Paper Review:Automated On-Ramp Merging for

Congested Traffic Situations

Emmanuel Sean Peters

Objectives & Results

Develop an automated merging system that:

I. Permits merging traffic to fluidly enter the major road to avoid congestion on the minor road

II. 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

Objectives & Results

Solved 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

Outline

I. Introduction

II. Control Architecture

III. Description of Vehicles & LCS

IV. Automated Ramp Entrance System

V. Experiments

VI.Conclusion

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

Outline: Architecture

AUTOPIA Program:

The development of automatic cars using mass produced vehicles and tests on real roads

Outline: Vehicles & LCS

Automated Vehicles- 2 Citroёn C3s (Gasoline-Propelled)- 1 Berlingo Citroёn (Electric)

Local Control Station (LCS) - Detecting risky traffic situations & advising the vehicles

involved

Outline: Automated Ramp Entrance System (I)

Design of system divided into 3 phases

I. Detection

II. Optimal Merging Algorithm

III. Intelligent Controller Design – uses reference data from optimal merging algorithm

Outline: Automated Ramp Entrance System (II)

Decision System

- Ensures sufficient headway is achieved by the time the merging point is reached

Control System

- Fuzzy logic

- Relies on SpeedError & Distance Error values

Outline: Experiments

Details: Decision System (I)

Details: Decision System (II)

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%

Details: Control System

Details: Control System

DistanceError

Difference between leading

& trailing vehicles’ speeds

SpeedError

Difference 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

Details: Control System

Output variable, Pedal [-1, 1], as a function of fuzzy input variables SpeedError and Distance Error; determines which actuator is pressed

.

Details: Control System

Output variable as Sugeno singletons

Details: Simulation Results

L=10m, initial speed=3m/s

Scenario 1: x20=-18 and x30=-8, Scenario 2: x20=-26 and x30=-16

Details: Simulation Results

Scenario 1 Scenario 2

Details: Simulation Results

Scenario 1 Scenario 2

Details: Experiments

http://www.iai.csic.es/autopia/Videos/Merging.wmv

Questions?