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NEO 2016 pp 3-44 | Cite as

Defensive Driving Strategy and Control for Autonomous Ground Vehicle in Mixed Traffic

  • Xiang Li
  • Jian-Qiao Sun
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 731)

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 and controls for AGVs to avoid problematic vehicles in the mixed traffic. The multi-objective optimization algorithms for local trajectory planning and adaptive cruise control are proposed. The dynamic predictive control is used to derive optimal trajectories in a rolling horizon. The intelligent driver model and lane-changing rules are employed to predict the movement of the vehicles. Multiple performance objectives are optimized simultaneously, including traffic safety, transportation efficiency, driving comfort, tracking error and path consistency. The multi-objective optimization problems are solved with the cell mapping method. Different scenarios are created to test the effectiveness of the defensive driving strategies and adaptive cruise control. Extensive experimental simulations show that the proposed defensive driving strategy and PID-form control are promising and may provide a new tool for designing the intelligent navigation system that helps autonomous vehicles to drive safely in the mixed traffic.

Keywords

Defensive driving Motion planning Trajectory planning Multi-objective optimization Adaptive cruise control 

Notes

Acknowledgements

The material in this chapter 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 for Mexico and the United States (UC MEXUS) and the Consejo Nacional de Ciencia y Tecnología de México (CONACYT) through the project “Hybridizing Set Oriented Methods and Evolutionary Strategies to Obtain Fast and Reliable Multi-objective Optimization Algorithms”.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of MechanicsTianjin UniversityTianjinChina
  2. 2.School of EngineeringUniversity of CaliforniaMercedUSA

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