Braking Strategy for an Autonomous Vehicle in a Mixed Traffic Scenario

During the early deployment phase of autonomous vehicles, autonomous vehicles will share roads with conventional manually driven vehicles. They will be required to adjust their driving dynamically taking into account not only preceding but also following conventional manually driven vehicles. This paper addresses the challenges of adaptive braking to avoid front-end and rear-end collisions, where an autonomous vehicle is followed by a conventional manually driven vehicle. We illustrate via simulations the consequences of independent braking in terms of collisions, on both autonomous and conventional vehicles, and propose an adaptive braking strategy for autonomous vehicles to coordinate with conventional manually driven vehicles to avoid front and rear-end collisions.

[1]  Thomas Sattel,et al.  On Automatic Collision Avoidance Systems , 2005 .

[2]  Vicente Milanés Montero,et al.  Autonomous Pedestrian Collision Avoidance Using a Fuzzy Steering Controller , 2011, IEEE Transactions on Intelligent Transportation Systems.

[3]  Yang Zheng,et al.  Multiple-Vehicle Longitudinal Collision Mitigation by Coordinated Brake Control , 2014 .

[4]  Umit Ozguner,et al.  Effect of inter-vehicle communication on rear-end collision avoidance , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[5]  Hannes Hartenstein,et al.  VANET: Is 95% probability of packet reception safe? , 2011, 2011 11th International Conference on ITS Telecommunications.

[6]  Andreas A. Malikopoulos,et al.  A Survey on the Coordination of Connected and Automated Vehicles at Intersections and Merging at Highway On-Ramps , 2017, IEEE Transactions on Intelligent Transportation Systems.

[7]  Dirk Helbing,et al.  Enhanced intelligent driver model to access the impact of driving strategies on traffic capacity , 2009, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[8]  Alcherio Martinoli,et al.  A new collision warning system for lead vehicles in rear-end collisions , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[9]  Yizhen Zhang,et al.  A new threat assessment measure for collision avoidance systems , 2006, 2006 IEEE Intelligent Transportation Systems Conference.

[10]  Henk Wymeersch,et al.  Cooperative receding horizon conflict resolution at traffic intersections , 2014, 53rd IEEE Conference on Decision and Control.

[11]  M. Durali,et al.  Collision avoidance maneuver for an autonomous vehicle , 2006, 9th IEEE International Workshop on Advanced Motion Control, 2006..

[12]  Dot Hs.  Alert Algorithm Development Program NHTSA Rear-End Collision Alert Algorithm , 2002 .

[13]  Carl D. Crane,et al.  The cognitive driving framework: Joint inference for collision prediction and avoidance in autonomous vehicles , 2015, 2015 15th International Conference on Control, Automation and Systems (ICCAS).

[14]  Peter Vortisch,et al.  Microscopic Traffic Flow Simulator VISSIM , 2010 .

[15]  Elias B. Kosmatopoulos,et al.  Collision avoidance analysis for lane changing and merging , 1999, IEEE Trans. Veh. Technol..

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

[17]  Klaus C. J. Dietmayer,et al.  Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios , 2009, IEEE Transactions on Intelligent Transportation Systems.

[18]  Dot Hs,et al.  Development of an FCW Algorithm Evaluation Methodology With Evaluation of Three Alert Algorithms , 2009 .

[19]  Bart van Arem,et al.  Effects of Cooperative Adaptive Cruise Control on traffic flow stability , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[20]  Rainer Wiedemann,et al.  SIMULATION DES STRASSENVERKEHRSFLUSSES. , 1974 .