Object-Level Attention Prediction for Drivers in the Information-Rich Traffic Environment

计算机科学 人工智能 认知 聚类分析 感知 可视化 机器学习 模式识别(心理学) 生物 神经科学
作者
Qingxiao Liu,Hui Yao,Chao Lu,H. Liu,Yangtian Yi,Huiyan Chen
出处
期刊:IEEE Transactions on Industrial Electronics [Institute of Electrical and Electronics Engineers]
卷期号:71 (6): 6396-6406
标识
DOI:10.1109/tie.2023.3294547
摘要

An object-level attention prediction framework for drivers in the urban environment with rich semantic and motion information is proposed in this article. The proposed framework is based on the visual working memory mechanism, which decomposes the perception process into three phases, external stimuli, cognitive constructing, and memory search. In the external stimuli phase, semantic and motion information of surrounding objects is obtained. In the cognitive constructing phase, the neighbor-based hierarchical clustering method is applied to extract both independent and dependent features of traffic participants and driving events. In the memory search phase, the heterogeneous motif graph neural network is utilized to construct visual memory layers and integrate multilevel features for attention reasoning. Finally, the feature embedding is fed into a multilayer perceptron to predict the object-level visual attention. Training and testing data are collected from crowded and dynamic traffic scenes. Experimental results show that the proposed framework can achieve a superior object-level prediction performance in the information-rich environments compared with the state-of-the-art methods. In addition, the proposed framework can reduce the time bias of visual attention effectively.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
美好乌冬面完成签到,获得积分10
刚刚
娜娜子发布了新的文献求助30
1秒前
我是老大应助最溜皮大爷采纳,获得10
1秒前
勤劳茗完成签到,获得积分20
1秒前
wj发布了新的文献求助10
2秒前
搜集达人应助彩色的诗桃采纳,获得10
2秒前
求知发布了新的文献求助100
2秒前
Sylvia发布了新的文献求助10
2秒前
悟空最可爱完成签到,获得积分20
3秒前
ding应助科研通管家采纳,获得10
3秒前
3秒前
小二郎应助科研通管家采纳,获得30
3秒前
爆米花应助科研通管家采纳,获得10
3秒前
闫雪艳完成签到 ,获得积分10
3秒前
在水一方应助科研通管家采纳,获得10
3秒前
乐乐应助科研通管家采纳,获得30
3秒前
英俊的铭应助北洛采纳,获得10
3秒前
完美世界应助科研通管家采纳,获得10
4秒前
Akim应助科研通管家采纳,获得10
4秒前
李爱国应助木木采纳,获得10
4秒前
上官若男应助科研通管家采纳,获得10
4秒前
ding应助科研通管家采纳,获得10
4秒前
FLMXene发布了新的文献求助10
4秒前
远看寒山发布了新的文献求助10
4秒前
汉堡包应助科研通管家采纳,获得10
4秒前
4秒前
5秒前
5秒前
情怀应助科研通管家采纳,获得10
5秒前
5秒前
脑洞疼应助科研通管家采纳,获得10
5秒前
5秒前
5秒前
5秒前
5秒前
5秒前
勤劳茗发布了新的文献求助10
5秒前
5秒前
5秒前
5秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Picture Books with Same-sex Parented Families: Unintentional Censorship 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 360
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3970802
求助须知:如何正确求助?哪些是违规求助? 3515474
关于积分的说明 11178714
捐赠科研通 3250627
什么是DOI,文献DOI怎么找? 1795390
邀请新用户注册赠送积分活动 875818
科研通“疑难数据库(出版商)”最低求助积分说明 805183