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

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.

[1]  Roman V. Belavkin,et al.  On emotion, learning and uncertainty : a cognitive modelling approach , 2003 .

[2]  Jie Wang,et al.  Electronic Stability Control Based on Motor Driving and Braking Torque Distribution for a Four In-Wheel Motor Drive Electric Vehicle , 2016, IEEE Transactions on Vehicular Technology.

[3]  David González,et al.  A Review of Motion Planning Techniques for Automated Vehicles , 2016, IEEE Transactions on Intelligent Transportation Systems.

[4]  J. Gregory Trafton,et al.  ACT-R/E , 2013, HRI 2013.

[5]  Gábor Orosz,et al.  Optimal Control of Connected Vehicle Systems With Communication Delay and Driver Reaction Time , 2017, IEEE Transactions on Intelligent Transportation Systems.

[6]  Osamu Nishihara,et al.  Obstacle Avoidance by Steering and Braking with Minimum Total Vehicle Force , 2016 .

[7]  John R. Williams,et al.  The EPIC crop growth model , 1989 .

[8]  Francesco Biral,et al.  Four wheel optimal autonomous steering for improving safety in emergency collision avoidance manoeuvres , 2014, 2014 IEEE 13th International Workshop on Advanced Motion Control (AMC).

[9]  Guan Zhou,et al.  Path planning and stability control of collision avoidance system based on active front steering , 2017 .

[10]  Cheng Chen,et al.  A Lithium-Ion Battery-in-the-Loop Approach to Test and Validate Multiscale Dual H Infinity Filters for State-of-Charge and Capacity Estimation , 2018, IEEE Transactions on Power Electronics.

[11]  Dongpu Cao,et al.  Development of a new integrated local trajectory planning and tracking control framework for autonomous ground vehicles , 2017 .

[12]  Andreas Kugi,et al.  Optimisation based path planning for car parking in narrow environments , 2016, Robotics Auton. Syst..

[13]  Dayal R. Parhi,et al.  IWO-based adaptive neuro-fuzzy controller for mobile robot navigation in cluttered environments , 2016 .

[14]  Le Yi Wang,et al.  A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter , 2017 .

[15]  Hao Mu,et al.  A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries , 2017 .

[16]  Byeong Ho Kang,et al.  Using a physics engine in ACT-R to aid decision making , 2016 .

[17]  A. Goodarzi,et al.  Design of a VDC System for All-Wheel Independent Drive Vehicles , 2007, IEEE/ASME Transactions on Mechatronics.

[18]  Hongwen He,et al.  A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[19]  Roman V. Belavkin Conflict resolution by random estimated costs , 2003 .

[20]  Nooshin Atashfeshan,et al.  Determination of the Proper Rest Time for a Cyclic Mental Task Using ACT-R Architecture , 2017, Hum. Factors.

[21]  O. P. Sahu,et al.  Real Time Navigation Approach for Mobile Robot , 2017, J. Comput..

[22]  Jiayi Cao,et al.  Reinforcement learning-based real-time power management for hybrid energy storage system in the plug-in hybrid electric vehicle , 2018 .

[23]  Michael Yu Wang,et al.  Trajectory planning and control of parallel manipulators , 2009, 2009 IEEE International Conference on Control and Automation.

[24]  Xiaohui Li,et al.  Real-Time Trajectory Planning for Autonomous Urban Driving: Framework, Algorithms, and Verifications , 2016, IEEE/ASME Transactions on Mechatronics.

[25]  Hyungseok Oh,et al.  Computational modeling of human performance in multiple monitor environments with ACT-R cognitive architecture , 2014 .

[26]  Shunming Li,et al.  The algorithm of obstacle avoidance based on improved fuzzy neural networks fusion for exploration vehicle , 2009 .

[27]  Liang Liu,et al.  Smooth trajectory planning for a parallel manipulator with joint friction and jerk constraints , 2016 .

[28]  John R. Anderson,et al.  A step-by-step tutorial on using the cognitive architecture ACT-R in combination with fMRI data , 2017 .