Predictive trajectory guidance for (semi-)autonomous vehicles in public traffic

The safe and reliable operation of autonomous and semi-autonomous vehicles in public traffic requires the tight integration of environmental sensing and vehicle dynamics control. In this paper, a predictive control framework is outlined that connects both areas. Specifically, a trajectory guidance module is posed as a nonlinear model predictive controller that computes the optimal future vehicle trajectory using information from environmental sensing for other objects as well as by imposing public traffic rules. It is also sought to minimize the number of vehicle specific parameters needed for the guidance by adopting a particular particle motion description for the vehicle. The computed control input set for the trajectory guidance is passed as a reference for lower-level vehicle dynamics control systems. The definitions of the objective functions and constraints and the adopted vehicle motion model allow for a unified predictive trajectory guidance scheme for fully autonomous and semi-autonomous vehicles in public traffic with multiple dynamic objects. The performance of the proposed scheme is illustrated via simulations of an autonomous and a semi-autonomous vehicle in a few traffic scenarios such as intersections and collision avoidance. Execution time considerations are also analyzed.

[1]  P. Falcone,et al.  A hierarchical Model Predictive Control framework for autonomous ground vehicles , 2008, 2008 American Control Conference.

[2]  Francesco Borrelli,et al.  Spatial Predictive Control for Agile Semi-Autonomous Ground Vehicles , 2012 .

[3]  Julius Ziegler,et al.  Optimal trajectory generation for dynamic street scenarios in a Frenét Frame , 2010, 2010 IEEE International Conference on Robotics and Automation.

[4]  Francesco Borrelli,et al.  Predictive Active Steering Control for Autonomous Vehicle Systems , 2007, IEEE Transactions on Control Systems Technology.

[5]  Moritz Diehl,et al.  High-speed moving horizon estimation based on automatic code generation , 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

[6]  Francesco Borrelli,et al.  Predictive control of an autonomous ground vehicle using an iterative linearization approach , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[7]  Moritz Diehl,et al.  An auto-generated real-time iteration algorithm for nonlinear MPC in the microsecond range , 2011, Autom..

[8]  M. Werling,et al.  Low-level controllers realizing high-level decisions in an autonomous vehicle , 2008, 2008 IEEE Intelligent Vehicles Symposium.

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

[10]  Günther Prokop,et al.  Modeling Human Vehicle Driving by Model Predictive Online Optimization , 2001 .

[11]  Francesco Borrelli,et al.  Robust Predictive Control for semi-autonomous vehicles with an uncertain driver model , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[12]  J. Asgari,et al.  Predictive control approach to autonomous vehicle steering , 2006, 2006 American Control Conference.

[13]  H. E. Tseng,et al.  Linear model predictive control for lane keeping and obstacle avoidance on low curvature roads , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[14]  Manfred Morari,et al.  Auto-generated algorithms for nonlinear model predictive control on long and on short horizons , 2013, 52nd IEEE Conference on Decision and Control.