1. Adaptive Cruise Control (ACC)
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Group Memebers:
Mirza Abdel Jabbar Baig ( )
Mohammad Ali Akbari ( )
Navid Moazzami ( )
Hasan Ashrafuzzaman ( )
ELG 4152 Project
Professor Riadh Habash
TA: Fouad Khalil

2. Reference
[1] A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go Scenarios Martinez, J.-J.; Canudas-de-Wit, C.; Volume 15,  Issue 2,  March 2007 Page(s):246 – 258
[2] Modeling a Cruise Control
[3] Highway Speed Controller
[4] W. Jones, “Keeping cars from crashing,” IEEE Spectrum, vol. 38, no.
9, pp. 40–45, Sep
[5] M. A. Goodrich and E. R. Boer, “Designing human-centered automation:
Tradeoffs in collision avoidance system design,” IEEE Trans. Intell.
Transp. Syst., vol. 1, no. 1, pp. 40–54, Mar

3. Problem Statement
The main problem regarding the normal Cruise Control technology is that it is not aware of other vehicles’s movement
The driver must be always aware. Hence, possibility of mistakes
Possibility of collision with the leading car if not manually slowed down

4. Proposed Solution
Introduce Adaptive Cruise Control for longitudinal control of the vehicle
Speed would be automatically adjusted for safe inter-distance
Once safe inter-distance is reached, the speed would return to the desired speed set by the driver

5. Technical Objectives To design a control system for ACC. No overshoot
Settling Time of about 4-7 seconds.
No oscillation (because no overshoot)
A steady-state error of 0

6. Vehicle Characteristics
If the inertia of the wheels is neglected, and it is assumed that friction (which is proportional to the car's speed) is what is opposing the motion of the car, then the problem is reduced to the simple mass and damper system shown in the next slide.

7. Vehicle Characteristics

8. System Block Diagram [2]
Add the block diagram of the system.

9. Controller Selection Which kind of Controller is the best?
No controller.
P controller.
PI controller.
PID controller.
PD controller.

10. Controller Selection P Controller No Controller Kp = 10000
Add the matlab output response.
Settling time = 76.7 s
Steady state error > 98%
Kp = 10000
Settling Time = 0.389s
Steady state error = 2%

11. Controller Selection PI Controller *Final choice is PI Controller*
Kp=800, Ki=40
Settling time = 4.89 s
Steady state error = 0

12. Distance Checking [1] Three scenarios:
dr > d0, cruises at desired speed, ACC inactive
dr < dc, danger zone, ACC enables to slow down
d0 < dr < d0, ACC is enable to reach safe inter-distance

13. Implementation of Distance Checking [3]
The distance checking algorithm only requires a minimum distance and a range.
The algorithm calculates the actual minimum distance (> provided distance) and maximum distance and then outputs the new speed of the vehicle.
The user can also provide a maximum and minimum speed for the vehicle.
Add matlab output.

14. Implementation of Distance Checking
temp=(300*(speedmax-speedmin))/(12*range)
minimum_Distance=(minimum_Distance*32)/10
max_Distance = minimum_Distance + (3*range)
if (distance > (max_Distance))
speed = speedmax;
if (distance < minimum_Distance)
speed = 0;
if ((distance < max_Distance) and (distance>minimum_Distance))
if leader_speed > 0
speed = ((100*speedmin-(kvit*(minimum_distance))) + temp * distance)/100;
else
speed = ((100*speedmin+(kvit*(max_Distance))) + temp * distance)/100;

15. Simulation Maximum follower vehicle speed = 100 m/s
The following parameters were used for the simulation:
Maximum follower vehicle speed = 100 m/s
Minimum follower vehicle speed = 0 m/s
Minimum distance = 40 m
Range = 20 m
Initial distance = 80 m
Kp = 800
Ki = 40
b = 50
m = 1000

16. Final Model (simplified)

17. Simulation Yellow: Distance between two vehicles
Blue: Speed of the leader vehicle
Purple: Speed of the follower vehicle

18. Limitations/Conclusion
Not a complete transfer function of the vehicle and environment.
Linear distance-checking model.
No limitations on the acceleration and jerk.
Our model is simplified compared to real-time models, but can be used to implement a practical ACC.