Behavior Signal Processing for Vehicle Applications
(0) Within the past decade, analyzing and modeling human behavior by processing large amounts of collected data has become an active research topic in advanced human-machine interaction systems. The research community strives to find improved ways to explain and represent meaningful behavioral characteristics of humans in order to develop efficient and effective cooperative interactions between humans, machines, and the environment. This paper provides a summary of progress achieved to date of our research on behavior signal processing, with a focus on the driver-vehicle-environment interaction. First, we describe the method of data collection used to develop our real-world driving corpus, which contains multimodal driving signals capturing relevant information regarding driver, vehicle, and environment. Then, the paper provides an overview of our signal processing and data-driven approaches used to analyze and model driver behavior for a wide range of practical vehicle applications. We then perform experimental validation using the realistic driving behavior of several drivers. In particular, the vehicle applications include driver identification, behavior prediction (i.e., car following and lane change), driver frustration (emotion) detection, and driver education. We hope that this paper will provide some insight to researchers who have interest in this field, and help identify areas and applications where further research is needed.
Chiyomi Miyajima Pongtep Angkititrakul Kazuya Takeda
Nagoya University, Nagoya, Japan
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-10
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）