汽车主动安全关键参数联合估计方法

Joint estimation method of key parameters for automotive active safety

针对汽车主动安全系统的需求,提出了一种包括纵向、侧向车速与附着系数的汽车主动安全参数的联合估计方法。基于3自由度车辆动力学模型和刷子轮胎模型,建立不同道路附着系数条件下的扩展卡尔曼滤波模型,利用交互多模型算法实现纵向、侧向车速的自适应估计,并根据计算出的各模型概率实现道路附着系数的实时估计。计算结果表明:该方法能在不同道路附着系数条件下进行车速的准确估计,纵向车速估计误差小于1%,侧向车速估计误差小于5%,与扩展卡尔曼方法相比误差减小了50%以上,且能够实时给出道路附着系数估计值,估计误差小于0.1,对路面突变的响应时间低于2s。

According to the requirements of automotive active safety system, a joint estimation method of key parameters for automotive active safety including automotive longitudinal velocity, lateral velocity and road friction coefficient was proposed. Based on automotive dynamics model with 3 degrees of freedom and brush tire model, the extended Kalman filter models under different road friction coefficient conditions were established. Automotive longitudinal velocity and lateral velocity were adaptively estimated by using the interacting multiple model, and the road friction coefficient could be real-timely estimated based on the calculated model probabilities. Calculation result shows that the method can accurately estimate automotive longitudinal and lateral velocities under different road friction coefficient conditions, the estimation error rates are less than 1% and 5% respectively. Compared with extended Kalman filter method, the estimation error of automotive velocity estimated by using the method reduces by more than 50%. When road condition mutates, the road friction coefficient can be real-timely estimated, the estimation error is less than 0.1, and the response time is less than 2 s. 4 tabs, 11 figs, 16 refs.