基于驾驶模拟器洗出算法的非线性放缩法研究

Study on Nonlinear Scaling Method Based on Washout Algorithm in Driving Simulator

驾驶模拟器洗出算法中非线性放缩法的放缩参数固定,参数的选取过度依赖专家经验,导致模拟器空间利用率低、模拟逼真度不高。针对上述问题,提出了基于PSO的非线性放缩法。对现有非线性放缩法进行优化时,综合考虑了真实驾驶员和模拟器驾驶员之间的感知误差、信号放缩前后的归一化相关系数以及模拟器的物理运动限制等因素,并结合经典洗出算法进行了仿真验证。结果表明,所提方法克服了现有非线性放缩法依赖经验确定参数和模拟器工作空间利用率低的问题,降低了人体感知误差,提高了模拟逼真度。

In the driving simulator washing algorithm, the parameters of the nonlinear scaling method are fixed, and the selection of parameters relies on expert experience, which leads to the low space utilization rate of the simulator and low simulation fidelity. To solve the above problems, a nonlinear scaling method based on PSO is proposed. When optimizing the existing nonlinear scaling method, the factors such as the perceptual error between the real driver and the simulator driver, the normalized correlation coefficient before and after the signal scaling and the physical motion limitation of the simulator were comprehensively considered, and the simulation was carried out by combining the classical washout algorithm. The results show that the proposed method overcomes the existing problems of empirically-dependent parameters and low utilization of the simulator workspace, reduces the human perception error and improves the simulation fidelity.