Systems and methods for autonomous vehicle navigation

• US 10,002,471 B2
• Filed: 11/24/2015
• Issued: 06/19/2018
• Est. Priority Date: 09/30/2015
• Status: Expired due to Fees

• Current Assignee: KAMI Vision Incorporated
• Original Assignee: ANTS TECHNOLOGY (HK) LIMITED
• Inventors: Blayvas, Ilya, Fridental, Ron, Da, Shengwei
• Primary Examiner(s): Do, Truc M
• Application Number:
  US14/949,919
• Publication Number:
  US 20170090478A1
• Time in Patent Office: 938 Days
• Field of Search: None
• CPC Class Codes: G01C 21/3453 Special cost functions, i.e. other than distance or default speed limit of road segments
• CPC Class Codes: G05D 1/0005 with arrangements to save energy
• CPC Class Codes: G05D 1/0217 in accordance with energy consumption, time reduction or distance reduction criteria
• CPC Class Codes: G06Q 10/04 Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" market predictions or forecasting for commercial activities G06Q30/0202
• CPC Class Codes: G06Q 50/40 Business processes related to the transportation industry shipping G06Q10/083
• CPC Class Codes: G07C 5/0808 Diagnosing performance data testing of vehicles G01M17/00; testing of electrical installation on vehicles G01R31/005

Abstract

Disclosed is a system for automatically navigating a vehicle, which system comprises:

a data storage containing one or more maps of one or more areas;

electronic interfaces for: (i) receiving data relating to the one or more areas from external data sources; (ii) receiving parameters relating to motion of the vehicle from physical sensors; and (iii) receiving a location of the vehicle from a positioning device;
a navigation processor comprising processing circuitry adapted to determine an initial optimal route for traversing the vehicle from a given point in the one or more areas to another point in the one or more areas by computationally resolving a constrained minimization problem.

Claims

1. A system for automatically navigating a vehicle, said system comprising:

a data storage for storing one or more data sets physically describing one or more areas;

physical sensors to continuously obtain parameters relating to motion of the vehicle;

a first electronic interface for receiving data relating to the one or more areas from external data sources;

a second electronic interface for receiving the parameters relating to motion of the vehicle;

a navigation processor comprising processing circuitry adapted to;

continuously calculate and update, based on the parameters and data, a physical vehicle model, a microenvironment model, a driver safety model and a motion control model;

receive a destination and priorities;

determine based on the calculated models and based on the received destination and priorities an initial optimal route for traversing the vehicle from a given point in the one or more areas to another point in the one or more areas by computationally resolving a constrained minimization problem, wherein;

(iii) constraints of the constrained minimization problem are physical capabilities and limits of the vehicle and a physical space within the one or more areas appropriate for traversing the vehicle; and

(iv) an optimization parameter of the constrained minimization problem is a cost function, which cost function comprises a weighted aggregation of cost values representing each of;

safety of passengers, safety of other people, safety of the vehicle and other property, obedience to traffic regulations, minimizing travel time and minimizing cost;

divide the initial route into the smaller sections for detailed planning based on the continuously undated models; and

until reaching the destination, perform;

during an execution of a current section, continuously check, based on the continuously updated models, for an appearance of an interruption requiring a correction in the planned route;

in case of an interruption, compare a current position to the planned route, calculate reaction to the interruption based on the continuously updated models and executed reaction; and

at an end of the current section, in case the destination is not reached, move to a next section of the route.

2. The system according to claim 1, wherein determining an initial optimal route includes applying equations of motion to find the vehicle trajectory minimizing said cost function.

3. The system according to claim 1, wherein said navigation processor includes route planning logic and a route execution unit, wherein:

a. The route planning logic is configured to;

i. receive;

a starting point, a destination point, a map from said data storage, environmental characteristics of an area in the map, and a set of navigation preferences, and

ii. construct the initial optimal route, optimal for the vehicle in terms of the received set of navigation preferences;

b. the route execution unit monitors traversal of the vehicle on the initial optimal route, continuously monitoring for interrupts of the initial optimal route, wherein interrupts of the initial optimal route are new circumstances by which a change or modification of the initial optimal route, or its execution, are desired based on a resolution of the constrained minimization problem factoring the new circumstances.

4. The system according to claim 1, further comprising a fourth electronic interface for controlling one or more controls of the vehicle.

5. The system according to claim 3, further comprising a fourth electronic interface for controlling one or more controls of the vehicle and wherein said system is adapted to use said fourth electronic interface to cause the vehicle to perform a maneuver in response to the new circumstance.

6. The system according to claim 4, wherein said system is adapted to use said fourth electronic interface to cause the vehicle to traverse the optimal path.

7. The system according to claim 6, wherein said system includes a vehicle physical model, a vehicle motion controllers model and a vehicle specific environment model,

wherein:

a. said vehicle physical model comprises;

a mass of the vehicle, dimensions of the vehicle, mass distribution of the vehicle, moment of inertia of the vehicle, aerodynamic or hydrodynamic properties of the vehicle;

b. said vehicle physical model is used to solve and simulate equations of motion to predict behavior of the vehicle in a particular set of conditions, thereby facilitating control of the vehicle via said fourth electronic interface;

c. said motion controllers model is used to calculate a signal to send to the one or more controls in order to achieve a given maneuver of the vehicle in a given set of circumstances; and

d. said vehicle specific environmental model includes models of forces being applied by external sources upon the vehicle.

8. The system according to claim 7, wherein said vehicle physical model is configured to factor malfunctions or damage to one or more components of the vehicle when solving and simulating equations of motion to predict behavior of the vehicle in a particular set of conditions.

9. The system according to claim 8, wherein said system is configured to update said vehicle physical model in real time upon occurrence of a malfunction of, or damage to, the vehicle.

10. The system according to claim 7, wherein said vehicle specific environmental model includes models of forces being applied by wind upon the vehicle and the data relating to the one or more areas from external data sources includes a wind direction and speed in the one or more areas.

11. The system according to claim 7, wherein said vehicle specific environmental model includes models of forces being applied by waves and current upon the vehicle and the data relating to the one or more areas from external data sources includes wave and current parameters in the one or more areas.

12. The system according to claim 7, wherein said vehicle specific environmental model includes models of forces being applied by a road upon the vehicle.

13. The system according to claim 1, wherein the vehicle is a robot.

14. The system according to claim 1, wherein the vehicle is an automobile.

15. The system according to claim 1, wherein the vehicle is a sailboat.

16. The system according to claim 1, wherein the vehicle is an airplane.

17. The system according to claim 1, further comprising an electronic communication unit adapted to communicate with public infrastructure, wherein said system is further adapted to factor traffic light cycles when performing navigational calculations.

18. The system according to claim 1, further comprising an electronic communication unit adapted to communicate with other vehicles and said system is further adapted to factor data received from other vehicles when performing navigational calculations.

19. A method for automatically navigating a vehicle, comprising:

receiving from a data storage, data sets physically describing one or more areas;

receiving from external data sources, data relating to the one or more areas;

continuously obtaining by physical sensors parameters relating to motion of the vehicle;

continuously calculating and updating, based on the parameters and data, a physical vehicle model, a microenvironment model, a driver safety model and a motion control model;

receiving a destination and priorities;

determining based on the calculated models and based on the received destination and priorities an initial optimal route for traversing the vehicle from a given point in the one or more areas to another point in the one or more areas by computationally resolving a constrained minimization problem, wherein;

(iii) constraints of the constrained minimization problem are physical capabilities and limits of the vehicle and a physical space within the one or more areas appropriate for traversing the vehicle; and

(iv) an optimization parameter of the constrained minimization problem is a cost function, which cost function comprises a weighted aggregation of cost values representing each of;

safety of passengers, safety of other people, safety of the vehicle and other property, obedience to traffic regulations, minimizing travel time and minimizing cost;

dividing the initial route into the smaller sections for detailed planning based on the continuously updated models; and

until reaching the destination, performing;

during an execution of a current section, continuously checking, based on the continuously updated models, for an appearance of an interruption requiring a correction in the planned route;

in case of an interruption, comparing a current position to the planned route, calculate reaction to the interruption based on the continuously updated models and executing reaction; and

at an end of the current section, in case the destination is not reached, moving to a next section of the route.