Automatic driving in expressways is generally considered to be the easiest for commercial landing, and vehicle behavior decision-making is the core of automatic driving technology, which directly affects the safety and comfort of vehicle driving. In this paper, an automatic lane change strategy based on prior knowledge and data-driven is proposed for expressway. Our method decouples autonomous driving decisions into two processes. Firstly, we propose an optimization model of lane change features based on prior knowledge, which not only provides a basis for the selection of vehicle features but also effectively reduces the interference of invalid features. The driving risk field is used to screen the vehicle targets that have potential influence on the lane change of the ego vehicle, and the maximal information coefficient evaluates the effectiveness of the features of the vehicle targets through the maximal information coefficient to optimize the lane change features. Then, a data-driven lane-changing model is proposed based on Attention-BiLSTM (Bi-directional Long Short-Term Memory), which transforms vehicle lane-changing into time series prediction, fully explores the relationship between lane-changing features in the context information, and effectively improves the anti-interference ability of the system against accidental errors. In addition, the attention mechanism can adaptively adjust the weight of lane-changing features and effectively capture the important information that needs attention in different lane-changing states, further improving the accuracy of the model. Finally, the validity of the model is verified by remote sensing data sets and real vehicle experiments. The test results show that the test accuracy of our method is 95.1% in data sets and 94.2% in real vehicle experiments, which fully meets the requirements of lane change decisions about automatic driving.

中文翻译：

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基于先验知识和数据驱动的高速公路自动驾驶变道策略

高速公路自动驾驶被普遍认为最容易商业落地，而车辆行为决策是自动驾驶技术的核心，直接影响车辆行驶的安全性和舒适性。本文提出了一种基于先验知识和数据驱动的高速公路自动变道策略。我们的方法将自动驾驶决策分解为两个过程。首先，我们提出了一种基于先验知识的变道特征优化模型，不仅为车辆特征的选择提供了依据，而且有效地减少了无效特征的干扰。利用驾驶风险场筛选对本车换道有潜在影响的车辆目标，最大信息系数通过最大信息系数评估车辆目标特征的有效性，优化换道特征。然后，提出了一种基于Attention-BiLSTM（双向长短期记忆）的数据驱动的换道模型，将车辆换道转化为时间序列预测，充分挖掘了车辆换道特征之间的关系。上下文信息，有效提高系统针对意外错误的抗干扰能力。此外，注意力机制可以自适应调整换道特征的权重，有效捕捉不同换道状态下需要关注的重要信息，进一步提高模型的准确性。最后通过遥感数据集和实车实验验证了模型的有效性。测试结果表明，该方法在数据集上的测试准确率为95.1%，在实车实验中的测试准确率为94.2%，完全满足自动驾驶换道决策的要求。