Anjan Rayamajhi PhD Dissertation Defense Clemson University

1. Exploring Smart Infrastructure Concepts to Improve the Reliability and Functionality of Safety Oriented Connected Vehicle Applications Anjan Rayamajhi PhD Dissertation Defense Clemson University

2. Introduction • Roadway fatalities have increased 14% since 2014 in the US • Every year 40,000 deaths occur because of road accidents • WHO states roadway accidents is one of the leading cause of deaths in the world.

3. Introduction • Motivated to reduce fatalities and improve roadway efficiency, USDOT and NHTSA have outlined two initiatives • Connected Vehicles (CV) • Autonomous Vehicles (AV) • Vehicle Platooning such as Cooperative Adaptive Cruise Control (CACC) will become front runner in future transportation systems • CACC is advantageous because it: • improves highway throughput • reduces energy consumption

4. Problem Statement • CACC systems and its engineering is not well understood for large scale deployment [Naus 2010] [Ploeg 2011] [Ploeg 2014] • Performance and Reliability of applications like CACC in actual DSRC based CV systems is not well understood [Lei 2011] [Ploeg 2015] • Performance under malicious activities is not very well studied [Biron 2017] • Challenges in blending historically independent research domains • Can CACC be made more reliable under realistic network conditions ?

5. Contribution • We show that platoons are affected by outages in wireless networks and fall back mechanisms have to be developed to prevent a platoon from becoming unstable and to avoid crashes. [Rayamajhi 2018] • We present the optimal approach to navigate a platoon with different number of vehicles under different network scenarios. Our approach is to find the most safest and stable criteria. • We develop and evaluate practical methods by which a platoon can adapt to better achieve maximum flow while ensuring stability.

6. Overview • Network Performance measurement of DSRC • Implications of network reliability in a CACC application • Heuristic solutions to improve traffic flow adhering to stability and safety

7. Network Performance Metrics • Mean burst Length (MBL) : average number of packets in loss burst • Mean Good Length (MGL) : average number of packets in good burst • Reliability( R) = ratio of number of packets received over total number of packets expected in time R = 1 - • Weighted Average Reliability

8. Observations made in DSRC tests Calculated Nominal transmission time Observed End to End Latency

9. DSRC Response in Congestion • How does DSRC behave in a congested scenario ? Burst Loss property in Congested DSRC Network • Two nodes with dedicated DSRC flows (Rate ½ QPSK Modulation) • Variable number of nodes in the vicinity • All nodes transmit at 40 pps. • Simulation area of 200 x 200 meters Number of packets Packet Error Rate Number of nodes Number of nodes

10. DSRC Response in Malicious attack • How does DSRC behave in an attack ? Burst Loss property in attack scenario • Total nodes = 51 • Two nodes (node 0, node 1) with dedicated DSRC flows (Rate ½ QPSK Modulation) • An attack node (node 50) placed midway between the two nodes • Attack node allowed to transmit at nominal rate • Simulation area of 200 x 200 meters Number of packets Modulation and Coding

11. CACC operation in a DSRC network • Observe the affect of DSRC network in an emulated CACC application ( Virtual Vehicle (CACC)

12. CACC Platoons • A platoon of N vehicles in single lane traffic composed of automated trucks • Each vehicle capable of V2V or V2I using DSRC • Platoon uses information only from vehicle ahead • Each vehicle equipped with RADAR or LiDAR • Each vehicle undergoes CACC calculations to compute target acceleration • Target acceleration is used by vehicle torque module to actually move the vehicle CACC capable vehicle

13. CACC platoons • Platoon Controller equation • Vehicle mobility (piecewise linear) CACC Platoon ** Either Target Acceleration or Current Acceleration

14. How to define Stability in CACC ? • Velocity should decrease or remain same towards the tail of the platoon • Distance Error decays towards the tail of the platoon • Mean absolute jerk (rate of change of acceleration) • Renewal period = one period of • = start of renewal period

15. Local Estimation (Kalman Filter based) control inputs • Sensor measurements • Prediction Phase • Correction Phase prediction correction

16. Parameters for Acceleration Estimation Jerk Distance traveled Velocity acceleration State variables Measurements

17. Simulation of CACC

18. Leader vehicle acceleration Step profile Sinusoidal profile Linear profile Us06 profile Clemson-Anderson (real) profile

19. Simulating burst packet loss • Two state Markov model No Loss state Loss state Table A

20. CACC Assessment Metrics • Crash Occurrence Time • Time instance when a crash occurs in the platoon • The platoon stops operating after the crash • Flow Rate • Number of vehicles per second • Ratio between number of vehicles and time between successive crossing of the same point in the road by first and last vehicle • Stability • max = maximum value of of all vehicles

21. Simulation parameters Configurable system parameters

22. Traditional ACC Crash Analysis Stability Analysis Crastime, sec Time headway, sec Time headway, sec

23. CACC with complete outage Crash Analysis Stability Analysis Crastime, sec Time headway, sec Time headway, sec

24. CACC with Congested network Crash Analysis Stability Analysis Crastime, sec Time headway, sec Time headway, sec

25. CACC with Malicious attack Stability Analysis Crash Analysis Crastime, sec Time headway, sec Time headway, sec

26. CACC with On-Off burst loss Crash Analysis Stability Analysis Crastime, sec Time headway, sec Time headway, sec

27. CACC with long burst loss Crash Analysis Stability Analysis Crastime, sec Time headway, sec Time headway, sec

28. CACC with no packet loss Stability Analysis Time headway, sec

29. Time headway vs network reliability N = 10, reliability calculated every 1 sec

30. Time headway vs network reliability vs N

31. Dynamic Headway Optimization Control channel Prediction Optimization framework Where,

32. Global Headway Controller • Global controller node located in • An infrastructure node with reliable network connectivity, or • A platoon vehicle with reliable coverage from all the platooning vehicles • Each vehicle submits periodic reliability and mean absolute jerk metrics to the Global controller node using a control channel. • Homogeneous assignment of time headway • The control channel is independent of platoon broadcast channel

33. Initialize: False True i If renewal in : Vehicles GHC

34. Local Headway Controller • Local controller located in every platooning vehicle • Each vehicle notes the reliability and mean absolute jerk metrics and decision is made locally • Heterogenous assignment of time headway

35. Initialize: False True i If renewal in : Local Headway Controller

36. Results: Simulation settings • Simulation parameters • Simulation loss process setup • 0 – T/3 = No packet loss • T/3 - 2T/3 = burst loss • 2T/3 – T = No packet loss

37. Results: Dynamic headway Scenario with in Clemson-Anderson acceleration profile

38. Results: Flow Rate Scenario with in Clemson-Anderson acceleration profile

39. Conclusion • CACC can improve the highway throughput in a safe and stable manner • We studied that CACC can be affected by wireless congestion and malicious attacks • We presented fall back mechanism which is safer than traditional ACC • We analyzed results relating network reliability with the safe headway • We developed and analyzed two heuristic algorithms that cohere to the main advantage of CACC i.e. improving flow rate. • We found that the Local Headway Controller gives larger flowrate compared to Global Headway Controller

40. References: [Naus 2010] G. Naus, R. Vugts, J. Ploeg, R. v. d. Molengraft, and M. Steinbuch. Cooperative adaptive cruise control, design and experiments. In Proceedings of the 2010 American Control Conference, pages 6145{6150, June 2010. doi: 10.1109/ACC. 2010.5531596. [Ploeg 2014] Jeroen Ploeg, Nathan Van De Wouw, and Henk Nijmeijer. Lp string stability of cascaded systems: Application to vehicle platooning. IEEE Transactions on Control Systems Technology, 22(2):786{793, 2014. [Biron 2017] ZoleikhaAbdollahi Biron, Satadru Dey, and Pierluigi Pisu. On resilient connected vehicles under denial of service. 05 2017. [Ploeg 2015] J. Ploeg, E. Semsar-Kazerooni, G. Lijster, N. van de Wouw, and H. Nijmeijer. “Graceful degradation of cooperative adaptive cruise control”, IEEE Transactions on Intelligent Transportation Systems, 16(1):488{497, Feb 2015. ISSN 1524-9050.doi: 10.1109/TITS.2014.2349498 [Lei 2011] C. Lei, E. M. van Eenennaam, W. K. Wolterink, G. Karagiannis, G. Heijenk, and J. Ploeg. Impact of packet loss on cacc string stability performance. In 2011 11th International Conference on ITS Telecommunications, pages 381{386, Aug 2011. doi: 10.1109/ITST.2011.6060086. [Ploeg 2011] J. Ploeg, B. T. M. Scheepers, E. van Nunen, N. van de Wouw, and H. Nijmeijer. Design and experimental evaluation of cooperative adaptive cruise control. In 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC), pages 260{265, Oct 2011. [Rayamajhi 2018] Anjan Rayamajhi, ZoleikhaAbdollahi Biron, Roberto Merco, Pierluigi Pisu, James M Westall, and Jim Martin. The impact of dedicated short range communication on cooperative adaptive cruise control. In 2018 IEEE International Conference on Communications (ICC), pages 1{7. IEEE, 2018.

41. Thank you

42. Network Diagram (congestion) Propagation and fading model used from - ** J. Benin, M. Nowatkowski and H. Owen, "Vehicular Network simulation propagation loss model parameter standardization in ns-3 and beyond," 2012 Proceedings of IEEE Southeastcon, Orlando, FL, 2012, pp. 1-5. Receiver 200 m 50 m Transmitter 200 m

43. Network Diagram (attack) Receiver Transmitter 200 m 50 m Attack Node 200 m

44. Observations made in DSRC tests