1. M. Tech. Project Presentation Automatic Cruise Control System By: Rupesh Sonu Kakade 05323014 Under the guidance of Prof. Kannan Moudgalya and Prof. Krithi Ramamritham Indian Institute of Technology, Bombay 10 July 2007

2. Overview Introduction Objectives Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

3. Introduction Conventional Cruise Control Difficulties: 1. Useful only in sparsely populated roads 2. Disengagement may result in driver loosing control of a car. Velocity control Driver Set Speed

4. Introduction Automatic Cruise Control (ACC) System Control Objectives: 1. Follow-the-leader car 2. Adapt to leader velocity

5. Introduction - ACC

6. Safe Inter-vehicle distance Rule: 1. Constant spacing policy – Safe distance is independent of vehicle parameters such as maximal velocity, deceleration, etc.

7. Introduction - ACC 2. Constant time-gap policy: Difficulties with ACC: 1. Federal and State laws prohibits the use of ACC system below certain speed value. 2. Human driving often results in excessive accelerations and decelerations. Thus violating comfort specifications.

8. Introduction Stop-and-go scenario demands a different behavior from vehicles. Control in stop-and-go scenario Control Objectives: 1. Safety Constraint: Stop the vehicle before it reaches a critical distance,. 2. Comfort specification: Keep the deceleration and jerk bounded.

9. Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

10. Objectives of Project Design control systems for 1. Speed control - in conventional cruise control 2. ACC controller 3. Controller for stop-and-go traffic and 4. Integrate controllers on low-cost platform

11. Approach used Zones: 1. Blue Zone: Cruise control 2. Green Zone: Automatic cruise control 3. Orange Zone: Stop-and-go traffic control 4. Red Zone: Safety critical zone

12. Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

13. Automatic Cruise Control Control Objectives: 1. Follow-the-leader car, i.e., distance error should be minimal. Distance error is computed from where, 2. Adapt to leader velocity, i.e., relative velocity between two vehicles should be minimal.

14. ACC Control Law: The first time-derivative of distance error is computed and solved the following equation which ensures the distance error reduces to zero. We have

15. ACC The control structure is similar to PD controller with, 1. Proportional gain 2. Derivative gain

16. ACC Control Scheme

17. Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

18. Control during stop-and-go scenario Control Objectives: 1. Safety Constraint: Stop the vehicle before it reaches a critical distance,. 2. Comfort specification: Keep the deceleration and jerk values bounded for all t. Reference model: Input: Lead vehicle velocity and Output: Reference distance and reference acceleration

19. Control during stop-and-go scenario

20. Reference model has twofold objectives: 1. Reference distance computation: 2. Reference acceleration computation: Safety and comfort constraints

21. Control during stop-and-go scenario Objectives: To find constraints on c and so that safety and comfort specifications are satisfied for all initial conditions and. Initial conditions are defined as where t = 0 s, is the time when Orange Zone is reached. Solvingand

22. Control during stop-and-go scenario where =

23. Control during stop-and-go scenario Solving the previous expression, we have The maximum penetration distance is This gives us a lower bound on c

24. Control during stop-and-go scenario Next we find upper bound on c. Substitute in expression for reference acceleration, i.e., The maximum value of reference breaking is computed from

25. Control during stop-and-go scenario Substitute in, we have

26. Control during stop-and-go scenario Now we consider comfort specification, i.e., jerk values must also be bounded. This gives us another upper limit on value for c. The maximum value of jerk is believed to depend on extremes of

27. Control during stop-and-go scenario The expression has two solutions. i.e., estimated lead velocity assumed to be zero. Therefore maximum value of jerk could be computed from

28. Control during stop-and-go scenario To proceed we assume i. e., negative acceleration is always greater than positive acceleration. The maximum jerk will be bounded as

29. Control during stop-and-go scenario Assuming sufficiently large forThe previous expression yields another upper bound on value for c. C1 and c2 are associated with safety Whereas c3 is associated with comfort

30. Control during stop-and-go scenario In the Orange Zone, priority is given to safety, i.e., Next we determine the lower bound on the value of. We use the above expression together with If takes the smallest value then c takes on the largest value.

31. Control during stop-and-go scenario

32. Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

33. Results We implemented ACC controller on Dexter-6C. This platform is relatively reach in a sense that it has 1. Independent steering controller 2. Independent drive controller 3. Independent controller for white line sensing Our objective was to implement control system on a low cost platform, such as CDBOT. The experimental results on CDBOT are also presented.

34. Results Figure: Dexter-6C, a test car

35. Results - On Dexter-6C Fig.: Speed control loop performanceFig.: Car-following (ACC) results

36. Results - On Dexter-6C Fig.: Time-gap results

37. Results – On CDBOT Inner speed control loop performance test

38. ACC Results – On CDBOT

39. Results – On CDBOT Control in stop-and-go scenario

40. Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

41. Conclusion Different traffic densities is found to demand different behavior from vehicles. Controllers for longitudinal speed control of cars during sparsely populated road, moderate traffic, and stop-and-go scenarios are designed. Controllers were integrated on robotic platform, CDBOT. Also ACC controller was implemented on Dexter-6C.

42. Overview Introduction Objectives of project Automatic Cruise Control (ACC) Control in stop-and-go traffic Results Conclusion Future Improvements

43. 1. ACC controller used PD structure. Due to its non perfect tracking, jerk values are some times higher. This aspect could be improved by using advanced controller such as controller based on adaptive control theory. 2. String (or platoon) stability problem is not analyzed here.