# US 12,118,880 B2 · Systems and methods for coordinated vehicle lane assignment

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- Number: US 12,118,880 B2
- Document ID: US12118880B2
- Status: Granted
- Kind: B2
- Application number: 17/698,265
- Filed: 2022-03-18
- Published: 2023-04-27
- Granted: 2024-10-15
- Earliest claimed: 2021-10-21
- Assignees: Toyota Motor Engineering & Manufacturing North America, Inc.
- Applicants: Toyota Motor Engineering & Manufacturing North America, Inc.
- Inventors: Abdul Rahman Kreidieh; Yashar Zeiynali Farid; Kentaro Oguchi
- Web page: https://patentgrep.com/patent/US12118880B2

## CPC

- G08G 1/0141: {for traffic information dissemination}
- B60W 30/18163: {Lane change; Overtaking manoeuvres}
- G05B 13/0265: {the criterion being a learning criterion}
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 3/044: Recurrent networks, e.g. Hopfield networks
- G06N 3/045: Combinations of networks
- G06N 3/08: Learning methods
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G08G 1/0116: {from roadside infrastructure, e.g. beacons}
- G08G 1/0133: {for classifying traffic situation}
- G08G 1/0145: {for active traffic flow control}
- G08G 1/096708: {where the received information might be used to generate an automatic action on the vehicle control}
- G08G 1/096725: {where the received information generates an automatic action on the vehicle control}
- G08G 1/096775: {where the origin of the information is a central station}
- G08G 1/096783: {where the origin of the information is a roadside individual element}
- G08G 1/167: {Driving aids for lane monitoring, lane changing, e.g. blind spot detection}
- H04W 4/46: for vehicle-to-vehicle communication [V2V]
- B60W 2552/10: Number of lanes
- B60W 2556/45: External transmission of data to or from the vehicle
- G06N 20/00: Machine learning
- G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]}
- G08G 1/0129: {for creating historical data or processing based on historical data}
- G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93})
- B60W: CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72)

## Abstract

Systems and methods described herein relate to coordinated vehicle lane assignment. One embodiment receives from a locality manager, at a section manager that communicates with one or more connected vehicles in a section of a roadway, target lateral flows for two or more lanes of the roadway in the section of the roadway; converts the target lateral flows to a target number of connected vehicles N at the section manager; selects for lane change, at the section manager, a set of N connected vehicles whose ranked distances from a following vehicle in a target lane are greatest among the one or more connected vehicles, when a direction of lane change is uniform among the set of N connected vehicles; and transmits lane-change actions from the section manager to the set of N connected vehicles.

## Claims

### Claim 1

A system for coordinated vehicle lane assignment, the system comprising:
a processor; and
a memory storing machine-readable instructions that, when executed by the processor, cause the processor to:
receive from a locality manager target lateral flows for two or more lanes of a roadway in a section of the roadway that includes one or more connected vehicles;
convert the target lateral flows to a target number of connected vehicles N;
select for lane change a set of N connected vehicles whose ranked distances from a following vehicle in a target lane are greatest among the one or more connected vehicles in the section of the roadway, when a direction of lane change is uniform among the set of N connected vehicles;
transmit lane-change actions to the set of N connected vehicles; and
control at least one connected vehicle in the set of N connected vehicles in performing a lane-change action, of the lane-change actions.

### Claim 2 (depends on 1)

The system of claim 1, wherein the machine-readable instructions include further instructions that, when executed by the processor, cause the processor to select for lane change the set of N connected vehicles in accordance with a multi-objective selection algorithm, when the direction of lane change differs among the set of N connected vehicles.

### Claim 3 (depends on 2)

The system of claim 2, wherein the multi-objective selection algorithm is a Gray Relational Optimization algorithm.

### Claim 4 (depends on 1)

The system of claim 1, wherein the machine-readable instructions to convert the target lateral flows to the target number of connected vehicles N include instructions that, when executed by the processor, cause the processor to account for missed vehicles that were unable to change lanes in response to previously transmitted lane-change actions.

### Claim 5 (depends on 1)

The system of claim 1, wherein the machine-readable instructions include further instructions that, when executed by the processor, cause the processor to receive from the one or more connected vehicles measured distances between the one or more connected vehicles and other vehicles on the roadway.

### Claim 6 (depends on 1)

The system of claim 1, wherein the machine-readable instructions include further instructions that, when executed by the processor, cause the processor to repeatedly receive the target lateral flows, convert the target lateral flows to the target number of connected vehicles N, select the set of N connected vehicles for lane change, and transmit the lane-change actions periodically at a predetermined time interval.

### Claim 7 (depends on 1)

The system of claim 1, wherein the one or more connected vehicles are autonomous vehicles.

### Claim 8

A non-transitory computer-readable medium for coordinated vehicle lane assignment and storing instructions that when executed by a processor cause the processor to:
receive from a locality manager target lateral flows for two or more lanes of a roadway in a section of the roadway that includes one or more connected vehicles;
convert the target lateral flows to a target number of connected vehicles N;
select for lane change a set of N connected vehicles whose ranked distances from a following vehicle in a target lane are greatest among the one or more connected vehicles in the section of the roadway, when a direction of lane change is uniform among the set of N connected vehicles;
transmit lane-change actions to the set of N connected vehicles; and
control at least one connected vehicle in the set of N connected vehicles in performing a lane-change action, of the lane-change actions.

### Claim 9 (depends on 8)

The non-transitory computer-readable medium of claim 8, wherein the instructions include further instructions that, when executed by the processor, cause the processor to select for lane change the set of N connected vehicles in accordance with a multi-objective selection algorithm, when the direction of lane change differs among the set of N connected vehicles.

### Claim 10 (depends on 9)

The non-transitory computer-readable medium of claim 9, wherein the multi-objective selection algorithm is a Gray Relational Optimization algorithm.

### Claim 11 (depends on 8)

The non-transitory computer-readable medium of claim 8, wherein the instructions to convert the target lateral flows to the target number of connected vehicles N include instructions that, when executed by the processor, cause the processor to account for missed vehicles that were unable to change lanes in response to previously transmitted lane-change actions.

### Claim 12 (depends on 8)

The non-transitory computer-readable medium of claim 8, wherein the instructions include further instructions that, when executed by the processor, cause the processor to receive from the one or more connected vehicles measured distances between the one or more connected vehicles and other vehicles on the roadway.

### Claim 13 (depends on 8)

The non-transitory computer-readable medium of claim 8, wherein the instructions include further instructions that, when executed by the processor, cause the processor to repeatedly receive the target lateral flows, convert the target lateral flows to the target number of connected vehicles N, select the set of N connected vehicles for lane change, and transmit the lane-change actions periodically at a predetermined time interval.

### Claim 14

A method, comprising:
receiving from a locality manager, at a section manager that communicates with one or more connected vehicles in a section of a roadway, target lateral flows for two or more lanes of the roadway in the section of the roadway;
converting the target lateral flows to a target number of connected vehicles N at the section manager;
selecting for lane change, at the section manager, a set of N connected vehicles whose ranked distances from a following vehicle in a target lane are greatest among the one or more connected vehicles, when a direction of lane change is uniform among the set of N connected vehicles;
transmitting lane-change actions from the section manager to the set of N connected vehicles; and
controlling at least one connected vehicle in the set of N connected vehicles in performing a lane-change action, of the lane-change actions.

### Claim 15 (depends on 14)

The method of claim 14, further comprising selecting for lane change, at the section manager, the set of N connected vehicles in accordance with a multi-objective selection algorithm, when the direction of lane change differs among the set of N connected vehicles.

### Claim 16 (depends on 15)

The method of claim 15, wherein the multi-objective selection algorithm is a Gray Relational Optimization algorithm.

### Claim 17 (depends on 14)

The method of claim 14, wherein the converting the target lateral flows to the target number of connected vehicles N at the section manager includes accounting for missed vehicles that were unable to change lanes in response to previously transmitted lane-change actions.

### Claim 18 (depends on 14)

The method of claim 14, further comprising receiving from the one or more connected vehicles, at the section manager, measured distances between the one or more connected vehicles and other vehicles on the roadway.

### Claim 19 (depends on 14)

The method of claim 14, further comprising repeating the receiving, the converting, the selecting, and the transmitting periodically at a predetermined time interval.

### Claim 20 (depends on 14)

The method of claim 14, wherein the one or more connected vehicles are autonomous vehicles.