Connected vehicles adaptive security signing and verification methodology and node filtering

• US 20160036558A1
• Filed: 10/15/2015
• Published: 02/04/2016
• Est. Priority Date: 10/07/2013
• Status: Active Grant

• Current Assignee: Harman International Industries Incorporated (Samsung Electronics Co. Ltd.)
• Original Assignee: Savari Inc. (Samsung Electronics Co. Ltd.)
• Inventors: Ibrahim, Faroog, Nagadevendra, Chenikkayala, SRIRAMA, VARUN PALATHOUR
• Granted Patent: US 9,559,804 B2
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G01S 19/24 Acquisition or tracking or demodulation of signals transmitted by the system synchronisation aspects of direct sequence spread spectrum modulation H04B1/7073
• CPC Class Codes: G08G 1/0112 from the vehicle, e.g. floating car data [FCD]
• CPC Class Codes: G08G 1/0129 for creating historical data or processing based on historical data
• CPC Class Codes: G08G 1/0141 for traffic information dissemination
• CPC Class Codes: G08G 1/015 with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
• CPC Class Codes: H04K 2203/22 for communication related to vehicles
• CPC Class Codes: H04K 3/22 including jamming detection and monitoring
• CPC Class Codes: H04K 3/92 related to allowing or preventing remote control
• CPC Class Codes: H04L 67/52 specially adapted for the location of the user terminal
• CPC Class Codes: H04W 4/027 using movement velocity, acceleration information
• CPC Class Codes: H04W 4/44 for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

We introduce a connected vehicles adaptive security signing and verification methodology. We also introduce an adaptive node filtering at receiver, using noise levels and received signal strength. This invention addresses two important pillars in connected vehicle technology and autonomous cars: The first one is related to the security 1609.2 format and its main two functions: signing and verification. The second one explains how the noise level and signal strength can be used to filter undesired connected nodes. In this presentation, we provide various examples and variations on these topics.

Claims

1. A method for adaptive node filtering for autonomous vehicles in a street or highway, said method comprising:

a central computer in a system receiving data from a host vehicle;

said central computer receiving data from multiple remote vehicles;

a receiving device computing noise power level in a first environment;

said receiving device obtaining a noise floor value with respect to said first environment;

if said noise power level in said first environment is bigger than said noise floor value, then said receiving device setting a new value for said noise floor value, to be at a level which is three decibels higher than said noise power level,

said receiving device setting said new value for said noise floor value for said system, and rejecting all signals below said new value for said noise floor value;

said receiving device obtaining a threshold error rate;

if said noise power level in said first environment is not bigger than said noise floor value, then if pocket error rate is higher than said threshold error rate, then said receiving device examining a list of said multiple remote vehicles, and said receiving device finding a first remote vehicle which has least received signal strength,

said receiving device setting said new value for said noise floor value for said system, to be one decibel higher than power of said first remote vehicle which has least received signal strength;

said receiving device obtaining a threshold number of vehicles;

if said noise power level in said first environment is not bigger than said noise floor value, and if pocket error rate is not higher than said threshold error rate, then if number of said multiple remote vehicles is bigger than said threshold number of vehicles, then said central computer finding said first remote vehicle which has least received signal strength;

said receiving device obtaining a threshold speed for vehicles;

if said noise power level in said first environment is not bigger than said noise floor value, and if pocket error rate is not higher than said threshold error rate, and if number of said multiple remote vehicles is not bigger than said threshold number of vehicles, then if speed of a second vehicle is smaller than said threshold speed for vehicles, then said central computer finding said first remote vehicle which has least received signal strength;

said receiving device obtaining an original range value;

if said noise power level in said first environment is not bigger than said noise floor value, and if pocket error rate is not higher than said threshold error rate, and if number of said multiple remote vehicles is not bigger than said threshold number of vehicles, and if speed of said second vehicle is smaller than said threshold speed for vehicles, then if a new range value is same as said original range value, then said central computer finding said first remote vehicle which has least received signal strength;

if said noise power level in said first environment is not bigger than said noise floor value, and if pocket error rate is not higher than said threshold error rate, and if number of said multiple remote vehicles is not bigger than said threshold number of vehicles, and if speed of said second vehicle is smaller than said threshold speed for vehicles, then if a new range value is same as said original range value, then said central computer finding said first remote vehicle which has least received signal strength;

if said noise power level in said first environment is not bigger than said noise floor value, and if pocket error rate is not higher than said threshold error rate, and if number of said multiple remote vehicles is not bigger than said threshold number of vehicles, and if speed of said second vehicle is smaller than said threshold speed for vehicles, then if said new range value is not same as said original range value, then said central computer repeating said method from first step again.

2. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring global positioning system data.

3. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring map data.

4. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring packet error rate statistics.

5. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring communication congestion data.

6. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring vehicle congestion data.

7. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

optimizing processing power and resources.

8. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring packet rates.

9. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring communication jamming devices.

10. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring communication hackers.

11. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring security holes.

12. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring suspicious activities.

13. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

changing communication range.

14. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

changing radius of interest.

15. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring noise.

16. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring thermal noise.

17. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring hardware noise.

18. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

monitoring quality of packets.

19. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

changing thresholds.

20. The method for adaptive node filtering for autonomous vehicles in a street or highway, as recited in claim 1, said method comprises:

adaptively changing thresholds.