Results for 2019

This year we worked on micro-macro models, adaptive cruise control and traffic control via AVs

Field experiments for adaptive cruise control vehicles

• [Achieved] To assess the string stability of ACC vehicles, we conducted a series of experiments with seven different make/ model of ACC vehicles (roughly 1,800 km of test driving) to observe how different ACC vehicles respond when following a lead vehicle. Using the experimental data, models of ACC vehicle behavior were learned and assessed for string stability. See our work below and a video of the experiments in the demos
• The following papers were submitted / published:
  1. G. Gunter, Y. Wang, D. Gloudemans, R. Stern, D. Work, M. L. Delle Monache, R. Bhadani, M. Buntingm R. Lysecky, J. Sprinkle, B. Seibold, B. Piccoli, WiP Abstract: String stability of commercial adaptive cruise control vehicles, 2019 ACM/IEEE 10th International conference on Cyber-Physical Systems (ICCPS), 328-329, Montreal, Canada. extended abstract, [preprint]
  2. G. Gunter, D. Gloudemans, R. E. Stern, S. McQuade, R. Bhadani, M. Bunting, M. L. Delle Monache, R. Lysecky, B. Seibold, J. Sprinkle, B. Piccoli, D. B. Work, Are commercially implemented adaptive cruise control systems string stable?, submitted. [ preprint ]
• The dataset of the experiments is available at
  1. G. Gunter, D. Gloudemans, R. E. Stern, S. McQuade, R. Bhadani, M. Bunting, M. L. Delle Monache, B. Seibold, J. Sprinkle, B. Piccoli, D. B. Work, Are commercially implemented adaptive cruise control systems string stable? Experimental data, 2019. dataset
• [Ongoing]We are working to calibrate more nuanced models of ACC vehicles to address the question of how ACC vehicles will influence the overall traffic flow behavior. This includes delay differential equations that take into account sensing and actuation delay of the ACC vehicle.

Construction of a micro-macro model for mixed traffic

• [Achieved] We have designed a weakly coupled partial differential equation - ordinary differential equation system (PDE-ODE) to describe the dynamics of traffic flow with autonomous vehicles. The term "weakly" means that the autonomous vehicle does not affect traffic flow. Thus, the autonomous vehicles act as tracer vehicles in the flow and collect measurements along their trajectory to estimate the bulk flow. We can theoretically and numerically reconstruct the correct traffic density at a certain time using only the measurements from the autonomous vehicles. Check our work on
  1. M. L. Delle Monache, T. Liard, B. Piccoli, R. Stern, D. Work, Traffic reconstruction using autonomous vehicles, SIAM Journal of Applied Mathematics, 79 (5), (2019), 1748-1767. paper, [ preprint ]
• [Ongoing] We are currently working on a strongly coupled partial differential equation - ordinary differential equation system (PDE-ODE) to describe the dynamics of traffic flow with autonomous vehicles. The term "strongly" means that the autonomous vehicle affects the traffic flow via a point flux constraint. Thus, the autonomous vehicle creates local instabilities for the PDE. Our goal is to understand what are the effects of these local instabilities on other drivers. We will work on the theoretical and numerical solution to the problems.

Control with autonomous vehicles

• [Achieved] We developed analytically, numerically and experimentally control algorithms for autonomous vehicles to control traffic when only a portion of the vehicles is autonomous and the rest is driven by humans. Check our work on
  1. M. L. Delle Monache, T. Liard, A. Rat, R. Stern, R. Bhadani, B. Seibold, J. Sprinkle, D. Work, B. Piccoli, Feedback control algorithms for the dissipation of traffic waves with autonomous vehicles, Chapter 12, Computational Intelligence and Optimization Methods for Control Engineering, (2019), 275-299.  paper, [ preprint ].
  2. R. Stern, Y. Chen, M. Churchill, F. Wu, M. L. Delle Monache, B. Piccoli, B. Seibold, J. Sprinkle, D. Work, Quantifying air quality benefits resulting from few autonomous vehicles stabilizing traffic, Transportation Research Part D, 67, (2019), 351-365. paper, [preprint ]
  3. F. Wu, R. Stern, S. Cui, M. L. Delle Monache, R. Bhadani, M. Bunting, M. Churchill, N. Hamilton, R. Haulcy, B. Piccoli, B. Seibold, J. Sprinkle, D. B. Work, Tracking vehicle trajectories and fuel rates in phantom traffic jams: methodology and data, Transportation Research Part C, 99, (2019), 82-109. paper, [preprint ]
  4. M. L. Delle Monache, J. Sprinkle, R. Vasudevan, D. Work, Autonomous vehicles: from vehicular traffic to traffic control, to appear, (2019), Proceedings of the Conference on Decision and Control, CDC 2019, Nice, France. [ preprint ]