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.
Highlights
In recent years, under the double pressure of energy and environment, electric vehicles became the focus of the automobile industry
In the 20 times simulation analysis, the two trajectory planning methods begin run based on the weight set WDz0 = [1, 1, 1, 1] generated by the state space trajectory planning method and the weight set WDa0 = [15, 3, 1, 1] generated by the trajectory planning method based on ACT-R cognitive model, respectively
This paper presents a new electric vehicle active obstacle avoidance system’s trajectory planning
Summary
Under the double pressure of energy and environment, electric vehicles became the focus of the automobile industry. Electric vehicle active collision avoidance system is one of the most effective methods to solve the rear-end accidents and it is a driving assistant system that is based on vehicle active safety technology. The controller will be decoupled of trajectory planning and motion control in the paper, the trajectory planning is used to provide real-time effective trajectory and the trajectory’s characteristic values [12], including the vehicle’s speed, acceleration, driving time, electricity consumption, and other state and control parameters [13,14]. The different digital constraints are applied to the evaluation function in the optimal control (OC) methods to get the optimal driving trajectory. Human’s cognitive behavioral characteristics are used in the trajectory planning method and the generated trajectory can meet different constraints and human’s cognitive characteristics. The simulation and experiment of different methods are conducted to test this paper method’s superiority
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