基于D-S证据信息融合方法的全地形车行驶工况辨识

磁流变阻尼器的全地形车智能悬架可以使车辆面对不同行驶工况下提供更好的减振效果，为了解决在传感器存在噪声或异常等情况下车辆行驶工况辨识困难的问题，文中提出了一种基于D-S(Dempster-Shafer)证据理论的多传感器信息特征值的融合技术提高行驶工况辨识的准确性。通过改进的距离评估方法对全地形车行驶工况的传感器敏感特征值进行了提取和筛选，采用区间估计将传感器的噪声和异常值当做不确定性信息。利用D-S合成对特征层的辨识结果进行决策层融合，基于可行区间的决策规则完成对车辆行驶工况的辨识。最后使用Carsim整车仿真试验平台，验证了基于D-S证据理论的决策层融合方法的有效性。

Driving condition identification of all-terrain vehicles based on D-S evidence information fusion method

Due to fast response and adjustable damping force with the application of magnetic fields, magnetorheological suspension of all-terrain vehicles (ATV) has significant advantages in vibration suppression, especially for the complicated driving conditions. However, it is a challenge to identify the vehicle driving conditions in the case of noise or abnormal sensors. This paper focused on a fusion technology of multi-sensor information eigenvalues based on D-S (Dempster-Shafer) evidence theory to improve the accuracy of driving cycle identification. Firstly, the improved distance estimation method was used to select and identify the sensor eigenvalues related to driving conditions, and then the noise and outliers of sensors were treated as uncertain information by interval estimation. The identification results of feature layer were fused by D-S synthesis, and the driving condition identification of ATV was completed based on the decision rule of feasible interval. Finally, the validity of the decision level fusion method with D-S evidence theory was verified in Carsim simulation software.