Stabilizing Traffic with Autonomous Vehicles

Autonomous vehicles promise safer roads, energy savings, and more efficient use of existing infrastructure, among many other benefits. Although the effect of autonomous vehicles has been studied in the limits (near-zero or full penetration), the transition range requires new formulations, mathematical modeling, and control analysis. In this article, we study the ability of small numbers of autonomous vehicles to stabilize a single-lane system of human-driven vehicles. We formalize the problem in terms of linear string stability, derive optimality conditions from frequency-domain analysis, and pose the resulting nonlinear optimization problem. In particular, we introduce two conditions which simultaneously stabilize traffic while imposing a safety constraint on the autonomous vehicle and limiting degradation of performance. With this optimal linear controller in a system with typical human driver behavior, we can numerically determine that only a 6% uniform penetration of autonomously controlled vehicles (i.e. one per string of up to 16 human-driven vehicles) is necessary to stabilize traffic across all traffic conditions.

[1]  Nakayama,et al.  Dynamical model of traffic congestion and numerical simulation. , 1995, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[2]  Y. Sugiyama,et al.  Traffic jams without bottlenecks—experimental evidence for the physical mechanism of the formation of a jam , 2008 .

[3]  Azim Eskandarian,et al.  Research advances in intelligent collision avoidance and adaptive cruise control , 2003, IEEE Trans. Intell. Transp. Syst..

[4]  Petros A. Ioannou,et al.  Throttle and Brake Control Systems for Automatic Vehicle following , 1994, J. Intell. Transp. Syst..

[5]  Martin Treiber,et al.  Traffic Flow Dynamics , 2013 .

[6]  Charles A. Desoer,et al.  A SYSTEM LEVEL STUDY OF THE LONGITUDINAL CONTROL OF A PLATOON OF VEHICLES , 1992 .

[7]  Alexandre M. Bayen,et al.  Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control , 2017, ArXiv.

[8]  D. Swaroop,et al.  String Stability Of Interconnected Systems: An Application To Platooning In Automated Highway Systems , 1997 .

[9]  Berthold K. P. Horn,et al.  Suppressing traffic flow instabilities , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[10]  Sertac Karaman,et al.  Polling-systems-based control of high-performance provably-safe autonomous intersections , 2014, 53rd IEEE Conference on Decision and Control.

[11]  S E Shladover,et al.  Automated vehicles for highway operations (automated highway systems) , 2005 .

[12]  Petros A. Ioannou,et al.  Autonomous intelligent cruise control , 1993 .

[13]  Stefan Krauss,et al.  MICROSCOPIC MODELING OF TRAFFIC FLOW: INVESTIGATION OF COLLISION FREE VEHICLE DYNAMICS. , 1998 .

[14]  Emilio Frazzoli,et al.  Toward a Systematic Approach to the Design and Evaluation of Automated Mobility-on-Demand Systems: A Case Study in Singapore , 2014 .

[15]  Gábor Stépán,et al.  Traffic jams: dynamics and control , 2010, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[16]  Maria Laura Delle Monache,et al.  Dissipation of stop-and-go waves via control of autonomous vehicles: Field experiments , 2017, ArXiv.

[17]  Huei Peng,et al.  Optimal Adaptive Cruise Control with Guaranteed String Stability , 1999 .

[18]  K. Hasebe,et al.  Structure stability of congestion in traffic dynamics , 1994 .

[19]  Dirk Helbing,et al.  General Lane-Changing Model MOBIL for Car-Following Models , 2007 .

[20]  Don MacKenzie,et al.  Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles , 2016 .

[21]  Mao-Bin Hu,et al.  Traffic Flow Characteristics in a Mixed Traffic System Consisting of ACC Vehicles and Manual Vehicles: A Hybrid Modeling Approach , 2009 .

[22]  Alireza Talebpour,et al.  Influence of connected and autonomous vehicles on traffic flow stability and throughput , 2016 .

[23]  Steven E Shladover,et al.  Modeling cooperative and autonomous adaptive cruise control dynamic responses using experimental data , 2014 .

[24]  D. Schrank,et al.  2012 Urban Mobility Report , 2002 .

[25]  J.K. Hedrick,et al.  String Stability Analysis for Heterogeneous Vehicle Strings , 2007, 2007 American Control Conference.

[26]  Maarten Steinbuch,et al.  String-Stable CACC Design and Experimental Validation: A Frequency-Domain Approach , 2010, IEEE Transactions on Vehicular Technology.

[27]  Peter Stone,et al.  A Multiagent Approach to Autonomous Intersection Management , 2008, J. Artif. Intell. Res..

[28]  Martin Treiber,et al.  Microscopic Calibration and Validation of Car-Following Models – A Systematic Approach , 2013, 1403.4990.

[29]  JOHN F. B. Mitchell,et al.  National Transportation Statistics (Annual Report, 1983) , 1983 .

[30]  R. E. Wilson,et al.  Car-following models: fifty years of linear stability analysis – a mathematical perspective , 2011 .

[31]  L. L. Hoberock,et al.  A Survey of Longitudinal Acceleration Comfort Studies in Ground Transportation Vehicles , 1977 .

[32]  Emilio Frazzoli,et al.  Hybrid centralized/distributed autonomous intersection control: Using a job scheduler as a planner and inheriting its efficiency guarantees , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[33]  J.K. Hedrick,et al.  Heavy-duty truck control: short inter-vehicle distance following , 2004, Proceedings of the 2004 American Control Conference.

[34]  Mike McDonald,et al.  Car-following: a historical review , 1999 .

[35]  Rajesh Rajamani,et al.  Semi-autonomous adaptive cruise control systems , 2002, IEEE Trans. Veh. Technol..

[36]  Emilio Frazzoli,et al.  Robotic load balancing for mobility-on-demand systems , 2012, Int. J. Robotics Res..

[37]  Helbing,et al.  Congested traffic states in empirical observations and microscopic simulations , 2000, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[38]  Susan Shaheen,et al.  Dynamic Ecodriving in Northern California: Study of Survey and Vehicle Operations Data from Ecodriving Feedback Device , 2013 .