The prediction and estimation of the lane-changing state of the host car and surrounding cars are important parts of an advanced driving assistant system, which mainly depend on the understanding of the driver lane-changing behavior. To learn driver lane-changing maneuver well, this article provides a novel stochastic driver lane-changing model based on an improved input–output hidden Markov model (IOHMM) framework. First, an improved IOHMM is proposed to address the deficiency that the traditional IOHMM cannot remember previous data and describe continuous output. Then, based on the improved IOHMM framework, a driver lane-changing model is established considering the intention and behavior of the driver in the lane-changing process. The model parameters can be learned from the collected lane-changing data using the maximum likelihood estimation and generalized estimation-maximization methods. Finally, the model is applied to a real driver lane-changing process. It is verified that the proposed model has good performance in predicting the future motion maneuver of the host vehicle and estimating the current motion state of the surrounding cars.

中文翻译：

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改进的基于IOHMM的随机驾驶员变道模型


对本车和周围车辆换道状态的预测和估计是高级驾驶辅助系统的重要组成部分，这主要取决于对驾驶员换道行为的理解。为了更好地学习驾驶员换道操作，本文提供了一种基于改进的输入输出隐马尔可夫模型（IOHMM）框架的新型随机驾驶员换道模型。首先，提出了一种改进的IOHMM，以解决传统IOHMM无法记住先前数据并描述连续输出的缺陷。然后，基于改进的IOHMM框架，考虑驾驶员在换道过程中的意图和行为，建立驾驶员换道模型。可以使用最大似然估计和广义估计最大化方法从收集的车道变换数据中学习模型参数。最后，将该模型应用于真实的驾驶员换道过程。经验证，该模型在预测本车未来运动操纵和估计周围车辆当前运动状态方面具有良好的性能。