ACT-R Cognitive Model Based Trajectory Planning Method Study for Electric Vehicle’s Active Obstacle Avoidance System

Aijuan Li, Wanzhong Zhao, Xibo Wang and Xuyun Qiu
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Aijuan Li: School of Automotive Engineering, Shan Dong Jiao Tong University, Jinan 250357, China
Wanzhong Zhao: Energy and Power Engineering College, Nanjing University of Aeronautics & Astronautics, Nanjing 210016, China
Xibo Wang: School of Automotive Engineering, Shan Dong Jiao Tong University, Jinan 250357, China
Xuyun Qiu: School of Automotive Engineering, Shan Dong Jiao Tong University, Jinan 250357, China

Energies, 2018, vol. 11, issue 1, 1-21

Abstract: The active obstacle avoidance system is one of the important components of the electric vehicle active safety system. In order to realize the active obstacle avoidance system driving the vehicle smoothly and without collision in complex road situation, a new dynamical trajectory planning method based on ACT-R (Adaptive Control of Thought-Rational) cognitive model is introduced. Firstly, the ACT-R cognitive architecture is introduced and the trajectory planning method’s framework structure based on ACT-R cognitive model is built. Secondly, the modeling method of ACT-R cognitive model is introduced, the main module of ACT-R cognitive model includes the initialized behavior module, trajectory planning module, estimated behavioral module, and weight adjustment behavior module. Finally, the verification of the trajectory planning method is conducted by the simulation and experiment results. The simulation and experiment results showed that the method of AR (ACT-R) is effective and feasible. The AR method is better than the methods that are based on the OC (Optimal Control) and FN (fuzzy neural network fusion); this paper’s method has more human behavior characteristics and can meet the demand of different constraints.

Keywords: the active obstacle avoidance system; ACT-R (Adaptive Control of Thought-Rational) cognitive model; trajectory planning method; modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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