Patent Description:
The invention relates to an autonomous transportation network and method for operating the same.

The term "automated transit network" or "automated transportation network" (abbreviated to ATN) is a relatively new designation for a specific transit mode that falls under the larger umbrella term of "automated guideway transits" (AGT). Before <NUM>, the name "personal rapid transit (PRT)" was used to refer to the ATN concept. In Europe, the ATN has been referred to in the past as "podcars". This document sets out the ATN concept and describes a novel method of operation of the ATN concept.

Like all forms of AGT, ATN is composed of automated vehicles that run on an infrastructure and are capable of carrying passengers from an origin to a destination. The automated vehicles are able to travel from the origin to the destination without any intermediate stops or transfers, such as are known on conventional transportation systems like buses, trams (streetcars) or trains. The ATN service is typically non-scheduled, like a taxi, and travelers are able to choose whether to travel alone in the vehicle or share the vehicle with companions.

The ATN concept is different from self-driving cars which are starting to be seen on public streets. The ATN concept has most often been conceived as a public transit mode similar to a train or bus rather than as an individually used consumer product such, as a car. Current design concepts of the ATN currently rely primarily on a central control management for controlling individually the operation of the autonomous vehicles on the ATN.

On the other hand, the self-driving cars are often described as being "autonomous", but in practice, there are different classes or levels of vehicle autonomy. The degree of vehicle autonomy is typically divided in five levels, as set out by the On-Road Automated Driving (ORAD) committee of the Society of Automotive Engineers (SAE) in "Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles" published in Recommended Practice SAE J <NUM> on <NUM> June <NUM>. Level <NUM> refers to a vehicle that has no driving automation. The driver of the vehicle is fully in charge of operating the movement of the vehicle. Vehicles of Level <NUM> may include safety systems such as, for example, a collision avoidance alert. Level <NUM> refers to vehicles having at least one driving assistance feature such as an acceleration or braking assist system. The driver is responsible for the driving tasks but is supported by the driving assist system which is capable of affecting the movement of the vehicle. Level <NUM> describes vehicles having more than one assist system for actively affecting the movement of the vehicle. The driver, in Level <NUM>, is still responsible for the driving tasks and must actively monitor the trajectory of the vehicle at all times. The driver is, however, actively supported by the assist systems. Level <NUM> describes a so-called "conditional automation" of the vehicle. The vehicle is capable of autonomously driving in certain situations and with limitations. The driver is not required to actively monitor the assist system but is, however, required to take control of a driving situation if requested by the assist system. Level <NUM> describes autonomously travelling vehicles which are capable of travelling specific routes under normal conditions without human supervision. The vehicles of Level <NUM> can therefore operate without a driver but might need remote human supervision in case of conflict situations, travelling in remote areas, or when travelling extreme weather conditions. Level <NUM> Automation describes fully autonomously driving vehicles. No human interaction is required at any time for the operation of the vehicles.

The reliance of the existing ATN networks on a central control management leads to a bottleneck in that each of the autonomous vehicles needs to be in almost continuous communication with the central control management. This can result in problems if the communications network is overloaded or there is a major incident somewhere in the ATN network that requires action from the central control management.

An example of such as central control management is outlined in <CIT>) in which the central control management receives a request from an autonomous vehicle for a route from the origin to the destination. The central control management calculates the route and sends to the autonomous vehicle a journey instruction set to allow the autonomous vehicle to navigate from the origin to the desired destination along the calculated route. The central control management in this system needs to transmit large amounts of data from the autonomous vehicles and gather data from the autonomous vehicles on a continuous basis. This requires a large amount of hardware and data bandwidth and can cause a problem if an autonomous vehicle enters an area in which connectivity is poor. In the event of a breakdown of the central control management, then the autonomous vehicles will no longer be able to navigate or recalculate journeys.

Many current ATN concepts rely on guideways being built as part of the infrastructure. This may have its advantages when dedicated infrastructure separate from other traffic flows or pedestrians can be designed. The cost of the provision of the guideways is significant and this will delay the development of the ATN network. One example of such a guideway is the infrastructure that can be seen in London Heathrow airport's Terminal <NUM>.

A report on "<NPL> reported that at the date of writing no ATN having more than ten stations had been implemented in the world. Currently the ATN networks operate on the principle of mapping each origin to all of the destinations. This leads to a matrix with <NUM> entries even for a simple five-station system as there are four possible destinations from each of the five origins. A ten-station system would have <NUM> possible routes and it will be seen that as the number of origins and destinations increases, then an O/D matrix listing all of the possible routes will expand out of hand. The current systems are therefore not scalable.

A further issue that has been identified in the ATN network is the handling of multiple vehicles and prioritizing of access for priority vehicles, such as paramedics or police. A solution is offered in <CIT>). The solution uses vehicle-to-vehicle communication to establish a priority zone as required.

<CIT> discloses an autonomous vehicle management system and a method for the controlling of a fleet of vehicles. The system comprises a plurality of autonomous vehicles having an onboard processor and vehicle memory for calculating of a route. The system further comprises a control management center and a plurality of beacons for communication between the control management center and the autonomous vehicles. The beacons may also be used for determining a location of the autonomous vehicle within a transportation network. The method comprises receiving a plurality of requests for rides in the autonomous vehicle by passengers, identifying demographic information related to each of the plurality of passengers, determining a vulnerability score and a priority for each of the plurality of the passengers; and causing a particular one of the autonomous vehicles to pick up the passengers. The vulnerability score is used to assess the vulnerability of the passenger in assigning and to calculate a priority to pick up each passenger. For example, passengers in higher crime areas and/or having demographics matching a vulnerable subset (e.g. senior citizens) of the population may be deemed to be more vulnerable and may be assigned higher vulnerability scores. patent further discloses sending and receiving of traffic information by the autonomous vehicles using the beacons. An independent calculation of the routes in the control management center and the onboard processor is not, however, disclosed.

<CIT> describes a method for equalizing traffic flows for autonomous vehicles in a transportation network using a control computer. The control computer stores information on the routes in the network as vectorized graphs representing the routes extending from an origin to a destination. The route network contains multiple branch points creating separate branches of the routes for the travelling of the autonomous vehicles. At least some of the autonomous vehicles send a unique vehicle identifier and their current position to the traffic control computer. The information sent by the autonomous vehicle includes the destination(s) to which the autonomous vehicles are travelling. The document also discloses a method for producing and transmitting a route recommendation to the vehicles. The route recommendation comprises sending a distribution ratio V to the vehicles. The autonomous vehicles themselves then calculate the alternate routes from the origin to the destination using a randomized selection scheme using the distribution ratio V. The document also teaches a method for transmitting the individual route recommendations to the vehicles. This method comprises briefly connecting a communication system, mounted close to the road via wireless communication to the autonomous vehicle. Different route recommendations are alternately sent to the passing autonomous vehicles. patent application remains silent on the issue of an independent route calculation by the control management center and an onboard processor of the autonomous vehicles.

<CIT> describes an automatic steering system for controlling a steering angle so that a vehicle traces a reference line provided along a road. The steering system comprises an administration center and a plurality of vehicles comprising an automatic steering apparatus. The administration center and the plurality of vehicles are connected wirelessly through beacons. The administration center is capable of sending a guiding signal to the vehicle to avoid a construction area or accident area. The European patent application does not describe a plurality of autonomous vehicles with an onboard processor and vehicle memory for calculating a route between an origin and a destination and a vehicle antenna for transmitting the calculated route. The European patent application does furthermore not describe that the control management processor of the control management center and the onboard processor of the autonomous vehicle independently calculate the route from an origin and a destination.

<CIT> describes a remote-control system for mobile objects. The mobile objects are capable of recognizing their own geographical position. The mobile objects are remote controlled by a main driving controller which is capable of receiving obstacle information from an obstacle detector and remote controlling the mobile objects to avoid a collision. The US patent application does not describe a plurality of autonomous vehicles with an onboard processor and vehicle memory for calculating a route between an origin and a destination and a vehicle antenna for transmitting the calculated route. The US patent application does furthermore not describe that the control management processor of the control management center and the onboard processor of the autonomous vehicle independently calculate the route from an origin and a destination.

<CIT> discloses a system and method for associating passenger docking locations with destinations. The document describes general information relating to autonomous vehicles but does not describe a plurality of autonomous vehicles with an onboard processor and vehicle memory for calculating a route between an origin and a destination and a vehicle antenna for transmitting the calculated route.

There is therefore a need for providing a resilient autonomous transportation network.

An autonomous transportation network is disclosed. The autonomous transportation network is aware of the movement of all autonomous vehicles travelling within the autonomous transportation network without the autonomous vehicles having to constantly communicate the position in the network to a control management center.

The autonomous transportation network comprises a plurality of autonomous vehicles with an onboard processor and vehicle memory for calculating a route between an origin and a destination and a vehicle antenna for transmitting the calculated route. A control management center for control of the transportation network comprises a control management processor and a central memory. A passenger can request to travel the route from an origin to a destination in the transportation network. The control management processor independently calculates the routes of the plurality of autonomous vehicles from the origin to the destination. A plurality of beacons is connected to the control management center and receives redirection information from the control management center for transmission to one or more of the plurality of autonomous vehicles in the event of a disturbance. The control management center is adapted for simulating a traffic demand and the routes of the autonomous vehicles, for comparing the routes calculated for the plurality of autonomous vehicles with the route independently calculated in the control management center, for determining corrected route instructions for the autonomous vehicles in case of a necessary redirection along the route, and for sending corrected route instructions to the one of the plurality of autonomous vehicles in the event of a disturbance.

This network enables the plurality of autonomous vehicles to travel along the route from the origin to the destination independently. The control management center is aware of the movement of the plurality of autonomous vehicles travelling in the autonomous transportation network without the autonomous vehicles having to constantly communicate with the control management center. This independent calculation of the routes hereby reduces the communication overhead of a communication infrastructure.

The control management center is adapted to determine a conflict situation on the routes of the plurality of autonomous vehicles.

A method of operation of an autonomous transportation network comprising a plurality of autonomous vehicles is also disclosed. The method comprises receiving an instruction for a journey from an origin to a destination, calculating in at least one of plurality of autonomous vehicles a route from the origin to the destination, independently calculating in a control management center the route of the autonomous vehicle from the origin to the destination, comparing the route calculated in the one of the plurality of autonomous vehicles with the route independently calculated in the control management center, wherein the comparing further comprises simulating a traffic demand and the routes of the autonomous vehicles and determining corrected route instructions for the autonomous vehicles in case of a necessary redirection along the route, and, in the event of a disturbance, sending corrected route instructions to the one of the plurality of autonomous vehicles. The sending of the corrected route instruction comprises sending the corrected route instructions to one of more beacons. The corrected route instructions can be one or more of speed instructions or diversion instructions.

<FIG> shows a first example of an autonomous transportation network <NUM> according to one aspect of this document. The autonomous transportation network has a plurality of autonomous vehicles <NUM> running on a plurality of tracks <NUM>. The tracks <NUM> form a network of tracks over which the autonomous vehicles <NUM> are able run. It will be appreciated that the tracks <NUM> may include guide rails, such as steel rails or concrete guidance elements, but could also comprise separated roadways. It is envisaged that the tracks <NUM> could also be incorporated into regular roadways and streets as long as sufficient safety measures are incorporated. The tracks <NUM> are provided with a plurality of beacons <NUM> (similar to rail balises) which monitor the progress of the autonomous vehicles <NUM> and can also send signals to the autonomous vehicles <NUM>.

The autonomous vehicles <NUM> can be parked in a parking place with a plurality of tracks <NUM> or be in motion along the tracks <NUM>. The autonomous vehicles <NUM> will be typically battery powered and can be charged, for example, when they are in the parking places.

The autonomous transportation network <NUM> has a control management center <NUM> which monitors the progress of the autonomous vehicles <NUM> but does not directly control the progress of the autonomous vehicles <NUM>, as will be explained below. The autonomous vehicles <NUM> can send and receive information to the control management center <NUM>, if necessary, and are connected to the control management center <NUM> through wireless connections using a vehicle antenna <NUM> located on the autonomous vehicle <NUM> in communication with the control management center <NUM> through the communications antenna <NUM> at the control management center <NUM>. The control management center <NUM> is provided with a processor <NUM> and a central memory <NUM>. The control management center <NUM> is connected to the beacons <NUM> using fixed communication lines <NUM> (although of course it would be possible to also use wireless connections over the distance between the beacons <NUM> and the control management center <NUM> or over part of the distance if required). The central memory <NUM> includes central geographic data <NUM> about the autonomous transportation network <NUM> including the location of the beacons <NUM>.

The autonomous transportation network <NUM> is provided with a plurality of stopping points (also termed stations), as is known from a railway, tram, or bus network. The stopping points will be clearly labelled to passengers <NUM> who wish to use the autonomous transportation network <NUM>. A vehicle memory <NUM> in the autonomous vehicle <NUM> stores a vehicle geographic data <NUM> in the form of a network map with the location of the plurality of stopping points and also a selection of precalculated routes along the tracks <NUM> between any two of the stopping points. There will generally be more than one pre-calculated route between two of the stopping points to allow for alternative paths to be followed, as will be explained later.

The autonomous vehicle <NUM> has not only the afore-mentioned vehicle antenna <NUM> and the vehicle memory <NUM> but will also include an onboard processor <NUM> which can control the autonomous vehicle <NUM> using the information in the vehicle memory <NUM> and any information received from the beacons <NUM>.

Suppose now that a passenger <NUM> at a first one of the stopping points, termed an origin <NUM>, wishes to travel to a second one of the stopping points, termed a destination <NUM>. <FIG> shows the flow of this method. In a first step <NUM> the passenger <NUM> will make a request <NUM> for an autonomous vehicle <NUM> and will give the destination <NUM>. This request <NUM> is made for example by telephone or using an app on a smartphone. It would also be possible to use a control and information point at the origin <NUM> if this is provided or indeed to phone a telephone help line to arrange for a pick-up at the origin <NUM> by one of the autonomous vehicles <NUM>.

The request <NUM> is received in step <NUM> by the control management center <NUM>. The request <NUM> will include details about the origin <NUM> of the passenger and the planned destination <NUM> of the passenger. The origin <NUM> can be determined by either using GPS coordinates transmitted in the request <NUM> from a smartphone or by transmitting the number of the stopping point in the app. The destination <NUM> of the passenger <NUM> will be determined in step <NUM> by either inputting the number of the stopping point corresponding to the destination <NUM>, or an address of the destination <NUM> or selecting a point representing the nearest stopping point to an address on a map displayed on the screen of the smartphone.

The control management center <NUM> stores the data received through the request <NUM> concerning the origin <NUM>, at which point the passenger <NUM> wishes to be picked up, and the destination <NUM>. The control management center <NUM> will then generally assign in step <NUM> the autonomous vehicle <NUM> closest to the passenger <NUM> to pick up the passenger <NUM> from the origin <NUM>. It will be appreciated, of course, that there may already be one of the autonomous vehicles <NUM> at the origin <NUM> and the passenger <NUM> may in fact be standing next to one of the autonomous vehicles <NUM> and other ways of communication, such as NFC communication or by scanning a bar code or QR code on the vehicle could be used to reserve the autonomous vehicle <NUM> for use by the passenger <NUM>. These examples are not limiting of the invention.

The autonomous vehicle <NUM> will then calculate in step <NUM> locally in a local processor <NUM> using the vehicle geographic data <NUM> (network map plus precalculated routes between the stopping points) stored in the vehicle memory <NUM> the route <NUM> to the destination <NUM> to which the passenger <NUM> wishes to go.

At around the same time in step <NUM> the control management system <NUM> will independently calculate using the control management processor <NUM> the route to the destination <NUM>. The vehicle geographic data <NUM> stored in the autonomous vehicle <NUM> is identical or substantially similar to the central geographic data <NUM> stored in the central memory <NUM> and thus the control management system <NUM> will know the route that the autonomous vehicle <NUM> will take between the origin <NUM> and the destination <NUM>. In other words, the central geographic data <NUM> stored in the central memory <NUM> comprises identical or similar data compared to the central geographic data <NUM> stored in the autonomous vehicle <NUM>. The central geographic data <NUM> might comprise, however, more detailed data as, for example, the simulated current traffic situation in the transportation network <NUM>. Hence, the route calculation in the autonomous vehicle <NUM> and the route calculation in the control management system <NUM> will be performed separately from each other in real-time based on the vehicle geographic data <NUM> and the central geographic data <NUM> and will initially not take into account any disturbances, such as but not limited to traffic accidents, traffic jams.

Once the route <NUM> has been calculated in the local processor <NUM>, the autonomous vehicle <NUM> will start its journey from the origin <NUM> to the destination <NUM>. Unlike in prior art systems, the autonomous vehicle <NUM> is not required to notify the calculated route <NUM> to the control management center <NUM>. The control management center <NUM> knows, as described above, the route of the autonomous vehicle <NUM> by calculating the route <NUM> in step <NUM>.

The purpose of this dual calculation of the routes is to enable the control management center <NUM> to determine what is happening in real-time in the autonomous transportation network <NUM>. There will not be a single passenger <NUM> requesting a single one of the autonomous vehicles <NUM>, but a number of passengers <NUM> requesting a number of autonomous vehicles <NUM> from a plurality of the origins <NUM> and going to a plurality of the destinations <NUM>. It is the role of the control management center <NUM> in step <NUM> to simulate the traffic demand and the routing of the autonomous vehicles <NUM> and, if necessary, make changes of the routes <NUM> or adjust the speed of travel of the autonomous vehicle <NUM> as will be described in more detail in the examples set out below.

In the event that the control management center <NUM> determines that the autonomous vehicle <NUM> needs to deviate or needs to be redirected from the calculated route <NUM>, then the control management center <NUM> sends corrected route instructions 50cor. The control management center <NUM> does not send these corrected route instructions 50cor directly to the autonomous vehicle <NUM>, but in step <NUM> corrected routing information is sent to one or more of the beacons <NUM> which can then redirect or slow the autonomous vehicle <NUM> in step <NUM>.

The communication between the beacons <NUM> and the autonomous vehicles <NUM> is carried out locally and does not require much power. Only those beacons <NUM> near the position of the autonomous vehicle <NUM> need to be provided with corrected routing instructions 50cor to be received by individual ones of the autonomous vehicles <NUM>. Unlike in prior art systems, only individual ones of the autonomous vehicles <NUM> need to change the route <NUM> if a possible conflict is detected. The control management center <NUM> knows, from independently calculating in step S250, the location of the autonomous vehicle <NUM> in the autonomous transportation network <NUM>. The control management center <NUM> therefore only needs to inform those beacons <NUM> near the position of the autonomous vehicle <NUM> of the corrected routing instructions 50cor. The local transmission of information between the beacon <NUM> and the autonomous vehicle <NUM> also reduces the risks of hacking of the autonomous transportation network <NUM> as the amount of data transmitted is very small and the distances of wireless transmission are also short.

These corrected route instructions 50cor will ensure that the autonomous vehicle <NUM> changes the route <NUM> or to alter its speed, as will be explained below. The autonomous vehicle <NUM> after redirection will recalculate (as in step <NUM>) the new best route 50new to the destination <NUM> using the vehicle geographic data <NUM> and continue the journey along the corrected new best route 50new to reach the destination <NUM>. The control management center <NUM> will also be able to determine the new best route 50new and will then be able to simulate the route (step <NUM>) to determine whether there are further issues that may need a further redirection of the autonomous vehicle <NUM>.

An example of a necessary correction to the originally calculated route <NUM> is shown in <FIG> in which the direct route 50dir is blocked at a blocked position <NUM> by, for example, a broken-down autonomous vehicle <NUM>'. The autonomous vehicle <NUM> starts at the origin <NUM> and calculates in step <NUM> the direct route 50dir in step <NUM>. The same calculated direct route 50dir is calculated in step <NUM> by the control management center <NUM>. The control management center <NUM> has, however, received information that the calculated direct route 50dir is not possible since the direct route 50dir is blocked by the broken-down autonomous vehicle <NUM>'. The control management center <NUM> sends to the beacon <NUM> located at a junction <NUM> information to redirect the autonomous vehicle <NUM> along an alternative route 50alt (step <NUM>). The autonomous vehicle <NUM> receives from the beacon <NUM> the alternative routing instruction 50cor to use the alternative route 50alt. After being redirected (step <NUM>) onto the alternative route 50alt, the autonomous vehicle <NUM> needs to calculate the new route 50new using the vehicle geographic data <NUM>.

There is no need for the control management center <NUM> to broadcast to all of the autonomous vehicles <NUM> in the autonomous transportation network <NUM> information about the blocked route at the position <NUM>. Only those autonomous vehicles <NUM> that have calculated the direct route 50dir which passes through the blocked position <NUM> will receive the redirection information locally from the beacon <NUM>. This eliminates much of the potential data traffic sent from the control management center <NUM>.

The vehicle memory <NUM> in the autonomous vehicle <NUM> does not need to store unnecessary information about the blocked routes. This simplifies the calculation of the new route 50new in the onboard processor <NUM> which results in a quicker calculation with the use of fewer resources. The vehicle memory <NUM> can be kept smaller.

The amount of resources used the control management center is also reduced since the control management processor <NUM> only needs to inform the beacons <NUM> at the start junction <NUM> of the blocked route that there is an obstruction due to a broken-down autonomous vehicle <NUM>'. There is no need to broadcast the information to all of the autonomous vehicles <NUM>.

A further example of the efficient management of the autonomous vehicles <NUM> is shown in <FIG> which shows three autonomous vehicles 20a-c sharing a common entrance to a roundabout <NUM> (also termed "traffic circle" or "rotaries") and a further vehicle 20d wishing to enter the roundabout <NUM>. The calculated route <NUM> programmed in all of the autonomous vehicles 20a-d to the destination <NUM> from different origins 30a-d means that all of the autonomous vehicles 20a-d arrive at the roundabout <NUM> at approximately the same time. The routes <NUM> from each of the autonomous vehicles 20a-d have been calculated by the control management center <NUM> in step <NUM> and the calculations made in the control management processor <NUM> identify the possible conflict between the merging ones of the autonomous vehicles 20a-c and at the roundabout <NUM> with the autonomous vehicle 20d.

The control management center <NUM> is able to send information to the autonomous vehicles in step <NUM> to the autonomous vehicles 20a-d using the beacons 17x and 17y located near the entries to the roundabout <NUM>. The information will not be needed to travel along another route 50alt, as shown in <FIG>, but will comprise instructions to reduce speed or increase speed to each of the four autonomous vehicles 20a-d to adjust their speed so that there is no conflict at the merging roads and also no conflict on the roundabout <NUM>. This enable efficient use of available road space by the autonomous vehicles 20a-d and can mean that there is no need to initiate a braking and stopping process, which is wasteful of energy.

<FIG> shows a workflow describing the method 110b for calculation of the route <NUM> from the origin <NUM> to the destination <NUM> in the onboard processor <NUM> of the autonomous vehicle <NUM>. In step <NUM>, the autonomous vehicle <NUM> receives the instruction for the journey from the origin <NUM> to the destination <NUM>. The onboard processor <NUM>, in step <NUM>, locally calculates a direct route 50dir from the origin <NUM> to the destination <NUM> for fulfilling the instruction. Locally calculating the direct route 50dir is done using the vehicle geographic data <NUM> stored in the vehicle memory <NUM>. In step <NUM>, the autonomous vehicle <NUM> receives corrected route instructions 50cor from the control management center <NUM> if the control management center <NUM> has determined corrected route instructions 50cor for the one of the plurality of autonomous vehicles <NUM>. Receiving the corrected route instruction 50cor is done using, for example, the vehicle antenna <NUM>. The corrected route instruction 50cor is addressed, by the control management center <NUM>, to at one of the plurality of the autonomous vehicles <NUM>. The corrected route instructions 50cor comprise, for example, instructions for changing the route <NUM> to the alternative route 50alt or altering the vehicle speed of the autonomous vehicle <NUM>. The corrected route instructions 50cor are used to indicate the blocked position <NUM> along the route <NUM> to at least one of the plurality of autonomous vehicle <NUM>. The corrected route instructions 50cor are also used to reduce speed or increase speed of at least one autonomous vehicle <NUM> so that there is no conflict when merging roads before or at a roundabout <NUM>.

The autonomous vehicle. <NUM> recalculates, in step <NUM>, the new best route 50new for the remainder of the route <NUM> of the autonomous vehicle <NUM> travelling to the destination <NUM>. The autonomous vehicles <NUM> include assist systems of Level <NUM> or Level <NUM>, as described above. The autonomous vehicle <NUM> is capable of autonomously travelling in the transportation network, using the vehicle geographic data <NUM> and the onboard processor <NUM>. The autonomous vehicles <NUM>, therefore, do not require a driver for driving of the autonomous vehicle <NUM>. The recalculating of the new best route 50new is done by the onboard processor <NUM> using the vehicle geographic data <NUM> stored in the vehicle memory <NUM> and the received corrected route instructions 50cor. The autonomous vehicle <NUM>, in step <NUM>, continues the journey along the corrected new best route 50new to the destination <NUM>.

shows a workflow describing the method 110c for the independent calculation of the route <NUM> from the origin <NUM> to the destination <NUM> in the control management processor <NUM>. In step <NUM>, the control management center <NUM> receives the request for the journey of the passenger <NUM> from the origin <NUM> to the destination <NUM>. In step <NUM>, the control management center <NUM> assigns one of the plurality of autonomous vehicles <NUM> for fulfilling the request of the passenger <NUM>. Assigning the request of the passenger to one of the plurality of the autonomous vehicles <NUM> is based on, for example, the proximity of the autonomous vehicle <NUM> to the passenger <NUM>.

The control management processor <NUM> of the control management center <NUM> independently calculates, in step <NUM>, the route <NUM> of the assigned one of the plurality of the autonomous vehicles <NUM> from the origin <NUM> to the destination <NUM>. Calculating the route <NUM> by the control management processor <NUM> is done using the central geographic data <NUM> stored in the central memory <NUM>. In step <NUM>, the control management center <NUM> simulates a traffic demand and the routing of the autonomous vehicles <NUM> in the transportation network <NUM>, using the requests received from the plurality of passengers <NUM> and the control management processor <NUM>. The control management center <NUM> determines, in step <NUM>, the corrected route instructions 50cor to enable the redirection of the assigned one of the plurality of autonomous vehicles <NUM> from the route <NUM>. Determining the corrected route instructions 50cor is done using the simulated traffic demand.

Claim 1:
An autonomous transportation network (<NUM>) comprising
a plurality of autonomous vehicles (<NUM>) with an onboard processor (<NUM>) and vehicle memory (<NUM>) for calculating (<NUM>) a route (<NUM>) between an origin (<NUM>) and a destination (<NUM>) and a vehicle antenna (<NUM>) for transmitting the calculated route (<NUM>);
a control management center (<NUM>) comprising a control management processor (<NUM>) and a central memory (<NUM>) for independently calculating the routes (<NUM>) of the plurality of autonomous vehicles (<NUM>); and
a plurality of beacons (<NUM>) connected to the control management center (<NUM>) and receiving redirection information from the control management center (<NUM>) for transmission to one or more of the plurality of autonomous vehicles (<NUM>) in the event of a disturbance,
wherein the control management center (<NUM>) is further adapted for simulating a traffic demand and the routes (<NUM>) of the autonomous vehicles (<NUM>), for comparing the routes (<NUM>) calculated for the plurality of autonomous vehicles (<NUM>) with the route independently calculated in the control management center (<NUM>), for determining corrected route instructions (50cor) for the autonomous vehicles (<NUM>) in case of a necessary redirection along the route (<NUM>), and for sending corrected route instructions (50cor) to the one of the plurality of autonomous vehicles (<NUM>) in the event of a disturbance.