Hierarchical Approach for Safety of Multiple Cooperating Vehicles

In this paper we present a hierarchical structure consisting of offline and online verification, ensuring the safety properties of cooperating vehicles; communicating either implicitly (e.g.: vehicle on left lane slowly opens gap, indicating the vehicle on right lane to merge in front of it) or explicitly via Car-to-Car communication with each other. The offline verification is based on the concept of reachability analysis of hybrid systems and aims at building formally correct and safe cooperative maneuvers for a group of vehicles. The online verification layer has the task of negotiating the possible cooperative maneuvers with other traffic participants and send the selected optimal plan to a low-level tube based Model Predictive Control (MPC). The MPC then calculates the control inputs to be applied to the actuators in order to guide the vehicle safely under the presence of model uncertainty and disturbance. In case MPC offers no feasible solution to the constraint optimization problem i.e. there occurs a constraint violation during the prediction horizon, the safety of the cooperative maneuver is ensured by an emergency planner, which aborts the current cooperative maneuver and brings the vehicle to safe state. The effectiveness and the performance of the hierarchical concept presented here are shown with an exemplary cooperative lane change scenario involving multiple vehicles.

[1]  Francisco Rodríguez,et al.  Robust tube-based predictive control for mobile robots in off-road conditions , 2011, Robotics Auton. Syst..

[2]  Goran Frehse,et al.  Flowpipe-Guard Intersection for Reachability Computations with Support Functions , 2012, ADHS.

[3]  Francisco Rodríguez,et al.  Online robust tube-based MPC for time-varying systems: a practical approach , 2011, Int. J. Control.

[4]  Johan Löfberg,et al.  YALMIP : a toolbox for modeling and optimization in MATLAB , 2004 .

[5]  Claire J. Tomlin,et al.  Reachability-based synthesis of feedback policies for motion planning under bounded disturbances , 2011, 2011 IEEE International Conference on Robotics and Automation.

[6]  Thomas A. Henzinger,et al.  The theory of hybrid automata , 1996, Proceedings 11th Annual IEEE Symposium on Logic in Computer Science.

[7]  Pravin Varaiya,et al.  Ellipsoidal Techniques for Reachability Analysis , 2000, HSCC.

[8]  Antoine Girard,et al.  Reachability Analysis of Hybrid Systems Using Support Functions , 2009, CAV.

[9]  Christian Löper,et al.  Safe Cooperation of Automated Vehicles , 2017 .

[10]  Claire J. Tomlin,et al.  Design of guaranteed safe maneuvers using reachable sets: Autonomous quadrotor aerobatics in theory and practice , 2010, 2010 IEEE International Conference on Robotics and Automation.

[11]  André Platzer,et al.  Efficiency analysis of formally verified adaptive cruise controllers , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[12]  S. Prajna Barrier certificates for nonlinear model validation , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[13]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[14]  Xin Chen,et al.  Flow*: An Analyzer for Non-linear Hybrid Systems , 2013, CAV.

[15]  Oded Maler,et al.  Recent progress in continuous and hybrid reachability analysis , 2006, 2006 IEEE Conference on Computer Aided Control System Design, 2006 IEEE International Conference on Control Applications, 2006 IEEE International Symposium on Intelligent Control.

[16]  Matthias Althoff,et al.  Formal verification of maneuver automata for parameterized motion primitives , 2014, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[17]  André Platzer,et al.  On Provably Safe Obstacle Avoidance for Autonomous Robotic Ground Vehicles , 2013, Robotics: Science and Systems.

[18]  Manfred Morari,et al.  Multi-Parametric Toolbox 3.0 , 2013, 2013 European Control Conference (ECC).

[19]  H. Eric Tseng,et al.  A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles , 2014 .