Scenario model predictive control for lane change assistance on highways

This paper presents a new algorithm for detecting the safety of lane changes on highways and for computing safe lane change trajectories. This task is considered as a building block for driver assistance systems and autonomous cars. The presented algorithm is based on recent results in Scenario Model Predictive Control (SCMPC). It accounts for the uncertainty in the traffic environment via a small number of future scenarios, which can be generated by any model-based or data-based approach. The paper describes the SCMPC design as well as the integration with scenario-based traffic predictions. The design procedure is simple and can be generalized to other control situations. An extensive case study demonstrates the effectiveness of the proposed SCMPC algorithm and its performance in lane change situations.

[1]  Georg Schildbach,et al.  Scenario-based optimization for multi-stage stochastic decision problems , 2014 .

[2]  F. Borrelli,et al.  Stochastic Predictive Control of Autonomous Vehicles in Uncertain Environments , 2014 .

[3]  Jonathan P. How,et al.  Threat assessment design for driver assistance system at intersections , 2010, 13th International IEEE Conference on Intelligent Transportation Systems.

[4]  Lars Petersson,et al.  Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling , 2008, IEEE Transactions on Intelligent Transportation Systems.

[5]  Phillipp Kaestner,et al.  Linear And Nonlinear Programming , 2016 .

[6]  T. Kanade,et al.  Monte Carlo road safety reasoning , 2005, IEEE Proceedings. Intelligent Vehicles Symposium, 2005..

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

[8]  Lorenzo Fagiano,et al.  Randomized Solutions to Convex Programs with Multiple Chance Constraints , 2012, SIAM J. Optim..

[9]  Marco C. Campi,et al.  The Exact Feasibility of Randomized Solutions of Uncertain Convex Programs , 2008, SIAM J. Optim..

[10]  Lorenzo Fagiano,et al.  The scenario approach for Stochastic Model Predictive Control with bounds on closed-loop constraint violations , 2013, Autom..

[11]  Giuseppe Carlo Calafiore,et al.  Uncertain convex programs: randomized solutions and confidence levels , 2005, Math. Program..

[12]  Masahiro Ono,et al.  A Probabilistic Particle-Control Approximation of Chance-Constrained Stochastic Predictive Control , 2010, IEEE Transactions on Robotics.

[13]  Chung Choo Chung,et al.  Comparative evaluation of dynamic and kinematic vehicle models , 2014, 53rd IEEE Conference on Decision and Control.

[14]  Marco C. Campi,et al.  A Sampling-and-Discarding Approach to Chance-Constrained Optimization: Feasibility and Optimality , 2011, J. Optim. Theory Appl..

[15]  Ulrich Kressel,et al.  Probabilistic trajectory prediction with Gaussian mixture models , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[16]  Rajesh Rajamani,et al.  Vehicle dynamics and control , 2005 .

[17]  Francesco Borrelli,et al.  Kinematic and dynamic vehicle models for autonomous driving control design , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[18]  Jan M. Maciejowski,et al.  Predictive control : with constraints , 2002 .

[19]  Jonas Jansson,et al.  Collision Avoidance Theory : with Application to Automotive Collision Mitigation , 2005 .

[20]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[21]  Amaury Nègre,et al.  Probabilistic Analysis of Dynamic Scenes and Collision Risks Assessment to Improve Driving Safety , 2011, IEEE Intelligent Transportation Systems Magazine.

[22]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[23]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014 .