Automotive drone deployment system

• US 10,642,279 B2
• Filed: 01/30/2017
• Issued: 05/05/2020
• Est. Priority Date: 07/16/2014
• Status: Expired due to Fees

• Current Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Original Assignee: Ford Global Technologies, LLC (Ford Motor Company)
• Inventors: Lockwood, John A., Stanek, Joseph F.
• Primary Examiner(s): Antonucci, Anne Marie
• Assistant Examiner(s): Stroud, James E
• Application Number:
  US15/419,814
• Publication Number:
  US 20170139421A1
• Time in Patent Office: 1,191 Days
• Field of Search: 701 3
• CPC Class Codes: B60R 16/02 electric constitutive elements
• CPC Class Codes: B60W 30/00 Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
• CPC Class Codes: B64C 2230/00 Boundary layer controls
• CPC Class Codes: B64C 29/0008 having its flight directional axis horizontal when grounded
• CPC Class Codes: B64C 29/0091 Accessories not provided for elsewhere
• CPC Class Codes: B64C 29/02 having its flight directional axis vertical when grounded
• CPC Class Codes: B64U 10/00 Type of UAV
• CPC Class Codes: B64U 2101/00 UAVs specially adapted for particular uses or applications
• CPC Class Codes: B64U 2101/20 for use as communications relays, e.g. high-altitude platforms
• CPC Class Codes: B64U 2101/30 for imaging, photography or videography
• CPC Class Codes: B64U 2101/64 for parcel delivery or retrieval
• CPC Class Codes: B64U 2201/00 UAVs characterised by their flight controls
• CPC Class Codes: B64U 2201/10 autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
• CPC Class Codes: B64U 2201/20 Remote controls
• CPC Class Codes: B64U 70/00 Launching, take-off or landing arrangements
• CPC Class Codes: B64U 70/30 for capturing UAVs in flight by ground or sea-based arresting gear, e.g. by a cable or a net
• CPC Class Codes: B64U 70/60 Take-off or landing of UAVs from a runway using their own power
• CPC Class Codes: B64U 70/80 Vertical take-off or landing, e.g. using rockets rotorcrafts B64U10/10; VTOL aircraft B64U10/20
• CPC Class Codes: B64U 70/83 using parachutes, balloons or the like
• CPC Class Codes: B64U 80/00 Transport or storage specially adapted for UAVs
• CPC Class Codes: B64U 80/86 : Land vehicles
• CPC Class Codes: G05D 1/0022 : characterised by the communication link data switching networks in general H04L12/00
• 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 1/0094 : involving pointing a payload, e.g. camera, weapon, sensor, towards a fixed or moving target
• CPC Class Codes: G05D 1/0202 : specially adapted to aircraft
• 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 1/0231 : using optical position detecting means position-fixing by using electromagnetic waves other than radio waves, e.g. optical position detecting means G01S5/16
• CPC Class Codes: G05D 1/0242 : using non-visible light signals, e.g. IR or UV signals
• CPC Class Codes: G05D 1/0255 : using acoustic signals, e.g. ultra-sonic singals sonar systems designed for anti-collision purposes G01S15/93
• CPC Class Codes: G05D 1/0257 : using a radar radar systems designed for anti-collision purposes between land vehicles or between land vehicle and fixed obstacles G01S13/931
• 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 1/028 : using a RF signal
• CPC Class Codes: G08C 17/00 : Arrangements for transmitting signals characterised by the use of a wireless electrical link
• CPC Class Codes: G08G 1/012 : from other sources than vehicle or roadside beacons, e.g. mobile networks
• CPC Class Codes: G08G 1/091 : Traffic information broadcasting broadcasting communication H04H

Abstract

This disclosure generally relates to an automotive drone deployment system that includes at least a vehicle and a deployable drone that is configured to attach and detach from the vehicle. More specifically, the disclosure describes the vehicle and drone remaining in communication with each other to exchange information while the vehicle is being operated in an autonomous driving mode so that the vehicle'"'"'s performance under the autonomous driving mode is enhanced.

Claims

1. A vehicle comprising:

leading sensor(s) having a sensing range for monitoring in front of the vehicle; and

processor(s) to;

set a vehicle speed cap based on the sensing range;

receive traffic data from a server;

determine a congestion location based on the traffic data;

when the congestion location is beyond the sensing range, instruct a drone to monitor the congestion location;

obtain, by way of the drone, navigational data comprising a new road not included in a navigational map stored onboard the vehicle used for the vehicle navigation, wherein the drone is configured to;

predict a path of the vehicle;

identify the new road that is not included the navigational map stored onboard the vehicle;

obtain a photograph of a road nameplate of the road that identifies a name of the new road;

generate a navigational path from a current position of the vehicle to the new road; and

update the navigational map stored onboard the vehicle, the update comprising adding the new road to the navigational map using data obtained from the photograph of the road nameplate that identifies the name of the new road; and

autonomously control the vehicle steering and acceleration, by way of the processor(s), to navigate to the new road, the autonomous control comprising continuously varying the leading sensor range based on the path of the vehicle predicted by the drone.

2. The vehicle of claim 1, wherein the processor(s) are configured to:

reference a stored navigational map;

determine a deficiency of the stored navigational map, the deficiency comprising the new road that is not included the navigational map;

determine a location of the deficiency; and

instruct the drone to fly toward and monitor the determined deficiency location.

3. The vehicle of claim 2, wherein the processor(s) are configured to amend the stored navigational map based on the drone data transmitted by the drone that relates to the deficiency location.

4. The vehicle of claim 1, wherein the processor(s) are configured to:

autonomously steer the vehicle based on sensing data collected by the leading sensor(s); and

autonomously accelerate the vehicle based on the sensing data.

5. The vehicle of claim 4, wherein the processor(s) are configured to vary the speed cap based on the drone data received from the drone.

6. The vehicle of claim 1, wherein, to monitor the congestion area, the processor(s) instruct the drone to detach from the vehicle and fly toward the congestion area, locate a second drone device that is already in a deployed state, and communicate with the second drone device to obtain the navigational data.

7. The vehicle of claim 1, wherein the processor(s) are configured to:

project a vehicle route based on a received destination;

determine the sensing range of the leading sensor(s) based on the projected vehicle route;

determine a desired sensing range of the leading sensor(s);

compare the sensing range with the desired sensing range; and

transmit an instruction to a drone based on the comparison.

8. The vehicle of claim 7, wherein the processor(s) are configured to continuously recalculate the sensing range as the vehicle traverses the vehicle route.

9. The vehicle of claim 8, wherein the processor(s) are configured to determine the sensing range of the leading sensor(s) based on the projected vehicle route and a navigational map.

10. The vehicle of claim 7, wherein the processor(s) are configured to adjust the sensing range based on a turn of the projected vehicle route.

11. The vehicle of claim 10, wherein the processor(s) are configured to amend the projected route based on the drone data received from the drone that includes information related to a drone sensor measurement.

12. A method of controlling a drone with a vehicle, the vehicle comprising:

a plurality of sensors, including one or more leading sensors configured to sense external objects leading the vehicle, and processor(s), the method comprising, via the processor(s);

storing traffic data received from a traffic server;

determining a traffic congestion location based on the received traffic data;

transmitting an instruction to a drone to fly toward and sense the determined traffic congestion location;

obtaining, by way of the drone, navigational data comprising a new road not included in a navigational map stored onboard the vehicle used for the vehicle navigation;

wherein the drone is configured to;

predict a path of the vehicle;

identify the new road that is not included the navigational map stored onboard the vehicle;

obtain a photograph of a road nameplate of the road that identifies a name of the new road;

generate a navigational path from a current position of the vehicle to the new road;

update the navigational map stored onboard the vehicle, the update comprising adding the new road to the navigational map using data obtained from the photograph of the road nameplate that identifies the name of the new road; and

receiving sensed drone data relating to the traffic congestion location;

autonomously controlling the vehicle steering and acceleration, by way of the processor(s), to navigate to the new road, the autonomous control comprising continuously varying the leading sensor range based on the path of the vehicle predicted by the drone;

enforcing a speed cap on the vehicle; and

varying the enforced speed cap based on an estimated leading sensing range of the one or more leading sensors.

13. The method of claim 12, comprising:

referencing a stored navigational map;

determining a deficiency of the stored navigational map, the deficiency comprising the new road that is not included the navigational map;

determining a location of the deficiency; and

transmitting an instruction to the drone to fly toward and sense the determined deficiency location.

14. The method of claim 13, comprising amending the stored navigational map based on data transmitted by the drone relating to the deficiency location.

15. The method of claim 12 comprising:

autonomously steering the vehicle based on inputs from the plurality of sensors; and

autonomously accelerating the vehicle based on inputs from the plurality of sensors.

16. The method of claim 12, comprising:

receiving information from the drone; and

varying the enforced speed cap based on the received information.

17. The method of claim 12 comprising:

determining a vehicle speed;

projecting a vehicle route based on a received destination;

estimating the estimated leading sensing range of the one or more leading sensors based on the projected vehicle route;

determining a desired leading sensing range of the one or more leading sensors;

comparing the estimated leading sensing range with the desired leading sensing range; and

transmitting an instruction to the drone based on the comparison.

18. The method of claim 17, comprising periodically recalculating the leading sensing range as the vehicle traverses the vehicle route.

19. The method of claim 17, comprising determining an upcoming turn in the vehicle route and reducing the estimated leading sensing range based on the determination.

20. A vehicle comprising:

multiple sensors, including leading sensor(s) configured to sense external objects leading the vehicle, and processor(s) configured to;

store traffic data received from a traffic server;

determine a traffic congestion location using the received traffic data;

transmit an instruction to a drone to fly toward and sense the determined traffic congestion location;

obtain, by way of the drone, navigational data comprising a new road not included in a navigational map stored onboard the vehicle used for the vehicle navigation;

wherein the drone is configured to;

predict a path of the vehicle;

identify the new road that is not included the navigational map stored onboard the vehicle;

obtain a photograph of a road nameplate of the road that identifies a name of the new road;

generate a navigational path from a current position of the vehicle to the new road; and

update the navigational map stored onboard the vehicle, the update comprising adding the new road to the navigational map using data obtained from the photograph of the road nameplate that identifies the name of the new road;

receive sensed drone data regarding the traffic congestion location;

amend the stored traffic data based on the received drone data;

autonomously steer the vehicle based on inputs from the sensors; and

autonomously control the vehicle steering and acceleration to navigate to the new road, the autonomous control comprising continuously varying the leading sensor range based on the path of the vehicle predicted by the drone.