Autonomous transportation network and method of operating the same

Abstract

An autonomous transportation network (10) and method of operation (100a, 100b, 100c) are disclosed. The autonomous transportation network (10) includes a plurality of autonomous vehicles (20) having an on-board processor (27) and a vehicle memory (28) for locally computing (240, 310) a route (50) between an origin (30) and a destination (40), and a vehicle antenna (25) for transmitting the computed route (50). A control management center (100) includes a control management processor (120) and a central memory (140) and independently computes (250, 520) routes (50) for the plurality of autonomous vehicles (20). A plurality of beacons (17) are connected to and receive redirection information from the control management center (100) for transmission to one or more of the plurality of autonomous vehicles (20).

Description

[0001] Cross-reference to related applications

[0002] This application claims priority to Luxembourg Patent Application No. LU101673, filed March 9, 2020, and UK Patent Application No. 2003395.7, filed March 9, 2020. The full disclosure of Luxembourg Patent Application No. LU101673 and UK Patent Application No. 2003395.7 is incorporated herein by reference. Technical Field

[0003] This invention relates to autonomous transportation networks and their operation methods. Background Technology

[0004] The term "Automated Transport Network" (ATN) is a relatively new name for a specific mode of transport belonging to the broader term "Automated Guided Transport" (AGT). Prior to 2010, the name "Personal Rapid Transit (PRT)" was used to refer to the ATN concept. In Europe, ATN was formerly known as "podcar." This article elucidates the ATN concept and describes its novel operational approach.

[0005] Like all forms of AGT, ATN consists of automated vehicles operating on infrastructure capable of transporting passengers from origin to destination. These automated vehicles are able to travel from origin to destination without any intermediate stops or transfers (as is known on traditional transport systems like buses, trams (streetcars), or trains). ATN services are typically irregular, like taxis, and travelers can choose to ride alone or share a vehicle with companions.

[0006] The ATN concept differs from the self-driving cars that are beginning to appear on public streets. The ATN concept is most often envisioned as a public transportation mode similar to a train or bus, rather than as a standalone consumer good like a car. Current ATN design concepts primarily rely on central control management for the individual control of autonomous vehicles operating on the ATN.

[0007] On the other hand, while these self-driving cars are often described as “autonomous,” in practice, different categories or levels of vehicle autonomy exist. The degree of vehicle autonomy is typically categorized into five levels, as outlined in “Classification and Definition of Terms Related to Driving Automation Systems for Road Motor Vehicles,” published by the Society of Automotive Engineers (SAE) Road Automated Driving (ORAD) Committee on June 15, 2018, in Recommended Practice SAE J 3016. Level 0 refers to vehicles without driving automation. The driver is fully responsible for operating the vehicle's movement. Level 0 vehicles may include safety systems such as collision avoidance warnings. Level 1 refers to vehicles with at least one driving assistance feature (such as acceleration or braking assist systems). The driver is responsible for driving tasks but is supported by driving assistance systems capable of influencing the vehicle's movement. Level 2 describes vehicles with more than one assistance system for actively influencing the vehicle's movement. In Level 2, the driver remains responsible for driving tasks and must always actively monitor the vehicle's trajectory. However, the driver is actively supported by the assistance systems. Level 3 describes vehicles with so-called “conditional automation.” In certain situations, the vehicle is capable of autonomous driving, but with limitations. The driver does not need to actively monitor the assistance system; however, the driver needs to take control of the driving situation if requested by the assistance system. Level 4 describes an autonomous vehicle capable of traveling a specific route under normal conditions without human supervision. Therefore, a Level 4 vehicle can operate without a driver, but may require remote human supervision in conflict situations, when traveling in remote areas, or in extreme weather conditions. Level 5 automation describes a fully autonomous vehicle. No human intervention is required at any time during vehicle operation.

[0008] The bottleneck in the existing ATN network's reliance on central control management lies in the fact that each autonomous vehicle needs to maintain near-constant communication with central control management. This can lead to problems if the communication network becomes overloaded or if a major event occurs somewhere in the ATN network that requires action from central control management.

[0009] An example of central control management is outlined in U.S. Patent No. 10,345,805 (Sailley, assigned to Podway Corporation), in which central control management receives a request for a route from the autonomous vehicle to its destination. Central control management calculates the route and sends a set of trip instructions to the autonomous vehicle, allowing it to navigate along the calculated route from the origin to the desired destination. Central control management in this system requires the transmission and continuous collection of large amounts of data from and from the autonomous vehicle. This demands significant hardware and data bandwidth and can cause problems if the autonomous vehicle enters an area with poor connectivity. In the event of a failure in central control management, the autonomous vehicle will no longer be able to navigate or recalculate the trip.

[0010] Many current ATN concepts rely on guide rails built as part of the infrastructure. This can be advantageous when dedicated infrastructure can be designed to separate it from other traffic flows or pedestrians. However, the cost of providing guide rails is significant, and this can delay the development of the ATN network. An example of such guide rails can be seen in the infrastructure of Terminal 5 at London Heathrow Airport.

[0011] A report titled "Automated Transit Networks (ATN): A Review of the State of the Industry and Prospects for the Future," published by the Mineta Transportation Institute in its September 2014 report No. 12-31, states that, as of the time of writing, no ATN with more than ten stations has been implemented worldwide. Currently, ATN networks operate on the principle of mapping each origin to all destinations. Even for a simple five-station system, this results in a matrix with 20 entries, as each of the five origins has four possible destinations. A ten-station system would have 90 possible routes, and it will be observed that as the number of origins and destinations increases, the O / D matrix listing all possible routes expands uncontrollably. Therefore, the current system is not scalable.

[0012] Another problem identified in ATN networks is the handling of multiple vehicles and the prioritization of access to priority vehicles, such as caregivers or police officers. A solution is provided in U.S. Patent 9,536,427 (Tonguz et al., assigned to Carnegie Mellon). This solution uses vehicle-to-vehicle communication to establish priority zones as needed.

[0013] US Patent 10,152,053B1 discloses an autonomous vehicle management system and a method for controlling a fleet of vehicles. The system includes multiple autonomous vehicles having onboard processors for route calculation and vehicle memory. The system further includes a control management center and multiple beacons for communication between the control management center and the autonomous vehicles. The beacons can also be used to determine the location of the autonomous vehicles within a transportation network. The method includes: receiving multiple requests from passengers for rides in the autonomous vehicles; identifying demographic information associated with each of the multiple passengers; determining a vulnerability score and priority for each of the multiple passengers; and causing a specific autonomous vehicle to pick up a passenger. The vulnerability score is used to assess the passenger's vulnerability at the time of allocation and to calculate the priority for picking up each passenger. For example, passengers in areas with high crime rates and / or with demographics matching a vulnerable subset of the population (e.g., the elderly) may be considered more vulnerable and assigned a higher vulnerability score. The US patent also discloses the use of beacons by autonomous vehicles to send and receive traffic information. However, it does not disclose the independent calculation of routes in the control management center and onboard processors.

[0014] US 2009 / 037086 A1 describes a method for balancing traffic flow among autonomous vehicles in a transportation network using a control computer. The control computer stores information about routes in the network as a vectorized map representing routes extending from origin to destination. The route network contains multiple branch points that create separate branches of routes for the autonomous vehicles' travel. At least some of these autonomous vehicles send a unique vehicle identifier and their current location to the traffic control computer. The information sent by the autonomous vehicles includes the destinations they are traveling to. The document also discloses a method for generating route recommendations and transmitting them to vehicles. Route recommendations include sending an allocation ratio V to the vehicles. The autonomous vehicles themselves then use the allocation ratio V to calculate alternative routes from origin to destination using a randomized selection scheme. The document also teaches a method for transmitting individual route recommendations to vehicles. This method involves simply connecting a roadside communication system to the autonomous vehicles via wireless communication. Different route recommendations are alternately sent to passing autonomous vehicles. The US patent application still does not address the issue of independent route calculation for the autonomous vehicle's control management center and onboard processor.

[0015] Therefore, there is a need to provide an adaptive autonomous transportation network. Summary of the Invention

[0016] An autonomous transportation network is disclosed. This autonomous transportation network knows the movement of all autonomous vehicles operating within the network, without these autonomous vehicles constantly transmitting their positions within the network to a control and management center.

[0017] The autonomous transportation network comprises multiple autonomous vehicles, each equipped with an onboard processor and vehicle memory for calculating routes between origin and destination, and vehicle antennas for transmitting the calculated routes. A control and management center for controlling the transportation network includes a control and management processor and a central memory. Passengers can request to travel along a route from origin to destination within the transportation network. The control and management processor independently calculates routes from origin to destination for the multiple autonomous vehicles. Multiple beacons located at, for example, intersections are connected to the control and management center and receive redirection information from the center for transmission to one or more of the autonomous vehicles.

[0018] This network enables multiple autonomous vehicles to travel independently along routes from origin to destination. The control and management center is aware of the movements of the autonomous vehicles traveling within the autonomous transportation network, without the autonomous vehicles needing to constantly communicate with the control and management center. Therefore, this independent route calculation reduces the communication overhead of the communication infrastructure.

[0019] The control and management center is adapted to identify conflict situations on the routes of multiple autonomous vehicles.

[0020] The control and management center is further used to simulate the traffic demand and routes of autonomous vehicles and to determine corrected route instructions for autonomous vehicles when it is necessary to reorient them along the route.

[0021] A method for operating an autonomous transportation network, comprising multiple autonomous vehicles, is also disclosed. The method includes: receiving instructions for a journey from origin to destination; calculating a route from the origin to the destination in at least one of the multiple autonomous vehicles; independently calculating the route from the origin to the destination in a control management center; comparing the route calculated in the one of the multiple autonomous vehicles with the route independently calculated in the control management center; and, in the event of interference, sending a corrected route instruction to the one of the multiple autonomous vehicles. Sending the corrected route instruction includes sending the corrected route instruction to one or more beacons. The corrected route instruction may be one or more speed instructions or steering instructions.

[0022] A method for calculating a route from origin to destination in an onboard processor of an autonomous vehicle, the method comprising receiving instructions for the trip from a control management center. The received instructions include the origin and destination of the trip and are relayed via, for example, a beacon. The method further comprises locally calculating a direct route from origin to destination. The local route calculation is performed using the onboard processor and geographic data stored in the vehicle's memory. In a further step, the method comprises receiving corrected route instructions from the control management center by an antenna of the autonomous vehicle. The corrected route instructions are addressed at one of the plurality of autonomous vehicles. After receiving the corrected route instructions, the onboard processor of the autonomous vehicle locally recalculates a new optimal route for the remainder of the route to the destination. The calculation of the new optimal route is completed using the onboard processor, geographic data stored in the vehicle's memory, and the received corrected route instructions. The autonomous vehicle then continues along the corrected new optimal route. The autonomous vehicle includes a Level 2 or Level 3 assistance system as described above. Using vehicle geographic data and the onboard processor, the autonomous vehicle is able to navigate autonomously within a transportation network. Therefore, the autonomous vehicle does not require a driver.

[0023] A method for calculating a route from origin to destination in a control management processor includes receiving a trip instruction. The trip instruction includes the origin and destination of the trip. A control management center assigns one of multiple autonomous vehicles to fulfill the passenger's request. The control management processor independently calculates the route to the destination using geographic data stored in central memory. Attached Figure Description

[0024] Figure 1 This provides an overview of the ATN in this paper.

[0025] Figure 2 The workflow is shown.

[0026] Figure 3A The correction of the route based on the blocked route is shown.

[0027] Figure 3B The diagram illustrates management at a roundabout (circular intersection).

[0028] Figure 4 The workflow for calculating routes in autonomous vehicles is shown.

[0029] Figure 5 The workflow for calculating routes in the control and management center is shown. Detailed Implementation

[0030] Figure 1A first example of an autonomous transportation network 10 according to one aspect of this document is shown. The autonomous transportation network has multiple autonomous vehicles 20 operating on multiple tracks 15. The tracks 15 form a network of tracks on which the autonomous vehicles 20 can operate. It should be understood that the tracks 15 may include guide rails, such as steel rails or concrete guide elements, but may also include separate roads. It is conceivable that the tracks 15 may also be integrated into conventional roads and streets, provided that sufficient safety measures are incorporated. The tracks 15 are equipped with multiple beacons 17 (similar to railway transponders) that monitor the progress of the autonomous vehicles 20 and may also send signals to the autonomous vehicles 20.

[0031] The autonomous vehicle 20 can be parked in or move along a parking space with multiple tracks 15. The autonomous vehicle 20 will typically be battery powered and can be charged, for example, when it is in a parking space.

[0032] The autonomous transportation network 10 has a control management center 100 that monitors the progress of autonomous vehicles 20 but does not directly control their progress, as will be explained below. If necessary, autonomous vehicles 20 can send and receive information to and from the control management center 100, and connect wirelessly to the control management center 100 using vehicle antennas 25 located on the autonomous vehicles 20. Vehicle antennas 25 communicate with the control management center 100 via communication antennas 110 located at the control management center 100. The control management center 100 is equipped with a processor 120 and a central memory 140. The control management center 100 is connected to beacon 17 using a fixed communication line 105 (although wireless connections may also be used over the distance between beacon 17 and the control management center 100, or, if necessary, over a portion of that distance). The central memory 140 contains central geographic data 124 about the autonomous transportation network 10, including the location of beacon 17.

[0033] The autonomous transportation network 10 is equipped with multiple stops (also called stations), as known in railway, tram, or bus networks. These stops are clearly marked for passengers 35 who wish to use the autonomous transportation network 10. The vehicle memory 28 in the autonomous vehicle 20 stores vehicle geographic data 24 in the form of a network map, which includes the locations of the multiple stops and pre-calculated route options along track 15 between any two of these stops. Typically, there are more than one pre-calculated path between two stops to allow for following alternative routes, as will be explained later.

[0034] The autonomous vehicle 20 not only has the aforementioned vehicle antenna 28 and vehicle memory 28, but will also include an on-board processor 27, which can use the information in the vehicle memory 28 and any information received from the beacon 17 to control the autonomous vehicle 20.

[0035] Now suppose that passenger 35, who is at the first stop (called origin 30), wants to travel to the second stop (called destination 40). Figure 2 The flow of the method is illustrated. In the first step 210, the passenger 35 will make a request 37 to the autonomous vehicle 20 and will provide a destination 40. This request 37 is made, for example, by telephone or using an application on a smartphone. A control and information point at the origin 30 may also be used if a telephone helpline is provided or a call is made to arrange for one of the autonomous vehicles 20 to pick the passenger up at the origin 30.

[0036] In step 220, the control management center 100 receives request 37. Request 37 will include details about the passenger's origin 30 and the passenger's planned destination 40. The origin 30 can be determined by using GPS coordinates sent from the smartphone in request 37 or by sending the stop number in the application. In step 225, the passenger's destination 40 will be determined by entering the stop number corresponding to destination 40 or the address of destination 40, or by selecting a point representing the stop closest to the address displayed on the map on the smartphone screen.

[0037] Control management center 100 stores data received via request 37 regarding the origin 30 and destination 40 where passenger 35 wishes to be picked up. Control management center 100 then typically allocates the autonomous vehicle 20 closest to passenger 35 from origin 30 in step 230. It should be understood that an autonomous vehicle 20 may already be present at origin 30, and passenger 35 may actually be located next to one, and other communication methods (such as NFC communication) or by scanning a barcode or QR code on the vehicle may be used to reserve an autonomous vehicle 20 for passenger 35. These examples are not intended to limit the invention.

[0038] Then, in step 240, the autonomous vehicle 20 will use the vehicle geographic data 24 (network map plus pre-calculated routes between stops) stored in the vehicle memory 28 to calculate the route 50 to the destination 40 that the passenger 35 wishes to go to in-situ in the local processor 27.

[0039] In step 250, at approximately the same time, the control management system 10 independently calculates the route to destination 40 using the control management processor 120. The vehicle geographic data 24 stored in the autonomous vehicle 20 is the same as or substantially similar to the central geographic data 124 stored in the central memory 140, and therefore the control management system 10 will know the route the autonomous vehicle 20 will take between origin 30 and destination 40. In other words, the central geographic data 124 stored in the central memory 140 includes data that is the same as or similar to the central geographic data 124 stored in the autonomous vehicle 20. However, the central geographic data 124 may include more detailed data, such as simulated current traffic conditions in the transportation network 10. Therefore, the route calculation in the autonomous vehicle 20 and the route calculation in the control management system 10 will be performed separately from each other in real time based on the vehicle geographic data 24 and the central geographic data 124, and initially without considering any interference, such as, but not limited to, traffic accidents or traffic congestion.

[0040] Once route 50 has been calculated in local processor 27, autonomous vehicle 20 will begin its journey from origin 30 to destination 40. Unlike existing systems, autonomous vehicle 20 does not need to notify control management center 100 of the calculated route 50. As described above, control management center 100 knows the route of autonomous vehicle 20 by calculating route 50 in step 250.

[0041] The purpose of this dual calculation of routes is to enable the control management center 100 to determine what is happening in real time within the autonomous transportation network 10. Instead of a single passenger 35 requesting a single autonomous vehicle 20, there are multiple passengers 35 requesting multiple autonomous vehicles 20 from multiple origins 30 and heading to multiple destinations 40. In step 260, the control management center 100 simulates the traffic demand and routes of the autonomous vehicles 20, and, if necessary, changes routes 50 or adjusts the speed of the autonomous vehicles 20, which will be described in more detail in the examples listed below.

[0042] If the control management center 100 determines that the autonomous vehicle 20 needs to deviate from the calculated route 40 or needs to redirect from the calculated route 40, the control management center 100 sends a corrected route instruction 50cor. Instead of sending these corrected route instructions 50cor directly to the autonomous vehicle 20, the control management center 100 sends the corrected route information to one or more beacons 17 in step 270, which can then redirect or slow down the autonomous vehicle 20 in step 275.

[0043] Communication between beacon 17 and autonomous vehicle 20 is performed locally and requires little power. Only those beacons 17 located near autonomous vehicle 20 need to be provided with corrected route instructions 50cor, which will be received by the individual autonomous vehicles in autonomous vehicle 20. Unlike prior art systems, if a potential collision is detected, only the individual autonomous vehicles in autonomous vehicle 20 need to change their routes 40. Control management center 100 knows the location of autonomous vehicle 20 in autonomous transportation network 10 from independent calculations in step S250. Control management center 100 therefore only needs to notify the corrected route instructions 50cor to those beacons 17 located near autonomous vehicle 20. The local transmission of information between beacon 17 and autonomous vehicle 20 also reduces the risk of autonomous transportation network 10 being hacked because the amount of data transmitted is very small and the wireless transmission distance is short.

[0044] These corrected route instructions 50cor will ensure that autonomous vehicle 20 changes route 50 or its speed, as will be explained below. After redirection, autonomous vehicle 20 will recalculate (as in step 240) a new optimal route 50new to destination 40 using vehicle geographic data 24, and continue its journey along the corrected new optimal route 50new to reach destination 40. Control management center 100 will also be able to determine the new optimal route 50new, and will then be able to simulate the route (step 260) to determine if there are any further issues that may require further redirection of autonomous vehicle 20.

[0045] Example 1: A blocked road

[0046] An example of necessary corrections to the original calculated route 50 is in Figure 3A As shown, the direct route 50dir is blocked at blockage location 55 by, for example, a malfunctioning autonomous vehicle 20'. Autonomous vehicle 20 departs from origin 30 and calculates the direct route 50dir in step 240. The same calculated direct route 50dir is calculated by control management center 100 in step 250. However, control management center 100 has received information that the calculated direct route 50dir is impossible because it is blocked by the malfunctioning autonomous vehicle 20'. Control management center 100 sends a message to beacon 17 located at intersection 56 to redirect autonomous vehicle 20 along alternative route 50alt (step 270). Autonomous vehicle 20 receives alternative route instruction 50cor from beacon 17 to use alternative route 50alt. After being redirected (step 275) to alternative route 50alt, autonomous vehicle 20 needs to calculate a new route 50new using vehicle geographic data 24.

[0047] Control and management center 100 does not need to broadcast information about the congested route at location 55 to all autonomous vehicles 20 in the autonomous transportation network 100. Only those autonomous vehicles 20 that have calculated the direct route 50dir passing through the congested location 55 will receive the redirection information locally from beacon 17. This eliminates a large amount of potential data traffic sent from control and management center 100.

[0048] The vehicle memory 28 in the autonomous vehicle 20 does not need to store unnecessary information about blocked routes. This simplifies the calculation of new routes 50new in the onboard processor 27, resulting in faster computation using fewer resources. The vehicle memory 28 can remain relatively small.

[0049] The amount of resources used by the control and management center is also reduced because the control and management processor 120 only needs to notify the beacon 17 at the starting intersection 56 of the blocked route due to obstacles caused by the malfunctioning autonomous vehicle 20'. There is no need to broadcast the information to all autonomous vehicles 20.

[0050] Example 2

[0051] exist Figure 3B The diagram illustrates another example of the effective management of autonomous vehicles 20, showing three autonomous vehicles 20a-c sharing a common entrance to roundabout 57 (also known as a "roundabout intersection" or "roundabout traffic hub"), and another vehicle 20d wishing to enter roundabout 57. The calculated routes 50 programmed in all autonomous vehicles 20a-d from their different origins 30a-d to their destination 40 mean that all autonomous vehicles 20a-d arrive at roundabout 57 approximately simultaneously. The routes 50 from each of the autonomous vehicles 20a-d have been calculated by the control management center 100 in step 250, and calculations performed in the control management processor 120 identify potential conflicts between merging autonomous vehicles in the autonomous vehicles 20a-c and with autonomous vehicle 20d at roundabout 57.

[0052] In step 270, the control management center 100 is able to send information to autonomous vehicles 20a-d using beacons 17x and 17y located near the entrance of roundabout 57. For example... Figure 3A As shown, this information will not require driving along another route 50alt, but will include instructions to reduce or increase the speed of each of the four autonomous vehicles 20a-d to adjust their speed, thus ensuring no conflict on the lane-changing road and no conflict at roundabout 57. This allows the autonomous vehicles 20a-d to make efficient use of available road space and may mean that there is no need to initiate energy-wasting braking and stopping processes.

[0053] Figure 4The flowchart of method 110b for calculating a route 50 from origin 30 to destination 40 in an onboard processor 27 of an autonomous vehicle 20 is shown. In step 300, the autonomous vehicle 20 receives instructions for the journey from origin 30 to destination 40. In step 310, the onboard processor 27 locally calculates a direct route 50dir from origin 30 to destination 40 to fulfill the instructions. The direct route 50dir is calculated locally using vehicle geographic data 24 stored in vehicle memory 28. In step 320, if the control management center 100 has determined a corrected route instruction 50cor for one of the multiple autonomous vehicles 20, then the autonomous vehicle 20 receives the corrected route instruction 50cor from the control management center 100. The corrected route instruction 50cor is received using, for example, a vehicle antenna 25. The corrected route instruction 50cor is addressed by the control management center 100 to one of the multiple autonomous vehicles 20. The corrected route instruction 50cor includes, for example, instructions to change route 50 to an alternative route 50alt or to change the speed of the autonomous vehicle 20. The corrected route instruction 50cor is used to indicate a congestion location 55 along route 50 to at least one of the autonomous vehicles 20. The corrected route instruction 50cor is also used to reduce or increase the speed of at least one autonomous vehicle 20 so that there is no conflict when merging lanes before or at roundabout 57.

[0054] In step 330, the autonomous vehicle 20 recalculates a new optimal route 50new for the remainder of its route 50 to destination 40. The autonomous vehicle 20 includes a Level 2 or Level 3 assistance system as described above. Using vehicle geographic data 24 and an onboard processor 27, the autonomous vehicle 20 is capable of autonomous driving within the transportation network. Therefore, the autonomous vehicle 20 does not require a driver. The recalculation of the new optimal route 50new is performed by the onboard processor 27 using the vehicle geographic data 24 stored in the vehicle memory 28 and the received corrected route instructions 50cor. In step 340, the autonomous vehicle 20 continues its journey to destination 40 along the corrected new optimal route 50new.

[0055] Figure 5The flowchart illustrates a method 110c for independently calculating a route 50 from origin 30 to destination 40 in a control management processor 120. In step 500, the control management center 100 receives a request from passenger 35 for a trip from origin 30 to destination 40. In step 510, the control management center 100 assigns one of a plurality of autonomous vehicles 20 to fulfill the passenger 35's request. The assignment of the passenger's request to one of the plurality of autonomous vehicles 20 is based on, for example, the proximity of the autonomous vehicle 20 to the passenger 35.

[0056] In step 520, the control management processor 120 of the control management center 100 independently calculates a route 50 from origin 30 to destination 40 for one of the multiple autonomous vehicles 20 assigned to it. The control management processor 120 calculates route 50 using central geographic data 124 stored in central memory 140. In step 530, the control management center 100 uses requests received from multiple passengers 35 and the control management processor 120 to simulate traffic demand and routes for the autonomous vehicles 20 in the transportation network 10. In step 540, the control management center 100 determines a corrected route instruction 50cor to enable redirection from route 50 to one of the multiple autonomous vehicles 20 assigned to it. The determination of the corrected route instruction 50cor is done using simulated traffic demand.

[0057] In step 550, the corrected route instruction 50cor is relayed to one of the assigned autonomous vehicles in autonomous vehicle 20 to redirect autonomous vehicle 20 to an alternative route 50alt or for recalculation of a new optimal route 50new by autonomous vehicle 20, such as... Figure 4 As detailed in the description above.

[0058] Figure Labels

[0059] 10 Autonomous Transportation Network

[0060] 15 tracks

[0061] 17 Beacon

[0062] 20 Autonomous Vehicles

[0063] 24 Geographic Data

[0064] 25 Vehicle antennas

[0065] 27. Vehicle-mounted processor

[0066] 28 Vehicle storage

[0067] 30 Origin

[0068] 35 passengers

[0069] 37 Request

[0070] 40 Destination

[0071] Route 50

[0072] 50cor Corrected route

[0073] 50alt Alternative Route

[0074] 50dir direct route

[0075] 50new new best routes

[0076] 55 Blockage Location

[0077] 56 intersections

[0078] 57 Roundabout

[0079] 100 Control and Management Center

[0080] 105 Fixed Line

[0081] 110 Communication Antenna

[0082] 120 Control Management Processor

[0083] 124 Central Geographic Data

[0084] 130 transmitter

[0085] 140 Central Memory

Claims

1. An autonomous transportation network (10), comprising: Multiple autonomous vehicles (20) have an onboard processor (27) and a vehicle memory (28) for calculating (240) a route (50) between a place of origin (30) and a destination (40), and a vehicle antenna (25) for transmitting the calculated route (50). A control management center (100), comprising a control management processor (120) and a central memory (140) for independently calculating (250) the routes (50) of the plurality of autonomous vehicles (20); and Multiple beacons (17) are connected to the control management center (100) and receive redirection information from the control management center (100) for transmission to one or more of the multiple autonomous vehicles (20), wherein the control management center (100) does not send the redirection information directly to the multiple autonomous vehicles (20). The control management center (100) is also adapted to: simulate the traffic demand of the autonomous vehicles (20) and the route (50); compare the route (50) calculated by the plurality of autonomous vehicles (20) with the route calculated independently in the control management center (100); determine the corrected route instruction (50cor) of the autonomous vehicles (20) if it is necessary to reorient along the route (50); and send the corrected route instruction (50cor) to one or more of the plurality of autonomous vehicles (20) in the event of interference.

2. The autonomous transportation network (10) according to claim 1, wherein the plurality of beacons (17) are located at intersections (56).

3. The autonomous transportation network (10) according to claim 1 or 2, wherein the control management center (100) is adapted to determine conflict situations in the routes (50) of the plurality of autonomous vehicles (20).

4. An operating method (100a) for an autonomous transportation network, said autonomous transportation network comprising a plurality of autonomous vehicles (20), said method comprising: - Receive instructions for the journey from the origin (30) to the destination (40); - Calculate (240) locally (50) the route (50) from the origin (30) to the destination (40) in at least one of the multiple autonomous vehicles (20); - The route (50) of the autonomous vehicle (20) from the origin (30) to the destination (40) is independently calculated (250) in the control management center (100); - The route (50) calculated in one of the plurality of autonomous vehicles (20) is compared (260) with the route calculated independently in the control management center (100), wherein the comparison further includes simulating the traffic demand of the autonomous vehicle (20) and the route (50) and determining the corrected route instructions (50cor) of the autonomous vehicle (20) if it is necessary to reorient along the route (50); and, in the event of interference, - Transmit (270) corrected route instructions (50cor) to one of the plurality of autonomous vehicles (20) via one or more beacons (17), wherein the control management center (100) does not send the corrected route instructions (50cor) directly to the plurality of autonomous vehicles (20).

5. The method of claim 4, wherein the corrected route instruction (50cor) includes one or more of a speed instruction or a steering instruction.

6. A method (100b) for calculating a route (50) from a point of origin (30) to a destination (40) in an onboard processor (27) of an autonomous vehicle (20), the method (100b) comprising: - Receive (300) instructions for a trip, the instructions including the origin (30) and the destination (40) of the trip; - Using the on-board processor (27) and vehicle geographic data (24) stored in the vehicle memory (28), calculate (240, 310) locally the direct route (50dir) from the origin (30) to the destination (40); - The autonomous vehicle (20) receives (320) a corrected route instruction (50cor) sent by the control management center (100) for one of the plurality of autonomous vehicles (20) via one or more beacons (17), wherein the control management center (100) is adapted to: simulate the traffic demand and routes of the autonomous vehicle (20); compare a locally calculated direct route (50dir) with a route independently calculated in the control management center (100); determine the corrected route instruction (50cor) of the autonomous vehicle (20) if it is necessary to redirect along the direct route (50dir); and send the corrected route instruction (50cor) to one or more of the plurality of autonomous vehicles (20) in the event of interference, wherein the control management center (100) does not send the corrected route instruction (50cor) directly to the autonomous vehicle (20). - Using the onboard processor (27), the vehicle geographic data (24) stored in the vehicle memory (28), and the received corrected route instructions (50cor), a new optimal route (50new) is locally recalculated (330) for the remaining portion of the route (50) to the destination (40); and -The autonomous vehicle (20) continues the journey (340) along the corrected new optimal route (50new).

7. A method (100c) for calculating a route (50) from a point of origin (30) to a destination (40) in a control management processor (120), the method (100c) comprising: -Receive (500) a request from the passenger (35) to the destination (30) of the trip; - Assign (510) one of the multiple autonomous vehicles (20) to fulfill the passenger's (35) request; - Using the control management processor (120) and the central geographic data (124) stored in the central memory (140), the route (50) to the destination (40) is calculated independently (240, 520); - Using the requests received from multiple passengers (35) and the control management processor (120), simulate (260, 530) the traffic demand and routes of the autonomous vehicle (20) in the transportation network (10); - Using simulated traffic demand (260, 530), determine (540) a corrected route instruction (50cor) for redirection of one of the plurality of autonomous vehicles (20) from the route (50), wherein determining (540) the corrected route instruction (50cor) includes comparing the route (50) calculated by the plurality of autonomous vehicles (20) with a route independently calculated in the control management processor (120), and determining the corrected route instruction (50cor) of the autonomous vehicle (20) if it is necessary to redirect along the route (50); and - The corrected route instruction (50cor) is relayed (270, 550) via one or more beacons (17) to one of the autonomous vehicles (20) to redirect the autonomous vehicle (20) to an alternative route (50alt) or to recalculate a new optimal route (50new) by the autonomous vehicle (20), wherein the corrected route instruction (50cor) is not sent directly to the one of the autonomous vehicles (20).

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