Dynamic modeling and simulation of an autonomous vehicle fleet using real-time autonomous vehicle sensor input

Patent metadata

Field	Value
Publication number	US-10438486-B2
Application number	US-201715645776-A
Country	US
Kind code	B2
Filing date	Jul 10, 2017
Priority date	Jul 10, 2017
Publication date	Oct 8, 2019
Grant date	Oct 8, 2019

Abstract

Embodiments provide techniques for autonomous vehicle fleet modeling and simulation, such as within a dynamic transportation matching system utilizing one or more vehicle types such as non-autonomous vehicles and autonomous vehicles. An autonomous fleet simulation model may be generated based on real-world parameters of an autonomous vehicle fleet, and the parameters may be modified in a simulation in order to determine optimized values that may be applied to the real-world autonomous vehicle fleet.

First claim

What is claimed is:

1. A computer-implemented method comprising:
accessing autonomous fleet data and a fleet simulation model for a geographical region, wherein the autonomous fleet data relate to and the fleet simulation model includes one or more autonomous vehicles;
determining one or more potential locations in the geographical region for a service entity to be represented in the fleet simulation model, wherein the service entity is configured to service at least one of the one or more autonomous vehicles;
configuring the fleet simulation model using at least the autonomous fleet data and at least one of the one or more potential locations for the service entity;
performing, based on the configured fleet simulation model, a simulation in which the service entity located at the at least one of the one or more potential locations is utilized by at least one of the one or more autonomous vehicles;
determining one or more utilization metrics for the service entity or at least one of the one or more autonomous vehicles based on the simulation;
selecting, for representation in the fleet simulation model, a location for the service entity from the one or more potential locations, wherein the location for the service entity is selected based on at least the one or more utilization metrics;
updating the fleet simulation model based at least on the selected location for the service entity;
determining a pre-position for at least one of the one or more autonomous vehicles to be available for predicted future transportation requests within the geographical region based at least on the updated fleet simulation model; and
causing at least one of the one or more autonomous vehicles to travel to the determined pre-position.

2. The computer-implemented method of claim 1 , wherein the pre-position is determined to reduce travel times to pickup locations for the predicted future transportation requests.

3. The computer-implemented method of claim 1 , further comprising: causing, based on the predicted future transportation requests, at least one of the one or more autonomous vehicles in the geographical region to be unavailable for servicing transportation requests during a future time.

4. The computer-implemented method of claim 1 , further comprising:
based at least on the fleet simulation model, determining a service schedule to increase utilization of at least one of the one or more autonomous vehicles; and
causing at least one of the one or more autonomous vehicles to be serviced according to the determined service schedule.

5. The computer-implemented method of claim 1 , wherein the service entity comprises a charging station, fueling station, parking facility, service facility, or cleaning facility.

6. The computer-implemented method of claim 1 , wherein the fleet simulation model is configured using one or more service parameters of the service entity, wherein the one or more service parameters comprise one or more of: type of service provided, time required for service, or number of autonomous vehicles that can be serviced.

7. The computer-implemented method of claim 1 , wherein the fleet simulation model is configured to simulate various fleet scenarios, comprising geographic constraints, autonomous fleet constraints, and infrastructure constraints, to predict one or more outcomes with respect to utilization of the one or more autonomous vehicles.

8. The computer-implemented method of claim 1 , wherein the autonomous fleet data comprises one or more of autonomous vehicle deployment locations, autonomous vehicle fleet size, current autonomous vehicle utilization, historical autonomous vehicle utilization, autonomous vehicle route data, or autonomous vehicle ride data.

9. The method of claim 1 , wherein the fleet simulation model uses one or more ride parameters to simulate the one or more utilization metrics.

10. The method of claim 9 , wherein the one or more ride parameters comprise a number of the one or more autonomous vehicles, a number of future transportation requests, a number of miles traveled by at least one of the one or more autonomous vehicles, cost per mile, or service schedule for the one or more autonomous vehicles.

11. The method of claim 1 , further comprising:
evaluating, using the simulation, effects on the one or more utilization metrics due to changes in a number of future transportation requests or a number of available autonomous vehicles.

12. The method of claim 1 , wherein the one or more utilization metrics comprise a number of ride miles traveled by at least one of the one or more autonomous vehicles, cost per mile traveled, total cost of servicing transportation requests in the geographic region, or an amount of time the one or more autonomous vehicles are serviced.

13. The method of claim 1 , where the autonomous fleet data comprises a status of each of the one or more autonomous vehicles, a current location of each of the one or more autonomous vehicles, capacity of each of the one or more autonomous vehicles, or traffic data.

14. A computer-implemented method comprising:
accessing autonomous fleet data and a fleet simulation model for a geographical region, wherein the autonomous fleet data relate to and the fleet simulation model includes one or more autonomous vehicles;
determining one or more potential locations in the geographical region for a service entity to be represented in the fleet simulation model, wherein the service entity is configured to service at least one of the one or more autonomous vehicles;
configuring the fleet simulation model using at least the autonomous fleet data and at least one of the one or more potential locations for the service entity;
performing, based on the configured fleet simulation model, a simulation in which the service entity located at the at least one of the one or more potential locations is utilized by at least one of the one or more autonomous vehicles;
determining one or more utilization metrics for the service entity or at least one of the one or more autonomous vehicles based on the simulation;
selecting, for representation in the fleet simulation model, a location for the service entity from the one or more potential locations, wherein the location for the service entity is selected based on at least the one or more utilization metrics;
updating the fleet simulation model based at least on the selected location for the service entity; and
causing at least one of the one or more autonomous vehicles to travel to a position within the geographical region based on the updated fleet simulation model.

15. The computer-implemented method of claim 14 , wherein the service entity comprises a charging station, fueling station, parking facility, service facility, or cleaning facility.

16. The computer-implemented method of claim 14 , wherein the fleet simulation model is configured using one or more service parameters of the service entity, wherein the one or more service parameters comprise one or more of:
type of service provided, time required for service, or number of autonomous vehicles that can be serviced.

17. The computer-implemented method of claim 14 , wherein the fleet simulation model is configured to simulate various autonomous fleet scenarios, comprising geographic constraints, autonomous fleet constraints, and infrastructure constraints, to predict one or more outcomes with respect to utilization of the one or more autonomous vehicles.

18. The computer-implemented method of claim 14 , wherein the autonomous fleet data comprises one or more of autonomous vehi

Assignees

• Lyft Inc

Inventors

• Selvam Krishna
• Chamandy Nicholas
• Kelman Jody

Classifications

• G08G1/202 Primary

  Dispatching vehicles on the basis of a location, e.g. taxi dispatching · CPC title

• G05B17/02

  electric · CPC title

• G08G1/123 Primary

  indicating the position of vehicles, e.g. scheduled vehicles; {Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams}(transmission of navigation instructions to vehicles G08G1/0968) · CPC title

• G06Q50/30

  Physics · mapped topic

• G01C21/00

  Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 (measuring distance traversed on the ground by a vehicle G01C22/00; control of position, course, altitude or attitude of vehicles G05D1/00; traffic control systems for road vehicles involving transmission of navigation instructions to the vehicle G08G1/0968) · CPC title