prepare to launch an outgoing autonomous vehicle from that location. The operational efficiency of the autonomous vehicle depends at least in part on the inbound and outbound operations. Summary of the invention

[0005] The present disclosure recognizes various problems and previously unmet needs associated with autonomous vehicle navigation, and more specifically, various problems and previously unmet needs associated with the lack of technology to efficiently establish and utilize resources to reliably launch an autonomous vehicle from a location and receive an autonomous vehicle at the location. For example, when an autonomous vehicle arrives at a given location, several operations may need to be performed to reliably receive the autonomous vehicle. If not optimized, these operations will cause delays in the inbound operation of the autonomous vehicle. In another example, when an autonomous vehicle leaves a given location to make a trip, several operations may need to be performed to prepare for the trip of the autonomous vehicle. If not optimized, these operations will cause delays in the outbound operation of the autonomous vehicle.

[0006] Certain embodiments of the present disclosure provide unique technical solutions to technical problems of current autonomous vehicle technology, including the above-mentioned problems, to reduce inbound and outbound delays and improve navigation of incoming and outgoing autonomous vehicles into and out of a given location. The present disclosure contemplates systems and methods for improving inbound and outbound operations of autonomous vehicles.

[0007] In one example scenario, an autonomous vehicle may be approached by a terminal. In order to improve the efficiency of the incoming autonomous vehicle's inbound navigation, several operations may be performed. The autonomous vehicle may travel from the entrance of the terminal to a landing pad, where the autonomous vehicle may stop and park. In some cases, the travel path from the entrance of the terminal to the landing pad may be blocked by one or more obstacles, such as technicians working at the terminal, other autonomous vehicles, animals, vehicle components, etc. In such cases, the travel path of the autonomous vehicle may result in potential hazards and accidents.

[0008] The disclosed system is configured to notify the control device of the autonomous vehicle of the location of an object in the travel path to the landing pad. In other words, the disclosed system notifies the control device of the autonomous vehicle of potential safety issues along its travel path to the landing pad, enabling the autonomous vehicle to navigate more efficiently and safely and avoid such potential safety issues. In this way, the disclosed system improves inbound autonomous vehicle navigation and reduces delays in inbound autonomous vehicle navigation.

[0009] In some cases, after arriving at a terminal, one or more operations may need to be performed on the autonomous vehicle. For example, the autonomous vehicle may need to unload a load, be serviced (e.g., refueled), be provided with updated map data, be loaded with new cargo, and / or any other operations. If not optimized, these operations can waste time and cause delays in the autonomous vehicle's navigation inside the terminal and thereby affect the autonomous vehicle's preparation for the next trip.

[0010] The disclosed system is configured to provide more efficient operations to reduce delays in autonomous vehicle navigation, unloading, and preparation for the next trip. For example, the system can implement an example assembly line of autonomous vehicles to track and supervise each operation on the assembly line to ensure that each operation is performed efficiently and does not cause delays. The example assembly line may include 1) an assembly line from a landing pad where the autonomous vehicle stops after a trip; 2) an assembly line to a trailer drop-off area where the trailer of the autonomous vehicle drops off; 3) an assembly line to a data communication area where updated data is uploaded to the autonomous vehicle; 4) an assembly line to a service area where the autonomous vehicle is provided with services; and 5) an assembly line to a tractor assembly area where the tractor of the autonomous vehicle is temporarily stored to indicate that it is ready for the next trip. Therefore, the disclosed system improves the overall operation of an autonomous freight network of autonomous vehicles operating outside a terminal.

[0011] In another example scenario, an autonomous vehicle may exit a terminal. In exit navigation, the autonomous vehicle may need to travel from a launch pad to exit the terminal. In some cases, the autonomous vehicle's travel path from the launch pad to the terminal's exit may be blocked by one or more obstacles, such as technicians working at the terminal, other autonomous vehicles, animals, vehicle components, etc. In this case, the autonomous vehicle's travel path may lead to potential dangers and accidents.

[0012] The disclosed system is configured to notify the outbound autonomous vehicle of the location of an object in the travel path from the launch station to the exit terminal. In other words, the disclosed system notifies the control device of the autonomous vehicle of potential safety issues along its travel path from the exit terminal, so that the autonomous vehicle can navigate more efficiently and safely and avoid such potential safety issues. In this way, the disclosed system improves outbound autonomous vehicle navigation and reduces delays in outbound autonomous vehicle navigation.

[0013] Thus, the disclosed system may be integrated into a practical application of improving autonomous vehicle technology by increasing the operational efficiency of a fleet of autonomous vehicles entering and exiting a terminal, e.g., by employing an assembly line (e.g., the example assembly line described above) for the autonomous vehicles to follow. Furthermore, the disclosed system may be integrated into an additional practical application of reducing inbound and outbound delays for autonomous vehicles entering and exiting a terminal. Thus, due to the reduction in inbound and outbound delays, vehicle congestion at the terminal, its entrances, and its exits is also reduced. This, in turn, provides an additional practical application of providing a safer driving experience for autonomous vehicles and other vehicles at the terminal.

[0014] In addition, the disclosed system can be integrated into another practical application of providing a safer driving path for an autonomous vehicle to land on a landing pad or launch from a launch pad. For example, by notifying the autonomous vehicle of the location of objects and obstacles in a terminal and more specifically in its vicinity, a safer driving path is determined for the autonomous vehicle to use the safer driving path to reach a specified landing pad or leave the terminal, wherein objects and obstacles are avoided when determining the safer driving path. In this process, information from sensors on the autonomous vehicle and information from sensors set around the terminal are used to determine the location of objects and obstacles in the terminal. Therefore, a more comprehensive and accurate map of the terminal (including objects and obstacles) is determined and used to navigate the autonomous vehicle.

[0015] In one embodiment, a system includes a fleet of autonomous vehicles, a terminal, and an autonomous freight network management device. The fleet of autonomous vehicles includes a first autonomous vehicle, wherein the first autonomous vehicle is configured to travel along a predetermined route. The terminal includes one or more dedicated zones and one or more sensors within a physical space. Each of the one or more dedicated zones is configured to facilitate a specific function for the first autonomous vehicle. The one or more dedicated zones include a landing pad shaped to accommodate the first autonomous vehicle. The landing pad is established by a set of boundary indicators set around the landing pad. Each of the one or more sensors is configured to detect an object within a detection range. The autonomous freight network management device is operably coupled to the fleet of autonomous vehicles. The autonomous freight network management device includes a first processor, the first processor is configured to receive information indicating that the first autonomous vehicle is inbound to the terminal. The first processor receives first sensor data indicating the location of an object within the terminal. The first processor determines at least one location of at least one object in the travel path of the first autonomous vehicle to the landing pad based at least in part on the first sensor data. The first processor determines a landing instruction including at least one location of the at least one object, wherein the landing instruction indicates avoiding at least one location of the at least one object. The first processor transmits the landing instruction to the first autonomous vehicle. The first autonomous vehicle includes a control device, the control device includes a second processor, the second processor is configured to receive a landing instruction. The second processor determines a first route for the first autonomous vehicle to take to reach the landing pad based at least in part on the landing instruction, wherein the first route is free of at least one object. The second processor instructs the first autonomous vehicle to travel according to the first route.

[0016] Certain embodiments of the present disclosure may include some, all, or none of these advantages. These advantages and other features will be more clearly understood through the following detailed description taken in conjunction with the accompanying drawings and claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] For a more complete understanding of the present disclosure, reference is now made to the following brief description taken in conjunction with the accompanying drawings and detailed description, wherein like reference numerals represent like parts.

[0018] Figure 1 An embodiment of a system configured to optimize inbound and outbound operations of autonomous vehicles entering and exiting a terminal is illustrated;

[0019] Figure 2 Picture shows Figure 1 Example operation flow of the system;

[0020] Figure 3 An embodiment of a method for implementing an inbound operation of an autonomous vehicle entering a terminal is illustrated;

[0021] Figure 4 An embodiment of a method for implementing an exit operation of an autonomous vehicle from a terminal is illustrated;

[0022] Figure 5 A block diagram of an example autonomous driving vehicle configured to implement autonomous driving operations is shown;

[0023] Figure 6 shows the use of Figure 5 An example system for autonomous driving operations used by an autonomous vehicle; and

[0024] Figure 7 Shows Figure 5 A block diagram of an onboard control computer included in an autonomous driving vehicle. DETAILED DESCRIPTION

[0025] As described above, the prior art has failed to provide an efficient, reliable and safe solution to implement the inbound and outbound operations of autonomous vehicles entering and leaving the terminal. The present disclosure provides various systems, methods and devices for implementing the inbound and outbound operations of autonomous vehicles entering and leaving the terminal to reduce or minimize inbound and outbound delays. Figures 1 to 7 The embodiments of the present disclosure and their advantages can be understood. Figures 1 to 7 Describes systems and methods for enabling inbound and outbound operations of autonomous vehicles entering and exiting a terminal to reduce or minimize inbound and outbound delays.

[0026] System Overview

[0027] Figure 1One embodiment of an autonomous vehicle inbound and outbound management system 100 is illustrated, which is configured to optimize inbound and outbound operations of an autonomous vehicle 502 entering and exiting a terminal 140. In certain embodiments, the system 100 includes an autonomous freight network (AFN) management device 150 communicatively coupled to the autonomous vehicle 502 and its components, including a control device 550 and sensors 146 associated with the terminal 140, and a computing device 104 associated with a user 102 via a network 110. The network 110 enables communication between the components of the system 100. The autonomous vehicle 502 includes a control device 550. The control device 550 includes a processor 122 in signal communication with a memory 126. The memory 126 stores software instructions 128 that, when executed by the processor 122, cause the control device 550 to perform one or more operations described herein. The AFN management device 150 includes a processor 152 in signal communication with a memory 158. The memory 158 stores software instructions 160 that, when executed by the processor 152, cause the AFN management device 150 to perform one or more operations described herein. For example, when the AFN management device 150 determines that the autonomous vehicle 502 is inbound to the terminal 140, the software instructions 160 are executed to generate landing instructions 162 for the inbound or upcoming autonomous vehicle 502. In another example, when the AFN management device 150 determines that the autonomous vehicle 502 is outbound from the terminal 140, the software instructions 160 are executed to generate launch instructions 166 for the outbound or outgoing autonomous vehicle 502. The landing instructions 162 are determined to optimize the navigation of the landing and inbound autonomous vehicle 502, and to reduce the navigation delay of the inbound autonomous vehicle 502. The launch instructions 166 are determined to optimize the navigation of the launch and outbound autonomous vehicle 502, and to reduce the navigation delay of the outbound autonomous vehicle 502. These operations are respectively described in Figure 2 The operation flow 200 of the system 100 described in Figure 3 and Figure 4 The system 100 described in more detail in methods 300 and 400 of the system 100 described in FIG. In other embodiments, the system 100 may not have all of the components listed, and / or may have other elements to replace or supplement the above elements. The system 100 may be configured as shown, or in any other configuration.

[0028] In the example scenario, the autonomous vehicle 502 may be inbound to the terminal 140. In order to improve the efficiency of the inbound navigation of the oncoming autonomous vehicle 502, several operations need to be performed. The autonomous vehicle 502 may travel from the entrance of the terminal 140 to the landing pad 142a (see Figure 2), the autonomous vehicle can stop and park at the landing pad. In some cases, the autonomous vehicle 502 goes from the entrance of the terminal 140 to the landing pad 142a (see Figure 2 ) may be blocked by one or more obstacles, such as technicians working at the terminal 140, other autonomous vehicles 502, animals, vehicle components, etc. In this case, the path of the autonomous vehicle 502 may lead to potential danger and accidents. The system 100 (e.g., via the AFN management device 150) is configured to notify the autonomous vehicle 502 of the landing pad 142a (see Figure 2 ) of the object in the driving path. In other words, the system 100 notifies the control device 550 of the autonomous vehicle 502 of the location of the object along its driving path to the landing pad 142a (see Figure 2 )'s driving path, so that the control device 550 can navigate the autonomous driving vehicle 502 more efficiently and safely and avoid such potential safety issues.

[0029] In some embodiments, the control device 550 of the autonomous vehicle 502 may use the sensor data 130 captured by the sensor 546 and the information received from the AFN management device 150 to determine a method for reaching the landing pad 142a (see Figure 2 ) of an obstacle-free route. In this way, the navigation of the inbound autonomous vehicle 502 is optimized and the delay in the navigation of the inbound autonomous vehicle 502 is reduced.

[0030] In some cases, upon arrival at terminal 140, one or more operations may need to be performed on autonomous vehicle 502. For example, autonomous vehicle 502 may need to unload its load, be serviced (e.g., refueled), be provided with updated map data 134 and / or software instructions 128, be loaded with a new load, and / or any other operations. If not optimized, these operations can waste time and result in delays in autonomous vehicle 502 navigation within terminal 140 and delays in preparing the autonomous vehicle for the next trip.

[0031] The system 100 (e.g., via the AFN management device 150) is configured to provide more efficient operations to reduce delays in unloading the autonomous vehicle and preparing for the next trip. For example, the system 100 can implement an example assembly line for the autonomous vehicle 502 to follow. The example assembly line can include starting from the landing pad 142a (see Figure 2 ) to the loading and unloading area 142c (see Figure 2 ) to trailer drop-off area 142b (see Figure 2 ) to the data communication area 142e (see Figure 2 ) to service area 142f (see Figure 2) to the tractor assembly area 142g (see Figure 2 ) to the launch pad 142h (see Figure 2 ) assembly line. In other examples, the assembly line can include any and any number of these operations in any suitable sequence. Thus, the system 100 improves the overall operation of the autonomous freight network of self-driving vehicles 502.

[0032] In another example scenario, the autonomous vehicle 502 may exit from the terminal 140. In the exit autonomous vehicle 502 navigation, the autonomous vehicle 502 may need to exit from the launch station 142h (see Figure 2 ) to depart from terminal 140. In some cases, autonomous vehicle 502 departs from launch pad 142h (see Figure 2 ) to exit the terminal 140 may be blocked by one or more obstacles, such as technicians working at the terminal 140, other autonomous vehicles 502, animals, vehicle components, etc. In this case, the driving path of the autonomous vehicle 502 may lead to potential dangers and accidents.

[0033] The system 100 (eg, via the AFN management device 150) is configured to notify the autonomous vehicle 502 of the transmission from the transmitting station 142h (see Figure 2 ) to the location of objects and obstacles in the driving path away from the terminal 140. In other words, the system 100 notifies the control device 550 of the autonomous vehicle 502 of potential safety issues along its driving path away from the terminal 140, so that the control device 550 can navigate the autonomous vehicle 502 more efficiently and safely and avoid such potential safety issues.

[0034] In certain embodiments, the autonomous vehicle 502 may use the sensor data 130 captured by the sensor 546 and the information received from the AFN management device 150 to determine an obstacle-free route for exiting the terminal 140. In this manner, navigation of the inbound autonomous vehicle 502 is optimized and delays in inbound autonomous vehicle navigation are reduced. Thus, the system 100 improves both inbound and outbound autonomous vehicle navigation of the autonomous vehicle 502.

[0035] System Components

[0036] The network 110 may include any interconnected system capable of transmitting audio, video, signals, data, messages, or any combination of the foregoing. The network 110 may include all or part of the following: a local area network (LAN), a wide area network (WAN), a metropolitan area network (MAN), a personal area network (PAN), a wireless PAN (WPAN), an overlay network, a software defined network (SDN), a virtual private network (VPN), a packet data network (e.g., the Internet), a mobile phone network (e.g., a cellular network such as 4G or 5G), a plain old telephone (POT) network, a wireless data network (e.g., WiFi, WiGig, WiMax, etc.), a long term evolution (LTE) network, a universal mobile telecommunications system (UMTS) network, a peer-to-peer (P2P) network, a Bluetooth network, a near field communication (NFC) network, a Zigbee network, a Z-wave network, a WiFi network, and / or any other suitable network.

[0037] Example autonomous vehicle

[0038] In one embodiment, the autonomous vehicle 502 may include a semi-truck tractor unit attached to a trailer to transport cargo or goods from one location to another (see Figure 5 ). The self-driving vehicle 502 is typically configured to travel along a road in an autonomous mode. The self-driving vehicle 502 may use Figure 5-Figure 7 The operation of the autonomous vehicle 502 is as follows: Figure 5-Figure 7 The corresponding description below includes a brief description of certain components of the autonomous vehicle 502.

[0039] The control device 550 may generally be configured to control the operation of the autonomous vehicle 502 and its components and facilitate autonomous driving of the autonomous vehicle 502. The control device 550 may also be configured to determine a path ahead of the autonomous vehicle 502 that is safe to travel and free of objects or obstacles, and to navigate the autonomous vehicle 502 to travel in the path. Figure 5-Figure 7 The control device 550 may generally include one or more computing devices that communicate signals with other components of the autonomous vehicle 502 (see Figure 5 ). In the present disclosure, the control device 550 may be interchangeably referred to as the on-board control computer 550 .

[0040] The control device 550 can be configured to detect objects on and around the road on which the autonomous vehicle 502 is traveling by analyzing the sensor data 130 and / or the map data 134. For example, the control device 550 can detect objects on and around the road by implementing the object detection machine learning module 132. The object detection machine learning module 132 can be implemented using a neural network and / or a machine learning algorithm to detect objects from images, videos, infrared images, point clouds, radar data, etc. The object detection machine learning module 132 will be described in more detail below. The control device 550 can receive sensor data 130 from a sensor 546 located on the autonomous vehicle 502 to determine a safe driving path. The sensor data 130 may include data captured by the sensor 546.

[0041] Sensor 546 can be configured to capture any object within its detection area or field of view, such as landmarks, lane markings, lane boundaries, road boundaries, vehicles, pedestrians, and road / traffic signs, etc. In some embodiments, sensor 546 can be configured to detect rain, fog, snow, and / or any other weather conditions. Sensor 546 may include detection and ranging (LiDAR) sensors, radar sensors, cameras, infrared cameras, ultrasonic sensor systems, gust detection systems, microphone arrays, thermocouples, humidity sensors, barometers, inertial measurement units, positioning systems, infrared sensors, motion sensors, rain sensors, etc. In some embodiments, sensors 546 can be positioned around the autonomous vehicle 502 to capture the environment around the autonomous vehicle 502. For further description of sensors 546, see Figure 5 The corresponding description of .

[0042] Control Equipment

[0043] The control device 550 Figure 5 126 and the network interface 124. In short, the control device 550 may include a processor 122 that communicates signals with the memory 126 and the network interface 124. The processor 122 may include one or more processing units that perform various functions as described herein. The memory 126 may store any data and / or instructions that the processor 122 uses to perform its functions. For example, the memory 126 may store software instructions 128 that, when executed by the processor 122, cause the control device 550 to perform one or more functions described herein.

[0044] The processor 122 may be Figure 51. The processor 122 includes one or more processors operably coupled to the memory 126. The processor 122 may be any electronic circuit system, including a state machine, one or more central processing unit (CPU) chips, a logic unit, a core (e.g., a multi-core processor), a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or a digital signal processor (DSP). The processor 122 may be a programmable logic device, a microcontroller, a microprocessor, or any suitable combination thereof. The processor 122 may be communicatively coupled to the network interface 124 and the memory 126 and perform signal communication therewith. One or more processors may be configured to process data and may be implemented in hardware or software. For example, the processor 122 may be 8-bit, 16-bit, 32-bit, 64-bit, or any other suitable architecture. The processor 122 may include an arithmetic logic unit (ALU) for performing arithmetic and logic operations, a processor register that provides operands to the ALU and stores the results of the ALU operations, and a control unit that retrieves instructions from the memory and executes instructions by directing the coordinated operation of the ALU, registers, and other components. One or more processors may be configured to implement various instructions. For example, one or more processors may be configured to execute software instructions 128 to implement the functionality disclosed herein, such as with respect to Figure 1-Figure 7 Some or all of the functions described. In some embodiments, the functions described herein are implemented using a logic unit, FPGA, ASIC, DSP, or any other suitable hardware or electronic circuitry.

[0045] The network interface 124 may be Figure 6 Components of the network communication subsystem 592 described in . The network interface 124 can be configured to enable wired and / or wireless communication. The network interface 124 can be configured to transmit data between the autonomous driving vehicle 502 and other devices, systems, or domains. For example, the network interface 124 may include an NFC interface, a Bluetooth interface, a Zigbee interface, a Z-wave interface, a radio frequency identification (RFID) interface, a WIFI interface, a local area network (LAN) interface, a wide area network (WAN) interface, a metropolitan area network (MAN) interface, a personal area network (PAN) interface, a wireless PAN (WPAN) interface, a modem, a switch, and / or a router. The processor 122 can be configured to send and receive data using the network interface 124. The network interface 124 can be configured to use any suitable type of communication protocol, as understood by those of ordinary skill in the art.

[0046] The memory 126 may be Figure 5Memory 126 may be one of the data storage devices 590 described in . Memory 126 may be volatile or non-volatile and may include read-only memory (ROM), random access memory (RAM), ternary content addressable memory (TCAM), dynamic random access memory (DRAM), and static random access memory (SRAM). Memory 126 may include one or more of a local database, a cloud database, a network attached storage device (NAS), etc. Memory 126 may store Figure 1-Figure 7 120, and any other data, instructions, logic, rules, or code that, when executed by processor 122, is operable to implement the functionality described herein. For example, memory 126 may store software instructions 128, sensor data 130, object detection machine learning module 132, map data 134, route plan 136, driving instructions 138, landing pad sensor signals 212, launch pad sensor signals 218, routes 230, 240, and / or any other data / instructions. Software instructions 128 include instructions that, when executed by processor 122, cause control device 550 to perform the functionality described herein (such as Figure 1-Figure 7 Memory 126 includes one or more disks, tape drives, or solid-state drives, and may be used as an off-the-shelf data storage device to store programs when such programs are selected for execution, and to store instructions and data that are read during program execution.

[0047] The object detection machine learning module 132 may be implemented by the processor 122 executing the software instructions 128, and may generally be configured to detect objects and obstacles from the sensor data 130. The object detection machine learning module 132 may be implemented using a neural network and / or a machine learning algorithm for detecting objects from any data type such as images, videos, infrared images, point clouds, radar data, etc.

[0048] In some embodiments, the object detection machine learning module 132 may be implemented using a machine learning algorithm, such as a support vector machine (SVM), naive Bayes, logistic regression, k-nearest neighbor, decision tree, etc. In some embodiments, the object detection machine learning module 132 may utilize multiple neural network layers, convolutional neural network layers, long short-term memory (LSTM) layers, bidirectional LSTM layers, recurrent neural network layers, etc., wherein the weights and biases of these layers are optimized during the training process of the object detection machine learning module 132. The object detection machine learning module 132 may be trained by a training data set, which may include samples of data types labeled with one or more objects in each sample. For example, the training data set may include sample images of objects (e.g., vehicles, lane markings, pedestrians, road signs, obstacles, etc.) labeled with (multiple) objects in each sample image. Similarly, the training data set may include samples of other data types labeled with (multiple) objects in each sample data, such as videos, infrared images, point clouds, radar data, etc. The object detection machine learning module 132 may be trained, tested, and refined by the training data set and the sensor data 130. The object detection machine learning module 132 uses the sensor data 130 (which is not labeled with objects) to improve the accuracy of their predictions when detecting objects. For example, supervised and / or unsupervised machine learning algorithms can be used to verify the predictions of the object detection machine learning module 132 when detecting objects in the sensor data 130.

[0049] Map data 134 may include a virtual map of a city or region that includes roads on which autonomous vehicle 502 travels. In some examples, map data 134 may include map 658 and map database 636 (see Figure 6 658 and map database 636). The map data 134 may include drivable areas such as roads, paths, highways, etc. and non-drivable areas such as terrain (determined by occupancy grid module 660, see Figure 6 See occupancy grid module 660 for a description.) Map data 134 may specify location coordinates of road signs, lanes, lane markings, lane boundaries, road boundaries, traffic lights, obstacles, and the like.

[0050] The route plan 136 may be a plan for traveling from a starting location (e.g., a first autonomous vehicle launch pad / landing pad) to a destination (e.g., a second autonomous vehicle launch pad / landing pad). For example, the route plan 136 may specify a combination of one or more streets, roads, and highways in a specific order from the starting location to the destination. The route plan 136 may specify stages, including a first stage (e.g., moving out from the starting location / launch pad), multiple intermediate stages (e.g., traveling along specific lanes of one or more specific streets / roads / highways), and a final stage (e.g., entering the destination / landing pad). The route plan 136 may include other information about the route from the starting location to the destination, such as road / traffic signs in the route plan 136, etc.

[0051] Driving instructions 138 can be implemented by planning module 662 (see Figure 6 ). Driving instructions 138 may include instructions and rules for adapting the autonomous driving of autonomous vehicle 502 according to the driving rules for each stage of route plan 136. For example, driving instructions 138 may include instructions for: staying within a speed range for the road on which autonomous vehicle 502 is traveling, adapting the speed of autonomous vehicle 502 relative to changes observed by sensors 546 (such as the speed of surrounding vehicles, the speed of objects within the detection area of ​​sensors 546), and so on.

[0052] Autonomous Freight Network Terminals

[0053] Autonomous freight network terminal 140 in Figure 2 134 and / or updated software instructions 128). Briefly, the autonomous freight network terminal 140 facilitates inbound and outbound operations of the autonomous vehicles 502. For example, the autonomous freight network terminal 140 facilitates the autonomous vehicles 502 entering the terminal 140, inspecting the autonomous vehicles 502, unloading loads carried by the trailers of the autonomous vehicles 502, servicing the autonomous vehicles 502, loading the trailers of the autonomous vehicles 502 with new loads, uploading updated data (e.g., updated map data 134 and / or updated software instructions 128) to the control devices 550 associated with the autonomous vehicles 502, and the autonomous vehicles 502 exiting the autonomous freight network terminal 140. The autonomous freight network terminal 140 may facilitate preparing the tractor units of the autonomous vehicles 502 for transportation of freight or goods along a route, from the launch pad 142h (see Figure 2 ) automatically dispatches the autonomous vehicle 502 and lands at the appropriate landing pad 142a (see Figure 2 ) to safely receive the oncoming autonomous vehicle 502. Terminal 140 can facilitate the operation of autonomous vehicles 502 driven by human drivers and traditional tractor units.

[0054] In certain embodiments, terminal 140 may include dedicated areas 142, boundary indicators 144, sensors 146, and data and control command center 148. Each of dedicated areas 142 may be configured to facilitate a specific function for any autonomous vehicle 502 in autonomous vehicle fleet 502. For example, dedicated areas 142 may include landing pads, launch pads, loading and unloading areas, trailer staging areas, maintenance and data communications areas, and tractor staging areas. These dedicated areas 142 may be configured to facilitate a specific function for any autonomous vehicle 502 in autonomous vehicle fleet 502. Figure 2 Described in more detail in .

[0055] In some embodiments, each dedicated zone 142 may be established by a set of boundary indicators 144 disposed around the corresponding dedicated zone 142. Examples of boundary indicators 144 may include location delineators, yellow areas, traffic cones, etc. In some embodiments, each dedicated zone 142 may be established by painting markings on concrete around the corresponding dedicated zone 142. In some embodiments, the boundary indicators 144 may be integrated with one or more sensors 146. In some embodiments, the boundary indicators 144 and the sensors 146 may be different devices.

[0056] The sensors 146 may include any sensor configured to detect an object within a detection range. Examples of the sensors 146 may include cameras, infrared sensors, motion sensors, thermal sensors, and light detection and ranging (LiDAR) sensors. Each sensor 146 is communicatively coupled to the AFN management device 150 and optionally communicatively coupled to other components of the system 100. Each sensor 146 may be disposed at various locations within the terminal 140. For example, some sensors 146 may be disposed around the perimeter of the dedicated area 142 and / or within the dedicated area 142.

[0057] Each sensor 146 is configured to detect an object within its detection range and generate a sensor signal 212, 218 (see Figure 2 ). The sensor 146 may transmit the sensor signals 212, 218 to the AFN management device 150 and / or any other component of the system 100 (see Figure 2 ). The AFN management device 150 may use the received sensor signals 212, 218 (see Figure 2 ) to determine the sensor signals 212, 218 (see Figure 2). The AFN management device 150 may determine landing instructions 162 for the oncoming autonomous vehicle 502 based on the received information. For example, the landing instructions 162 may indicate avoiding the location in the travel path of the oncoming autonomous vehicle 502 where the object was detected. The AFN management device 150 may also determine launch instructions 166 for the outgoing autonomous vehicle 502 based on the received information. For example, the launch instructions 166 may indicate avoiding the location in the travel path of the outgoing autonomous vehicle 502 where the object was detected. These operations are described in detail in detail. Figure 2 Described in more detail in .

[0058] The data and control command center 148 may include a physical space where the AFN management equipment 150 is located. The data and control command center 148 is typically a space where the administrator of the terminal 140 is located to oversee operations at the terminal 140. In some embodiments, the data and control command center 148 houses the AFN management equipment 150, which communicates with the autonomous vehicle 502 and the sensors 146 associated with the dedicated area 142 (including the launch pad and the landing pad) to implement the various functions of the launch pad and the landing pad described in this disclosure. These operations are Figure 2 Described in more detail in .

[0059] Computing equipment

[0060] Each of computing devices 104a and 140b is an instance of computing device 104. Computing device 104 is generally any device configured to process data and interact with user 102. Examples of computing device 104 include, but are not limited to, personal computers, desktop computers, workstations, servers, laptop computers, tablet computers, mobile phones (such as smart phones), etc. Computing device 104 may include a user interface, such as a display, microphone, keypad, or other appropriate terminal device available to the user. Computing device 104 may include a hardware processor, memory, and / or circuit system (not explicitly shown) configured to perform any function or action of computing device 104 described herein. For example, a software application designed using software code may be stored in a memory and executed by a processor to perform the functions of computing device 104. Computing device 104 is configured to communicate with other devices, such as AFN management device 150, via network 110.

[0061] Autonomous freight network management equipment

[0062] The autonomous freight network (AFN) management device 150 may include one or more processing and computing devices and is generally configured to optimize inbound and outbound navigation of the autonomous vehicle 502 and reduce inbound and outbound delays. The AFN management device 150 may also be configured to oversee the operation of the autonomous vehicle 502 while it is in transit (e.g., on the road) and within the terminal 140. Examples of the AFN management device 150 may include a server, a workstation, a server cloud, etc.

[0063] The AFN management device 150 may include a processor 152, a network interface 154, a user interface 156, and a memory 158. The components of the AFN management device 150 may be operably coupled to each other. The processor 152 may include one or more processing units that perform various functions of the AFN management device 150. The memory 158 may store any data and / or instructions used by the processor 152 to perform its functions. For example, the memory 158 may store software instructions 160 that, when executed by the processor 152, cause the AFN management device 150 to perform one or more functions described herein. The AFN management device 150 may be configured as shown or in any other suitable configuration.

[0064] In one embodiment, the AFN management device 150 may be implemented by a cluster of computing devices that may be used to oversee the operation of the autonomous vehicles 502 and the operations performed on each autonomous vehicle 502 in the terminal 140. For example, the AFN management device 150 may be implemented by a plurality of computing devices using a distributed computing and / or cloud computing system. In another example, the AFN management device 150 may be implemented by a plurality of computing devices in one or more data centers. Thus, in one embodiment, the AFN management device 150 may include more processing power than the control device 550. The AFN management device 150 communicates signals with the autonomous vehicles 502 and their components (e.g., the control device 550), the computing device 104, and the sensor 146.

[0065] Processor 152 includes one or more processors. Processor 152 is any electronic circuit system including a state machine, one or more CPU chips, logic units, cores (e.g., multi-core processors), FPGAs, ASICs, or DSPs. Processor 152 may be a programmable logic device, a microcontroller, a microprocessor, or any suitable combination thereof. Processor 152 may be communicatively coupled to network interface 154, user interface 156, and memory 158 and communicate signals therewith. One or more processors are configured to process data and may be implemented in hardware or software. For example, processor 152 may be 8-bit, 16-bit, 32-bit, 64-bit, or any other suitable architecture. Processor 152 may include an ALU for performing arithmetic and logical operations, processor registers that provide operands to the ALU and store the results of the ALU operations, and a control unit that retrieves instructions from memory and executes instructions by directing the coordinated operation of the ALU, registers, and other components. One or more processors are configured to implement various instructions. For example, one or more processors are configured to execute software instructions 160 to implement the functions disclosed herein, such as regarding Figure 1-Figure 7 Some or all of the functions described. In some embodiments, the functions described herein may be implemented using a logic unit, FPGA, ASIC, DSP, or any other suitable hardware or electronic circuitry.

[0066] The network interface 154 may be configured to enable wired and / or wireless communication with the AFN management device 150. The network interface 154 may be configured to transmit data between the AFN management device 150 and other devices, servers, autonomous vehicles 502, systems, or domains. For example, the network interface 154 may include an NFC interface, a Bluetooth interface, a Zigbee interface, a Z-wave interface, an RFID interface, a WIFI interface, a LAN interface, a WAN interface, a PAN interface, a modem, a switch, and / or a router. The processor 152 may be configured to send and receive data using the network interface 154. The network interface 154 may be configured to use any suitable type of communication protocol, as understood by those of ordinary skill in the art.

[0067] The user interface 156 may include one or more user interfaces configured to interact with a user, such as a remote operator 180. The remote operator 180 may be an administrator working at the terminal 140. The remote operator 180 may access the AFN management device 150 using the user interface 156. The user interface 156 may include peripherals of the AFN management device 150, such as a display, keyboard, mouse, track pad, touch pad, microphone, webcam, speaker, etc. The remote operator 180 may access the memory 158 using the user interface 156 to view any data stored in the memory 158, such as the post-trip inspection report 164 and the pre-trip inspection report 168. The remote operator 180 may confirm, update, and / or override the landing instructions 162, the launch instructions 166, and / or any other data stored in the memory 158.

[0068] The memory 158 may be volatile or non-volatile and may include ROM, RAM, TCAM, DRAM, and SRAM. The memory 158 may include one or more of a local database, a cloud database, NAS, etc. The memory 158 may store Figure 1-Figure 7 150, and any other data, instructions, logic, rules, or code operable to implement the functionality described herein when executed by the processor 152. For example, the memory 158 may store the software instructions 160, the sensor data 130, the object detection machine learning module 132, the map data 134, the route plan 136, the driving instructions 138, the landing instructions 162, the post-trip inspection report 164, the launch instructions 166, the pre-trip inspection report 168, the trip 170, the landing pad sensor signal 212, the launch pad sensor signal 218, and / or any other data / instructions. The software instructions 160 may include code that, when executed by the processor 152, causes the AFN management device 150 to perform the functionality described herein, such as Figure 1-Figure 7 The memory 158 includes one or more magnetic disks, tape drives, or solid-state drives and may be used as an off-the-shelf data storage device to store programs when such programs are selected for execution and to store instructions and data read during program execution.

[0069] Example Autonomous Freight Network Terminal

[0070] Figure 2 An example embodiment of an autonomous freight network terminal 140 is illustrated. In the illustrated embodiment, the terminal 140 includes a dedicated zone 142, a boundary indicator 144, sensors 146, and a data and control command center 148.

[0071] The dedicated area 142 may include a landing pad 142a, a trailer drop-off and staging area 142b, a loading and unloading area 142c, a maintenance and data communications area 142d (which includes a data communications area 142e and a maintenance / service area 142f), a tractor staging area 142g, and a launch pad 142h.

[0072] The landing pad 142a is typically a predefined area or zone that facilitates the oncoming autonomous vehicle 502 to safely arrive at the predefined area and park therein. The landing pad 142a may include a set of landing pad corridors 210. Each landing pad corridor 210 may be associated with a landing pad 142a. Each landing pad 142a may be associated with an identifier (ID) number. In some embodiments, the boundaries of the landing pad 142a are determined by boundary indicators 144 disposed around the edges of each landing pad corridor 210. The landing pad 142a may be established by painting markings around the perimeter of each landing pad 142a. The landing pad 142a may have an associated sensor 146 for determining whether the area is free of obstacles that would prevent the autonomous vehicle 502 from safely arriving at the area and parking therein. For example, the landing pad 142a may be a physical platform (e.g., constructed of concrete or any suitable material) that includes, is embedded with, and / or is surrounded by sensors 146. Examples of sensors 146 are shown in FIG. Figure 1 In some embodiments, a post-trip check 222 of the autonomous vehicle 502 may be performed while the autonomous vehicle 502 is in the landing pad 142a. This process is described in more detail below.

[0073] In the illustrated embodiment, each landing pad 142a is observed by four sensors 146. In other embodiments, any suitable number of sensors 146 may be deployed to observe each landing pad 142a. In the illustrated embodiment, the landing pad 142a is shown as having three landing pads 142a. In other embodiments, the landing pad 142a may include any number of landing pads 142a.

[0074] Although Figure 2 The example shows sensors 146 near landing pad 142a and launch pad 13h, but it will be understood that any number of sensors 146 may be located at any suitable location within terminal 140, for example, along the travel path between the entrance of terminal 140 to landing pad 142a and launch pad 142h.

[0075] Although Figure 2The example terminal 140 of illustrative embodiments shows a separate landing pad 142a and launching pad 142h, but in other embodiments, the same structure (i.e., a predefined area including appropriate sensors for detecting whether the area is clear of obstacles) can be used as both landing pad 142a and launching pad 142h. In some embodiments, landing pad 142a can be bidirectional, meaning that the autonomous vehicle 502 can reach the landing pad 142a from two directions and park therein. In some embodiments, landing pad 142a can be unidirectional, meaning that the autonomous vehicle 502 can reach the landing pad 142a from one direction and park therein.

[0076] The sensors 146 are configured to detect objects on and around the landing pad 142a and the launch pad 142h. The sensors 146 associated with (e.g., observing) the landing pad 142a may generate a landing pad sensor signal 212 that indicates the location of objects detected in and near the landing pad 142a. The sensors 146 may transmit the landing pad sensor signal 212 to the AFN management device 150.

[0077] In certain embodiments, the AFN management device 150 may use the landing pad sensor signal 212 to determine whether there are any obstacles or objects in the travel path of the upcoming autonomous vehicle 502 to the landing pad 142a.

[0078] If the AFN management device 150 determines that there is an object in the travel path of the oncoming autonomous vehicle 502 to the landing pad 142a, the AFN management device 150 can determine a landing instruction 162 that instructs to avoid the location of the object detected from the sensor signal 212. The AFN management device 150 can transmit the landing instruction 162 to the oncoming autonomous vehicle 502.

[0079] The control device 550 of the oncoming autonomous vehicle 502 can use the landing instructions 162 and the sensor data 130 captured by its sensors 546 to instruct the autonomous vehicle 502 to take a route 230 to reach the landing pad 142a that is free of obstacles and objects detected in any of the sensor signals 212 and the sensor data 130. An example operational flow 200 for determining and transmitting the landing instructions 162 to the oncoming autonomous vehicle 502 is further described below.

[0080] In certain embodiments, the AFN management device 150 can use the landing pad sensor signal 212 to identify a landing pad 122a (e.g., landing pad lane 210) that is free of obstacles and available to receive an oncoming autonomous vehicle 502. The identified landing pad 142a can be communicated to the oncoming autonomous vehicle 502 so that the autonomous vehicle 502 can safely and efficiently navigate to the landing pad 142a (or a landing pad known to be free of obstacles). This significantly reduces the complexity of the navigation of the inbound autonomous vehicle 502, which otherwise can only be determined based on the information detected by the sensor 546. If the inbound autonomous vehicle 502 detects an obstacle in the inbound lane 204 leading to the identified landing pad 142a, the autonomous vehicle 502 can transmit the obstacle as sensor data 130 to the AFN management device 150, and the AFN management device 150 can identify a new landing pad 142a that can be reached using a different inbound lane 203 and without the obstacle, or using a different inbound lane 204 leading to the assigned landing pad 142a.

[0081] In some embodiments, the landing pad sensor signal 212 and the sensor data 130 from the autonomous vehicle 502 requesting to enter the landing pad 142a are provided to the AFN management device 150. The AFN management device 150 can use the landing pad sensor signal 212 and the sensor data 130 from the autonomous vehicle 502 to determine whether the landing pad 142a is free of obstacles. If it is determined that the landing pad 142a is free of obstacles, the AFN management device 150 allows the autonomous vehicle 502 to begin moving into the landing pad 142a. In some cases, the AFN management device 150 can also determine an inbound lane 204 (e.g., with a specific ID) for the autonomous vehicle 502 to take in order to enter the terminal 140 and arrive at the designated landing pad 142a identified by the ID.

[0082] The trailer staging area 142b generally includes areas within the terminal 140 that are used to store trailers when they are not in use or when they are attached to an autonomous vehicle 502 that is a tractor unit. For example, a first trailer staging area 142b may store trailers from an oncoming autonomous vehicle 502 that arrives at the terminal 140, and a second trailer staging area 142b may store outgoing trailers that are to be attached to a tractor unit autonomous vehicle 502 that is to be launched from a launch pad 142h.

[0083] The loading and unloading areas 142c generally include areas within the terminal 140 that are used to load merchandise onto outbound trailers of autonomous vehicles 502 for carry and transport, and to unload merchandise from recently arrived autonomous vehicles 502.

[0084] The data communication area 142e generally includes areas within the terminal 140 that are used to upload data to and download data from the autonomous vehicle 502. Computing equipment, connecting cables, Ethernet cables, switches, and / or routers (not explicitly shown) can be placed in the data communication area 142e to facilitate data communications with the autonomous vehicle 502. The data communication area 142e can be equipped with equipment for facilitating wired and / or wireless communications with the autonomous vehicle 502. For example, when preparing the autonomous vehicle 502 for a trip, if it is determined that the map data 134 of the autonomous vehicle 502 needs to be updated (see Figure 1 ) and / or software instructions 128 (see Figure 1 ), the autonomous driving vehicle 502 is driven into the data communication area 142e and the updated map data 134 (see Figure 1 ) and / or software instructions 128 (see Figure 1 ) may be uploaded to the autonomous vehicle 502, more specifically to the control device 550. In another example, when the autonomous vehicle 502 arrives at the terminal 140, it may be driven to the data communication zone 142e to download the sensor data 130 captured by the sensor 546 during its trip.

[0085] The maintenance and service area 142f generally includes an area within the terminal 140 that is used to provide services to the autonomous vehicle 502. These services may include sensor calibration, sensor housing cleaning, fuel filling, oil filling, tire air filling, coolant filling, and any other services that the autonomous vehicle 502 may need to operate. In the present disclosure, the maintenance and service area 142f may be interchangeably referred to as the service area 132f. The service area 142f may include resources for providing services to the autonomous vehicle 502. For example, the service area 142f may include resources such as fuel pumps for refueling the autonomous vehicle 502 and any other vehicles operating outside of the terminal 140.

[0086] The tractor staging area 142g generally includes an area of ​​the terminal 140 that is used to prepare the tractor units of the autonomous vehicles 502 before the autonomous vehicles 502 set out to begin traveling along the road 202. The tractors of the autonomous vehicles 502 are queued up in sequence on the lanes of the tractor staging area 142g as they prepare to begin traveling on the road 202.

[0087] The launch pad 142h is generally a predefined area or zone that facilitates the safe and efficient autonomous departure of the autonomous vehicle 502 from the terminal 140. For example, the launch pad 142h can be a physical station (e.g., composed of concrete or any suitable material) that includes, is embedded with, and / or is surrounded by sensors 146. The launch pad 142h can include a set of launch pad channels 216. Each launch pad channel 216 can be associated with a launch pad 142h. Each launch pad 142h can be associated with an ID number.

[0088] In some embodiments, the boundaries of the launch pads 142h are determined by boundary indicators 144 set around the edges of each launch pad 142h (e.g., each launch pad lane 216). The launch pads 142h can be established by painting markings around the perimeter of each launch pad 142h. The launch pads 142h can be associated with sensors 146 that are used to determine whether the area is free of obstacles that would prevent the autonomous vehicle 502 from safely reaching the area and parking therein. Examples of sensors 146 are shown in FIG. Figure 1 Maintenance and pre-trip diagnostics / testing (i.e., pre-trip check 234) may be performed on the autonomous vehicle 502 in launch pad 142h. This process will be described in more detail below.

[0089] In the illustrated embodiment, each launch pad 142h is associated with (e.g., observed by) four sensors 146. In other embodiments, any suitable number of sensors 146 may be included to observe each launch pad 142h. In the illustrated embodiment, the launch pad 142h is shown as having three launch pads 142h. In other implementations, the landing pad 142a may include any number of launch pads 142h.

[0090] In some embodiments, launch pad 142h may be bidirectional, which means that autonomous vehicle 502 can reach launch pad 142h from two directions and park therein. In some embodiments, launch pad 142h may be unidirectional, which means that autonomous vehicle 502 can reach launch pad 142h from one direction and park therein.

[0091] The sensors 146 associated with the launch pad 142h may generate a launch pad sensor signal 218 that indicates the location of objects detected within and near the launch pad 142h. The sensors 146 may transmit the launch pad sensor signal 218 to the AFN management device 150.

[0092] In certain embodiments, the AFN management device 150 may use the launch pad sensor signal 218 from the sensor 146 of the launch pad 142h to determine whether there are any obstacles or objects in the travel path of the outgoing autonomous vehicle 502 from the launch pad 142h to the exit from the terminal 140.

[0093] If the AFN management device 150 determines that there is an object in the travel path of the outgoing autonomous vehicle 502 leaving the terminal 140 from the transmitter 142h, the AFN management device 150 can determine a transmit instruction 166 that instructs to avoid the location of the object detected from the transmitter sensor signal 218. The AFN management device 150 can transmit the transmit instruction 166 to the outgoing autonomous vehicle 502. The control device 550 of the outgoing autonomous vehicle 502 can use the transmit instruction 166 and the sensor data 130 captured by its sensor 546 to instruct the autonomous vehicle 502 to take a route 240 to leave the terminal 140, which is free of obstacles and objects detected in any of the transmitter sensor signal 218 and the sensor data 130. An example operational flow 200 for determining and transmitting the transmit instruction 166 to the outgoing autonomous vehicle 502 is further described below.

[0094] In some embodiments, the launch pad sensor signal and sensor data 130 from the autonomous vehicle 502 requesting to leave the launch pad 142h are provided to the AFN management device 150. The AFN management device 150 can use the launch pad sensor signal 218 and the sensor data 130 to determine whether the area or zone around the autonomous vehicle 502 is free of obstacles. If the area around the autonomous vehicle 502 is determined to be free of obstacles, the AFN management device 150 allows the autonomous vehicle 502 to begin moving out of the launch pad 142h. In some cases, the AFN management device 150 can also determine an exit lane 206 (e.g., a specific exit lane 206 with a specific ID) for the autonomous vehicle 502 to take in order to leave the terminal 140 and arrive at the road 202, which is used to reach the route to the specified destination. In some embodiments, the control device 550 can use the sensor data 130 and the launch pad sensor signal 218 to determine the route 240 for leaving the terminal 140.

[0095] The data and control command center 220 is typically a space where administrators of the terminal 140 are located to oversee operations at the terminal 140. In the illustrated embodiment, the data and control command center 220 houses the AFN management equipment 150, which communicates with the autonomous vehicle 502 and the sensors 146 of the launch pad 142h and the landing pad 142a to implement the various functions of the launch pad 142h and the landing pad 142a described in this disclosure. Figure 2 The example of shows that the AFN management device 150 is located in the data and control command center 220, but it should be understood that the AFN management device 150 can be located at any suitable location and / or can be a distributed computing system. The terminal 140 can also include one or more vehicle parking spaces and one or more pass-through parking spaces for autonomous vehicles 502 and non-autonomous vehicles.

[0096] Example operation flow for an inbound autonomous vehicle

[0097] Figure 2 Further illustrating the inbound and outbound operations of the autonomous vehicle 502 relative to the terminal 140 Figure 1 An example operational flow 200 of the system 100 is shown. In the current technology, the inbound and outbound operations of the autonomous vehicle 502 introduce delays, which reduce the efficiency of navigating the autonomous vehicle 502 into and out of the terminal 140, preparing the autonomous vehicle 502 for the next trip 170, providing services to the autonomous vehicle 502, and sending and receiving data to and from the autonomous vehicle 502.

[0098] Figure 1 The system 100 is configured to implement the operational process 200 to reduce inbound and outbound delays and improve the efficiency of the autonomous vehicle 502 navigating into and out of the terminal 140, preparing the autonomous vehicle 502 for the next trip, providing services to the autonomous vehicle 502, and sending and receiving data to and from the autonomous vehicle 502.

[0099] Upcoming inbound operations for autonomous vehicles

[0100] In the example scenario, assume that the autonomous vehicle 502 is inbound to the terminal 140. For example, the autonomous vehicle 502 may be on the road 202 and traveling toward the terminal 140. When the AFN management device 150 determines that the autonomous vehicle 502 is inbound to the terminal 140, the operational flow 200 may begin. For example, the AFN management device 150 may receive information indicating that the autonomous vehicle 502 is inbound to the terminal 140.

[0101] For example, the control device 550 may send sensor data 130 including location coordinates (e.g., global positioning system (GPS) location coordinates) and a trajectory of the autonomous vehicle 502 to the AFN management device 150. Based on the sensor data 130, the AFN management device 150 may determine that the autonomous vehicle 502 is inbound to the terminal 140.

[0102] Determine the driving path to the landing pad

[0103] The AFN management device 150 may receive a landing pad sensor signal 212 from a sensor 146 associated with the landing pad 142a. The landing pad sensor signal 212 may indicate whether there are objects or obstacles along the travel path of the upcoming autonomous vehicle 502 to the landing pad 142a. For example, the landing pad sensor signal 212 may indicate the location of users 102a-b, other autonomous vehicles 502, other vehicles, vehicle equipment, animals, and other objects. Based on the landing pad sensor signal 212, the AFN management device 150 may determine the location of objects in the travel path of the upcoming autonomous vehicle 502 to the landing pad 142a. In this process, the AFN management device 150 may feed the sensor signal 212 to the object detection machine learning module 132 (see Figure 1 ) to detect the object and its location from the sensor signal 212. In response, the AFN management device 150 can determine landing instructions 162 for the upcoming autonomous vehicle 502. The landing instructions 162 can include the location of the object detected from the landing pad sensor signal 212. The landing instructions 162 can instruct to avoid the location of the detected object. For example, the landing instructions 162 can include route instructions for avoiding the location of the detected object.

[0104] In certain embodiments, the landing instructions 162 may also include an identifier of the landing pad 142a-1, an identifier of the inbound lane 204 that the oncoming autonomous vehicle 502 should take, a path to the landing pad 142a-1 from the specified inbound lane 204 to the specified landing pad 142a-1, and a time window during which the oncoming autonomous vehicle 502 is allowed to move into the specified landing pad 142a-1. The time window may indicate a duration of time that the landing pad 142a-1 is available to receive the oncoming autonomous vehicle 502 to arrive and park therein.

[0105] The AFN management device 150 may transmit the landing instruction 162 to the oncoming autonomous vehicle 502. The control device 550 associated with the oncoming autonomous vehicle 502 may receive the landing instruction 162. The control device 550 may determine the route 230 to be taken in order to reach the landing pad 142a, such that the determined route 230 is free of objects. The control device 550 may instruct the autonomous vehicle 502 to travel according to the route 230 to reach the specified landing pad 142a (e.g., landing pad 142a-1).

[0106] In some embodiments, the control device 550 may receive sensor data 130 indicating objects detected by the sensor 546 associated with the oncoming autonomous vehicle 502. The sensor data 130 may indicate objects detected along the travel path of the oncoming autonomous vehicle 502 to the landing pad 142a. In addition to the landing pad sensor signal 212, the control device 550 may also use the sensor data 130 to determine a route 230 to an available and unoccupied landing pad 142a. Thus, determining the route 230 may be based on the landing pad sensor signal 212 and the sensor data 130. The control device 550 may instruct the autonomous vehicle 605 to travel according to the determined route 230 to reach the identified landing pad 142a and park therein.

[0107] Upon reaching the specified landing pad 142a-1, the control device 550 may instruct the oncoming autonomous vehicle 502 to park and stop inside the landing pad 142a-1. In the process, the control device 550 may determine whether the boundary indicator 144 is detected in the sensor data 130. If the control device 550 detects the boundary indicator 144 from the sensor data 130, the control device 550 instructs the oncoming autonomous vehicle 502 to drive into the landing pad 142a-1 until it is a certain distance (e.g., two feet, three feet, etc.) from the boundary indicator set 144.

[0108] In some embodiments, before determining the landing instructions 162, the AFN management device 150 may determine whether the landing pad 142a-1 is occupied by the second autonomous vehicle 502 or any object that prevents the upcoming autonomous vehicle 502 from landing inside the landing pad 142a-1. If it is determined that the landing pad 142a-1 is not occupied by another autonomous vehicle 502 or any object that prevents the upcoming autonomous vehicle 502 from landing inside the landing pad 142a-1, the AFN management device 150 may determine the landing instructions 162. For example, assume that the AFN management device 150 detects that the landing pad 142a-2 is occupied by the second autonomous vehicle 502 based on analyzing the landing pad sensor signal 212. In this example, the AFN management device 150 may indicate in the landing instructions 162 that the landing pad 142a-2 is occupied. In response, the control device 550 may exclude the landing pad 142a-2 from landing consideration. In another example, assume that the AFN management device 150 detects the object 208 and its location by analyzing the landing pad sensor signal 212. The AFN management device 150 can include the location of the object 208 in the landing instruction 162. Since the object 208 is in the travel path to the landing pad 142a-3, the control device 550 can exclude the landing pad 142a-3 from landing considerations.

[0109] In certain embodiments, in response to receiving the landing pad sensor signal 212, the AFN management device 150 may determine a specific landing pad 142a for the upcoming autonomous vehicle 502 to land. For example, the AFN control device 150 may determine that the landing pad 142a-2 is occupied by another autonomous vehicle 502, the travel path to the landing pad 142a-3 is blocked by the object 208, and the landing pad 142a-1 is not occupied by any object and is ready to receive the upcoming autonomous vehicle 502. In response, the AFN management device 150 may determine the landing instructions 162 that indicate landing in the landing pad 142a-1. In this example, the landing instructions 162 may include an identifier of the landing pad 142a-1, an identifier of the inbound lane 204, a path to the landing pad 142a-1 from the specified inbound lane 204 to the specified landing pad 142a-1, and a time window during which the upcoming autonomous vehicle 502 is allowed to move into the specified landing pad 142a-1. The time window may indicate a duration in which landing pad 142a - 1 may be available to receive an incoming autonomous vehicle 502 to arrive and park.

[0110] If the AFN management device 150 determines that the landing pad 142a is occupied by the second autonomous vehicle 502 or any object that prevents the upcoming autonomous vehicle 502 from landing inside the landing pad 142a, the AFN management device 150 can identify another landing pad 142a, e.g., one that has no obstacles and a travel path to it that has no obstacles, i.e., is available to receive the upcoming autonomous vehicle 502. The AFN management device 150 can determine updated landing instructions 162 that include a route to an available landing pad 142a.

[0111] The AFN management device 150 may transmit the updated landing instructions 162 to the oncoming autonomous vehicle 502. The control device 550 associated with the oncoming autonomous vehicle 502 may instruct the autonomous vehicle 502 to travel according to the updated landing instructions 162.

[0112] Post-trip inspection

[0113] When the oncoming autonomous vehicle 502 arrives at the designated landing pad 142a-1 and parks there, a post-trip inspection 222 of the autonomous vehicle 502 may be performed. The post-trip inspection 222 may be performed by the user 102a who is a certified technician or inspector. During the post-trip inspection 222, the functionality and health levels of various components of the autonomous vehicle 502 are inspected and tested, such as Figure 5 104a. The user 102a can indicate the functionality and health levels of the components of the autonomous vehicle 502 in the computing device 104a and generate a post-trip inspection report 232. The user 102a can transmit the post-trip inspection report 232 from the computing device 104a to the AFN management device 150. In this way, the AFN management device 150 determines that the post-trip inspection 222 of the autonomous vehicle 502 is performed.

[0114] Trailer drop-off

[0115] like Figure 1 As described in the description of the autonomous vehicle 502 and Figure 2 and Figure 5 As shown, the autonomous vehicle 502 can be a tractor unit attached to a trailer carrying a load. When the autonomous vehicle 502 is on the landing pad 142a-1, the technician (e.g., user 102a) can disconnect the tractor from the trailer.

[0116] Assume that the oncoming autonomous vehicle 502 has transported a load to the terminal 140. The tractor of the autonomous vehicle 502 can be moved to the loading and unloading area 142c so that the load can be unloaded from the trailer, such as by a worker working at the terminal 140 or by another automated system.

[0117] The AFN management device 150 may determine a trailer drop-off zone 142b to drop off the trailer of the autonomous vehicle 502. The trailer drop-off zone 142b may have occupied plots and available plots. In some embodiments, the AFN management device 150 may determine a specific plot in the trailer drop-off zone 142b for dropping off the trailer of the autonomous vehicle 502. The AFN management device 150 may transmit the determined drop-off zone 142b available for receiving the trailer (e.g., an available plot in the drop-off zone 142b) to a computing device (e.g., computing device 104a or 120b) associated with a driver of a vehicle configured to move the trailer (e.g., user 102a or 102b). The driver may transport the trailer of the autonomous vehicle 502 to the determined drop-off zone 142b available for receiving the trailer. The driver may confirm on the computing device that the trailer is moved to the specified drop-off zone 142b. The driver may transmit a message from the computing device to the AFN management device 150 indicating that the trailer is moved to the specified drop-off zone 142b. The AFN management device 150 may receive a confirmation message from the computing device. The driver may refer to the control system of an automatic or autonomous system configured to move the trailer to the appropriate location. Such an automatic or autonomous system may include an automatic yard dog (e.g., an autonomous vehicle), or any other device or system configured to connect / disconnect the trailer and transport it to the appropriate location within the terminal yard or port-side yard.

[0118] Uploading data to self-driving vehicles

[0119] In some embodiments, after the post-trip inspection 222 is performed on the autonomous vehicle 502, the post-trip inspection report 232 may indicate that the map data 134 (see Figure 1 ) and / or software instructions 128 (see Figure 1 ) is not the latest. Map data 134 (see Figure 1 ) may include routes and location coordinates of objects on the road 202 and routes within the driving range of the autonomous vehicle 502. Software instructions 128 (see Figure 1 ) may include autonomous software code that facilitates autonomous functions for the autonomous vehicle 502. For example, when the autonomous vehicle 502 is in transit, the map data 134 (see Figure 1 ) and / or software instructions 128 (see Figure 1) may have been updated. In this way, the AFN management device 150 can determine that the map data 134 (see Figure 1 ) and software instructions 128 (see Figure 1 ) needs to be updated. In response, the AFN management device 150 may determine a data communication zone 142e. For example, the AFN management device 150 may determine a parcel of land within the data communication zone 142e that is available for receiving the autonomous vehicle 502. The data communication zone 142e is configured to facilitate the transmission of data, such as map data 134 (see Figure 1 ) and software instructions 128 (see Figure 1 ), and receives data, such as sensor data 130, from the autonomous vehicle 502.

[0120] The AFN management device 150 may transmit the data communication zone 142e to a computing device (e.g., computing device 104a or 120b) associated with a driver (e.g., user 102a or 102b). The driver may manually drive the tractor of the autonomous vehicle 502 to the data communication zone 142e. The driver may transmit a message from the computing device to the AFN management device 150 indicating that the tractor of the autonomous vehicle 502 is moved to the data communication zone 142e. The AFN management device 150 may receive a confirmation message from the computing device indicating that the tractor of the autonomous vehicle 502 is moved to the data communication zone 142e.

[0121] For example, updated map data 134 (see Figure 1 ) and / or updated software instructions 128 (see Figure 1 ) may be uploaded to the control device 550 of the autonomous vehicle 502, for example using wired and / or wireless communications. For example, the AFN management device 150 may transmit the updated map data 134 (see Figure 1 ) and / or updated software instructions 128 (see Figure 1 In another or the same example, a computing device, server, router, Ethernet cable, and / or communication device residing in the data communication zone 142e and communicatively coupled to the AFN management device 150 may be used to transmit the updated map data 134 to the control device 550 (see Figure 1 ) and / or updated software instructions 128 (see Figure 1 ).

[0122] In some embodiments, while the autonomous vehicle 502 is in transit, the sensors 546 may have captured sensor data 130, which may include recent changes on the road 202 (e.g., construction zones, road closures, etc.), performance reports of components of the autonomous vehicle 502 (e.g., sensors 546 and vehicle subsystems 540 (see Figure 6 )’s performance) and any other information.

[0123] The information captured by the sensors 546 while the autonomous vehicle 502 is in transit may be large (e.g., more than 1 Gigabit (Gb), 2 Gb, etc.) and may require a large network communication bandwidth to transmit. The captured information may be downloaded from the control device 550 at the data communication zone 142e, for example, by the AFN management device 150 via a computing device residing at the data communication zone 142e and communicatively coupled to the AFN management device 150. The AFN management device 150 may use the captured information to update the map data 134 (see Figure 1 ), software instruction 129 (see Figure 1 ), object detection machine learning module 132 (see Figure 1 ), and any other data used to operate and navigate the autonomous vehicle 502.

[0124] Services for autonomous vehicles

[0125] In some embodiments, after the post-trip check 222 of the autonomous vehicle 502 is performed, the post-trip check report 232 may indicate that the autonomous vehicle 502 requires service. The service may include sensor calibration, sensor housing cleaning, fuel filling, oil filling, tire air filling, coolant filling, battery charging, battery replacement, and any other service that enables the autonomous vehicle 502 to operate. In this way, the AFN management device 150 can determine that the autonomous vehicle 502 requires service based on the post-trip check report 232. In response, the AFN management device 150 can determine a service area 142f. For example, the AFN control device 150 can determine a location within the service area 142f that can be used to receive the autonomous vehicle 502.

[0126] The AFN management device 150 may transmit the service area 142f to a computing device (e.g., computing device 104a or 120b) associated with a driver (e.g., user 102a or 102b) or an autonomous yard dog or other mechanism configured to attach to or detach from the trailer for moving the trailer. The driver drives the tractor of the autonomous vehicle 502 to the service area 142f. The driver may transmit a message from the computing device to the AFN management device 150 indicating that the tractor of the autonomous vehicle 502 is moved to the service area 142f. The AFN management device 150 may receive a confirmation message from the computing device indicating that the tractor of the autonomous vehicle 502 is moved to the service area 142f. Service (e.g., indicated in the post-trip inspection report 232) may be provided to the tractor of the autonomous vehicle 502 at the service area 142f, for example, by a technician. After the service is completed, the tractor of the autonomous vehicle 502 may be moved to the tractor staging area 142g.

[0127] In certain embodiments, after the post-trip check 222 of the autonomous vehicle 502 is performed (and optionally after providing the autonomous vehicle 502 with the required services and / or updating the data for operating the autonomous vehicle 502 as described above), the AFN management device 150 may determine that the tractor of the autonomous vehicle 502 is ready for the next trip 170. For example, if the AFN management device 150 determines that the tractor of the autonomous vehicle 502 is suitable for driving on the road, and the control device 550 is operational and updated, the AFN management device 150 may determine that the tractor of the autonomous vehicle 502 is ready for the next trip 170. In response, the AFN management device 150 may determine a tractor staging area 142g. For example, the AFN control device 150 may determine a parcel of land within the tractor staging area 142g that is available to receive a tractor. The tractor staging area 142g may be an area where the tractor is positioned to indicate that the tractor is ready for the next trip.

[0128] The AFN management device 150 may transmit the tractor staging area 142g to a computing device (e.g., computing device 104a or 120b) associated with a driver (e.g., user 102a or 102b). The driver may drive the tractor to the tractor staging area 142g. The driver may transmit a message to the AFN management device 150 indicating that the tractor of the autonomous vehicle 502 is moved from the computing device to the tractor staging area 142g. The AFN management device 150 may receive a confirmation message from the computing device indicating that the tractor is moved to the tractor staging area 142g. The tractor is now placed in a queue of tractors ready for the next trip 170. When the next trip 170 or task is received, the AFN management device 150 may assign the next trip 170 to the first tractor in the queue of tractors.

[0129] Outbound Operations

[0130] In the example scenario, assume that the autonomous vehicle 502 exits the terminal 140 (e.g., from the remote operator 180 (see FIG. 1 )). The AFN management device 150 may determine that the autonomous vehicle 502 exits the terminal 140 in response to receiving the trip 170 or the task. For example, the AFN management device 150 may receive a message indicating that the autonomous vehicle 502 exits the terminal 140 (e.g., from the remote operator 180 (see FIG. 1 )). Figure 1 )) outbound information. In response, the AFN management device 150 can access the trip 170 scheduled for the autonomous vehicle 502. The trip 1710 can indicate a starting location (e.g., a terminal 140), a load (to be transported by the autonomous vehicle 502), a departure time window, an arrival time window, and a destination (e.g., another terminal 140). In response, the autonomous vehicle 502 can prepare for the trip 170. In the process, the trailer can be moved to the loading and unloading area 142c to be loaded with the load indicated in the trip 170. The remote operator 180 (see Figure 1 ) may indicate the ID of the trailer to be used for the trip 170. One or more technicians (e.g., users 102a and / or 102b) may load the load onto the trailer. The remote operator 180 (see Figure 1 ) can transmit the ID of the trailer to the AFN management device 150.

[0131] The AFN management device 150 may identify the trailer carrying the load of the trip 170 based on the provided trailer ID. In some embodiments, the AFN management device 150 may identify a particular transmitting station 142h (e.g., any one of transmitting stations 142h-1, 142h-2, and 142h-3) that may be used to receive the trailer.

[0132] In the illustrated example, the AFN management device 150 may determine that the launch pad 142h-1 is available to receive the trailer. The AFN management device 150 may transmit the identified launch pad 142h-1 to a computing device (e.g., computing device 104a or 120b) associated with the driver (e.g., user 102a or 102b). The driver (e.g., user 102a or 102b) may move the loaded trailer to the launch pad 142h-1. For example, in response to receiving a message from a computing device associated with the driver, the AFN management device 150 may determine that the trailer is moved to the specified launch pad 142h-1.

[0133] Similarly, for example, in response to receiving a message from a computing device associated with the driver, the AFN management device 150 may determine that the tractor is moved to the specified launch pad 142h-1. Now that both the tractor and the trailer are in the launch pad 142h-1, the user 102b may attach the tractor to the trailer. This process may result in the assembly of the autonomous vehicle 502. The user 102b may transmit a message from the computing device 104b to the AFN management device 150 indicating that the tractor is attached to the trailer. For example, in response to receiving the message from the computing device 104b, the AFN management device 150 may determine that the tractor is attached to the trailer at the launch pad 142h-1.

[0134] Pre-trip inspection

[0135] The user 102b may perform a pre-trip inspection 234 on the autonomous vehicle 502. During the pre-trip inspection 234, the functionality and health level of components of the autonomous vehicle 502, such as the vehicle subsystems 540 (see FIG. Figure 6 ).

[0136] In certain embodiments, the pre-trip check 234 may include determining the health level of components of the autonomous vehicle 502, including the vehicle subsystems 540 (see Figure 5 ). If the health level of the components of the autonomous vehicle 502 exceeds a threshold percentage (e.g., exceeds 90%, 95%, etc.), it is determined that the autonomous vehicle 502 has passed the pre-trip check 234. In this case, the autonomous vehicle 502 is allowed to proceed with the trip 170. Otherwise, it is determined that the autonomous vehicle 502 has not passed the pre-trip check 234. In this case, the autonomous vehicle 502 has not received a transmission permit. The user 102b can generate a pre-trip check report 236 and send it from the computing device 104b to the AFN management device 150. In this way, the AFN management device 150 can determine that the pre-trip check 234 is completed. The AFN management device 150 can determine that the autonomous vehicle 502 is allowed to transmit based on the pre-trip check report 236. Determining a travel path for leaving the terminal

[0137] The AFN management device 150 may receive a launch pad sensor signal 218 from a sensor 146 associated with the launch pad 142h. The launch pad sensor signal 218 may indicate the location of objects within and near the launch pad 142h, for example, in the driving path of the autonomous vehicle 502 from the launch pad 142h-1 to the exit terminal 140.

[0138] The AFN management device 150 may determine the transmit instructions 166 based on the launch station sensor signal 218. The transmit instructions 166 may include the location of the object in the travel path of the autonomous vehicle 502 from the launch station 142h-1 to the exit terminal 140. In some embodiments, the transmit instructions 166 may also include the time window during which the autonomous vehicle 502 is allowed to leave the launch station 142h-1, and the ID of the exit lane 206 that the autonomous vehicle 502 should take in order to leave the terminal 140. The transmit instructions 166 may instruct to avoid the location of the object detected in the exit travel path of the autonomous vehicle 502. For example, the transmit instructions 166 may include routing instructions for avoiding such a location of the object.

[0139] The AFN management device 150 may transmit the transmit instruction 166 to the autonomous vehicle 502 located at the transmitter station 142h-1. The control device 550 of the autonomous vehicle 502 may receive the transmit instruction 166. The control device 550 may determine that the route 240 is for the autonomous vehicle 502 to take in order to leave the terminal 140 and start the trip 170. The route 240 is free of objects detected from the transmitter station sensor signal 218. The control device 550 may instruct the autonomous vehicle 502 to travel according to the route 240.

[0140] In some embodiments, the control device 550 may receive sensor data 130 from the sensor 546, wherein the sensor data 130 may indicate objects and their locations along the travel path of the autonomous vehicle 502. The control device 550 may use the sensor data 130 and the transmission instructions 166 to determine the route 240.

[0141] In some embodiments, determining launch instructions 166 may be in response to determining that launch pad 142h-1 is not occupied by a second autonomous vehicle 502 or any object that would prevent outbound autonomous vehicle 502 from landing within launch pad 142h-1.

[0142] If the AFN management device 150 determines that the launch pad 142h is occupied by the second autonomous vehicle 502 or any object that prevents the outbound autonomous vehicle 502 from landing within the launch pad 142h, the AFN management device 150 can identify another launch pad 142h (e.g., launch pad 142h-1) that is not occupied by another autonomous vehicle 502 or any object that prevents the outbound autonomous vehicle 502 from landing within the launch pad 142h.

[0143] The AFN management device 150 may determine updated transmission instructions 166 that include a second route from the available transmission station 142h to the exit terminal 140. The AFN management device 150 may transmit the updated transmission instructions 166 to the outbound autonomous vehicle 502. The control device 550 may instruct the outbound autonomous vehicle 502 to travel according to the updated transmission instructions 166.

[0144] Example method for inbound operations

[0145] Figure 3 An example flow chart of a method 300 for implementing an oncoming inbound operation of an autonomous vehicle 502 to a terminal 140 is illustrated. Modifications, additions, or omissions may be made to the method 300. The method 300 may include more, fewer, or other operations. For example, the operations may be performed in parallel or in any suitable order. Although sometimes discussed as the system 100, the autonomous vehicle 502, the control device 550, the AFN management device 150, or any component thereof performing an operation, any suitable system or component of the system may perform one or more operations of the method 300. For example, one or more operations of the method 300 may be performed at least in part by the system 100, the autonomous vehicle 502, the control device 550, the AFN management device 150, or any component thereof. Figure 1 and Figure 5 The software instructions 128, 160 and processing instructions 580 are implemented in the form of the instructions stored in a non-transitory, tangible, machine-readable medium (e.g., from Figure 1 and Figure 5 The instructions are executed by one or more processors (e.g., respectively, from memory 126, memory 158, and data storage device 590). Figure 1 and Figure 5 When executed by processors 122, 152 and 570), it can cause one or more processors to perform operations 302-314.

[0146] At 302, the AFN management device 150 receives information indicating that the autonomous vehicle 502 is inbound to the terminal 140. For example, the AFN management device 150 may receive sensor data 130 from the sensor 546 of the autonomous vehicle 502, the sensor data including GPS location coordinates and a track of the autonomous vehicle 502. Based on the sensor data 130, the AFN management device 150 may determine that the autonomous vehicle 502 is inbound to the terminal 140.

[0147] At 304, the AFN management device 150 receives sensor data indicating the location of an object within the terminal 140. For example, the AFN management device 150 may receive a landing pad sensor signal 212 indicating the location of an object along the travel path of the autonomous vehicle 502 to the landing pad 142a, similar to Figure 2 As described in.

[0148] At 306, the AFN management device 150 selects the landing pad 142a. The AFN management device 150 may iteratively select the landing pad 142a until there are no landing pads 142a to evaluate. For example, assume that the AFN management device 150 selects the landing pad 142a-1.

[0149] At 308, the AFN management device 150 determines whether the landing pad 142a-1 is available to receive the autonomous vehicle 502. If it is determined that the landing pad 142a-1 is available to receive the autonomous vehicle 502, the method 300 continues to 310. Otherwise, the method 300 returns to 306. If there is no object at the landing pad 142a-1, the AFN management device 150 may determine that the landing pad 142a-1 is available to receive the autonomous vehicle 502, similar to Figure 2 As described in.

[0150] At 310, the AFN management device 150 determines the location of objects in the driving path of the autonomous vehicle 502 to the landing pad 142a-1. In this process, the AFN management device 150 can use the landing pad sensor signal 212, similar to Figure 2 As described in.

[0151] At 312, the AFN management device 150 may determine a landing instruction 162 that includes the location of an object in the path of the autonomous vehicle 502 to the landing pad 142a-1. The landing instruction 162 may instruct to avoid the location of the object in the path of the autonomous vehicle 502 to the landing pad 142a-1, similar to Figure 2 As described in.

[0152] At 314, the AFN management device 150 may transmit the landing instruction 162 to the autonomous vehicle 502. The control device 550 may receive the landing instruction 162. The control device 550 may determine the route 230 for the autonomous vehicle 502 to take to reach the landing pad 142a-1, and instruct the autonomous vehicle 502 to travel according to the route 230, similar to Figure 2 As described in.

[0153] In some embodiments, the AFN management device 150 may determine the route 230 based on the landing pad sensor signal 212 and the sensor data 130, similar to Figure 2 In some embodiments, the control device 550 can determine the route 230 based on the landing pad sensor signal 212 and the sensor data 130, similar to Figure 2 As described in.

[0154] Example method for outbound operations

[0155] Figure 4 An example flow chart of a method 400 for implementing an exit operation of an outgoing autonomous vehicle 502 from a terminal 140 is illustrated. Modifications, additions, or omissions may be made to the method 400. The method 400 may include more, fewer, or other operations. For example, the operations may be performed in parallel or in any suitable order. Although sometimes discussed as the system 100, the autonomous vehicle 502, the control device 550, the AFN management device 150, or any component thereof performing an operation, any suitable system or component of the system may perform one or more operations of the method 400. For example, one or more operations of the method 400 may be performed at least in part by the system 100, the autonomous vehicle 502, the control device 550, the AFN management device 150, or any component thereof. Figure 1 and Figure 5 The software instructions 128, software instructions 160 and processing instructions 580 are implemented in the form of software instructions 128, software instructions 160 and processing instructions 580, which are stored in a non-transitory, tangible, machine-readable medium (e.g., from Figure 1 and Figure 5 The instructions are executed by one or more processors (e.g., respectively, from memory 126, memory 158, and data storage device 590). Figure 1 and Figure 5 When executed by processors 122, 152 and 570), it can cause one or more processors to perform operations 402-422.

[0156] At 402 , the AFN management device 150 receives information indicating that the autonomous vehicle 502 is exiting from the terminal 140 . For example, the AFN management device 150 may receive a message from the remote operator 180 indicating that the autonomous vehicle 502 is exiting from the terminal 140 .

[0157] At 404 , the AFN management device 150 accesses the trip 170 scheduled for the autonomous vehicle 502 . The trip 170 may be provided by the remote operator 180 .

[0158] At 406, the AFN management device 150 may identify the trailer carrying the load for the trip 170. For example, the trip 170 may include a load ID, a trailer ID, a parcel ID where the trailer is located at the trailer staging area 142b, a starting location, a departure time window, an arrival time window, a destination, a tractor ID, and other information.

[0159] At 408, the AFN management device 150 selects a transmission station 142h. The AFN management device 150 may iteratively select a transmission station 142h until no transmission stations 142h are available for evaluation. For example, assume that the AFN management device 150 selects a transmission station 142h-1.

[0160] At 410, the AFN management device 150 determines whether the transmitter station 142h-1 is available to receive the autonomous vehicle 502. If it is determined that the transmitter station 142h-1 is available to receive the autonomous vehicle 502, the method 400 continues to 412. Otherwise, the method 400 returns to 408. If there is no obstacle at the transmitter station 142h-1, the AFN management device 150 may determine that the transmitter station 142h-1 is available to receive the autonomous vehicle 502, similar to Figure 2 As described in.

[0161] At 412, the AFN management device 150 determines that the trailer and tractor associated with the autonomous vehicle 502 are moved to the launch station 142h-1. For example, the AFN management device 150 may receive a message from a computing device (e.g., computing device 104a and / or 120b) associated with a driver (e.g., user 102a and / or 102b) indicating that the trailer and tractor are moved to the launch station 142h-1, similar to Figure 2 As described in.

[0162] At 414, the AFN management device 150 determines that the trailer is attached to the tractor. For example, the AFN management device 150 may receive a message from a computing device (e.g., computing device 104a or 120b) associated with a technician (e.g., user 102a or 102b) indicating that the trailer is attached to the tractor, similar to Figure 2 As described in.

[0163] At 416, the AFN management device 150 determines that the pre-trip check 234 of the autonomous vehicle 502 is performed. For example, the AFN management device 150 may receive a message from a computing device associated with the user 102b (e.g., computing device 104b) indicating that the pre-trip check 234 of the autonomous vehicle 502 is performed, similar to Figure 2 As described in.

[0164] At 418, the AFN management device 150 receives sensor data indicating the location of an object in the travel path from the launch station 142h-1 to the autonomous vehicle 502 exiting the terminal 140. For example, the AFN management device 150 may receive a launch station sensor signal 218 from a sensor 146 associated with the launch station 142h, similar to Figure 2 As described in.

[0165] At 420, the AFN management device 150 determines a transmit instruction 166 that includes the location of an object in the travel path from the transmitting station 142h-1 to the autonomous vehicle 502 exiting the terminal 140. The transmit instruction 166 may indicate to avoid the location of the object. The transmit instruction 166 may include an exit lane 206 for the autonomous vehicle 502 to take in order to exit the terminal 140.

[0166] At 422, the AFN management device 150 transmits the launch instruction 166 to the autonomous vehicle 502. The control device 550 receives the launch instruction 166. The control device 550 may determine a route 240 for the autonomous vehicle 502 to take in order to leave the terminal 140 and begin the trip 170, similar to Figure 2 As described in.

[0167] In some embodiments, the AFN management device 150 may determine the route 240 based on the transmitter sensor signal 218 and the sensor data 130, similar to Figure 2 In some embodiments, control device 550 may determine route 240 based on launch station sensor signal 218 and sensor data 130, similar to Figure 2 As described in.

[0168] Example autonomous vehicle and its operation

[0169] Figure 5 A block diagram of an example vehicle ecosystem 500 is shown in which autonomous driving operations may be determined. Figure 5As shown, the autonomous vehicle 502 may be a semi-trailer truck. The vehicle ecosystem 500 may include several systems and components that may generate and / or deliver one or more information / data sources and related services to an onboard control computer 550 that may be located in the autonomous vehicle 502. The onboard control computer 550 may communicate data with a plurality of vehicle subsystems 540, all of which may reside in the autonomous vehicle 502. A vehicle subsystem interface 560 may be provided to facilitate data communication between the onboard control computer 550 and the plurality of vehicle subsystems 540. In some embodiments, the vehicle subsystem interface 560 may include a controller area network (CAN) controller to communicate with devices in the vehicle subsystem 540.

[0170] The autonomous vehicle 502 may include various vehicle subsystems that support the operation of the autonomous vehicle 502. The vehicle subsystem 540 may include a vehicle drive subsystem 542, a vehicle sensor subsystem 544, a vehicle control subsystem 548, and / or a network communication subsystem 592. Figure 5 The components or devices shown of vehicle drive subsystem 542, vehicle sensor subsystem 544, and vehicle control subsystem 548 are examples. Autonomous vehicle 502 can be configured as shown, or according to any other configuration.

[0171] The vehicle drive subsystem 542 may include components operable to provide powered movement for the autonomous vehicle 502. In an example embodiment, the vehicle drive subsystem 542 may include an engine / motor 542a, wheels / tires 542b, a transmission 542c, an electrical subsystem 542d, and a power source 542e.

[0172] The vehicle sensor subsystem 544 may include a plurality of sensors 546 configured to sense information about the environment or condition of the autonomous vehicle 502. The vehicle sensor subsystem 544 may include one or more cameras 546a or image capture devices, a radar unit 546b, one or more temperature sensors 546c, a wireless communication unit 546d (e.g., a cellular communication transceiver), an inertial measurement unit (IMU) 546e, a laser rangefinder / LiDAR unit 546f, a global positioning system (GPS) transceiver 546g, and a wiper control system 546h. The vehicle sensor subsystem 544 may also include sensors configured to monitor the internal systems of the autonomous vehicle 502 (e.g., an O2 monitor, a fuel gauge, an engine oil temperature, etc.).

[0173] The IMU 546e may include any combination of sensors (e.g., accelerometers and gyroscopes) configured to sense changes in the position and orientation of the autonomous vehicle 502 based on inertial acceleration. The GPS transceiver 546g may be any sensor configured to estimate the geographic location of the autonomous vehicle 502. To this end, the GPS transceiver 546g may include a receiver / transmitter operable to provide information about the position of the autonomous vehicle 502 relative to the earth. The radar unit 546b may represent a system that utilizes radio signals to sense objects within the local environment of the autonomous vehicle 502. In some embodiments, in addition to sensing objects, the radar unit 546b may also be configured to sense the speed and heading of objects close to the autonomous vehicle 502. The laser rangefinder or LiDAR unit 546f may be any sensor configured to use lasers to sense objects in the environment in which the autonomous vehicle 502 is located. The camera 546a may include one or more devices configured to capture multiple images of the environment of the autonomous vehicle 502. The camera 546a may be a still image camera or a motion video camera.

[0174] The vehicle control subsystem 548 may be configured to control the operation of the autonomous vehicle 502 and its components. Thus, the vehicle control subsystem 548 may include various elements, such as a throttle and gear selector 548a, a brake unit 548b, a navigation unit 548c, a steering system 548d, and / or an autonomous control unit 548e. The throttle and gear selector 548a may be configured to control, for example, the operating speed of the engine, and in turn control the speed of the autonomous vehicle 502. The throttle and gear selector 548a may be configured to control the gear selection of the transmission. The brake unit 548b may include any combination of mechanisms configured to decelerate the autonomous vehicle 502. The brake unit 548b may decelerate the autonomous vehicle 502 in a standard manner, including by using friction to decelerate the wheels or engine braking. The brake unit 548b may include an anti-lock braking system (ABS) that may prevent the brakes from locking when the brakes are applied. The navigation unit 548c may be any system configured to determine the driving path or route of the autonomous vehicle 502. The navigation unit 548c may also be configured to dynamically update the driving path while the autonomous vehicle 502 is operating. In some embodiments, the navigation unit 548c may be configured to combine data from the GPS transceiver 546g and one or more predetermined maps to determine the driving path of the autonomous vehicle 502. The steering system 548d may represent any combination of mechanisms that may be operable to adjust the heading of the autonomous vehicle 502 in either an autonomous mode or a driver-controlled mode.

[0175] The autonomous control unit 548e may represent a control system that is configured to identify, evaluate, and avoid or otherwise negotiate potential obstacles or obstructions in the environment of the autonomous vehicle 502. In general, the autonomous control unit 548e may be configured to control the autonomous vehicle 502 for operation without a driver, or to provide driver assistance in controlling the autonomous vehicle 502. In some embodiments, the autonomous control unit 548e may be configured to incorporate data from the GPS transceiver 546g, radar unit 546b, LiDAR unit 546f, camera 546a, and / or other vehicle subsystems to determine a travel path or trajectory for the autonomous vehicle 502.

[0176] The network communication subsystem 592 may include a network interface, such as a router, a switch, a modem, etc. The network communication subsystem 592 may be configured to establish communications between the autonomous vehicle 502 and other systems, servers, etc. The network communication subsystem 592 may also be configured to send and receive data to and from other systems.

[0177] Many or all of the functions of the autonomous vehicle 502 may be controlled by an onboard control computer 550. The onboard control computer 550 may include at least one data processor 570 (which may include at least one microprocessor) that executes processing instructions 580 stored in a non-transitory computer-readable medium (such as a data storage device 590 or a memory). The onboard control computer 550 may also represent a plurality of computing devices that may be used to control individual components or subsystems of the autonomous vehicle 502 in a distributed manner. In some embodiments, the data storage device 590 may contain executable data executable by the data processor 570 to perform various methods and / or functions of the autonomous vehicle 502 (including data regarding Figure 1-Figure 7 The method and functions described herein may be those of the processing instructions 580 (eg, program logic).

[0178] The data storage device 590 may also contain additional instructions, including instructions for transmitting data to, receiving data from, interacting with, or controlling one or more of the vehicle drive subsystem 542, the vehicle sensor subsystem 544, and the vehicle control subsystem 548. The onboard control computer 550 may be configured to include a data processor 570 and a data storage device 590. The onboard control computer 550 may control functions of the autonomous vehicle 502 based on inputs received from various vehicle subsystems (e.g., the vehicle drive subsystem 542, the vehicle sensor subsystem 544, and the vehicle control subsystem 548).

[0179] Figure 6An exemplary system 600 for providing precise autonomous driving operation is shown. The system 600 may include several modules that may operate in the onboard control computer 550, such as Figure 5 The vehicle control computer 550 may include: Figure 6 , wherein the sensor fusion module 602 can perform at least four image or signal processing operations. The sensor fusion module 602 can obtain images from a camera located on the autonomous vehicle to perform image segmentation 604 to detect the presence of moving objects (e.g., other vehicles, pedestrians, etc.) and / or static obstacles (e.g., stop signs, speed bumps, terrain, etc.) located around the autonomous vehicle. The sensor fusion module 602 can obtain LiDAR point cloud data items from a LiDAR sensor located on the autonomous vehicle to perform LiDAR segmentation 606 to detect the presence of objects and / or obstacles located around the autonomous vehicle.

[0180] The sensor fusion module 602 can perform instance segmentation 608 on the image and / or point cloud data items to identify contours (e.g., boxes) around objects and / or obstacles located around the autonomous vehicle. The sensor fusion module 602 can perform temporal fusion 610, in which objects and / or obstacles from one image and / or one frame of point cloud data items are correlated or associated with objects or obstacles from one or more images or frames subsequently received in time.

[0181] The sensor fusion module 602 may fuse objects and / or obstacles from images acquired from a camera and / or point cloud data items acquired from a LiDAR sensor. For example, the sensor fusion module 602 may determine, based on the positions of the two cameras, that an image including half of a vehicle located in front of the autonomous vehicle from one of the cameras is the same as the vehicle captured by the other camera. The sensor fusion module 602 may send the fused object information to the interference module 646 and send the fused obstacle information to the occupancy grid module 660. The onboard control computer may include an occupancy grid module 660 that may retrieve landmarks from a map database 658 stored in the onboard control computer. The occupancy grid module 660 may determine a drivable area and / or obstacles based on the fused obstacles acquired from the sensor fusion module 602 and the landmarks stored in the map database 658. For example, the occupancy grid module 660 may determine that the drivable area may include a speed bump obstacle.

[0182] Below the sensor fusion module 602, the onboard control computer 550 may include a LiDAR-based object detection module 612 that may perform object detection 616 based on point cloud data items acquired from a LiDAR sensor 614 located on the autonomous vehicle. The object detection 616 technique may provide the location of the object (e.g., in 3D world coordinates) from the point cloud data items. Below the LiDAR-based object detection module 612, the onboard control computer may include an image-based object detection module 618 that may perform object detection 624 based on images acquired from a camera 620 located on the autonomous vehicle. The object detection 618 technique may employ deep machine learning techniques 624 to provide the location of the object (e.g., in 3D world coordinates) from the images provided by the camera 620.

[0183] Radar 656 on the autonomous vehicle can scan the area in front of the autonomous vehicle or the area the autonomous vehicle is heading towards. Radar data can be sent to sensor fusion module 602, which can use the radar data to associate objects and / or obstacles detected by radar 656 with objects and / or obstacles detected from both LiDAR point cloud data items and camera images. Radar data can also be sent to interference module 646, which can perform data processing on the radar data to track objects through object tracking module 648, as further described below.

[0184] The onboard control computer may include an interference module 646 that receives the location of the object from the point cloud and the object from the image, and receives the fused object from the sensor fusion module 602. The interference module 646 also receives radar data, with which the interference module 646 can track the object from one point cloud data item and one image acquired at one time to another (or next) point cloud data item and another image acquired at another subsequent time via the object tracking module 648.

[0185] The interference module 646 may perform object attribute estimation 650 to estimate one or more attributes of an object detected in an image or point cloud data item. The one or more attributes of the object may include the type of the object (e.g., a pedestrian, a car, or a truck, etc.). The interference module 646 may perform behavior prediction 652 to estimate or predict the motion pattern of the object detected in the image and / or point cloud. The behavior prediction 652 may be performed to detect the position of an object in a set of images (e.g., consecutive images) received at different time points or in a set of point cloud data items (e.g., sequential point cloud data items) received at different time points. In some embodiments, the behavior prediction 652 may be performed for each image received from the camera and / or each point cloud data item received from the LiDAR sensor. In some embodiments, the interference module 646 may be executed (e.g., run or performed) to reduce the computational load by performing the behavior prediction 652 every other image received from the camera or after every predetermined number of point cloud data items received from the LiDAR sensor (e.g., after every two images or after every three point cloud data items).

[0186] The behavior prediction 652 feature can determine the speed and direction of objects around the autonomous vehicle based on radar data, where the speed and direction information can be used to predict or determine the object's motion pattern. The motion pattern may include predicted trajectory information of the object within a predetermined length of time in the future after receiving an image from the camera. Based on the predicted motion pattern, the interference module 646 can assign a motion pattern context label to the object (e.g., "located at coordinates (x, y)", "stopped", "traveling at 50 mph", "accelerating", or "decelerating"). The context label can describe the object's motion pattern. The interference module 646 can send one or more object attributes (e.g., the type of object) and the motion pattern context label to the planning module 662. The interference module 646 can use any information obtained by the system 600 and any number and combination of components thereof to perform environmental analysis 654.

[0187] The onboard control computer may include a planning module 662 that receives object attributes and motion pattern context labels from the interference module 646, and receives drivable areas and / or obstacles, as well as vehicle position and posture information from the fused positioning module 626 (described further below).

[0188] The planning module 662 may perform navigation planning 664 to determine a set of trajectories on which the autonomous vehicle can travel. The set of trajectories may be determined based on drivable area information, one or more object attributes of an object, a motion mode scenario tag of an object, the location of obstacles, and drivable area information. In some embodiments, the navigation planning 664 may include determining an area next to a road where the autonomous vehicle can be safely parked in an emergency. The planning module 662 may include a behavioral decision 666 to determine a driving action (e.g., steering, braking, throttle) in response to determining a changing condition on the road (e.g., a traffic light turns yellow, or the autonomous vehicle is in an unsafe driving condition because another vehicle is driving in front of the autonomous vehicle and in an area within a predetermined safety distance of the location of the autonomous vehicle). The planning module 662 performs trajectory generation 668 and selects a trajectory from the set of trajectories determined by the navigation planning operation 664. The selected trajectory information may be sent by the planning module 662 to the control module 670.

[0189] The onboard control computer may include a control module 670 that receives the proposed trajectory from the planning module 662 and receives the position and attitude of the autonomous vehicle from the fused positioning module 626. The control module 670 may include a system identifier 672. The control module 670 may perform model-based trajectory refinement 674 to refine the proposed trajectory. For example, the control module 670 may apply filtering (e.g., a Kalman filter) to smooth the proposed trajectory data and / or minimize noise. The control module 670 may perform robust control 676 by determining the amount of brake pressure to be applied, the steering angle, the throttle amount for controlling the vehicle speed, and / or the transmission gear based on the information of the refined proposed trajectory and the current position and / or attitude of the autonomous vehicle. The control module 670 may send the determined brake pressure, steering angle, throttle amount, and / or transmission gear to one or more devices in the autonomous vehicle to control and facilitate the precise driving operation of the autonomous vehicle.

[0190] The depth image-based object detection 624 performed by the image-based object detection module 618 can also be used to detect landmarks on the road (e.g., stop signs, speed bumps, etc.). The onboard control computer can include a fused localization module 626 that obtains landmarks detected from the image, landmarks obtained from a map database 636 stored on the onboard control computer, landmarks detected from the point cloud data items by the LiDAR-based object detection module 612, speed and displacement from the odometer sensor 644 or rotary encoder, and the estimated position of the autonomous vehicle from the GPS / IMU sensor 638 (i.e., GPS sensor 640 and IMU sensor 642) located on or in the autonomous vehicle. Based on this information, the fused localization module 626 can perform a localization operation 628 to determine the position of the autonomous vehicle, which can be sent to the planning module 662 and the control module 670.

[0191] The fused positioning module 626 can estimate the pose 630 of the autonomous vehicle based on the GPS and / or IMU sensors 638. The pose of the autonomous vehicle can be sent to the planning module 662 and the control module 670. The fused positioning module 626 can also estimate the state of the trailer unit (e.g., position, possible movement angle) based on (e.g., trailer state estimation 634) information (e.g., angular rate and / or linear velocity) provided by the IMU sensor 642. The fused positioning module 626 can also check the map content 632.

[0192] Figure 7 5 shows an exemplary block diagram of an onboard control computer 550 included in an autonomous vehicle 502. The onboard control computer 550 may include at least one processor 704 and a memory 702, on which instructions (e.g., Figure 1 and Figure 5 When executed by the processor 704, the instructions configure the vehicle control computer 550 and / or various modules of the vehicle control computer 550 to perform Figure 1-Figure 7 The operations described in . Transmitter 706 can transmit information or data to or receive information or data from one or more devices in the autonomous vehicle. For example, transmitter 706 can send instructions to one or more motors of the steering wheel to turn the autonomous vehicle. Receiver 708 receives information or data transmitted or sent by one or more devices. For example, receiver 708 receives the current speed status from the odometer sensor, or receives the current transmission gear from the transmission. Transmitter 706 and receiver 708 can also be configured as described above in Figure 5 and Figure 6 The plurality of vehicle subsystems 540 described in the specification communicate with the on-board control computer 550.

[0193] Although several embodiments are provided in the present disclosure, it should be understood that the disclosed systems and methods may be embodied in many other specific forms without departing from the spirit or scope of the present disclosure. The present embodiments are considered to be illustrative rather than restrictive, and are not intended to be limited to the details given herein. For example, various elements or components may be combined or integrated into another system, or certain features may be omitted or not implemented.

[0194] In addition, without departing from the scope of the present disclosure, the techniques, systems, subsystems and methods described and illustrated as discrete or separate in various embodiments may be combined or integrated with other systems, modules, techniques or methods. Other items shown or discussed as coupled or directly coupled or communicating with each other may be indirectly coupled or communicated through some interface, device or intermediate component, whether electrical, mechanical or other. Other examples of changes, substitutions and variations are within the ability of those skilled in the art to determine and may be made without departing from the spirit and scope disclosed herein.

[0195] To assist the Patent Office and any reader of any patent issuing based upon this application in interpreting the claims appended hereto, applicants note that unless “means” or “step” is expressly used in a particular claim, no invocation is intended to invoke 35 U.S.C. § 112(f), as it existed on the date of filing this application.

[0196] The implementation of the present disclosure may be described according to the following sections, and its features may be combined in any reasonable manner.

[0197] Section 1. An autonomous vehicle inbound and outbound management system, comprising:

[0198] A fleet of autonomous vehicles, including a first autonomous vehicle, wherein the first autonomous vehicle is configured to travel along a predetermined route;

[0199] A terminal comprising one or more dedicated zones and one or more sensors within a physical space, wherein:

[0200] Each of the one or more dedicated zones is configured to facilitate a specific function for the first autonomous vehicle;

[0201] The one or more dedicated areas include a landing pad shaped to accommodate the first autonomous vehicle;

[0202] The landing pad is established by a set of boundary indicators disposed around the landing pad; and

[0203] Each of the one or more sensors is configured to detect an object within a detection range;

[0204] an autonomous freight network management device operably coupled to the fleet of autonomous vehicles and comprising a first processor configured to:

[0205] receiving information indicating that the first autonomous driving vehicle is entering the terminal;

[0206] receiving first sensor data indicating a location of an object within the terminal;

[0207] determining at least one position of at least one object in a travel path of the first autonomous vehicle to the landing pad based at least in part on the first sensor data;

[0208] determining landing instructions including at least one position of the at least one object, wherein the landing instructions indicate avoiding the at least one position of the at least one object;

[0209] transmitting the landing instruction to the first autonomous vehicle;

[0210] The first autonomous driving vehicle includes a control device, the control device includes a second processor, and the second processor is configured to:

[0211] receiving the landing instruction;

[0212] determining a first route based at least in part on the landing instructions, the first route for the first autonomous vehicle to take to reach the landing pad, wherein the first route is free of the at least one object; and

[0213] Instruct the first autonomous driving vehicle to travel according to the first route.

[0214] Section 2. A system according to Section 1, wherein before determining the landing instructions for reaching the landing pad, the first processor is also configured to determine whether the landing pad is occupied by a second autonomous vehicle or any object that prevents the first autonomous vehicle from landing inside the landing pad.

[0215] Section 3. A system according to Section 2, wherein determining the landing instruction is in response to determining that the landing pad is not occupied by the second autonomous vehicle or any object that prevents the first autonomous vehicle from landing inside the landing pad.

[0216] Section 4. The system according to Section 1, wherein:

[0217] The second processor is configured to receive second sensor data from at least one sensor associated with the first autonomous vehicle, wherein the second sensor data includes a location of an object detected by the at least one sensor; and

[0218] Determining the first route is also based at least in part on the second sensor data.

[0219] Section 5. The system of Section 4, wherein instructing the first autonomous driving vehicle to travel according to the first route comprises:

[0220] determining whether the set of boundary indicators associated with the landing pad is detected in the second sensor data; and

[0221] In response to determining that the set of boundary indicators is indicated in the second sensor data, instructing the first autonomous vehicle to travel into the landing pad until a certain distance from the set of boundary indicators.

[0222] Section 6. A system according to Section 1, wherein the first autonomous vehicle is a tractor attached to a trailer.

[0223] Section 7. The system of Section 2, wherein the first processor is further configured to, in response to determining that the landing pad is occupied by the second autonomous vehicle or any object that prevents the first autonomous vehicle from landing inside the landing pad:

[0224] Identify another landing pad;

[0225] determining updated landing instructions including a second route to the another landing pad; and

[0226] Transmitting the updated landing instructions to the first autonomous vehicle.

[0227] Section 8. The system according to Section 6, wherein the first processor is further configured to:

[0228] determining that a post-trip check of the first autonomous vehicle is performed;

[0229] Determine a drop-off area for dropping off passengers on the trailer where:

[0230] The drop-off area is configured to facilitate trailer drop-off; and

[0231] The drop-off zone is one of the one or more dedicated zones;

[0232] transmitting the drop-off zone to a computing device associated with a driver of a vehicle configured to move the trailer; and

[0233] A confirmation is received from the computing device indicating that the trailer was moved to the drop-off area.

[0234] Section 9. The system according to Section 1, wherein the first processor is further configured to:

[0235] determining that a post-trip check of the first autonomous vehicle is performed;

[0236] determining, based at least in part on the post-trip review, that at least one of map data and autonomous software code associated with the first autonomous vehicle needs to be updated;

[0237] Determine the data communication area; where:

[0238] The data communications zone is configured to facilitate transmission of at least one of the map data and autonomous software code to the first autonomous vehicle;

[0239] The data communication area is one of the one or more dedicated areas;

[0240] transmitting the data communication area to a computing device associated with the driver;

[0241] receiving a confirmation from the computing device indicating that the first autonomous driving vehicle was moved to the data communication zone; and

[0242] Transmitting at least one of updated map data and updated autonomous software code to the first autonomous vehicle.

[0243] Section 10. The system according to Section 9, wherein:

[0244] The map data includes a route within the driving range of the first autonomous driving vehicle and position coordinates of objects on the route; and

[0245] The autonomous software code facilitates autonomous functionality of the first autonomous vehicle.

[0246] Section 11. The system according to Section 1, wherein the first processor is further configured to:

[0247] determining that a post-trip check of the first autonomous vehicle is performed;

[0248] determining that the first autonomous vehicle requires service based at least in part on the post-trip inspection;

[0249] Determine the service area, where:

[0250] The service area is configured to facilitate the service to be provided to the first autonomous vehicle; and

[0251] The service area is one of the one or more dedicated areas;

[0252] transmitting the service area to a computing device associated with the driver; and

[0253] A confirmation is received from the computing device indicating that the first autonomous driving vehicle was moved to the service area.

[0254] Chapter 12. A system according to Chapter 11, wherein the service includes at least one of sensor calibration, sensor housing cleaning, fuel filling, oil filling, tire air filling, and cooling fluid filling.

[0255] Section 13. The system according to Section 1, wherein the first processor is further configured to:

[0256] determining that a post-trip check of the first autonomous vehicle is performed, wherein the first autonomous vehicle comprises a tractor attached to a trailer;

[0257] determining that the tractor is ready for a next trip based at least in part on the post-trip inspection;

[0258] Identify assembly areas where:

[0259] The staging area is where the tractor is positioned to indicate that the tractor is ready to proceed with the next trip; and

[0260] The staging area is one of the one or more dedicated areas; and

[0261] Information is received from a computing device associated with a driver indicating that the tractor is moved to the staging area.

[0262] Chapter 14. The system of Chapter 13, wherein determining that the post-trip inspection was performed is in response to receiving a message from a computing device associated with an inspector indicating that the post-trip inspection was performed.

[0263] Section 15. An autonomous vehicle inbound and outbound management system, comprising:

[0264] A fleet of autonomous vehicles, including a first autonomous vehicle, wherein:

[0265] The first autonomous vehicle is configured to travel along a predetermined route; and

[0266] The first autonomous vehicle includes a tractor attached to a trailer;

[0267] A terminal comprising one or more dedicated zones and one or more sensors within a physical space, wherein:

[0268] Each of the one or more dedicated zones is configured to facilitate a specific function for the first autonomous vehicle;

[0269] The one or more dedicated areas include a launch pad configured to accommodate the first autonomous vehicle;

[0270] The launch pad is established by a set of boundary indicators arranged around the launch pad;

[0271] The launch pad is a location where a pre-trip check is performed on the first autonomous vehicle; and

[0272] Each of the one or more sensors is configured to detect an object within a detection range;

[0273] An autonomous freight network management device operably coupled to the fleet of autonomous vehicles and comprising a first processor configured to:

[0274] receiving information indicating that the first autonomous driving vehicle exits the terminal;

[0275] accessing a trip scheduled for the first autonomous vehicle, wherein the trip indicates at least one of a starting location, a load, a departure time, an arrival time, and a destination;

[0276] identifying the trailer carrying the load for the trip;

[0277] determining that the trailer is moved to the launch pad;

[0278] determining that the tractor is moved to the launch pad;

[0279] determining that the tractor is attached to the trailer at the launching station;

[0280] determining that the pre-trip check is complete, wherein the pre-trip check includes transmitting the trip to the first autonomous vehicle and determining that a health level associated with a component of the first autonomous vehicle is greater than a threshold percentage;

[0281] receiving sensor data indicating a location of an object within the terminal;

[0282] determining, based at least in part on the sensor data, at least one position of at least one object in a travel path of the first autonomous vehicle from the launch station to departure from the destination;

[0283] determining a firing instruction including at least one position of the at least one object, wherein the firing instruction indicates avoiding the at least one position of the at least one object; and

[0284] transmitting the launch instruction to the first autonomous driving vehicle;

[0285] The first autonomous driving vehicle includes a control device, the control device includes a second processor, and the second processor is configured to:

[0286] receiving the transmitting instruction;

[0287] determining, based at least in part on the launch instruction, a first route for the first autonomous vehicle to take in order to depart the terminal and begin the trip, wherein the first route is free of the at least one object; and

[0288] Instruct the first autonomous driving vehicle to travel according to the first route.

[0289] Section 16. A system according to Section 15, wherein before determining the launch instruction, the first processor is also configured to determine whether the launch pad is occupied by a second autonomous vehicle or any object that prevents the first autonomous vehicle from landing inside the launch pad.

[0290] Section 17. A system according to Section 16, wherein determining the launch instruction is in response to determining that the launch pad is not occupied by the second autonomous vehicle or any object that prevents the first autonomous vehicle from landing inside the launch pad.

[0291] Chapter 18. A system according to Chapter 15, wherein

[0292] The second processor is configured to receive second sensor data from at least one sensor associated with the first autonomous vehicle, wherein the second sensor data includes a location of an object detected by the at least one sensor; and

[0293] Determining the first route is also based at least in part on the second sensor data.

[0294] Section 19. A system according to Section 15, wherein the one or more sensors include a camera sensor, a light detection and ranging (LiDAR) sensor, and an infrared sensor.

[0295] Section 20. The system of Section 16, wherein the first processor is further configured to, in response to determining that the launch pad is occupied by the second autonomous vehicle or any object that prevents the first autonomous vehicle from landing inside the launch pad:

[0296] Identify another transmitter;

[0297] determining updated launch instructions including a second route to the another launch station; and

[0298] Transmitting the updated transmission instructions to the first autonomous vehicle.

[0299] Chapter 21. A method comprising one or more operations according to any one of Chapters 1 to 14.

[0300] Chapter 22. A method comprising one or more operations according to any one of Chapters 15 to 20.

[0301] Chapter 23. An apparatus comprising components for performing one or more operations according to any of Chapters 1 to 20.

[0302] Chapter 24. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform one or more operations according to any one of Chapters 1 to 14.

[0303] Chapter 25. A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform one or more operations according to any one of Chapters 15 to 20.

Claims

1. An autonomous driving vehicle inbound and outbound management system (100), comprising: A fleet of autonomous vehicles (502), including a first autonomous vehicle (502), wherein the first autonomous vehicle (502) is configured to travel along a predetermined route; A terminal (140) includes one or more dedicated areas (142) and one or more sensors (146) within a physical space, wherein: Each of the one or more dedicated zones (142) is configured to facilitate a specific function for the first autonomous vehicle (502); The one or more dedicated areas (142) include a landing pad (142a) shaped to accommodate the first autonomous vehicle (502); The landing pad (142a) is established by a set of boundary indicators (144) disposed around the landing pad (142a); and Each of the one or more sensors (146) is configured to detect an object within a detection range; An autonomous freight network management device (150) is operably coupled to the autonomous vehicle fleet (502) and includes a first processor (152) configured to: receiving information indicating that the first autonomous driving vehicle (502) has inbounded the terminal (140); receiving first sensor data (130) indicating a location of an object within the terminal (140); determining at least one position of at least one object (208) in a travel path of the first autonomous vehicle (5020) to the landing pad (142a) based at least in part on the first sensor data (130); determining landing instructions (162) including at least one position of the at least one object (208), wherein the landing instructions (162) indicate avoiding at least one position of the at least one object (208); transmitting the landing instruction (162) to the first autonomous vehicle (502); The first autonomous driving vehicle (502) includes a control device (550), the control device includes a second processor (570), and the second processor is configured to: receiving the landing instruction (162); determining a first route (230) based at least in part on the landing instructions (162) for the first autonomous vehicle (502) to take in order to reach the landing pad (142a), wherein the first route (230) is free of the at least one object (208); and Instruct the first autonomous driving vehicle (502) to travel according to the first route (230).

2. The system of claim 1 , wherein before determining the landing instructions (162) for reaching the landing pad (142a), the first processor (152) is further configured to determine whether the landing pad (142a) is occupied by a second autonomous vehicle or any object that prevents the first autonomous vehicle (502) from landing inside the landing pad (142a).

3. The system of claim 2, wherein determining the landing instruction (162) is in response to determining that the landing pad (142a) is not occupied by the second autonomous vehicle or any object that would prevent the first autonomous vehicle (502) from landing inside the landing pad (142a).

4. The system of claim 1, wherein: The second processor (570) is configured to receive second sensor data (130) from at least one sensor (546) associated with the first autonomous vehicle (502), wherein the second sensor data (130) includes a location of an object detected by the at least one sensor (546); and Determining the first route (320) is also based at least in part on the second sensor data (130).

5. The system of claim 4, wherein instructing the first autonomous vehicle (502) to travel according to the first route (230) comprises: determining whether the set of boundary indicators (144) associated with the landing pad (142a) is detected in the second sensor data (130); as well as In response to determining that the boundary indicator set (144) is indicated in the second sensor data (130), instructing the first autonomous vehicle (502) to travel into the landing pad (142a) until a certain distance from the boundary indicator set (144).

6. The system of claim 1, wherein the first autonomous vehicle (502) is a tractor attached to a trailer.

7. The system of claim 2, wherein the first processor (152) is further configured to, in response to determining that the landing pad (142a) is occupied by the second autonomous vehicle or any object that prevents the first autonomous vehicle (502) from landing inside the landing pad (142a): identifying another landing pad (142a); determining updated landing instructions (162), the updated landing instructions including a second route (230) to the other landing pad (142a); and The updated landing instructions (162) are transmitted to the first autonomous vehicle (502).

8. The system of claim 6, wherein the first processor (152) is further configured to: determining that a post-trip check (222) of the first autonomous vehicle (502) is performed; A drop-off zone (142b) is determined for dropping off passengers from the trailer, wherein: The drop-off area (142b) is configured to facilitate trailer drop-off; and The drop-off area (142b) is a dedicated area among the one or more dedicated areas (142); transmitting the drop-off zone (142b) to a computing device associated with a driver of a vehicle configured to move the trailer; and A confirmation is received from the computing device indicating that the trailer was moved to the drop-off area (142b).

9. The system of claim 1, wherein the first processor (152) is further configured to: determining that a post-trip check (222) of the first autonomous vehicle (502) is performed; determining, based at least in part on the post-trip review (222), that at least one of map data (134) and autonomous software code (128) associated with the first autonomous vehicle (502) needs to be updated; Determine a data communication area (142e); wherein: The data communication area (142e) is configured to facilitate transmitting the at least one of the map data (134) and the autonomous software code (128) to the first autonomous vehicle (502); The data communication area (142e) is a dedicated area among the one or more dedicated areas (142); transmitting the data communication area (142e) to a computing device associated with the driver; receiving a confirmation from the computing device indicating that the first autonomous vehicle (502) was moved to the data communication zone (142e); as well as At least one of updated map data (134) and updated autonomous software code (128) is transmitted to the first autonomous vehicle (502).

10. The system of claim 9, wherein: The map data (134) includes a route within the driving range of the first autonomous driving vehicle (502) and position coordinates of objects on the route; and The autonomous software code (128) facilitates autonomous functions of the first autonomous vehicle (502).

11. The system of claim 1, wherein the first processor (152) is further configured to: determining that a post-trip check (222) of the first autonomous vehicle (502) is performed; determining that the first autonomous vehicle (502) requires service based at least in part on the post-trip inspection (222); Determine the service area (142f), where: The service area (142f) is configured to facilitate the service to be provided to the first autonomous vehicle (502); and The service area (142f) is a dedicated area among the one or more dedicated areas (142); transmitting the service area to a computing device associated with the driver (142f); and A confirmation is received from the computing device indicating that the first autonomous driving vehicle (502) was moved to the service area (142f).

12. The system of claim 11, wherein the service includes at least one of sensor calibration, sensor housing cleaning, fuel fill, oil fill, tire air fill, and cooling fluid fill.

13. The system of claim 1, wherein the first processor (152) is further configured to: determining that a post-trip check (222) of the first autonomous vehicle (502) is performed, wherein the first autonomous vehicle (502) includes a tractor attached to a trailer; determining that the tractor is ready for a next trip based at least in part on the post-trip check (222); Determine the staging area (142g), where: The staging area (142g) is where the tractor is positioned to indicate that the tractor is ready to make the next trip; and The staging area (142g) is a dedicated area among the one or more dedicated areas (142); and Information is received from a computing device associated with a driver indicating that the tractor is moved to the staging area (142g).

14. The system of claim 13, wherein determining that the post-trip inspection (222) was performed is in response to receiving a message from a computing device associated with an inspector indicating that the post-trip inspection (222) was performed.

15. An autonomous driving vehicle inbound and outbound management system (100), comprising: A fleet of autonomous vehicles (502), including a first autonomous vehicle (502), wherein: The first autonomous vehicle (502) is configured to travel along a predetermined route; and The first autonomous vehicle (502) includes a tractor attached to a trailer; A terminal (140) includes one or more dedicated areas (142) and one or more sensors (146) within a physical space, wherein: Each of the one or more dedicated zones (502) is configured to facilitate a specific function for the first autonomous vehicle (502); The one or more dedicated areas (142) include a launch pad (142h) configured to accommodate the first autonomous vehicle (502); The launch pad (142h) is established by a set of boundary indicators (144) arranged around the launch pad (142h); The launch station (142h) is a location for performing a pre-trip check (234) on the first autonomous vehicle (502); and Each of the one or more sensors (146) is configured to detect an object within a detection range; An autonomous freight network management device (150) is operably coupled to the autonomous vehicle fleet (502) and includes a first processor (122) configured to: receiving information indicating that the first autonomous driving vehicle (502) exits the terminal (140); accessing a trip (170) scheduled for the first autonomous vehicle (502), wherein the trip (170) indicates at least one of a starting location, a load, a departure time, an arrival time, and a destination; Identifying the trailer carrying the load for the trip (1700); Determining that the trailer is moved to the launch pad (142h); Determining that the tractor is moved to the launch pad (142h); determining that the tractor is attached to the trailer at the launch station (142h); determining that the pre-trip check (234) is complete, wherein the pre-trip check (234) includes transmitting the trip (170) to the first autonomous vehicle (502) and determining that a health level associated with a component of the first autonomous vehicle (502) is greater than a threshold percentage; receiving sensor data (130) indicating a location of an object (208) within the terminal (140); determining at least one location of at least one object (208) based at least in part on the sensor data (130), the at least one object being in a travel path of the first autonomous vehicle (502) from the launch station (142h) to exiting the terminal (140); determining a launch instruction (166) including at least one position of the at least one object (208), wherein the launch instruction (166) instructs to avoid the at least one position of the at least one object (208); and transmitting the launch instruction (166) to the first autonomous vehicle (502); The first autonomous driving vehicle (502) includes a control device (550), the control device includes a second processor (122), and the second processor is configured to: receiving the transmission instruction (166); determining, based at least in part on the launch instructions (166), a first route (240) for the first autonomous vehicle (502) to take in order to depart the terminal (140) and begin the trip, wherein the first route (240) is free of the at least one object (208); and Instruct the first autonomous driving vehicle (502) to travel according to the first route (240).

16. The system of claim 15, wherein before determining the launch instruction (166), the first processor (152) is further configured to determine whether the launch pad (142h) is occupied by a second autonomous vehicle or any object that prevents the first autonomous vehicle (502) from landing inside the launch pad (142h).

17. The system of claim 16, wherein determining the launch instruction (166) is in response to determining that the launch pad (142h) is not occupied by the second autonomous vehicle or any object that would prevent the first autonomous vehicle (502) from landing inside the launch pad (142h).

18. The system of claim 15, wherein The second processor (122) is configured to receive second sensor data (130) from at least one sensor (546) associated with the first autonomous vehicle (502), wherein the second sensor data (130) includes a location of an object detected by the at least one sensor (546); and Determining the first route (240) is also based at least in part on the second sensor data (130).

19. The system of claim 15, wherein the one or more sensors (546) include a camera sensor, a light detection and ranging (LiDAR) sensor, and an infrared sensor.

20. The system of claim 16, wherein the first processor (152) is further configured to, in response to determining that the launch pad (142h) is occupied by the second autonomous vehicle or any object that prevents the first autonomous vehicle (502) from landing inside the launch pad (142h): Identify another transmitting station (142h); determining updated launch instructions (166), the updated launch instructions including a second route to the other launch station (142h); and The updated transmission instructions (166) are transmitted to the first autonomous vehicle (502).

21. A method (300) comprising one or more operations according to any one of claims 1 to 14.

22. A method (400) comprising one or more operations according to any one of claims 15 to 20.

23. An apparatus comprising means for performing one or more operations according to any one of claims 1 to 20.

24. A non-transitory computer-readable medium (126, 158) storing instructions that, when executed by one or more processors (122, 152), cause the one or more processors (122, 152) to perform one or more operations according to any one of claims 1 to 14.

25. A non-transitory computer-readable medium (126, 158) storing instructions that, when executed by one or more processors (122, 152), cause the one or more processors (122, 152) to perform one or more operations according to any one of claims 15 to 20.

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