Paper Review: Automated On-Ramp Merging for Congested Traffic Situations
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Transcript of Paper Review: Automated On-Ramp Merging for Congested Traffic Situations
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Paper Review:Automated On-Ramp Merging for
Congested Traffic Situations
Emmanuel Sean Peters
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Objectives & ResultsDevelop an automated merging system that:
I. Permits merging traffic to fluidly enter the major road to avoid congestion on the minor roadII. Modifies the speed of vehicles on main road to minimize the effect on the already congested main road
A fuzzy controller and decision algorithm that uses Vehicle-to-Infrastructure communication is designed and tested using three production vehicles
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Objectives & ResultsSolved one of the major causes of congestion in
urban environments: merging from minor to major roads.
• validated using simulations and real experiments involving mass produced vehicles
• system successfully & safely merged a car from ramp onto the main road at low speeds
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OutlineI. IntroductionII. Control ArchitectureIII. Description of Vehicles & LCSIV. Automated Ramp Entrance SystemV. ExperimentsVI.Conclusion
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Outline: Introduction• Increase in number of drivers and vehicles and
cars on road over past few decades• Urban environments are most congested• Advanced Driver-Assisted Systems (ADAS)– Ultimate decision is the driver’s; driver may be
incorrect• Simulations are encouraging but…– Gasoline-propelled vehicle dynamics at very low
speeds are highly non-linear and difficult to model
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Outline: ArchitectureAUTOPIA Program:
The development of automatic cars using mass produced vehicles and tests on real roads
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Outline: Vehicles & LCSAutomated Vehicles- 2 Citroёn C3s (Gasoline-Propelled)- 1 Berlingo Citroёn (Electric)
Local Control Station (LCS) - Detecting risky traffic situations & advising the vehicles
involved
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Outline: Automated Ramp Entrance System (I)
Design of system divided into 3 phasesI. DetectionII. Optimal Merging AlgorithmIII. Intelligent Controller Design – uses reference
data from optimal merging algorithm
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Outline: Automated Ramp Entrance System (II)
Decision System- Ensures sufficient headway is achieved by the
time the merging point is reachedControl System - Fuzzy logic - Relies on SpeedError & Distance Error values
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Outline: Experiments
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Details: Decision System (I)
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Details: Decision System (II)
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Details: Control SystemFuzzy Logic- Solutions based on vague information- Mamdani Inference: max-min method- Membership functions – maps input a value between 0 and 1
Inputs: - SpeedError- DistanceError
Output:- Pedal [-1,1]
Weights:- Throttle – 40%- Break – 10%
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Details: Control System
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Details: Control SystemDistanceErrorDifference between leading& trailing vehicles’ speeds
SpeedErrorDifference between leading& trailing vehicles’ speeds - [-3,3]kmh
Three membership functions & three linguistic labels - Positive linguistic used to accelerate/brake for SpeedError/DistanceError - Negative linguistic used to brake/accelerate for SpeedError/DistanceError - Center linguistic used to indicate that trailing car maintaining target speed or distance
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Details: Control System
Output variable, Pedal [-1, 1], as a function of fuzzy input variables SpeedError and Distance Error; determines which actuator is pressed
.
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Details: Control System
Output variable as Sugeno singletons
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Details: Simulation Results
L=10m, initial speed=3m/s
Scenario 1: x20=-18 and x30=-8, Scenario 2: x20=-26 and x30=-16
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Details: Simulation ResultsScenario 1 Scenario 2
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Details: Simulation ResultsScenario 1 Scenario 2
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Details: Experiments
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Questions?