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

ARBB MOCS Workshop - EricssonFebruary 17, 2016

A Machine ConsciousnessApproach to Urban Traffic SignalControl

IntroductionUrban Traffic Signal Control

ConsciousnessMaterials and Methods

MethodsResults

Conclusion

What is the motivation for Engineering TSC?Why Studying Consciousness in Animals?Why Applying Machine Consciousness to Urban Traffic Control?Main Contributions

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MethodsResults

Conclusion


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MethodsResults

Conclusion


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MethodsResults

Conclusion


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MethodsResults

Conclusion


Contribution

The main contribution of this work is the application of amachine consciousness approach to urban traffic signalcontrol, with the design and implementation of a solution tothe problem.

Part of the team which designed and implemented thefoundations of the Cognitive Systems Toolkit.

Designed and implemented the Global WorkspaceConsciousness Codelet as subsystem of CST, available as partof the architecture.

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MethodsResults

Conclusion

Isolated Signalized Junction

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MethodsResults

Conclusion

Bernard J. Baars and The Dynamic Global WorkspaceTheory

consciousness is like aninformation gateway to thebrain, because it allows awidespread structure ofneuronal networks tooperate in order to integrate,provide access, andcoordinate the processing ofmany specialized brain sites,which would otherwiseoperate autonomously.

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MethodsResults

Conclusion

Machine Consciousness

Spotlight

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MethodsResults

Conclusion

SUMONetwork Models - Test BedOur CST Architecture - The Traffic Controllers

SUMO - Simulation of Urban Mobility

Microscopic simulation;

Online interaction - TraCI

Time schedules of traffic lights can be imported or generatedautomatically by SUMO;

No artificial limitations in network size and number ofsimulated vehicles;

Supported map import formats: OpenStreetMap, VISUM,VISSIM, NavTeq.

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MethodsResults

Conclusion


Simple T Network Model

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MethodsResults

Conclusion


twin T Model

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MethodsResults

Conclusion


Corridor Network Model

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MethodsResults

Conclusion


Manhattan Network Model

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MethodsResults

Conclusion


Downtown Campinas Network Model

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MethodsResults

Conclusion


Traffic Controllers

Fixed Times

Parallel Reactive

Machine Consciousness

Available athttps://github.com/CST-Group/traffic-signal-control-app

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MethodsResults

Conclusion


Network Model

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MethodsResults

Conclusion


Contribution - Consciousness GWT Codelet

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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MethodsResults

Conclusion


Working 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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MethodsResults

Conclusion


Purely Reactive Behavior

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MethodsResults

Conclusion


Most Critical Traffic Situation

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MethodsResults

Conclusion


Broadcast, interference, deliberative behavior

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MethodsResults

Conclusion


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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MethodsResults

Conclusion


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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MethodsResults

Conclusion


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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MethodsResults

Conclusion


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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MethodsResults

Conclusion

Methods

Four simulations scenario for each one of the five networkmodels.

Random generated scenarios, using 5400 seconds: P=0.1,P=0.4, P=0.7, P=1.0.

20 experimental scenarios, which were run 10 times each,summing up a total of 200 experiments, run for each one ofthe 3 controllers.

Controlled experiments: SimpleT and P=1.0.

Available at https://github.com/CST-Group/traffic-signal-control-app-experiments.

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MethodsResults

Conclusion

Simple T Result

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Twin T Result

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MethodsResults

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Corridor Result

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MethodsResults

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Manhattan Result

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MethodsResults

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Manhattan Result

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MethodsResults

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Conclusion

A consistent gain in performance with the ”ArtificialConsciousness” traffic signal controller during all simulationtime, throughout different simulated scenarios, could beobserved, ranging from around 10% to more than 20%.

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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MethodsResults

Conclusion

Limitations and Future Work

varying conscious thresholds for codelet activation;

applying different heuristics in the unconscious automaticcodelets;

more complex traffic networks and scenarios;

real traffic data;

evolving our consciousness model is to implement automatiza-tion and deautomatization of behaviours;

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MethodsResults

Conclusion

Thank you! Q&A

email: [email protected]

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

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