A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling

Jun Gao; Jiangang Yi; Honghui Zhu; Yi Lu Murphey

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A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling

机译：基于深度学习和空间型模型的高级驾驶员辅助系统个性化车道改变模型

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摘要

Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data.

机译：车道的变化是司机的压力演习，特别是在高速交通流动期间。然而，由于驾驶行为的复杂性和不确定性，建模驾驶员的车道改变决策和实施过程具有挑战性。为解决此问题，本文为基于深度学习方法的高级驾驶员辅助系统（ADAS）提供了一个个性化的车道更改模型（LCM）。 LCM包含三个主要的计算组件。首先，具有较丰富的根剩余网络（Root-Reset），LCM能够从正视图数据数据中利用更多本地信息。

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来源
《SAE international journal of transportation safety》 |2019年第2期|共12页
作者
Jun Gao; Jiangang Yi; Honghui Zhu; Yi Lu Murphey;
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作者单位

Jianghan University;

Jianghan University;

Wuhan University of Technology;

University of Michigan-Dearborn;

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原文格式 PDF
正文语种 eng
中图分类运营技术;
关键词
Driver assistance systems; Education and training; Simulation and modeling;

机译：驾驶员援助系统;教育和培训;模拟和建模;

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专利

1. A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling [J] . Jun Gao, Jiangang Yi, Honghui Zhu, SAE international journal of transportation safety . 2019,第2期

机译：基于深度学习和空间型模型的高级驾驶员辅助系统个性化车道改变模型
2. Modelling the Intrusive Feelings of Advanced Driver Assistance Systems Based on Vehicle Activity Log Data: Case Study for the Lane Keeping Assistance System [J] . Park Kyudong, Kwahk Jiyoung, Han Sung H., International Journal of Automotive Technology . 2019,第3期

机译：基于车辆活动日志数据模拟高级驾驶员辅助系统的侵入性感受：车道保持辅助系统的案例研究
3. Quantitative model of the driver's reaction time during daytime fog – application to a head up display-based advanced driver assistance system [J] . Halmaoui Houssam, Joulan Karine, Hautière Nicolas, Intelligent Transport Systems, IET . 2015,第4期

机译：白天雾中驾驶员反应时间的定量模型–在基于平视显示器的高级驾驶员辅助系统中的应用
4. A Personalized Model for Driver Lane-Changing Behavior Prediction Using Deep Neural Network [C] . Jun Gao, Honghui Zhu, Yi Lu Murphey International Conference on Artificial Intelligence and Big Data . 2019

机译：基于深度神经网络的驾驶员变行行为预测的个性化模型
5. Object Detection Using Feature Extraction and Deep Learning for Advanced Driver Assistance Systems [D] . Reza, Tasmia. 2018

机译：使用特征提取和深度学习的高级驾驶员辅助系统进行对象检测
6. Moving Object Detection Based on Optical Flow Estimation and a Gaussian Mixture Model for Advanced Driver Assistance Systems [O] . Jaechan Cho, Yongchul Jung, Dong-Sun Kim, 2019

机译：基于光流估计和高斯混合模型的高级驾驶员辅助系统运动目标检测
7. Modelling the Intrusive Feelings of Advanced Driver Assistance Systems Based on Vehicle Activity Log Data: Case Study for the Lane Keeping Assistance System [O] . Kyudong Park, Jiyoung Kwahk, Sung H. Han, 2019

机译：基于车辆活动日志数据模拟高级驾驶员辅助系统的侵入性感受：案例研究车道保持援助系统

A Personalized Lane-Changing Model for Advanced Driver Assistance System Based on Deep Learning and Spatial-Temporal Modeling

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