Intelligent Driving Agents - TU Delft€¦ · Presentation Master’s thesis Patrick Ehlert January...
Transcript of Intelligent Driving Agents - TU Delft€¦ · Presentation Master’s thesis Patrick Ehlert January...
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Intelligent Driving AgentsIntelligent Driving Agents
The agent approach to tactical driving in The agent approach to tactical driving in autonomous vehicles and traffic simulationautonomous vehicles and traffic simulation
Presentation Master’s thesisPresentation Master’s thesisPatrick EhlertPatrick EhlertJanuary 29January 29thth, 2001, 2001
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Imagine….Imagine….
SensorsSensorsIntelligenceIntelligence
ActuatorsActuators
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Overview of presentationOverview of presentation
�� Project and theoryProject and theory
�� Design Design
�� SimulationSimulation
�� Conclusions and recommendationsConclusions and recommendations
�� Short demonstrationShort demonstration
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ProjectProject
�� Study the use of intelligent agents Study the use of intelligent agents controlling a vehicle in an urban controlling a vehicle in an urban environmentenvironment
�� Two cases: Two cases: 1. Real life vehicles1. Real life vehicles2. Simulated vehicles2. Simulated vehicles
�� Focus on Focus on tacticaltactical--level level drivingdriving
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Theory: tactical drivingTheory: tactical driving
Driving task separated in three levels:Driving task separated in three levels:
�� strategicstrategic
�� tacticaltactical
�� operationaloperational
longlong--term decisions, determine goalsterm decisions, determine goals
shortshort--term decisions, current situationterm decisions, current situation
actual performed actionsactual performed actions
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Theory: what are agents?Theory: what are agents?
DefinitionDefinition: autonomous computerized entity : autonomous computerized entity capable of sensing its environment and capable of sensing its environment and acting intelligently based on its perception.acting intelligently based on its perception.
“smart creature inside computer”“smart creature inside computer”
�� Ability to perform a given taskAbility to perform a given task�� AutonomousAutonomous�� Adaptive / capable of learningAdaptive / capable of learning
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Design: driving agentDesign: driving agent
�� Perform tactical drivingPerform tactical driving
�� Real time controlReal time control
�� SafetySafety
�� ExpandibilityExpandibility
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Behavioural rules
Sensors Communication Vehicle
Supervisor /other agents
ArbiterController
&Memory
Envi
ronm
ent
Design: driving agent Design: driving agent (continued)(continued)
Para
met
ers
Behavioural rules
Collisionavoidance
Roadfollowing
Overtaking Trafficlights
Carfollowing
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�� Decided to create new prototype Decided to create new prototype traffic simulation programtraffic simulation program
ImplementationImplementation: simulator: simulator
�� Used Borland Delphi 5 languageUsed Borland Delphi 5 language•• Suitable for fast prototypingSuitable for fast prototyping•• ExperienceExperience
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Implementation: simulatorImplementation: simulatorSimulationcontroller
User interface
1: update
Environment
Simulated objects
Traffic lightcontrollers
Traffic lights
Vehicles
Roads
IntersectionsPicture ofenvironment
2: visualfeedback
Timer
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Implementation: agentImplementation: agent
Agentsb: send orders
c: sleepReasoning
Sensors
Environment
Simulated objects
Traffic lightcontrollers
Traffic lights
Vehicles
Roads
Intersectionsa: get information
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Implementation: rulesImplementation: rules
�� Implemented and tested oneImplemented and tested one--byby--oneone
�� Behaviour rules are directly coded into Behaviour rules are directly coded into the programthe program
example: example: IfIf (agent speed < preferred speed)(agent speed < preferred speed)thenthen Accelerate (normal)Accelerate (normal)
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Implementation: exampleImplementation: example
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ConclusionsConclusions
�� Designed driving agent can control Designed driving agent can control vehiclesvehicles
�� Advantages agentAdvantages agent--based simulationbased simulation•• increased realismincreased realism•• flexibleflexible•• distributed processing possibledistributed processing possible
�� DisadvantagesDisadvantages•• increase computational loadincrease computational load•• many parametersmany parameters
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RecommendationsRecommendations / Future work/ Future work
�� Improve simulator and agentImprove simulator and agent
�� Use distributed approachUse distributed approach
�� Use agent to control real vehicles ?Use agent to control real vehicles ?
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DemonstrationDemonstration
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**** End of presentation ****
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Theory: senseTheory: sense--planplan--actact
Sensors
Perc
eptio
n
Wor
ld m
odel
ing
Plan
ning
Task
exe
cutio
n
Actuators
Traditional model, popular in 70’s and 80’sTraditional model, popular in 70’s and 80’s
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Theory: subsumptionTheory: subsumption
Explore
Build maps
Avoid objects
WanderSensors Actuators
Rodney Brooks, MIT 1986Rodney Brooks, MIT 1986
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Design: behaviour rulesDesign: behaviour rules
�� Any method may be used within Any method may be used within constraintsconstraints
�� Use behavioural parametersUse behavioural parameters•• preferred speedpreferred speed•• acceleration & deceleration rateacceleration & deceleration rate•• gap acceptancegap acceptance•• reaction timereaction time•• sensor range (visibility)sensor range (visibility)
�� Specialised and fast procedures that Specialised and fast procedures that propose an actionpropose an action
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Implementation: agentImplementation: agent
�� Agent execution loopAgent execution loop
1. Get input from sensors1. Get input from sensors2. Send input to memory2. Send input to memory
3. Determine action proposals3. Determine action proposals
4. Arbiter selects best proposal4. Arbiter selects best proposal
5. Send proposal to vehicle5. Send proposal to vehicle
6. Sleep until next loop6. Sleep until next loop
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Implementation: rules Implementation: rules (continued)(continued)
�� Example Road FollowingExample Road Following
Drive atpreferred
speedStay in lane Adjust speed
for curveBrake for end
of road
Action proposal
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Example .MDF fileExample .MDF file
DESCRIPTION="Demo scenario DESCRIPTION="Demo scenario -- Intersection"Intersection"SCALE=40SCALE=40MAPWIDTH=300MAPWIDTH=300MAPHEIGHT=300MAPHEIGHT=300ROAD (road1, [000,100], [100,100], 350, 350,1,1)ROAD (road1, [000,100], [100,100], 350, 350,1,1)ROAD (road2, [100,100], [300,100], 350, 350,1,1)ROAD (road2, [100,100], [300,100], 350, 350,1,1)ROAD (road3, [100,100], [100,000], 350, 350,1,1)ROAD (road3, [100,100], [100,000], 350, 350,1,1)ROAD (road4, [100,100], [100,300], 350, 350,1,1)ROAD (road4, [100,100], [100,300], 350, 350,1,1)TRAFFICLIGHT (light1, [087,113], road1, 1, right)TRAFFICLIGHT (light1, [087,113], road1, 1, right)TRAFFICLIGHT (light3, [113,087], road2, 1, left)TRAFFICLIGHT (light3, [113,087], road2, 1, left)TRAFFICLIGHT (light4, [087,087], road3, 1, left)TRAFFICLIGHT (light4, [087,087], road3, 1, left)TRAFFICLIGHT (light2, [113,113], road4, 1, left)TRAFFICLIGHT (light2, [113,113], road4, 1, left)LIGHTCONTROLLER (lc1, 5000, light1, light2, light3, light4)LIGHTCONTROLLER (lc1, 5000, light1, light2, light3, light4)
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ExperimentsExperiments
�� Low preferred speedLow preferred speed�� Large gap acceptanceLarge gap acceptance�� Low deceleration rateLow deceleration rate
�� High preferred speedHigh preferred speed�� Small gap acceptanceSmall gap acceptance�� High deceleration rateHigh deceleration rate
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Experiments Experiments (continued)(continued)