基于长短期记忆模型的跟车距离预测研究  

Research on Predicting Following Distance Based on Long Short-Term Memory Model

作　　者：张胤 ZHANG Yin(School of Automobile,Chang'an University,Xi'an 710064,China)

机构地区：[1]长安大学汽车学院,陕西西安710064

出　　处：《汽车实用技术》2024年第5期97-101,共5页Automobile Applied Technology

摘　　要：当前不少前向碰撞预警系统以预警距离作为预警的特征量对驾驶人进行预警,因此,提高对跟车距离的预测准度能够直观有效提高该前向碰撞预警系统的预警能力。研究通过驾驶模拟器构建跟车场景,收集了41名驾驶员的跟车行为数据,按照3:1的比例将试验数据划分为训练集和测试集。将驾驶人的跟车距离与速度作为长短期记忆模型的输入,跟车距离作为模型的输出,对驾驶人的跟车距离进行了预测分析研究。结果表明,利用该数据集的模型能够很好的预测驾驶人的跟车行为,泛化性能较好,没有过度拟合现象。并且通过输入不同时间窗口长度的测试集发现,随着测试集长度的降低,预测结果的误差会更大。能够为提高前向碰撞预警系统的精准度提供理论支持,从而增加驾驶员对预警系统的接受度。Many forward collision warning systems currently rely on warning distance as a feature to alert drivers,so improving the accuracy of predicting following distance can effectively enhance the warning capability of such systems.In this study,a driving simulator is used to construct following scenarios,and behavior data of 41 drivers are collected.The experimental data are divided into training and testing sets in a 3:1 ratio.By using the drivers'following distance and speed as inputs to a long short-term memory model,the following distance is predicted and analyzed.The results show that the model using this dataset could accurately predict the drivers'following behavior,with good generalization performance and no overfitting.Furthermore,by testing different time window lengths,it is found that as the length of the testing set decreased,the prediction error increased.This study provides theoretical support for improving the accuracy of forward collision warning systems,thereby increasing drivers'acceptance of these systems.

关 键 词：长短期记忆模型 神经网络 跟车距离 

分 类 号：U471.3[机械工程—车辆工程]