a machine consciousness approach to urban traffic signal control

Copyright@2011 DCA-FEEC-UNICAMP

Andre Luis Ogando ParaenseDCA-FEEC-UNICAMP

Klaus Raizer, Ricardo Ribeiro GudwinDCA-FEEC-UNICAMP

BICA 2015 - Lyon,FR

A Machine ConsciousnessApproach to Urban Traffic SignalControl

ContributionMotivation

Cognitive Systems ToolkitMachine Consciouness Controller

ResultsConclusion

Q&A

Contribution

The main contribution of this work is the application of amachine consciousness approach, based on the GlobalWorkspace Theory (Baars, 1988), to urban traffic signalcontrol, with the design and implementation of a solution tothe problem.

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ResultsConclusion

Q&A

Traffic Jam Problems

Traffic Jam Problems

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ResultsConclusion

Q&A

CoreArchitecture

Core

proc()In

B

Out

A

Codelet

Coderack Raw Memory

Coalition

T I

Memory Object (Sign)4 / 25



ResultsConclusion

Q&A

CoreArchitecture

Architecture

Procedural Memory

Working Memory

Perceptual Memory

Sensory Memory

Motor Memory

Episodic Memory Semantic Memory

Phonologic Loop

Visual Sketchpad

Episodic Buffer

Sensory Codelets

Perceptual Codelets

Attention Codelets

Coderack Raw Memory

Emotional Codelets Learning Codelets

Consciousness

Language Codelets Consciousness Codelets

Imagination and Planning Codelets

Behavioral Codelets

MotorCodelets

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ResultsConclusion

Q&A

Purely Reactive Behavior

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ResultsConclusion

Q&A

Most Critical Traffic Situation

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ResultsConclusion

Q&A

Broadcast, interference, deliberative behavior

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ResultsConclusion

Q&A

Network Model

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ResultsConclusion

Q&A

CST ControllerWorking Memory

Senso

ry

Cod

ele

ts

lane a: LaneSensor

In

B

Out

A

lane b: LaneSensor

In

B

Out

A

lane c: LaneSensor

In

B

Out

A

lane d: LaneSensor

In

B

Out

A

lane e: LaneSensor

In

B

Out

A

lane f: LaneSensor

In

B

Out

A

lane g: LaneSensor

In

B

Out

A

lane h: LaneSensor

In

B

Out

A

lane i: LaneSensor

In

B

Out

A

lane j: LaneSensor

In

B

Out

A

lane k: LaneSensor

In

B

Out

A

Sensory Memory

lane a : VelocitiesMO

lane a: VehicleNumberMO

lane a: DistancesFromLightMO

lane k : VelocitiesMO

lane k: VehicleNumberMO

lane k: DistancesFromLightMO

.

.

.Motor Memory

junction west: TrafficLightActuator

In

B

Out

A

junction east: TrafficLightActuator

In

B

Out

A

Moto

r C

odele

ts

junction east: PhaseMO

junction west: PhaseMO

Behavio

ral

Codele

tsC

onscio

us n

ess

Codele

tsjunction east:

OpenSlowNearLightVehiclesLanes

In

B

Out

A

junction west: OpenSlowNearLight

VehiclesLanes

In

B

Out

A

consciousness: SpotlightBroadcast

Controller

In

B

Out

A

junction east: ActivationMO

junction west: ActivationMO

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ResultsConclusion

Q&A

Simple TCorridorManhattan

Simple T Network Model

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ResultsConclusion

Q&A


Simple T Result

00:00 02:00 04:00 06:00 08:00 10:00 12:00time (h)

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mea

n tr

avel

tim

e [s

]

Fixed TimesParallel ReactiveArtificial Consciousness

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ResultsConclusion

Q&A


Corridor Network Model

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ResultsConclusion

Q&A


Corridor Result

00:00 02:00 04:00 06:00 08:00 10:00 12:00time (h)

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e [s

]


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ResultsConclusion

Q&A


Manhattan Network Model

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ResultsConclusion

Q&A


Manhattan Result

00:00 10:00 20:00 30:00 40:00 50:00 60:00time (h)

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]


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ResultsConclusion

Q&A

Conclusion

A consistent gain in performance with the ”ArtificialConsciousness” traffic signal controller during all simulationtime, throughout different simulated scenarios, could beobserved, ranging from around 13.8% to more than 21%.

This work supports the hypothesis that an artificialconsciousness mechanism, using global workspace theory canbring advantages to the global task performed by a society ofparallel agents working together for a common goal.

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ResultsConclusion

Q&A

Next steps - Downtown Campinas

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ResultsConclusion

Q&A

Thank you!

email: [email protected]

twitter: @AndreLOParaense

You can find more at https://github.com/CST-Group/cst

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ResultsConclusion

Q&A

Reactive Controller Action Selection

1 Calculates its level of activation, which is given by equation 1.

a(t) =

∑c∈C

(1− αVc(t)− βXc(t))

|C |(1)

2 Determines the best phase among the possible ones.

3 Goes back to 1.

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ResultsConclusion

Q&A

Reactive Controller Action Selection

1. Lane activation 2. Junction East 3. Best phase

∑c∈C

(1− αVc(t)− βXc(t))

(2)

∑i=1→n

AT (i)

n(3)

∑tl∈G

AT (tl) (4)

AT(g) = 0.09AT(h) = 0.3AT(i) = 0.85AT(j) = 0.05AT(k) = 0.13

ATJe = 0.858 Possible phases1. G,G,R,G,G = 0.572. G,R,G,R,R = 0.943. R,R,G,R,G =0.98Best phase

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ResultsConclusion

Q&A

Machine Consciousness Controller Behavior

1 Defines the junction codelet with greater activation level,which gains access to conscious global workspace whilerespecting a minimum threshold. If none of the codeletsreaches the threshold, the system works unconsciously andglobal workspace remains empty.

2 Broadcasts the sensory information of the conscious codelet.

3 Goes back to 1.

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ResultsConclusion

Q&A

Broadcast interference rules

1 If one incoming lane of my junction is topologically connectedto one incoming lane of the conscious junction that has a redlight in its chosen phase, I must close it with a red light.

2 If one incoming lane of my junction is topologically connectedto one incoming lane of the conscious junction that has agreen light in its chosen phase, or if it is not connected at all,I must open it with a green light.

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ResultsConclusion

Q&A

Machine Consciousness Definition

Spotlight

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ResultsConclusion

Q&A

Coalitions in CST

proc()In

B

Out

A

Codelet

Coderack Raw Memory

Coalition

T I

Memory Object (Sign)25 / 25

a machine consciousness approach to urban traffic signal control

Engineering