.png){kind=icon}
返回
Driving Segment Embedding and Patterns Dictionary Generation from Real-World Data Using Self-Supervised Learning
.png){kind=icon}
PRE
AI
DOI：10.1007/s42154-024-00300-4.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The design and testing of autonomous vehicles crucially depend on an understanding of human driving characteristics due to the coexistence of automated and human-driven vehicles. Previous studies had relied on personalized background vehicle models, which required either calibrating microscopic traffic model parameters or training supervised models. This paper proposes a method that considers human driving behavior as a collection of some empirical driving patterns. A pattern dictionary generation algorithm is presented that distinguishes real-world driving data into pattern representations in continuous space. Driving patterns extraction based on transformer (DPFormer), a self-supervised deep learning-based method, is proposed to embed high-dimensional driving data into a compact form without manually labeled driving maneuvers. The model is optimized using the pretext task which utilizes attention mechanisms to embed the current driving segment as the posterior embedding of driving context. The embedding representation facilitates the analysis of diverse driving behaviors. Furthermore, a driving characteristics analysis method is proposed based on the Wasserstein distance between driving patterns' transition probability matrices to quantify the differences between different drivers. Finally, the effectiveness of this proposed model is validated using a large amount of real-world driving data collected from 171 human drivers.
Keyword:
Driving pattern
Self-supervised deep learning
Multimodal driving behavior
Driving characteristics analysis
Attention mechanism
期刊
A
IF:
5
论文数:
363
被引数:
1.0K
机构
引用论文
.png){kind=icon}
Extracting Human-Like Driving Behaviors From Expert Driver Data Using Deep Learning使用深度学习从专家驾驶员数据中提取类似人的驾驶行为
OAAI
OAAI分享收藏分享收藏Driver behavior detection and classification using deep convolutional neural networks基于深度卷积神经网络的驾驶员行为检测与分类
PREAI
分享收藏分享收藏The production of social science research in Nigeria: status and systemic determinants尼日利亚社会科学研究的成果: 地位和系统决定因素
OAAI
OAAI分享收藏分享收藏
查看更多内容