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Adaptive Cruise Control (ACC)

Adaptive Cruise Control (ACC). ELG 4152 Project Professor Riadh Habash TA: Fouad Khalil. Group Memebers: Mirza Abdel Jabbar Baig (3256498) Mohammad Ali Akbari (3299852) Navid Moazzami (3413826) Hasan Ashrafuzzaman (3384661). Reference.

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Adaptive Cruise Control (ACC)

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  1. Adaptive Cruise Control (ACC) ELG 4152 Project Professor Riadh Habash TA: Fouad Khalil Group Memebers: Mirza Abdel Jabbar Baig (3256498) Mohammad Ali Akbari (3299852) Navid Moazzami (3413826) Hasan Ashrafuzzaman (3384661)

  2. Reference [1] A Safe Longitudinal Control for Adaptive Cruise Control and Stop-and-Go ScenariosMartinez, J.-J.; Canudas-de-Wit, C.; Volume 15,  Issue 2,  March 2007 Page(s):246 – 258 [2] Modeling a Cruise Control http://www.library.cmu.edu/ctms/ctms/examples/cruise/cc.htm [3] Highway Speed Controller http://www.site.uottawa.ca/~misbah/elg4392/HC12CodeWarriorC/HighwaySpeedController/project.c [4] W. Jones, “Keeping cars from crashing,” IEEE Spectrum, vol. 38, no. 9, pp. 40–45, Sep. 2001. [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. 2000.

  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]

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

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

  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.

  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 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.

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