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Automatic Merge Control

Automatic Merge Control. Vipul Shingde Department of Computer Science and Engineering, IIT Bombay under the guidance of Prof. Krithi Ramamritham. Outline. Introduction Problem Definition Our approaches Optimization Formulation Head of Lane Approach Observations Conclusions Future Work.

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Automatic Merge Control

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  1. Automatic Merge Control Vipul Shingde Department of Computer Science and Engineering, IIT Bombay under the guidance of Prof. Krithi Ramamritham

  2. Outline • Introduction • Problem Definition • Our approaches • Optimization Formulation • Head of Lane Approach • Observations • Conclusions • Future Work

  3. Motivation • Modern vehicles are being equipped with • Onboard sensors • Wireless communication • Processing power • To increase road-safety, vehicle throughput, overall trip quality we need to exploit these capabilities • Drive-by-wire, ACC, Automatic Lane Merging, Intersection Management.

  4. Introduction to AMC system • Ensure safe vehicle maneuver at road intersections • Primary Aim • Safety • Secondary Aim • Throughput  • Fuel Efficiency  • Latency 

  5. Related Work [4] • Virtual Vehicle • Vehicle mapped onto another lane to maintain safe distance • How to determine Merge sequence? Lane1 Lane2

  6. Our AMC System • Vehicles • Autonomous • Communication ability • Road-side controller at intersection • Track of Vehicle Profile • Determine • Merge Sequence • Future Behavior • Send commands to vehicles

  7. Optimization Problem • Formulated lane-merging problem as a non-linear optimization problem • Constraints • Precedence Constraint (Same Lane) • Mutual Exclusion Constraint (Different Lane) • Bound on Vehicle Velocity and Acceleration • Safety Criteria (Safety distance)

  8. Optimization Problem Formulation • Objective Functions • Minimization of average latency • Minimize (t1 + t2 + ::: + tn) / n • Maximize throughput • Minimize max (ti) System Input: i,j sij ,uij ,S System Output: i,j tij

  9. Optimization Problem (ctd..) • Using tij, we determine • Merge sequence • Future Behavior of vehicles • Aij - Acceleration (constant) of each vehicle • Vij(t) - Velocity of each vehicle • Acceleration commands are then sent to respective vehicles

  10. Head of Lane (HoL) • Basic Idea: • Consider foremost vehicles (HoL) in each lane • Determine which head vehicle should go first • Consider next set of head vehicles • Metric: Average Latency • Accelerate vehicle whenever possible

  11. HoL Algorithm Determine independently future behavior of head vehicles using current profile and maximum acceleration Vehicles Interfere Yes No Strong Interference Follow the above order No Yes Find cost of both orders. Choose the one with low cost

  12. Cost of Merge Order • Nearest Head: • Allow the vehicle with least velocity to go first • Cascading Effect: • Effect of particular order on the vehicles behind • Number of vehicles affected • Net deceleration introduced

  13. Is HoL Optimal? • Optimality can be w.r.t: • Which vehicles to consider for determining the merge sequence • The metric being used • HoL is optimal w.r.t. the first one • Proof by Induction & Contradiction

  14. Extending to Continuous stream of vehicles • Above algorithms • Applicable at particular instant • Zonal Partitioning • Apply algorithm to vehicles in Zone2 • When vehicles from Zone3  Zone2, reapply algorithm

  15. Implementation • Optimization formulation implemented in matlab • fmincon function • Execution time • Variable • Order of 1-4 second • Error: “Maximum number of function evaluations exceeded.” • HoL approach with nearest head implemented in C++ • Execution time • Order of 0.1 second

  16. Input System Parameters: • Amax = 4m/s2 Amin = -4m/s2 • Vmax = 27m/s Vmin = 0m/s • L = 5m Vehicle Profile at t=0

  17. Output

  18. Results (Optimization)

  19. Results (HoL)

  20. Conclusion • Safety criteria introduced in optimization formulation is effective in maintaining safety criteria at all time instants • HoL approach is optimal w.r.t. which vehicles to consider for determining merge sequence • Results obtained from HoL approach comparable to that of optimization formulation • Need to check for inconsistencies in constraints in the two approaches • Execution time of HoL very small compared to that of optimization formulation

  21. Future Work • Implement HoL using cascading effect and compare the results • Extend the system to deal with continuous stream of vehicles • Develop real-time support for the system and demonstrate the concept on robotic vehicular platforms • Look at the decentralized solution and compare with the current centralized one

  22. References • Gurulingesh Raravi; Jatin Bharadia; Krithi Ramamritham. “Towards Intelligent Vehicles: Automatic Merge Control”. • Tornsten Bruns; Eckehard Munch. “Intersection management as self-organisation of mechatronic systems”. In Proceedings of January 2006. • Tsugawa S. “Inter-vehicle communications and their applications to intelligent vehicles: an overview”. IEEE Intelligent Vehicle Symposium, 2:564-569, 2002. • T. Uno; A. Sakaguchi; S. Tsugawa. “A merging control algorithm based on inter-vehicle communication.” In IEEE International Conference on Intelligent Transportation Systems, pages 783-787, Tokyo, Japan, 1999.

  23. Thank You !!

  24. Case 1 • HoL Matlab • 79.450531 79.4546

  25. Optimization (Case 3)

  26. HoL (Case 3)

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