Autonomous vehicle repositioning

• US 10,331,124 B2
• Filed: 07/19/2018
• Issued: 06/25/2019
• Est. Priority Date: 07/20/2017
• Status: Active Grant

• Current Assignee: Nuro, Inc.
• Original Assignee: Nuro, Inc.
• Inventors: Ferguson, David, Zhu, Jiajun, Leipold, Cosimo, Jirapinyo, Pichayut
• Primary Examiner(s): Sample, Jonathan L
• Application Number:
  US16/040,446
• Publication Number:
  US 20190025820A1
• Time in Patent Office: 341 Days
• Field of Search: 701 23- 28
• 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: 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 1/0088 characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours using knowledge based models G06N5/00
• CPC Class Codes: G05D 1/0291 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 16/29 Geographical information databases
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/025 Extracting rules from data
• CPC Class Codes: G06Q 30/0205 Location or geographical consideration
• CPC Class Codes: G06T 2207/10044 Radar image
• CPC Class Codes: G06T 2207/30184 Infrastructure
• CPC Class Codes: G06T 2207/30256 Lane; Road marking
• CPC Class Codes: G06T 7/0004 Industrial image inspection
• CPC Class Codes: G06V 20/56 exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/582 of traffic signs
• CPC Class Codes: G06V 20/584 of vehicle lights or traffic lights
• CPC Class Codes: G06V 20/586 of parking space
• CPC Class Codes: G06V 20/588 Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
• CPC Class Codes: G06V 20/597 Recognising the driver's state or behaviour, e.g. attention or drowsiness
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0175 : by photographing vehicles, e.g. when violating traffic rules
• CPC Class Codes: G08G 1/143 : inside the vehicles
• CPC Class Codes: G08G 1/148 : Management of a network of parking areas
• CPC Class Codes: G08G 1/20 : Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles

Abstract

Provided herein is a platform for distributing and navigating an autonomous or semi-autonomous fleet throughout a plurality of pathways. The platform may employ demand distribution prediction algorithms, and interim repositioning algorithms to distribute the autonomous or semi-autonomous fleet for performing orders and tasks.

Claims

1. A platform for distributing and navigating an autonomous or semi-autonomous fleet throughout a plurality of pathways, the platform comprising:

a) the fleet comprising a plurality of autonomous or semi-autonomous vehicles wherein each autonomous or semi-autonomous vehicle comprises;

(i) an autonomous or semi-autonomous propulsion system;

(ii) a locational sensor configured to measure a current vehicle location of the vehicle;

(iii) a condition sensor configured to measure a current vehicle status; and

(iv) a communication device configured to transmit the current vehicle location and the current vehicle status; and

b) a server processor configured to provide a server application comprising;

(i) a database comprising a map of the plurality of pathways, wherein each pathway is associated with a pathway parameter comprising an autonomous driving safety factor and a speed factor;

(ii) a demand database comprising a historical demand data associated with a geographic area, and wherein the geographic area comprises at least the task destination;

(iii) a demand forecasting module applying a prediction algorithm to determine a predicted demand schedule for each of the autonomous or semi-autonomous vehicles based at least on the historical demand data, wherein the predicted demand schedule comprises a predicted demand task location within the geographic area and a predicted demand period;

(iv) a communication module receiving the current vehicle location and the current vehicle status from the communication device;

(v) a dispatch module assigning one or more of the plurality of autonomous or semi-autonomous vehicles to a task destination based at least on the current vehicle location and the current vehicle status;

(vi) a navigation module applying a route calculation algorithm to determine a vehicle task route from the current vehicle location to the task destination based at least on the pathway parameter and the current vehicle status, wherein the vehicle task route comprises at least a portion of one of the plurality of pathways; and

(vii) an interim repositioning module assigning an interim repositioning mode to each of the plurality of autonomous or semi-autonomous vehicles based at least on one or more of the predicted demand task location, the predicted demand period, the task destination, and the current vehicle status, the interim repositioning modes comprising;

i. a depot mode corresponding to a depot location;

ii. a parking mode associated with one of a plurality of parking spot locations; and

iii. a hover mode associated with a set threshold hover distance from the task destination or the predicted demand task location,

wherein the communication device further directs the autonomous or semi-autonomous propulsion system of the autonomous or semi-autonomous vehicle based on the vehicle task route.

2. The platform of claim 1, wherein the database further comprises the plurality of parking spot locations within the geographic area.

3. The platform of claim 2, wherein the application further comprises a parking distribution module determining a selected parking spot location for one or more of the plurality of autonomous or semi-autonomous vehicles based on the parking mode, the plurality of the parking spot locations, and at least one of the task destination and the predicted demand task location.

4. The platform of claim 3, wherein the navigation module further applies the route calculation algorithm to determine a vehicle repositioning route from the task destination:

a) to the depot location, based on the depot mode;

b) to the selected parking spot location, based on the parking mode;

or

c) to a vehicle hover route based on the hover mode.

5. The platform of claim 4, wherein the vehicle hover route comprises at least a portion of one of the plurality of pathways within the set threshold hover distance from the task destination or the predicted demand task location.

6. The platform of claim 4, wherein the communication device further directs the autonomous or semi-autonomous propulsion system of the autonomous or semi-autonomous vehicle to remain at the depot location, the selected parking spot location, or within the vehicle hover route for the predicted demand period.

7. The platform of claim 1, wherein the route calculation algorithm comprises a machine learning algorithm, a rule-based algorithm, or both.

8. The platform of claim 1, wherein the prediction algorithm comprises a machine learning algorithm, a rule-based algorithm, or both.

9. The platform of claim 1, wherein the current vehicle status comprises a vehicle power level, a vehicle stock, a vehicle hardware status, or any combination thereof.

10. The platform of claim 1, wherein at least one of the speed factor and the autonomous driving safety factor comprises a speed limit, an average speed, an average speed, a time-dependent average speed, a number of intersections, a number of turns, a turn type, an accident indicator, a stopped vehicle indicator, a number of lanes, a number of lanes, a one-way street indicator, a cellular reception parameter, a road slope, a maximum road slope, an average road slope, an average pedestrian density, a maximum pedestrian density, a minimum pedestrian density, a time-dependent pedestrian density, an average cyclist density, an unprotected turn parameter, a road smoothness parameter, a road visibility parameter, or any combination thereof.

11. The platform of claim 1, wherein the autonomous or semi-autonomous vehicle further comprises a sensor configured to measure a sensed data.

12. The platform of claim 11, wherein the database further stores at least one of the current vehicle location, the current vehicle status, and the sensed data.

13. The platform of claim 12, wherein at least one of the safety factor and speed factor is based on the sensed data.

14. The platform of claim 11, wherein the sensed data enables crowd sourced safety factor and speed factor determination.

15. The platform of claim 14, wherein the application further comprises a pathway parameter prediction module predicting a future pathway parameter based at least on the sensed data.

16. The platform of claim 15, wherein the route calculation algorithm further determines the vehicle task route based on the predicted road parameter.

17. The platform of claim 1, wherein the autonomous or semi-autonomous vehicle further comprises a sensor configured to measure a sensed data, and wherein the sensed data corresponds to a parking spot status of one or more of the plurality of parking spot locations within the geographic area.

18. The platform of claim 17, wherein the parking distribution module further determines the selected parking spot location based on the parking spot status.

19. The platform of claim 2, wherein the server application further comprises a display module displaying at least one of the current vehicle location, the current vehicle status, the task destination, the pathway parameter, the task route, the selected parking spot location, and the predicted demand task location.