IP Library › Granted Patent US 12,567,328
Granted Patent B2
US 12,567,328 · App. 18/299,187 · Granted Mar 3, 2026

Context-based identification of vehicle connectivity

Inventors: Sergei S. Avedisov (Cupertino, CA); Hongsheng Lu (Mountain View, CA); Onur Altintas (Mountain View, CA)
Assignees: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC.; TOYOTA JIDOSHA KABUSHIKI KAISHA
G08G1/096805G08G1/0112G08G1/0133G08G1/017G08G1/052H04L67/12
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Quick Facts
Patent No.
US 12,567,328
App. No.
18/299,187
Granted
Mar 3, 2026
Kind
B2
Abstract

An example operation includes one or more of identifying, via an ego vehicle, one or more surrounding vehicles of the ego vehicle based on sensor data from the ego vehicle, determining a state of an ego vehicle and a state of the one or more surrounding vehicles of the ego vehicle, dynamically determining parameters for identifying connected vehicles based on the determined states of the ego vehicle and the one or more surrounding vehicles, and detecting a connected vehicle from among the one or more surrounding vehicles via an exchange of messages between the ego vehicle and the one or more surrounding vehicles based on the dynamically determined parameters.

Claims (39)

1 . An apparatus comprising:

a processor configured to

identify surrounding vehicles of an ego vehicle based on sensor data from the ego vehicle;

determine a rate of speed of the ego vehicle;

determine parameter weights of a cost function of a connected vehicle algorithm and a number of iterations of a search based on the rate of speed;

modify a size of a search area based on execution of the connected vehicle algorithm with the determined parameter weights of the cost function;

transmit messages from the ego vehicle based on the modified size of the search area and the number of iterations of the search; and

detect a connected vehicle in the surrounding vehicles based on the messages.

2 . The apparatus of claim 1 , wherein the processor is further configured to determine a density of the surrounding vehicles with respect to the ego vehicle based on the sensor data, and determine the parameter weights and the number of iterations based on the determined density.

3 . The apparatus of claim 1 , wherein the processor is further configured to determine an accuracy of a sensor that transmitted the sensor data, and determine the parameter weights and the number of iterations based on the accuracy of the sensor data.

4 . The apparatus of claim 1 , wherein the processor is configured to modify at least one weight of at least one parameter based on a type of application requesting the search.

5 . The apparatus of claim 1 , wherein the processor is further configured to exchange the messages between the ego vehicle and the surrounding vehicles based on a periodic rate.

6 . The apparatus of claim 1 , wherein the processor is further configured to receive at least one of positions, speeds, and sizes of the surrounding vehicles, and detect the connected vehicle based on the at least one of the positions, speeds, and sizes of the surrounding vehicles.

7 . The apparatus of claim 1 , wherein the processor is further configured to generate and send a routing instruction to a user interface of the ego vehicle based on detection of the connected vehicle.

8 . A method comprising:

identifying surrounding vehicles of an ego vehicle based on sensor data from the ego vehicle;

determining a rate of speed of the ego vehicle;

determining parameter weights of a cost function of a connected vehicle algorithm and a number of iterations of a search based on the rate of speed;

modifying a size of a search area based on execution of the connected vehicle algorithm with the determined parameter weights of the cost function;

transmitting messages from the ego vehicle based on the modified size of the search area and the number of iterations of the search; and

detecting a connected vehicle in the surrounding vehicles based on the messages.

9 . The method of claim 8 , further comprising determining a density of the surrounding vehicles with respect to the ego vehicle based on the sensor data, wherein the determining the parameter weights comprises determining the parameter weights and the number of iterations based on the determined density.

10 . The method of claim 8 , further comprising determining an accuracy of a sensor that transmitted the sensor data, wherein the determining the parameter weights comprises determining the parameter weights and the number of iterations based on the accuracy of the sensor data.

11 . The method of claim 8 , wherein the determining comprises modifying at least one weight of at least one parameter based on a type of application requesting the search.

12 . The method of claim 8 , wherein the detecting comprises exchanging the messages between the ego vehicle and the surrounding vehicles based on a periodic rate.

13 . The method of claim 8 , wherein the identifying comprises receiving messages from the surrounding vehicles with at least one of positions, speeds, and sizes of the surrounding vehicles.

14 . The method of claim 8 , wherein the method further comprises generating and sending a routing instruction to a user interface of the ego vehicle based on detection of the connected vehicle.

15 . A non-transitory computer-readable storage medium comprising instructions, that when read by a processor, cause the processor to perform a method comprising:

identifying surrounding vehicles of an ego vehicle based on sensor data from the ego vehicle;

determining a rate of speed of the ego vehicle;

determining parameter weights of a cost function of a connected vehicle algorithm and a number of iterations of a search based on the rate of speed;

modifying a size of a search area based on execution of the connected vehicle algorithm with the determined parameter weights of the cost function;

transmitting messages from the ego vehicle based on the modified size of the search area and the number of iterations of the search; and

detecting a connected vehicle in the surrounding vehicles based on the messages.

16 . The non-transitory computer-readable storage medium of claim 15 , wherein the processor is further configured to perform determining a density of the surrounding vehicles with respect to the ego vehicle based on the sensor data, wherein the determining the parameter weights comprises determining the parameter weights and the number of iterations based on the determined density.

17 . The non-transitory computer-readable storage medium of claim 15 , wherein the processor is further configured to perform determining an accuracy of a sensor that transmitted the sensor data, wherein the determining the parameter weights comprises determining the parameter weights and the number of iterations based on the accuracy of the sensor data.

18 . The non-transitory computer-readable storage medium of claim 15 , wherein the determining comprises modifying at least one weight of at least one parameter based on a type of application requesting the search.

19 . The non-transitory computer-readable storage medium of claim 15 , wherein the identifying comprises receiving messages from the surrounding vehicles with at least one of positions, speeds, and sizes of the surrounding vehicles.

20 . The non-transitory computer-readable storage medium of claim 15 , wherein the method further comprises generating and sending a routing instruction to a user interface of the ego vehicle based on detection of the connected vehicle.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 24, 2026
From: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC.
To: TOYOTA JIDOSHA KABUSHIKI KAISHA
Reel/Frame 075070/0817 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 12, 2023
From: AVEDISOV, SERGEI S.; LU, HONGSHENG; ALTINTAS, ONUR
To: TOYOTA MOTOR ENGINEERING & MANUFACTURING NORTH AMERICA, INC.; TOYOTA JIDOSHA KABUSHIKI KAISHA
Reel/Frame 063299/0076 →
Continuity (1)
Related Publication 20240346927A1 · Oct 17, 2024
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