计算机科学
概率逻辑
预测(人工智能)
高级驾驶员辅助系统
动态时间归整
凝视
隐马尔可夫模型
背景(考古学)
人机交互
集合(抽象数据类型)
固定(群体遗传学)
撞车
人工智能
人口
程序设计语言
古生物学
人口学
社会学
生物
作者
Min Wu,Tyron Louw,Morteza Lahijanian,Wenjie Ruan,Xiaowei Huang,Natasha Merat,Marta Kwiatkowska
标识
DOI:10.1109/iros40897.2019.8967779
摘要
Anticipating a human collaborator's intention enables safe and efficient interaction between a human and an autonomous system. Specifically, in the context of semiautonomous driving, studies have revealed that correct and timely prediction of the driver's intention needs to be an essential part of Advanced Driver Assistance System (ADAS) design. To this end, we propose a framework that exploits drivers' time-series eye gaze and fixation patterns to anticipate their real-time intention over possible future manoeuvres, enabling a smart and collaborative ADAS that can aid drivers to overcome safety-critical situations. The method models human intention as the latent states of a hidden Markov model and uses probabilistic dynamic time warping distributions to capture the temporal characteristics of the observation patterns of the drivers. The method is evaluated on a data set of 124 experiments from 75 drivers collected in a safety-critical semi-autonomous driving scenario. The results illustrate the efficacy of the framework by correctly anticipating the drivers' intentions about 3 seconds beforehand with over 90% accuracy.
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