1. Paper Review: Automated On-Ramp Merging for Congested Traffic Situations
Emmanuel Sean Peters

2. 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

3. 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

4. Outline Introduction Control Architecture
Description of Vehicles & LCS
Automated Ramp Entrance System
Experiments
Conclusion

5. 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

6. Outline: Architecture
AUTOPIA Program:
The development of automatic cars using mass produced vehicles and tests on real roads

7. 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

8. Outline: Automated Ramp Entrance System (I)
Design of system divided into 3 phases
Detection
Optimal Merging Algorithm
Intelligent Controller Design – uses reference data from optimal merging algorithm

9. 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

10. Outline: Experiments

11. Details: Decision System (I)

12. Details: Decision System (II)

13. Details: Control System
Fuzzy 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%

14. Details: Control System

15. 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

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

17. Details: Control System
Output variable as Sugeno singletons

18. Details: Simulation Results
Scenario 1: x20=-18 and x30=-8,
Scenario 2: x20=-26 and x30=-16
L=10m, initial speed=3m/s

19. Details: Simulation Results
Scenario 1
Scenario 2

20. Details: Simulation Results
Scenario 1
Scenario 2

21. Details: Experiments

24. Questions?