Autonomous vehicle enhancement system

• US 10,739,149 B2
• Filed: 05/08/2018
• Issued: 08/11/2020
• Est. Priority Date: 05/08/2017
• Status: Active Grant

• Current Assignee: Arnold Chase, William Chase
• Original Assignee: Arnold Chase, William Chase
• Inventors: Chase, Arnold, Chase, William
• Primary Examiner(s): Cheung, Calvin
• Application Number:
  US15/973,987
• Publication Number:
  US 20180321688A1
• Time in Patent Office: 826 Days
• CPC Class Codes: G01C 21/3438 Rendez-vous, i.e. searching a destination where several users can meet, and the routes to this destination for these users; Ride sharing, i.e. searching a route such that at least two users can share a vehicle for at least part of the route
• CPC Class Codes: G01C 21/3492 employing speed data or traffic data, e.g. real-time or historical traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968
• CPC Class Codes: G05D 1/0022 characterised by the communication link data switching networks in general H04L12/00
• CPC Class Codes: G05D 1/0027 involving a plurality of vehicles, e.g. fleet or convoy travelling fleet control of land vehicles from a control room G05D1/0297; traffic control systems for road vehicles G08G1/00; for marine craft G08G3/00; for aircraft G08G5/00
• CPC Class Codes: G05D 1/0055 with safety arrangements
• CPC Class Codes: G05D 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0212 with means for defining a desired trajectory involving a plurality of land vehicles G05D1/0287
• CPC Class Codes: G05D 1/0276 using signals provided by a source external to the vehicle involving a plurality of vehicles G05D1/0287; automatically controlling vehicle speed responsive to externally generated signals B60K31/0058
• CPC Class Codes: G05D 1/0291 Fleet control monitoring fleets in traffic control systems for road vehicles G08G1/127, G08G1/127
• CPC Class Codes: G05D 1/0297 by controlling means in a control room
• CPC Class Codes: G06F 15/76 Architectures of general purpose stored program computers with program plugboard G06F15/08; multicomputers G06F15/16
• CPC Class Codes: G06F 3/0481 based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
• CPC Class Codes: G06F 3/0488 using a touch-screen or digitiser, e.g. input of commands through traced gestures
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/01 Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 10/02 Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 10/06311 Scheduling, planning or task assignment for a person or group
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G06V 20/59 : inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
• CPC Class Codes: G07C 5/008 : communicating information to a remotely located station transmission systems for measured values G08C
• CPC Class Codes: G08G 1/005 : including pedestrian guidance indicator
• CPC Class Codes: G08G 1/012 : from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/0175 : by photographing vehicles, e.g. when violating traffic rules
• CPC Class Codes: G08G 1/202 : Dispatching vehicles on the basis of a location, e.g. taxi dispatching
• CPC Class Codes: G10L 15/22 : Procedures used during a speech recognition process, e.g. man-machine dialogue
• CPC Class Codes: G10L 2015/223 : Execution procedure of a spoken command
• CPC Class Codes: H04L 63/08 : for authentication of entities cryptographic mechanisms or cryptographic arrangements for entity authentication H04L9/32
• CPC Class Codes: H04L 63/083 : using passwords cryptographic mechanisms or cryptographic arrangements for entity authentication using a predetermined code H04L9/3226
• CPC Class Codes: H04N 7/188 : Capturing isolated or intermittent images triggered by the occurrence of a predetermined event, e.g. an object reaching a predetermined position signal generation from motion picture films H04N5/253
• CPC Class Codes: H04W 12/06 : Authentication
• CPC Class Codes: H04W 12/72 : Subscriber identity
• CPC Class Codes: H04W 4/44 : for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]
• CPC Class Codes: H04W 4/90 : Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]

Abstract

The present application is directed to an Autonomous Vehicle Enhancement System (AVES) and method for monitoring and managing a virtual or existing fleet of autonomous vehicles in a transportation network and dispatching the autonomous vehicles to users. The AVES includes an AVES Central Operations Center (COC) that communicates with AVES vehicle equipment installed in the autonomous vehicles and AVES applications installed on computing devices accessible by the users. The AVES improves the operating efficiency of a transportation network by monitoring the condition of autonomous vehicles, optimizing the geographical distribution of the autonomous vehicles and optimizing assignment of the autonomous vehicles to users requesting services. The AVES COC monitors and controls the autonomous vehicles via the AVES vehicle equipment. The AVES COC communicates with the users via the AVES applications to provide services to the users. The AVES vehicle equipment and the AVES applications installed on the computing devices communicate with each other to fulfill user request for services.

Claims

1. An Autonomous Vehicle Enhancement System (AVES) for managing a fleet of autonomous vehicles in an existing or virtual transportation network, comprising:

AVES vehicle equipment installed on each autonomous vehicle; and

an AVES Central Operations Center (COC) in communication with the AVES vehicle equipment on each autonomous vehicle;

wherein the AVES COC monitors the fleet of autonomous vehicles via the AVES vehicle equipment installed on each autonomous vehicle to determine real-time vehicle location, real-time vehicle availability, real-time vehicle operational state, real-time vehicle condition and real-time vehicle fuel/power-charge level of each autonomous vehicle in the fleet;

wherein the AVES COC assigns autonomous vehicles in the fleet to an active pool based on pre-defined criteria comprising if the autonomous vehicles are available, are in acceptable operational state, are in acceptable condition and have acceptable fuel/power-charge level;

wherein the AVES COC receives trip requests from hailing users, selects autonomous vehicles from the active pool to respond to trip requests and guides the selected autonomous vehicles to the hailing users;

wherein the AVES COC guides autonomous vehicles in the fleet, which are not in acceptable operational state or condition, to service locations for maintenance service;

wherein the AVES COC guides autonomous vehicles in the fleet, which do not have acceptable fuel/power-charge level, to fueling/power-charging stations for fueling/power-charging;

wherein the AVES COC guides autonomous vehicles in the active pool, which are not assigned to respond to trip requests from hailing users, to standby locations throughout the transportation network'"'"'s geographic area; and

wherein the real-time vehicle operational state of each autonomous vehicle in the fleet includes information regarding the operational state of the mechanical and electrical systems of the autonomous vehicle, and the real-time vehicle condition of each autonomous vehicle in the fleet includes information regarding state of cleanliness of the autonomous vehicle.

2. The Autonomous Vehicle Enhancement System (AVES) according to claim 1,

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base;

and

wherein the Artificial Intelligence System executes a Vehicle Distribution Optimization Algorithm to determine standby locations for the autonomous vehicles in the fleet.

3. The Autonomous Vehicle Enhancement System (AVES) according to claim 2,

wherein the knowledge base includes at least one of real-time vehicle location data for the autonomous vehicles in the active pool, historical information regarding trip requests, real-time information regarding trip requests and information regarding scheduled events in the transportation network'"'"'s geographic area;

and

wherein the Vehicle Distribution Optimization Algorithm uses the knowledge base to determine standby locations for the autonomous vehicles in the transportation network'"'"'s geographic area to achieve a vehicle distribution that is optimally suited for fulfilling potential trip requests.

4. The Autonomous Vehicle Enhancement System (AVES) according to claim 3, wherein the real-time information regarding trip requests and information regarding scheduled events in the transportation network'"'"'s geographic area are received by the AVES COC from public messaging systems and/or the AVES vehicle equipment installed on the autonomous vehicles in the fleet.

5. The Autonomous Vehicle Enhancement System (AVES) according to claim 1,

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base;

and

wherein the Artificial Intelligence System executes the Matchmaking Algorithm by using the knowledge base to select autonomous vehicles from the active pool that are optimally suited to respond to the trip requests.

6. The Autonomous Vehicle Enhancement System (AVES) according to claim 5, wherein the knowledge base includes user preference(s) and at least one of real-time vehicle location data, real-time vehicle availability data, real-time vehicle operational state, real-time vehicle condition data, real-time vehicle fuel/power-charge level indication, vehicle owner specified restriction(s) and vehicle description for each of the autonomous vehicles in the fleet.

7. The Autonomous Vehicle Enhancement System (AVES) according to claim 1, wherein standby locations include fueling/power-charging stations, street parking spaces, parking garages and taxi queues.

8. The Autonomous Vehicle Enhancement System (AVES) according to claim 1, wherein the real-time vehicle availability of each autonomous vehicle in the fleet includes information regarding whether the autonomous vehicle is in the active pool, has been assigned to respond a trip request from a hailing user, is waiting or en route to pickup a hailing user for an assigned trip request, has picked up a hailing user for an assigned trip request or has dropped of a hailing user after completion of an assigned trip request.

9. The Autonomous Vehicle Enhancement System (AVES) according to claim 1, wherein the real-time vehicle fuel/power-charge level of each autonomous vehicle in the fleet includes information regarding at least one of a vehicle travel distance range, a percentage of fuel tank or electrical battery capacity available and units of available power or energy.

10. The Autonomous Vehicle Enhancement System (AVES) according to claim 1, wherein the AVES COC is configured to select and substitute a newly available autonomous vehicle in the active pool to respond to a trip request of the trip requests and guide the newly available autonomous vehicle to a hailing user of the hailing users after an initial autonomous vehicle of the autonomous vehicles was selected to respond to the trip request and at least partly guided to the hailing user if the AVES COC determines that the newly available autonomous vehicle is better suited for fulfilling the trip request of the hailing user.

11. The Autonomous Vehicle Enhancement System (AVES) according to claim 10, wherein the AVES COC is configured to refrain from selecting and substituting the newly available autonomous vehicle for fulfilling the trip request if the scheduled pickup time for the trip request is within a specified period of time and/or if the information about the initial autonomous vehicle was sent to the hailing user and/or an application installed on a computing device accessible by the hailing user.

12. The Autonomous Vehicle Enhancement System (AVES) according to claim 1,

wherein the information regarding the state of cleanliness of the autonomous vehicle comprises information regarding a condition of the interior of the autonomous vehicle and/or a condition of the user inside the autonomous vehicle;

wherein the AVES vehicle equipment includes an interior video camera for sending images of the interior of the autonomous vehicle to the AVES COC so that the condition of the interior of the autonomous vehicle and/or the condition of the user inside the autonomous vehicle can be inspected.

13. The Autonomous Vehicle Enhancement System (AVES) according to claim 12, wherein the condition of the interior of the autonomous vehicle comprises the interior of the autonomous vehicle being soiled, littered, in disrepair, and/or containing an item belonging to a previous user.

14. The Autonomous Vehicle Enhancement System (AVES) according to claim 13, wherein the AVES COC is configured to remove the autonomous vehicle from the active pool if the autonomous vehicle contains the item belonging to the previous user, wherein the AVES COC is configured to guide the autonomous vehicle to a service location for retrieval of the item belonging to the previous user, and wherein the AVES COC is configured to notify the previous user that the item belonging to the previous user was forgotten.

15. The Autonomous Vehicle Enhancement System (AVES) according to claim 12, wherein the condition of the user inside the autonomous vehicle comprises the user remaining in the interior of the autonomous vehicle following fulfillment of the trip request, and wherein the AVES COC is configured to remove the autonomous vehicle from the active pool if the condition of the user inside the autonomous vehicle is that the user remains in the interior of the autonomous vehicle following fulfillment of the trip request.

16. An Autonomous Vehicle Enhancement System (AVES) for managing a fleet of autonomous vehicles and dispatching the autonomous vehicles in the fleet to users, comprising:

AVES vehicle equipment installed on each autonomous vehicle;

AVES applications installed on computing devices accessible by the users; and

an AVES Central Operations Center (COC) in communication with the AVES vehicle equipment on each autonomous vehicle and the AVES applications installed on the computing devices;

wherein the AVES COC receives a unique vehicle ID, real-time vehicle location, real-time vehicle availability, real-time vehicle operational state, real-time vehicle condition and real-time vehicle fuel/power-charge level for each autonomous vehicle from the AVES vehicle equipment installed on each autonomous vehicle;

wherein the AVES COC receives a trip request from a hailing user via a selected AVES application on a selected computing device, the trip request including a unique user ID associated with the hailing user and trip information including pickup location, pickup date/time, destination location, and any intermediate stop location(s);

wherein the AVES COC determines a trip route based on the trip information for the trip request;

wherein the AVES COC selects one of the autonomous vehicles to fulfill the trip request based on the trip route, real-time vehicle location, real-time vehicle availability, real-time vehicle operational state, real-time vehicle condition and real-time vehicle fuel/power-charge level;

wherein the AVES COC generates a unique trip ID for the trip request, and associates the user ID for the hailing user and the vehicle ID for the selected autonomous vehicle with the unique trip ID; and

wherein the AVES COC sends the trip information and/or trip route to the AVES vehicle equipment of the selected autonomous vehicle to guide the selected autonomous vehicle to the pickup location at the pickup time.

17. The Autonomous Vehicle Enhancement System (AVES) according to claim 16,

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base;

and

wherein the Artificial Intelligence System executes a Route Determining Algorithm to determine the trip route for the trip request.

18. The Autonomous Vehicle Enhancement System (AVES) according to claim 17,

wherein the knowledge base includes at least one of historical traffic conditions, real-time traffic conditions, scheduled road closures, scheduled events and scheduled construction;

and

wherein the Artificial Intelligence System executes the Route Determining Algorithm by using the trip information and knowledge base to determine the trip route that is optimally suited for fulfilling the trip request.

19. The Autonomous Vehicle Enhancement System (AVES) according to claim 18, wherein the real-time traffic conditions, scheduled road closures, scheduled events and scheduled construction are received by the AVES COC from public messaging systems and/or the AVES vehicle equipment installed on the autonomous vehicles in the fleet.

20. The Autonomous Vehicle Enhancement System (AVES) according to claim 17, wherein the Artificial Intelligence System executes a Matchmaking Algorithm to select the autonomous vehicle that is optimally suited to fulfill the trip request.

21. The Autonomous Vehicle Enhancement System (AVES) according to claim 20,

wherein the knowledge base includes user preference(s) and at least one of real-time vehicle location data, real-time vehicle availability data, real-time vehicle operational state, real-time vehicle condition data, real-time vehicle fuel/power-charge level indication, vehicle owner specified restriction(s) and vehicle description for each of the autonomous vehicles in the fleet;

and

wherein the Matchmaking Algorithm uses the determined trip route and knowledge base to select the autonomous vehicle that is optimally suited for fulfilling the trip request.

22. The Autonomous Vehicle Enhancement System (AVES) according to claim 21, wherein if, before the completion of a scheduled pickup date/time for the trip request, the Matchmaking Algorithm determines that a newly available autonomous vehicle is better suited for fulfilling the trip request than the previously selected autonomous vehicle, the Artificial Intelligence System selects the newly available autonomous vehicle to fulfill the trip request in lieu of the previously selected vehicle.

23. The Autonomous Vehicle Enhancement System (AVES) according to claim 16, wherein if the trip request exceeds an operational range of any available autonomous vehicle in the fleet, the AVES COC implements sequential multi-hop routing to select and coordinate two or more autonomous vehicles to fulfill the trip request.

24. The Autonomous Vehicle Enhancement System (AVES) according to claim 16, wherein if the trip request exceeds a passenger capacity of any available autonomous vehicle in the fleet, the AVES COC implements simultaneous multi-vehicle routing to select and coordinate two or more autonomous vehicles to fulfill the trip request.

25. The Autonomous Vehicle Enhancement System (AVES) according to claim 16, wherein the AVES COC selects and unilaterally, unequivocally and automatically assigns the autonomous vehicle to fulfill the trip request.

26. The Autonomous Vehicle Enhancement System (AVES) according to claim 16,

wherein the AVES COC generates a trip PIN corresponding to the unique trip ID for the trip request;

and

wherein the AVES COC sends the unique trip ID and corresponding unique trip PIN to the AVES vehicle equipment of the selected autonomous vehicle and to the selected AVES application on the selected computing device of the hailing user.

27. The Autonomous Vehicle Enhancement System (AVES) according to claim 16,

wherein the selected AVES application transmits a signal encoded with the unique trip ID via the selected computing device;

wherein the AVES vehicle equipment includes an antenna that scans for the AVES application'"'"'s signal encoded with the unique trip ID;

wherein the AVES vehicle equipment detects the AVES application'"'"'s signal encoded with the unique trip ID and guides the autonomous vehicle toward the selected computing device, stops the autonomous vehicle, unlocks the doors of the autonomous vehicle and waits for the hailing user to enter the autonomous vehicle.

28. The Autonomous Vehicle Enhancement System (AVES) according to claim 16,

wherein the AVES vehicle equipment includes a signal emitter that transmits a signal encoded with the unique trip ID;

and

wherein the selected AVES application activates a camera on the selected computing device, senses the signal encoded with the unique trip ID and identifies on an image captured by the camera the selected autonomous vehicle transmitting the signal encoded with the unique trip ID.

29. The Autonomous Vehicle Enhancement System (AVES) according to claim 16,

wherein the AVES COC stores a vehicle description for each autonomous vehicle in the fleet;

and

wherein the AVES COC sends the vehicle description for the selected autonomous vehicle to the selected AVES application on the selected computing device of the hailing user.

30. The Autonomous Vehicle Enhancement System (AVES) according to claim 16,

wherein the AVES vehicle equipment includes an AVES vehicle control system operably connected to a vehicle computer system of the autonomous vehicle;

and

wherein the AVES vehicle control system transmits the trip information and/or trip route for the trip request to the vehicle computer system for guiding the autonomous vehicle to the pickup location at the pickup time.

31. The Autonomous Vehicle Enhancement System (AVES) according to claim 16,

wherein the AVES vehicle equipment includes an AVES vehicle control system operably connected to a vehicle computer system of the autonomous vehicle;

and

wherein the AVES vehicle control system transmits driving commands to the vehicle computer system of autonomous vehicle based on the trip information and/or trip route for guiding the autonomous vehicle to the pickup location at the pickup time.

32. The Autonomous Vehicle Enhancement System (AVES) according to claim 16, wherein the AVES vehicle equipment includes an interior video camera for sending images of the interior of the autonomous vehicle to the AVES COC so that a condition of the interior of the autonomous vehicle and/or a condition of the user inside the autonomous vehicle can be inspected.

33. The Autonomous Vehicle Enhancement System (AVES) according to claim 16, wherein the AVES vehicle equipment includes an Interactive Voice Response (IVR) system with a microphone and speaker and/or an interactive touchscreen for the user inside the autonomous vehicle and the AVES COC to communicate.

34. The Autonomous Vehicle Enhancement System (AVES) according to claim 33, wherein the IVR system and/or the interactive touchscreen identifies the unique trip ID and prompts the user for the unique trip PIN to authenticate the trip request.

35. The Autonomous Vehicle Enhancement System (AVES) according to claim 33,

wherein at least one of the IVR system, the interactive touchscreen and the AVES application receives a modification request from the hailing user to alter the trip information and sends the modification request to alter the trip information to the AVES COC;

wherein the AVES COC determines whether the selected autonomous vehicle is capable of fulfilling the modification request and determines an altered trip route;

wherein if the selected autonomous vehicle is capable of fulfilling the modification request to alter the trip information and there is sufficient payment capacity, the AVES COC confirms that the user accepts the altered trip route via the IVR system, the interactive touchscreen or the AVES application and guides the selected autonomous vehicle according to the altered trip route; and

wherein if the selected autonomous vehicle is not capable of fulfilling the modification request to alter the trip information, the AVES COC selects an alternative autonomous vehicle for fulfilling the modification request to alter the trip information, confirms that the user accepts the alternative autonomous vehicle via the IVR system, the interactive touchscreen or the AVES application and implements multi-hop routing to coordinate the selected autonomous vehicle and the alternative autonomous vehicle to fulfill the trip request according to the altered trip route.

36. The Autonomous Vehicle Enhancement System (AVES) according to claim 33,

wherein at least one of the IVR system, the interactive touchscreen and the AVES application receives a user emergency alert and sends the user emergency alert to the AVES COC;

wherein the AVES COC initiates an emergency protocol where the interior video camera and the IVR are activated so that AVES COC can assess the user emergency; and

wherein the AVES COC reroutes the autonomous vehicle to a hospital, police station or fire station, and/or calls for emergency response service.

37. The Autonomous Vehicle Enhancement System (AVES) according to claim 16, wherein the AVES vehicle equipment is operably connected to a computer system of the autonomous vehicle to control unlocking/locking of doors of the autonomous vehicle and to detect whether the doors of the autonomous vehicle are open/closed.

38. A method of managing a fleet of autonomous vehicles in a transportation network and dispatching the autonomous vehicles in the fleet to transportation network users, comprising the steps of:

providing an Autonomous Vehicle Enhancement System (AVES) comprising an AVES Central Operations Center (COC) in communication with AVES vehicle equipment installed on each autonomous vehicle in the fleet and with AVES applications installed on computing devices accessible by the users;

the AVES COC monitoring the fleet of autonomous vehicles via the AVES vehicle equipment installed on each autonomous vehicle to determine real-time vehicle location, real-time vehicle availability, real-time vehicle operational state, real-time vehicle condition and real-time vehicle fuel/power-charge level of each autonomous vehicle in the fleet;

the AVES Central Operations Center (COC) assigning autonomous vehicles in the fleet to an active pool if the autonomous vehicles are available, are in acceptable operational state, are in acceptable condition and have acceptable fuel/power-charge level;

the AVES COC receiving trip requests from hailing users, selecting autonomous vehicles from the active pool to respond to the trip requests and guiding the selected autonomous vehicles to the hailing users;

the AVES COC guiding autonomous vehicles in the fleet, which are not in acceptable operational state or condition, to service locations for maintenance service;

the AVES COC guiding autonomous vehicles in the fleet, which do not have acceptable fuel/power-charge level, to fueling/power-charging stations for fueling/power-charging;

the AVES COC guiding autonomous vehicles in the active pool, which are not assigned to respond to trip requests from hailing users, to standby locations throughout the transportation network'"'"'s geographic area;

wherein the real-time vehicle operational state of each autonomous vehicle in the fleet includes information regarding the operational state of the mechanical and electrical systems of the autonomous vehicle, and the real-time vehicle condition of each autonomous vehicle in the fleet includes information regarding state of cleanliness of the autonomous vehicle.

39. The method according to claim 38,

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base;

and

the Artificial Intelligence System executing a Vehicle Distribution Optimization Algorithm to determine standby locations for the autonomous vehicles in the fleet.

40. The method according to claim 39,

wherein the knowledge base includes at least one of real-time vehicle location data for the autonomous vehicles in the active pool, historical information regarding trip requests, real-time information regarding trip requests and information regarding scheduled events in the transportation network'"'"'s geographic area;

and

the Vehicle Distribution Optimization Algorithm using the knowledge base to determine standby locations for the autonomous vehicles in the transportation network'"'"'s geographic area to achieve a vehicle distribution that is optimally suited for fulfilling trip requests.

41. The method according to claim 40, wherein the real-time information regarding trip requests and information regarding scheduled events in the transportation network'"'"'s geographic area are received by the AVES COC from public messaging systems, the AVES applications installed on computing devices accessible by the users or the AVES vehicle equipment installed on the autonomous vehicles in the fleet.

42. The method according to claim 38,

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base;

and

the Artificial Intelligence System executing the Matchmaking Algorithm by using the knowledge base to select autonomous vehicles from the active pool that are optimally suited to respond to the trip requests.

43. The method according to claim 42, wherein the knowledge base includes user preference(s) and at least one of real-time vehicle location data, real-time vehicle availability data, real-time vehicle operational state, real-time vehicle condition data, real-time vehicle fuel/power-charge level indication, vehicle owner specified restriction(s), vehicle description for each of the autonomous vehicles in the fleet.

44. The method according to claim 38, wherein standby locations include fueling/power-charging stations, street parking spaces, parking garages and taxi queues.

45. The method according to claim 38, wherein the real-time vehicle availability of each autonomous vehicle in the fleet includes information regarding whether the autonomous vehicle is in the active pool, has been assigned to respond a trip request from a hailing user, is waiting or en route to pickup a hailing user for an assigned trip request, has picked up a hailing user for an assigned trip request or has dropped off a hailing user after completion of an assigned trip request.

46. The method according to claim 38, wherein the real-time vehicle fuel/power-charge level of each autonomous vehicle in the fleet includes information regarding at least one of a vehicle travel distance range, a percentage of fuel tank or electrical battery capacity available and units of available power or energy.

47. An Autonomous Vehicle Enhancement System (AVES) for managing a fleet of autonomous vehicles in an existing or virtual transportation network, comprising:

AVES vehicle equipment installed on each autonomous vehicle; and

an AVES Central Operations Center (COC) in communication with the AVES vehicle equipment on each autonomous vehicle;

wherein the AVES COC monitors the fleet of autonomous vehicles via the AVES vehicle equipment installed on each autonomous vehicle to determine real-time vehicle location, real-time vehicle availability, real-time vehicle operational state, real-time vehicle condition and real-time vehicle fuel/power-charge level of each autonomous vehicle in the fleet;

wherein the AVES COC assigns autonomous vehicles in the fleet to an active pool based on pre-defined criteria comprising if the autonomous vehicles are available, are in acceptable operational state, are in acceptable condition and have acceptable fuel/power-charge level;

wherein the AVES COC receives trip requests from hailing users, selects autonomous vehicles from the active pool to respond to trip requests and guides the selected autonomous vehicles to the hailing users;

wherein the AVES COC guides autonomous vehicles in the fleet, which are not in acceptable operational state or condition, to service locations for maintenance service;

wherein the AVES COC guides autonomous vehicles in the fleet, which do not have acceptable fuel/power-charge level, to fueling/power-charging stations for fueling/power-charging;

wherein the AVES COC guides autonomous vehicles in the active pool, which are not assigned to respond to trip requests from hailing users, to standby locations throughout the transportation network'"'"'s geographic area;

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base; and

wherein the Artificial Intelligence System executes a Vehicle Distribution Optimization Algorithm to determine standby locations for the autonomous vehicles in the fleet.

48. The Autonomous Vehicle Enhancement System (AVES) according to claim 47,

wherein the knowledge base includes at least one of real-time vehicle location data for the autonomous vehicles in the active pool, historical information regarding trip requests, real-time information regarding trip requests and information regarding scheduled events in the transportation network'"'"'s geographic area;

and

wherein the Vehicle Distribution Optimization Algorithm uses the knowledge base to determine standby locations for the autonomous vehicles in the transportation network'"'"'s geographic area to achieve a vehicle distribution that is optimally suited for fulfilling potential trip requests.

49. The Autonomous Vehicle Enhancement System (AVES) according to claim 48, wherein the real-time information regarding trip requests and information regarding scheduled events in the transportation network'"'"'s geographic area are received by the AVES COC from public messaging systems and/or the AVES vehicle equipment installed on the autonomous vehicles in the fleet.

50. An Autonomous Vehicle Enhancement System (AVES) for managing a fleet of autonomous vehicles in an existing or virtual transportation network, comprising:

AVES vehicle equipment installed on each autonomous vehicle; and

an AVES Central Operations Center (COC) in communication with the AVES vehicle equipment on each autonomous vehicle;

wherein the AVES COC monitors the fleet of autonomous vehicles via the AVES vehicle equipment installed on each autonomous vehicle to determine real-time vehicle location, real-time vehicle availability, real-time vehicle operational state, real-time vehicle condition and real-time vehicle fuel/power-charge level of each autonomous vehicle in the fleet;

wherein the AVES COC assigns autonomous vehicles in the fleet to an active pool based on pre-defined criteria comprising if the autonomous vehicles are available, are in acceptable operational state, are in acceptable condition and have acceptable fuel/power-charge level;

wherein the AVES COC receives trip requests from hailing users, selects autonomous vehicles from the active pool to respond to trip requests and guides the selected autonomous vehicles to the hailing users;

wherein the AVES COC guides autonomous vehicles in the fleet, which are not in acceptable operational state or condition, to service locations for maintenance service;

wherein the AVES COC guides autonomous vehicles in the fleet, which do not have acceptable fuel/power-charge level, to fueling/power-charging stations for fueling/power-charging;

wherein the AVES COC guides autonomous vehicles in the active pool, which are not assigned to respond to trip requests from hailing users, to standby locations throughout the transportation network'"'"'s geographic area;

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base; and

wherein the Artificial Intelligence System executes the Matchmaking Algorithm by using the knowledge base to select autonomous vehicles from the active pool that are optimally suited to respond to the trip requests.

51. The Autonomous Vehicle Enhancement System (AVES) according to claim 50, wherein the knowledge base includes user preference(s) and at least one of real-time vehicle location data, real-time vehicle availability data, real-time vehicle operational state, real-time vehicle condition data, real-time vehicle fuel/power-charge level indication, vehicle owner specified restriction(s) and vehicle description for each of the autonomous vehicles in the fleet.

52. A method of managing a fleet of autonomous vehicles in a transportation network and dispatching the autonomous vehicles in the fleet to transportation network users, comprising the steps of:

providing an Autonomous Vehicle Enhancement System (AVES) comprising an AVES Central Operations Center (COC) in communication with AVES vehicle equipment installed on each autonomous vehicle in the fleet and with AVES applications installed on computing devices accessible by the users;

the AVES COC monitoring the fleet of autonomous vehicles via the AVES vehicle equipment installed on each autonomous vehicle to determine real-time vehicle location, real-time vehicle availability, real-time vehicle operational state, real-time vehicle condition and real-time vehicle fuel/power-charge level of each autonomous vehicle in the fleet;

the AVES Central Operations Center (COC) assigning autonomous vehicles in the fleet to an active pool if the autonomous vehicles are available, are in acceptable operational state, are in acceptable condition and have acceptable fuel/power-charge level;

the AVES COC receiving trip requests from hailing users, selecting autonomous vehicles from the active pool to respond to the trip requests and guiding the selected autonomous vehicles to the hailing users;

the AVES COC guiding autonomous vehicles in the fleet, which are not in acceptable operational state or condition, to service locations for maintenance service;

the AVES COC guiding autonomous vehicles in the fleet, which do not have acceptable fuel/power-charge level, to fueling/power-charging stations for fueling/power-charging;

the AVES COC guiding autonomous vehicles in the active pool, which are not assigned to respond to trip requests from hailing users, to standby locations throughout the transportation network'"'"'s geographic area;

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base; and

the Artificial Intelligence System executing a Vehicle Distribution Optimization Algorithm to determine standby locations for the autonomous vehicles in the fleet.

53. The method according to claim 52,

wherein the knowledge base includes at least one of real-time vehicle location data for the autonomous vehicles in the active pool, historical information regarding trip requests, real-time information regarding trip requests and information regarding scheduled events in the transportation network'"'"'s geographic area;

and

the Vehicle Distribution Optimization Algorithm using the knowledge base to determine standby locations for the autonomous vehicles in the transportation network'"'"'s geographic area to achieve a vehicle distribution that is optimally suited for fulfilling trip requests.

54. The method according to claim 53, wherein the real-time information regarding trip requests and information regarding scheduled events in the transportation network'"'"'s geographic area are received by the AVES COC from public messaging systems, the AVES applications installed on computing devices accessible by the users or the AVES vehicle equipment installed on the autonomous vehicles in the fleet.

55. A method of managing a fleet of autonomous vehicles in a transportation network and dispatching the autonomous vehicles in the fleet to transportation network users, comprising the steps of:

providing an Autonomous Vehicle Enhancement System (AVES) comprising an AVES Central Operations Center (COC) in communication with AVES vehicle equipment installed on each autonomous vehicle in the fleet and with AVES applications installed on computing devices accessible by the users;

the AVES COC monitoring the fleet of autonomous vehicles via the AVES vehicle equipment installed on each autonomous vehicle to determine real-time vehicle location, real-time vehicle availability, real-time vehicle operational state, real-time vehicle condition and real-time vehicle fuel/power-charge level of each autonomous vehicle in the fleet;

the AVES Central Operations Center (COC) assigning autonomous vehicles in the fleet to an active pool if the autonomous vehicles are available, are in acceptable operational state, are in acceptable condition and have acceptable fuel/power-charge level;

the AVES COC receiving trip requests from hailing users, selecting autonomous vehicles from the active pool to respond to the trip requests and guiding the selected autonomous vehicles to the hailing users;

the AVES COC guiding autonomous vehicles in the fleet, which are not in acceptable operational state or condition, to service locations for maintenance service;

the AVES COC guiding autonomous vehicles in the fleet, which do not have acceptable fuel/power-charge level, to fueling/power-charging stations for fueling/power-charging;

the AVES COC guiding autonomous vehicles in the active pool, which are not assigned to respond to trip requests from hailing users, to standby locations throughout the transportation network'"'"'s geographic area;

wherein the AVES COC comprises an Artificial Intelligence System including a knowledge base; and

the Artificial Intelligence System executing the Matchmaking Algorithm by using the knowledge base to select autonomous vehicles from the active pool that are optimally suited to respond to the trip requests.

56. The method according to claim 55, wherein the knowledge base includes user preference(s) and at least one of real-time vehicle location data, real-time vehicle availability data, real-time vehicle operational state, real-time vehicle condition data, real-time vehicle fuel/power-charge level indication, vehicle owner specified restriction(s), vehicle description for each of the autonomous vehicles in the fleet.