Aiming at the problems that a data source is single, a sequence model difficultly captures a lane changing intention in a long sequence range and long-term dependence exists in lane changing intentionidentification, the invention provides a long-term short-term memory network vehicle lane changing intention identification model under a time information weighting index loss function. The method comprises the steps: firstly, conducting a highway driving experiment through a driving simulation cabin and an eye tracker, and collecting vehicle operation data and driver eye movement data; constructing a vehicle lane changing intention identification model in a highway environment based on an LSTM structural unit, and optimizing the model weight through a proposed index loss function based on time information weighting; and finally, verifying the proposed model by using the vehicle operation data and the driver eye movement data and comparing the proposed model with other models, wherein the lane changing identification accuracy of the proposed model is 96.78%, the precision is 95.72%, the recall rate is 95.83%, and the F1 value is 95.73%. The LSTM network has good resolution capabilityfor a long-sequence lane changing intention identification process, and the proposed loss function has a good effect on model weight optimization.
针对车道变换意图识别中数据源单一,序列模型难以捕获长序列范围内换道意图且存在长期依赖问题,提出一种时间信息加权指数损失函数下的长短时记忆网络车辆换道意图识别模型。首先,利用驾驶模拟舱,眼动仪进行高速公路驾驶实验,采集车辆运行数据和驾驶员眼动数据;基于LSTM结构单元构建高速公路环境下车辆换道意图识别模型,提出的基于时间信息加权的指数损失函数对模型权重进行优化;最后,利用车辆运行数据和驾驶员眼动数据对所提模型加以验证并与其它模型进行对比,所提模型换道识别的准确率为96.78%,精确率为95.72%,召回率为95.83%,F1值为95.73%。长短时记忆网络对于长序列换道意图识别过程具有较好的分辨能力,提出的损失函数对模型权重优化具有良好的效果。
Lane changing intention identification method based on LSTM under multi-source exponential weighting loss
基于多源指数加权损失下LSTM的换道意图识别方法
2020-05-08
Patent
Electronic Resource
Chinese
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion |
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