ARTIFICIAL INTELLIGENCE VALET SYSTEMS AND METHODS

• US 20160327949A1
• Filed: 07/15/2016
• Published: 11/10/2016
• Est. Priority Date: 03/05/2012
• Status: Abandoned Application

• Current Assignee: Florida Agricultural and Mechanical University (State University System of Florida)
• Original Assignee: Florida Agricultural and Mechanical University (State University System of Florida)
• Inventors: Wilson, Sihle K., Benson, Ronald E.
• Application Number:
  US15/211,163
• Publication Number:
  US 20160327949A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: B60W 2554/00 Input parameters relating to objects
• CPC Class Codes: B60W 2554/406 Traffic density
• CPC Class Codes: B60W 2555/60 Traffic rules, e.g. speed limits or right of way
• CPC Class Codes: B60W 2900/00 Indexing codes relating to the purpose of, or problem solved of road vehicle drive control systems not otherwise provided for in groups B60W30/00
• CPC Class Codes: B60W 30/09 Taking automatic action to avoid collision, e.g. braking and steering
• CPC Class Codes: B60W 60/0025 specially adapted for specific operations
• CPC Class Codes: G01C 21/3415 Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
• CPC Class Codes: G01C 21/3484 Personalized, e.g. from learned user behaviour or user-defined profiles
• 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 1/0246 using a video camera in combination with image processing means
• CPC Class Codes: G05D 1/0274 using mapping information stored in a memory device navigation using map-matching G01C21/30
• CPC Class Codes: G05D 1/0278 using satellite positioning signals, e.g. GPS

Abstract

Various examples are described for an artificial intelligence valet system. In one example, among others, a distributed information sharing system can obtain route information associated with a geographic area and provide the route information to a user vehicle in response to a request. In another example, an autonomous user vehicle can receive a request to autonomously proceed to a user defined location; obtain route information; and determine a route to the user defined location using the route information. In another example, a collision avoidance system can determine if an object is in a path of travel of a vehicle based at least in part upon sensory data and maneuver the vehicle based at least in part upon an object determination. In another example, an accident reporting system can determine an occurrence of a violation of a vehicle; obtain recordings of the environment surrounding the vehicle; and report the violation.

Claims

1. An autonomous user vehicle configured to:

receive a user request to autonomously proceed to a user defined pick-up location associated with a geographic area and arrive at the user defined pick-up location at a defined pick-up time, the user request is received from a user device and includes the user defined pick-up location and the defined pick-up time;

in response to the user request, obtain route information associated with the geographic area, the route information comprising traffic patterns of the geographic area associated with the defined pick-up time; and

determine a route to the user defined pick-up location based upon the route information.

2. The autonomous user vehicle of claim 1, wherein the route information is obtained by the autonomous user vehicle from a remotely located datastore.

3. The autonomous user vehicle of claim 1, further configured to:

obtain additional route information comprising current traffic conditions associated with the geographic area while autonomously proceeding to the user defined pick-up location; and

alter the route to the user defined pick-up location based upon the additional route information and a current location of the autonomous user vehicle.

4. The autonomous user vehicle of claim 1, wherein the route information includes driving patterns of a user of the autonomous user vehicle.

5. The autonomous user vehicle of claim 1, further configured to learn the driving patterns of the user of the autonomous user vehicle.

6. The autonomous user vehicle of claim 1, further configured to communicate with a distributed information sharing system (DISS) to obtain the route information.

7. The autonomous user vehicle of claim 6, further configured to provide transit information corresponding to the determined route to the DISS, which is remotely located from the autonomous user vehicle.

8. The autonomous user vehicle of claim 7, wherein the transit information includes a time to reach the user defined pick-up location.

9. The autonomous user vehicle of claim 7, wherein the transit information includes a speed at which the autonomous user vehicle is moving along the determined route.

10. The autonomous user vehicle of claim 6, wherein the route information further comprises shared transit information obtained from other users.

11. The autonomous user vehicle of claim 1, wherein the user defined pick-up location is a preferred parking location.

12. The autonomous user vehicle of claim 1, wherein the user defined pick-up location is a location where the user was dropped off by the autonomous user vehicle.

13. The autonomous user vehicle of claim 1, wherein the user defined pick-up location is a location other than where the user was dropped off by the autonomous user vehicle.

14. A collision avoidance system, comprising:

a plurality of sensors distributed about a motor vehicle;

an object classifier configured to;

obtain sensory data from the plurality of sensors while the motor vehicle is traveling along a path of travel;

determine if an object is currently located in the path of travel or will be located in the path of travel based at least in part upon the sensory data; and

responsive to the object being located in the path of travel, identify the object as a non-living object or a living object, where a living object classification is determined in response to the object being identified as a living object; and

a maneuver controller configured to maneuver the motor vehicle in response to the object being identified as a living object, the maneuver based at least in part upon the living object classification.

15. The collision avoidance system of claim 14, wherein determining the living object classification comprises determining if the living object is human or non-human.

16. The collision avoidance system of claim 15, wherein the object classifier is configured to, in response to a determination that a plurality of objects are in the path of travel, identify which objects of the plurality of objects are a living object and determine the living object classification for each of the objects identified as a living object.

17. The collision avoidance system of claim 16, wherein the maneuver controller maneuvers the motor vehicle to avoid impact with living objects that are determined to be human while minimizing impact with other living objects that are not determined to be human.

18. The collision avoidance system of claim 17, wherein the maneuver controller further maneuvers the motor vehicle to avoid impact with living objects while minimizing impact with non-living objects.

19. The collision avoidance system of claim 18, wherein minimizing the impact with non-living objects includes colliding with at least one non-living object.

20. The collision avoidance system of claim 14, wherein the maneuver controller is further configured to maneuver the motor vehicle based at least in part upon local traffic laws.