System and method of providing recommendations to users of vehicles

• US 10,372,129 B1
• Filed: 04/17/2017
• Issued: 08/06/2019
• Est. Priority Date: 10/05/2010
• Status: Active Grant

• Current Assignee: Waymo LLC (Alphabet Inc.)
• Original Assignee: Waymo LLC (Alphabet Inc.)
• Inventors: Urmson, Christopher Paul, Dolgov, Dmitri A., Chatham, Andrew Hughes, Nemec, Philip
• Primary Examiner(s): Chen, Shelley
• Application Number:
  US15/488,836
• Time in Patent Office: 841 Days
• Field of Search: None
• CPC Class Codes: B60R 1/23 with a predetermined field of view
• CPC Class Codes: B60R 2300/30 characterised by the type of image processing
• CPC Class Codes: B60T 17/18 Safety devices; Monitoring
• CPC Class Codes: B60T 17/221 Procedure or apparatus for checking or keeping in a correct functioning condition of brake systems hydraulic pressure systems in general F15B19/00, F15B21/04; testing structures or apparatus G01M
• CPC Class Codes: B60T 2201/022 Collision avoidance systems
• CPC Class Codes: B60T 2210/32 Vehicle surroundings
• CPC Class Codes: B60T 2270/406 Test-mode; Self-diagnosis
• CPC Class Codes: B60T 7/22 initiated by contact of vehicle, e.g. bumper, with an external object, e.g. another vehicle , or by means of contactless obstacle detectors mounted on the vehicle
• CPC Class Codes: B60T 8/00 Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force by changing number of effective brake cylinders in power brake systems B60T17/10
• CPC Class Codes: B60T 8/17 Using electrical or electronic regulation means to control braking detecting or indicating faulty operation B60T8/885
• CPC Class Codes: B60T 8/885 using electrical circuitry
• CPC Class Codes: B60W 2050/0292 Fail-safe or redundant systems, e.g. limp-home or backup systems
• CPC Class Codes: B60W 2420/403 Image sensing, e.g. optical camera
• CPC Class Codes: B60W 2420/408 Radar; Laser, e.g. lidar
• CPC Class Codes: B60W 2552/05 Type of road, e.g. motorways, local streets, paved or unpaved roads
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2556/10 Historical data
• CPC Class Codes: B60W 2556/50 of positioning data, e.g. GPS [Global Positioning System] data
• CPC Class Codes: B60W 30/08 Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
• CPC Class Codes: B60W 30/186 excessive wear or burn out of friction elements, e.g. clutches
• CPC Class Codes: B60W 40/06 : Road conditions
• CPC Class Codes: B60W 50/14 : Means for informing the driver, warning the driver or prompting a driver intervention
• CPC Class Codes: B62D 6/00 : Arrangements for automatically controlling steering depending on driving conditions sensed and responded to, e.g. control circuits means for initiating a change in direction B62D1/00; steering valves B62D5/06; combined with means for inclining the vehicle body or wheels on bends B62D9/00
• CPC Class Codes: G01C 21/3617 : using user history, behaviour, conditions or preferences, e.g. predicted or inferred from previous use or current movement
• CPC Class Codes: G01S 13/865 : Combination of radar systems with lidar systems
• CPC Class Codes: G01S 13/867 : Combination of radar systems with cameras
• CPC Class Codes: G01S 17/86 : Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
• CPC Class Codes: G01S 17/931 : of land vehicles
• CPC Class Codes: G05B 2219/2637 : Vehicle, car, auto, wheelchair
• 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 1/021 : specially adapted to land vehicles
• 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 1/0214 : in accordance with safety or protection criteria, e.g. avoiding hazardous areas monitoring the location of vehicles within a certain area, e.g. forbidden or allowed areas, in traffic control systems for road vehicles G08G1/13
• CPC Class Codes: G05D 1/024 : in combination with a laser lasers per se H01S
• CPC Class Codes: G05D 1/0246 : using a video camera in combination with image processing means
• 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 1/0274 : using mapping information stored in a memory device navigation using map-matching G01C21/30
• 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 1/0278 : using satellite positioning signals, e.g. GPS
• CPC Class Codes: G06Q 10/02 : Reservations, e.g. for tickets, services or events
• CPC Class Codes: G06Q 30/02 : Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0207 : Discounts or incentives, e.g. coupons or rebates
• CPC Class Codes: G06Q 40/08 : Insurance
• CPC Class Codes: G06T 2207/10004 : Still image; Photographic image
• CPC Class Codes: G06T 2207/10028 : Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/30236 : Traffic on road, railway or crossing
• CPC Class Codes: G06T 2207/30252 : Vehicle exterior; Vicinity of vehicle
• CPC Class Codes: G06T 2207/30261 : Obstacle
• CPC Class Codes: G06T 7/20 : Analysis of motion motion estimation for coding, decoding, compressing or decompressing digital video signals H04N19/43, H04N19/51
• CPC Class Codes: G06T 7/223 : using block-matching
• CPC Class Codes: G06T 7/231 : using full search
• CPC Class Codes: G06T 7/521 : from laser ranging, e.g. using interferometry; from the projection of structured light
• CPC Class Codes: G06T 7/74 : involving reference images or patches
• CPC Class Codes: G06V 10/255 : Detecting or recognising potential candidate objects based on visual cues, e.g. shapes
• CPC Class Codes: G06V 20/56 : exterior to a vehicle by using sensors mounted on the vehicle
• CPC Class Codes: G06V 20/58 : Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
• 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 20/64 : Three-dimensional objects
• CPC Class Codes: G07C 9/00563 : using personal physical data of the operator, e.g. finger prints, retinal images, voicepatterns

Abstract

A system and method are arranged to provide recommendations to a user of a vehicle. In one aspect, the vehicle navigates in an autonomous mode and the sensors provide information that is based on the location of the vehicle and output from sensors directed to the environment surrounding the vehicle. In further aspects, both current and previous sensor data is used to make the recommendations, as well as data based on the sensors of other vehicles.

Claims

1. A method of generating recommendations for destination locations, the method comprising:

maneuvering, by one or more processors, a vehicle in an autonomous driving mode at a particular location at a first point in time;

identifying, by the one or more processors, a particular point of interest is proximate to the particular location;

identifying, by the one or more processors, a period of time during which a passenger stayed at the particular point of interest at a second point in time earlier than the first point in time;

determining, by the one or more processors, whether the period of time meets a threshold period of time; and

when the period of time meets the threshold period of time, generating, by the one or more processors, a recommendation suggesting that the vehicle transport the passenger to the particular point of interest; and

providing, by the one or more processors, the recommendation to the passenger.

2. The method of claim 1, wherein the particular point of interest is a restaurant.

3. The method of claim 1, further comprising, after providing the recommendation, automatically maneuvering the vehicle in the autonomous driving mode to the particular point of interest.

4. The method of claim 1, wherein the threshold period of time is at least 30 minutes.

5. The method of claim 1, further comprising, prior to generating the recommendation:

receiving instructions to maneuver the vehicle in order to transport the passenger to a particular destination location; and

maneuvering the vehicle in the autonomous driving mode along a first route towards the particular destination location based on the instructions, wherein maneuvering the vehicle at the particular location at the first point in time corresponds to maneuvering the vehicle along the first route.

6. The method of claim 5, wherein identifying the particular point of interest is further based on a second route traveled by the vehicle at a third point in time that is at least one day prior to the first point in time, the second route being different from the first route.

7. The method of claim 5, wherein identifying the particular point of interest is further based on a second route traveled by the vehicle at a third point in time that is at least one day prior to the first point in time, the second route being a same route as the first route.

8. The method of claim 5, wherein identifying the particular point of interest is further based on a second route traveled by the vehicle at a third point in time that is at least one day prior to the first point in time, and the second route at least partially overlaps with the first route at the first point in time.

9. The method of claim 5, wherein generating the recommendation is further based on the particular destination location.

10. The method of claim 1, wherein identifying the particular point of interest of is further based on pre-stored information identifying that the vehicle has visited the particular point of interest in the past.

11. The method of claim 1, wherein the period of time corresponds to a period of time during which the vehicle stayed at the particular point of interest.

12. A system for generating recommendations for destination locations, the system comprising one or more processors configured to:

maneuver a vehicle in an autonomous driving mode at a particular location at a first point in time;

identify a particular point of interest is proximate to the particular location;

identify a period of time during which a passenger stayed at the particular point of interest at a second point in time earlier than the first point in time;

determine whether the period of time meets a threshold period of time; and

when the period of time meets the threshold period of time, generate a recommendation suggesting that the vehicle transport the passenger to the particular point of interest; and

providing, by the one or more processors, the recommendation to the passenger.

13. The system of claim 12, wherein the one or more processors are further configured to, after providing the recommendation, automatically maneuver the vehicle in the autonomous driving mode to the particular point of interest.

14. The system of claim 12, wherein the threshold period of time is at least 30 minutes.

15. The system of claim 12, wherein the one or more processors are further configured to, prior to generating the recommendation:

receive instructions to maneuver the vehicle in order to transport the passenger to a particular destination location; and

maneuver the vehicle in the autonomous driving mode along a first route towards the particular destination location based on the instructions, wherein maneuvering the vehicle at the particular location at the first point in time corresponds to maneuvering the vehicle along the first route.

16. The system of claim 15, wherein identifying the particular point of interest is further based on a second route traveled by the vehicle at a third point in time that is at least one day prior to the first point in time, the second route being different from the first route.

17. The system of claim 15, wherein identifying the particular point of interest is further based on a second route traveled by the vehicle at a third point in time that is at least one day prior to the first point in time, the second route being a same route as the first route.

18. The system of claim 15, wherein identifying the particular point of interest is further based on a second route traveled by the vehicle at a third point in time that is at least one day prior to the first point in time, and the second route at least partially overlaps with the first route at the first point in time.

19. The system of claim 15, wherein generating the recommendation is further based on the particular destination location.

20. The system of claim 12, further comprising the vehicle.