1. EXTENDED DRIVER-ASSISTED MERGING PROTOCOL BRIAN CHOI EMMANUEL PETERS SHOU-PON LIN

2. OBJECTIVE & RESULTS Tested driver assisted merge protocol and recommended improvements to increase average vehicle throughput and reduce delay Increase in maximum throughput from ~1725 veh/h to ~1825 veh/h and a reduction in average delay on highway from 100s to 19s

3. OUTLINE Merging Models Projection and Decision Making Algorithm Simulation Environment Assumptions Parameters Performance Metrics Results Additional Improvements & Results Conclusion

4. MERGING MODELS Lane Change Assistant Merging car chooses nearest front & back car in target lane to merge between No consideration for velocity of traffic in target lane Forces back car to break suddenly Projection Based Proactive Merging Protocol Merging car chooses front and back car by pooling target lane for suitable neighbors Choice based on: Target Velocity - Average Velocity of highway Target Time – Time needed to accelerate to Target Velocity Target Merge Point

5. PROJECTION & DECISION MAKING ALGORITHM IDM Cruise Control Poll Road Velocity, Positions of neighbors Wait Incr. Safety Spacing Project Positions Accelerate Merge Maneuver Abort Merge Protocol IDM Cruise Control Car on ramp NOT participating in merge Wait Timer expires No suitable neighbors found in X trials No suitable neighbors. No. of Trials <X Neighbors found No neighbors found Select Neighbors for Merge Achieve Target Velocity Merge Completed Unexpected event: Sudden breaking, etc. Car leaves ramp

6. PROJECTION & DECISION MAKING ALGORITHM

7. SIMULATION ENVIRONMENT: ASSUMPTIONS No erratic behaviors on the highway The subsystems of the merge protocol work All the drivers on the highway have the same safety spacing requirement and desired speed No trucks on the highway Vehicle-to-Infrastructure communication systems

8. SIMULATION ENVIRONMENT: PARAMETERS 1 Hour simulation time Simulation Variables: Participation Rate Throughput Percentage of traffic from ramp 200 runs

9. SIMULATION ENVIRONMENT: PERFORMANCE METRICS Congestion Average speed of traffic on first half of main road is less than 14.4km/h (8.9mph) Probability of Congestion Percentage of 200 runs that result in congestion Maximum throughput Throughput at which probability of congestion is less than or equal to 5% Highway Delay Difference between average travelling time on main road with and without ramp traffic Ramp Delay Time between car entering and leaving on ramp

10. RESULTS: PROBABILITY OF CONGESTION

11. RESULTS: HIGHWAY DELAY

12. RESULTS: RAMP DELAY

13. ADDITIONAL IMPROVEMENTS Eliminating requirement for front car Easier to find one car to participate in merge, than two cars Policing Communicating with cars on highway to create space for cars on ramp that do not use protocol Platooning Merging multiple cars at once More Intelligent Acceleration/Deceleration Fuzzy logic

14. IMPROVEMENTS: BACK CAR ONLY RESULTS

15. SIMULATION DEMO

16. CONCLUSION By taking into consideration the average speed on the high and the time needed to match the highway speed the merging protocol can more intelligently select the cars that should participate in a ramp to highway merge maneuver The projection of possible merging participants results in higher throughput and lower delay on the highway

17. REFERENCES [1] B.H. Kim and N.F. Maxemchuk, “A Safe Driver Assisted Merge Protocol” [2] Ziyuan Wang, Lars Kulik, and Kotagiri Ramamohanarao. 2007. Proactive traffic merging strategies for sensor-enabled cars. In Proceedings of the fourth ACM international workshop on Vehicular ad hoc networks (VANET '07). ACM, New York, NY, USA, 39-48. DOI=10.1145/1287748.1287755 http://doi.acm.org/10.1145/1287748.1287755 http://doi.acm.org/10.1145/1287748.1287755 [3] A. Kanaris, E. B. Kosmatopoulos, and P. A. Loannou, “Strategies and spacing requirements for lane changing and merging in automated highway systems,” IEEE Transactions on Vehicular Technology, vol. 50, no. 6, pp. 1568-1581, Nov. 2001. [4] Ioannou, P.A.; Stefanovic, M.;, "Evaluation of ACC vehicles in mixed traffic: lane change effects and sensitivity analysis," Intelligent Transportation Systems, IEEE Transactions on, vol.6, no.1, pp. 79- 89, March 2005 doi: 10.1109/TITS.2005.844226 URL:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1402432&isnumber=30446http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1402432&isnumber=30446 [5] X.-Y. Lu, H.-S. Tan, S. E. Shladover, and J. K. Hedrick, “Automated Vehicle Merging Maneuver Implementation for AHS,” Vehicle System Dynamics, vol. 41, no. 2, pp. 85–107, Jan. 2004. [6] Riener, A., Zia, K., Ferscha, A., Beltran, C., and Jesus, J. AMI technology helps to sustain speed while merging – A data driven simulation study on Madrid motoryway ring M30. 2010 14 th IEEE/ACM Symposium on Distributed Sumulation and Real-Time Applications [7] Milanés, V., Godoy, J., Villagrá, J., Pérez, J., “Automated On-Ramp Merging System for Congested Traffic Situations”, Intelligent Transportation Systems, IEEE Transactions on, vol. 12, no. 2, pp. 500-508, June 2011.

18. THANK YOU