东南大学学报（自然科学版）2017，Vol.47Issue(4)：832-838,7.DOI:10.3969/j.issn.1001-0505.2017.04.031
基于深度学习DBN算法的高速公路危险变道判别模型
Dangerous lane-change detecting modelon highway based on deep learning DBN algorithm
摘要
Abstract
Aiming at the problem that the vehicle lane-changing process cannot be quantitatively analyzed and accurately discriminated, a new quantitative discriminant model based on the DBN (deep belief networks) algorithm and the classification analysis method is presented.Twenty-eight participants were recruited.The participators took part in real-scene simulation experiments using the simulation driving platform.The detailed data of vehicle traveling and driving environment was required and used as the input of the model.With the SVM (support vector machine) algorithm as the classifier of the output layer, the discriminant model DBN-SVM and corresponding training method are set up.The discriminant accuracy of the DBN-SVM is 93.78%, increasing by 20.11% and 14.45% compared with the Na?ve Bayes model and BP-ANN (back propagation-artificial neural networks), respectively.And, the results are stable with adjusted parameters.The DBN-SVM model can predict and discriminate coming dangerous lane-changing according to drivers` driving history data, and warn drivers.As a result, it can reduce the chance of traffic accidents.This study provides theoretical support for lane-changing discrimination and warning under the connected vehicle environment.关键词
危险变道判别/模拟车试验/智能交通/深度信任网络/自动驾驶/车联网Key words
dangerous lane-changing discriminant/vehicle simulation experiments/intelligent transportation/deep belief network/automatic driving/connected vehicle分类
交通工程引用本文复制引用
赵玮,徐良杰,冉斌,汪济洲..基于深度学习DBN算法的高速公路危险变道判别模型[J].东南大学学报（自然科学版）,2017,47(4):832-838,7.基金项目
教育部社科青年基金资助项目(16YJCZH157)、国家重点基础研究发展计划(973计划)资助项目(2012CB725405)、内蒙古科技大学创新基金资助项目(2015QDL27) （16YJCZH157）
