GB2592923A

GB2592923A - Autonomous transportation network and method for operating the same

Info

Publication number: GB2592923A
Application number: GB2003395.7A
Authority: GB
Inventors: Dürr Martin
Original assignee: Dromos Technologies AG
Current assignee: Dromos Technologies AG
Priority date: 2020-03-09
Filing date: 2020-03-09
Publication date: 2021-09-15
Also published as: GB202003395D0

Abstract

An autonomous transportation network 10 comprises a plurality of autonomous vehicles 20 with an onboard processor 27 and vehicle memory 28 for calculating (240, figure 2) a route 50 between an origin 30 and a destination 40 and a vehicle antenna 25 for transmitting the calculated route. A control management centre 100 includes a control management processor 120 and a central memory 140 and calculates (250) the routes of the plurality of autonomous vehicles. A plurality of beacons 17 are connected to the control management centre and receive redirection information from the centre 100 for transmission to one or more of the vehicles. Further provided is a method of operating the network including a step of comparing the route calculated in one of the plurality of vehicles with the route calculated in the control management centre and, in the event of a disturbance, sending corrected route instructions to one of the vehicles.

Description

Title: Autonomous Transportation Network and Method for Operating the Same

Cross Reference to Related Applications

[0001] None

Summary of the Invention

[0002] The invention relates to an autonomous transportation network and method for operating the same

Background to the Invention

[0003] 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 2010, 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.

[0004] 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.

[0005] The ATN concept is different from self-driving cars which are starting to be seen on city 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. By comparison, self-driving cars are autonomous and rely on self-contained sensors to navigate, operate within restricted rights-of-way, and respond to other vehicles or obstacles.

[0006] 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.

[0007] An example of such as central control management is outlined in US Patent No. 10,345,805 (Seally, assigned to Podway Inc.) 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.

[0008] 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 5.

[0009] A report on "Automated Transit Networks (ATN): A Review of the State of the Industry and Prospects for the Future" published by the Mineta Transportation Institute, Report No 12-31 in September 2014 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 20 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 90 possible routes and it will be seen that as the number of origins and destinations increases, then an OLD matrix listing all of the possible routes will expand out of hand. The current systems are therefore not scalable.

[0010] 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 US Patent 9,536,427 (Tonguz et al, assigned to Carnegie Mellon). The solution uses vehicle-to-vehicle communication to establish a priority zone as required.

[0011] There is therefore a need for an ATN network which is able to overcome these 10 issues.

Summary of the Invention

[0012] An autonomous transportation network is disclosed. 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 comprises a control management processor and a central memory and calculates the routes of the plurality of autonomous vehicles. A plurality of beacons, located for example at junction, 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.

[0013] The control management center is adapted to determine conflict situation in the routes of the plurality of autonomous vehicles.

[0014] 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, calculating in a control management center the route from the origin to the destination, comparing the route calculated in the one of the plurality of autonomous vehicles with the route calculated in the control management center 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.

Description of the Figures

[0015] Fig. 1 shows an overview of the KEN of this document. [0016] Fig. 2 shows a workflow [0017] Fig. 3A shows correction to route based on blocked route [0018] Fig. 3B shows management at a roundabout (traffic circle)

Detailed Description of the Invention

[0019] Fig. 1 shows a first example of an autonomous transportation network 10 according to one aspect of this document. The autonomous transportation network has a plurality of autonomous vehicles 20 running on a plurality of tracks 15. The tracks 15 form a network of tracks over which the autonomous vehicles 20 are able run. It will be appreciated that the tracks IS may include guide rails, such as steel rails or concrete guidance elements, but could also comprise separated roadways. It is envisaged that the tracks 15 could also be incorporated into regular roadways and streets as long as sufficient safety measures are incorporated. The tracks 15 are provided with a plurality of beacons 17 (similar to rail balises) which monitor the progress of the autonomous vehicles 20 and can also send signals to the autonomous vehicles 20.

[0020] The autonomous vehicles 20 can be parked in a parking place with a plurality of tracks 15 or be in motion along the tracks 15. The autonomous vehicles 20 will be typically battery powered and can be charged, for example, when they are in the parking places.

[0021] The autonomous transportation network 10 has a control management center 100 which monitors the progress of the autonomous vehicles 20 but does not directly control the progress of the autonomous vehicles 20, as will be explained below. The autonomous vehicles 20 can send and receive information to the control management center 100, if necessary, and are connected to the control management center 100 through wireless connections using a vehicle antenna 25 located on the autonomous vehicle 20 in communication with the control management center 100 through the communications

S

antenna 110 at the control management center 100. The control management center 100 is provided with a processor 120 and a central memory 140. The control management center 100 is connected to the beacons 17 using fixed communication lines 105 (although of course it would be possible to also use wireless connections over the distance between the beacons 17 and the control management center 100 or over part of the distance if required).

The central memory 140 includes geographic data about the autonomous transportation network 10 including the location of the beacons 17.

[0022] The autonomous transportation network 10 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 35 who wish to use the autonomous transportation network 10. A vehicle memory 28 in the autonomous vehicle 20 stores a geographic data 24 in the form of a network map with the location of the plurality of stopping points and also a selection of pre-calculated routes along the tracks 15 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.

[0023] The autonomous vehicle 20 has not only the afore-mentioned vehicle antenna 28 and the vehicle memory 25 but will also include an onboard processor 27 which can control the autonomous vehicle 20 using the information in the vehicle memory 25 and any information received from the beacons 17.

[0024] Suppose now that a passenger 35 at a first one of the stopping points, termed an origin 30, wishes to travel to a second one of the stopping points, termed a destination 40. Fig. 2 shows the flow of this method. In a first step 210 the passenger 35 will make a request 37 for an autonomous vehicle 20 and will give the destination 40. This request 37 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 30 if this is provided or indeed to phone a telephone help line to arrange for a pick-up at the origin 30 by one of the autonomous vehicles 20.

[0025] The request 37 is received in step 220 by the control management center 100. The request 37 will include details about the origin 30 of the passenger and the planned destination 40 of the passenger. The origin 30 can be determined by either using UPS coordinates transmitted in the request 37 from a smartphone or by transmitting the number of the stopping point in the app. The destination 40 of the passenger 35 will be determined in step 225 by either inputting the number of the stopping point corresponding to the destination 40, or an address of the destination 40 or selecting a point representing the nearest stopping point to an address on a map displayed on the screen of the smartphone.

[0026] The control management center 100 stores the data received through the request 37 concerning the origin 30, at which point the passenger 35 wishes to be picked up, and the destination 40. The control management center 100 will then generally assign in step 230 the autonomous vehicle 20 closest to the passenger 35 to pick up the passenger 35 from the origin 30. It will be appreciated, of course, that there may already be one of the autonomous vehicles 20 at the origin 30 and the passenger 35 may in fact be standing next to one of the autonomous vehicles 20 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 20 for use by the passenger 35. These examples are not limiting of the invention.

[0027] The autonomous vehicle 20 will then calculate in step 240 locally in a local processor 27 using the geographic data 24 (network map plus pre-calculated routes between the stopping points) stored in the local memory 28 the route 50 to the destination 40 to which the passenger 35 wishes to go.

[0028] At around the same time in step 250 the control management system 10 will independently calculate using the control management processor 120 the route to the destination 40. The geographic data 24 stored in the autonomous vehicle 20 is identical to that stored in the central memory 140 and thus the control management system 10 will know the route that the autonomous vehicle 20 will take between the origin 30 and the destination 40. In other words, 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 geographic data 24 and will initially not take into account any disturbances, such as but not limited to traffic accidents, traffic jams.

[0029] Once the route 50 has been calculated in the local processor 27, the autonomous vehicle 20 will start its journey from the origin 30 to the destination 40. Unlike in prior art systems, the autonomous vehicle 20 will not notify the calculated route 50 to the control management center 100.

[0030] The purpose of this dual calculation of the routes is to enable the control management center 100 to determine what is happening in real-time in the autonomous transportation network 10. There will not be a single passenger 35 requesting a single one of the autonomous vehicles 20, but a number of passengers 35 requesting a number of autonomous vehicles 20 from a plurality of the origins 30 and going to a plurality of the destinations 40. It is the role of the control management center 100 in step 260 to simulate the traffic demand and the routing of the autonomous vehicles 20 and, if necessary, make changes of the routes 50 or adjust the speed of travel of the autonomous vehicle 20 as will be described in more detail in the examples set out below.

[0031] In the event that the control management center 100 determines that the autonomous vehicle 20 needs to deviate from the calculated route 40, then the control management center 100 can sends corrected route instructions 50cor. The control management center 100 does not send these corrected route instructions 60 directly to the autonomous vehicle 20, but in step 270 corrected routing information is sent to one or more of the beacons 17 which can then re-direct or slow the autonomous vehicle 20 in step 275.

[0032] The communication between the beacons 17 and the autonomous vehicles 20 is carried out locally and does not require much power. Only those beacons 17 near the position of the autonomous vehicle 20 need to be provided with corrected routing instructions 50cor. The local transmission of information between the beacon 17 and the autonomous vehicle also reduces the risks of hacking of the autonomous transportation network 10 as the amount of data transmitted is very small and the distances of wireless transmission are also short.

[0033] These corrected route instructions 50cor will ensure that the autonomous vehicle 20 changes the route 50 or to alter its speed, as will be explained below. The autonomous vehicle 20 after re-direction will recalculate (as in step 240) the best route 50new to the destination 40 using the geographic data 24 and continue the journey along the corrected new best route 50new to reach the destination 40. The control management center 100 will also be able to determine the new best route 50new and will then be able to simulate the route (step 260) to determine whether there are further issues that may need a further re-direction of the autonomous vehicle 20.

Example 1: Blocked Road [0034] An example of a necessary correction to the originally calculated route 50 is shown in Fig. 3A in which the direct route 50dir is blocked at a blocked position 55 by, for example, a broken-down autonomous vehicle 20'. The autonomous vehicle 20 starts at the origin 30 and calculates in step 240 the direct route 50dir in step 240. The same calculated direct route 50dir is calculated in step 250 by the control management center 100. The control management center 100 has, however, received information that the calculated direct route 50d is not possible since the direct route 50dir is blocked by the broken-down autonomous vehicle 20'. The control management center 100 sends to the beacon 17 located at a junction 56 information to redirect the autonomous vehicle 20 along an alternative route 50alt (step 270). The autonomous vehicle 20 receives from the beacon 17 the alternative routing instruction 50cor to use the alternative route 50alt. After being redirected (step 275) onto the alternative route 50alt, the autonomous vehicle 20 needs to calculate the new route 50new using the geographic data 24.

[0035] There is no need for the control management center 100 to broadcast to all of the autonomous vehicles 20 in the autonomous transportation network 100 information about the blocked route at the position 55. Only those autonomous vehicles 20 that have calculated the direct route 50dir which passes through the blocked position 55 will receive the redirection information locally from the beacon 17. This eliminates much of the potential data traffic sent from the control management center 100.

[0036] The vehicle memory 28 in the autonomous vehicle 20 does not need to store unnecessary information about the blocked routes. This simplifies the calculation of the new route 50new in the onboard processor 27 which results in a quicker calculation with the use of fewer resources. The local memory 28 can be kept smaller.

[0037] The amount of resources used the control management center is also reduced since the control management processor 120 only needs to inform the beacons 17 at the start junction 56 of the blocked route that there is an obstruction due to a broken-down autonomous vehicle 20'. There is no need to broad the information to all of the autonomous vehicles 20.

Example 2

[0038] A further example of the efficient management of the autonomous vehicles 20 is shown in Fig. 3B which shows three autonomous vehicles 20a-c sharing a common entrance to a roundabout 57 (also termed 'traffic circle-or -rotaries") and a further vehicle 20d wishing to enter the roundabout 57. The calculated route 50 programmed in all of the autonomous vehicles 20a-d to the destination 40 from different origins 30a-d means that all of the autonomous vehicles 20a-d arrive at the roundabout 57 at approximately the same time. The routes 50 from each of the autonomous vehicles 20a-d have been calculated by the control management center 100 in step 250 and the calculations made in the control management processor 120 identify a possible conflict between the merging ones of the autonomous vehicles 20a-c and at the roundabout 57 with the autonomous vehicle 20d. [0039] The control management center 100 is able to send information to the autonomous vehicles in step 270 to the autonomous vehicles 20a-d using the beacons 17x and 17y located near the entries to the roundabout 57. The information will not be the need to travel along another route 50alt, as shown in Fig. 3A, but will comprises 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 57. 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.

Reference Numerals Autonomous transportation network Tracks 17 Beacons Autonomous vehicles 24 Geographic data Vehicle antenna 27 Onboard Processor 28 Vehicle memory Origin Passenger 37 Request Destination 50 Route 50cor Corrected route 50alt Alternative route 50dir Direct route 50new New route 55 Blocked position 56 Junction 57 Roundabout Control management center Fixed lines 110 Communication antenna Control management processor 130 Transmitter Central memory

Claims

Claims An autonomous transportation network (10) comprising a plurality of autonomous vehicles (20) with an onboard processor (27) and vehicle memory (28) for calculating (240) a route (50) between an origin (30) and a destination (40) and a vehicle antenna for transmitting the calculated route; a control management center (100) comprising a control management processor (120) and a central memory (140) for calculating (250) the routes (50) of the plurality of autonomous vehicles (20); and a plurality of beacons (17) connected to the control management center (100) and receiving redirection information from the control management center (100) for transmission to one or more of the plurality of autonomous vehicles (20).
The autonomous transportation network (10) of claim 1_, wherein the plurality of beacons (17) are located at junctions 56.
The autonomous transportation network ( 10) of claim I or 2, wherein the control management center (100) is adapted to determine conflict situation in the routes (50) of the plurality of autonomous vehicles (20).
A method of operation of an autonomous transportation network comprising a plurality of autonomous vehicles (20), the method comprising: - receiving an instruction for a journey from an origin (30) to a destination (40); - calculating (240) in at least one of plurality of autonomous vehicles (20) a route (50) from the origin (30) to the destination (40); - calculating (250) in a control management center (100) the route (50) from the origin (30) to the destination (40); - comparing (260) the route (50) calculated in the one of the plurality of autonomous vehicles (20) with the route calculated in the control management center (100); and, in the event of a disturbance, - sending (270) corrected route instructions (50cor) to the one of the plurality of autonomous vehicles (20). 2.
The method of claim 4, wherein the sending (270) of the corrected route instruction (50cor) comprises sending the corrected route instructions (50cor) to one of more beacons (17).
6. The method of claim 4 or 5, wherein the corrected route instnict ons (50cor) comprises one or more of speed instnictions or diversion instructions