中文摘要：

为了提高驾驶员换道意图的辨识率,提出了一种基于隐马尔可夫模型（HMM）和支持向量机（SVM）的混合模型。通过驾驶员在环仿真实验平台采集1.2 s时间窗内的驾驶员方向盘转角、油门踏板操作信息,匹配时序性良好的各个HMM模型（紧急左换道、正常左换道、紧急右换道、正常右换道和车道保持五种HMM模型）。然后结合各个HMM模型输出的最大似然估计值,由SVM进行分类,从而辨识出驾驶员当前的换道意图。仿真结果表明：相比单独的HMM或SVM,该混合模型能够更准确地辨识驾驶员的换道意图,辨识率高达98%,且耗时仅需0.006 s,具有较好的实时性。

英文摘要：

A hybrid model based on hidden Markov model （HMM） and support vector machine （SVM） is proposed to improve the recognition rate of the driver＇ s lane change intention. The driver＇ s steering wheel angle and accel- erator pedal data in 1.2 second time window are collected by the Driver-in-Loop （DiL） simulation experiments, these data could be matched with five HMM models （emergency left lane change, normal left lane change, emer- gency right lane change, normal right lane change and lane keep these five HMM models）, which possess an out- standing characteristic of time sequence. SVM can classify the maximum likelihood estimation which is outputted by HMM models. Finally, it can recognize the driver＇ s lane change intention. The simulation results show that this proposed hybrid model can recognize the driver＇ s lane change intention more accurately when compared with the classified approach only with the HMM or SVM, the recognition rate is reached as high as 98% , and takes only 0. 006 second, which shows that it has an excellent performance in real time.