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ISSN:2164-6376 (print)
ISSN:2164-6414 (online)
Discontinuity, Nonlinearity, and Complexity

Dimitry Volchenkov (editor), Dumitru Baleanu (editor)

Dimitry Volchenkov(editor)

Mathematics & Statistics, Texas Tech University, 1108 Memorial Circle, Lubbock, TX 79409, USA

Email: dr.volchenkov@gmail.com

Dumitru Baleanu (editor)

Cankaya University, Ankara, Turkey; Institute of Space Sciences, Magurele-Bucharest, Romania

Email: dumitru.baleanu@gmail.com

Defensive Driving Strategy for Autonomous Ground Vehicle in Mixed Traffic

Discontinuity, Nonlinearity, and Complexity 6(1) (2017) 87--103 | DOI:10.5890/DNC.2017.03.008

Xiang Li$^{1}$ , Jian-Qiao Sun$^{2}$

$^{1}$ School of Engineering, University of California at Merced, Merced, CA 95343, USA

$^{2}$ Department of Mechanics, Tianjin University, Tianjin, 300072, China

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Abstract

One of the challenges of autonomous ground vehicles (AGVs) is to interact with human driven vehicles in the traffic. This paper develops defensive driving strategies for AGVs to avoid problematic vehicles in the mixed traffic. A multi-objective optimization algorithm for local trajectory planning is proposed. The dynamic predictive control is used to derive optimal trajectories in a rolling horizon. The intelligent driver model and lanechanging rules are employed to predict the movement of the vehicles. Multiple performance objectives are optimized simultaneously, including traffic safety, transportation efficiency, driving comfort and path consistency. The multi-objective optimization problem is solved with the cell mapping method. Different and relatively simple scenarios are created to test the effectiveness of the defensive driving strategies. Extensive experimental simulations show that the proposed defensive driving strategy is promising and may provide a new tool for designing the intelligent navigation system that helps autonomous vehicles to drive safely in the mixed traffic.

Acknowledgments

The material in this paper is based on work supported by grants (11172197, 11332008 and 11572215) from the National Natural Science Foundation of China, and a grant from the University of California Institute forMexico and the United States (UCMEXUS) and the Consejo Nacional de Ciencia y Tecnolog´ıa deM´exico (CONACYT) through the project “Hybridizing Set Oriented Methods and Evolutionary Strategies to Obtain Fast and Reliable Multi-objective Optimization Algorithms”. The first author would like to thank the China Scholarship Council (CSC) for sponsoring his study in the United States of America.

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